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1
Chen, Li-Sheng, Chih-Hsiang Ho, Cheng-Chang Chen, Yu-Shan Liang, and Sy-Yen Kuo. "Repetition with Learning Approaches in Massive Machine Type Communications." Electronics 11, no. 22 (2022): 3649. http://dx.doi.org/10.3390/electronics11223649.
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In the 5G massive machine type communication (mMTC) scenario, user equipment with poor signal quality requires numerous repetitions to compensate for the additional signal attenuation. However, an excessive number of repetitions consumes additional wireless resources, decreasing the transmission rate, and increasing the energy consumption. An insufficient number of repetitions prevents the successful deciphering of the data by the receivers, leading to a high bit error rate. The present study developed adaptive repetition approaches with the k-nearest neighbor (KNN) and support vector machine (SVM) to substantially increase network transmission efficacy for the enhanced machine type communication (eMTC) system in the 5G mMTC scenario. The simulation results showed that the proposed repetition with the learning approach effectively improved the probability of successful transmission, the resource utilization, the average number of repetitions, and the average energy consumption. It is therefore more suitable for the eMTC system in the mMTC scenario than the common lookup table.
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Bockelmann, Carsten, Nuno K. Pratas, Gerhard Wunder, et al. "Towards Massive Connectivity Support for Scalable mMTC Communications in 5G networks." IEEE Access 6 (May 1, 2018): 28969–92. https://doi.org/10.1109/ACCESS.2018.2837382.
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The fifth generation of cellular communication systems is foreseen to enable a multitude of new applications and use cases with very different requirements. A new 5G multi-service air interface needs to enhance broadband performance as well as provide new levels of reliability, latency, and supported number of users. In this paper, we focus on the massive Machine Type Communications (mMTC) service within a multi-service air interface. Specifically, we present an overview of different physical and medium access techniques to address the problem of a massive number of access attempts in mMTC and discuss the protocol performance of these solutions in a common evaluation framework.
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Yang, Bei, Fengsheng Wei, Xiaoming She, et al. "Intelligent Random Access for Massive-Machine Type Communications in Sliced Mobile Networks." Electronics 12, no. 2 (2023): 329. http://dx.doi.org/10.3390/electronics12020329.
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With the emerging Internet of Things paradigm, massive Machine-Type Communication (mMTC) has been identified as one of the prominent services that enables a broad range of applications with various Quality of Service (QoS) requirements for 5G-and-beyond networks. However, it is very difficult to employ a monolithic physical network to support various mMTC applications with differentiated QoS requirements. Moreover, in ultra-dense mobile networks, the scarcity of the preamble and Physical Downlink Control CHannel (PDCCH) resources may easily lead to resource collisions when a large number of devices access the network simultaneously. To tackle these issues, in this paper, we propose a network slicing-enabled intelligent random access framework for mMTC. First, by tailoring a gigantic physical network into multiple lightweight network slices, fine-grained QoS provisioning can be accomplished, and the collision domain of Random Access (RA) can be effectively reduced. In addition, we propose a novel concept of sliced preambles (sPreambles), based on which the transitional RA procedure is optimized, and the issue of preamble shortage is effectively relieved. Furthermore, with the aim of alleviating PDCCH resource shortage and improving transmission efficiency, we propose a learning-based resource-sharing scheme that can intelligently multiplex the PDCCH resources in the naturally dynamic environment. Simulation results show that the proposed framework can efficiently allocate resources to individual mMTC devices while guaranteeing their QoS requirements in random access processes.
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Zheng, Tongyi, Lei Ning, Qingsong Ye, and Fan Jin. "An XGB-Based Reliable Transmission Method in the mMTC Scenarios." Security and Communication Networks 2021 (December 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/9929051.
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Massive machine-type communications (mMTCs) for Internet of things are being developed thanks to the fifth-generation (5G) wireless systems. Narrowband Internet of things (NB-IoT) is an important communication technology for machine-type communications. It supports many different protocols for communication. The reliability and performance of application layer communication protocols are greatly affected by the retransmission time-out (RTO) algorithm. In order to improve the reliability and performance of machine-type communications, this study proposes a novel RTO algorithm UDP-XGB based on the user datagram protocol (UDP) and NB-IoT. It combines traditional algorithms with machine learning. The simulation results show that real round-trip time (RTT) is close to the RTO, which is obtained by this algorithm, and the reliability and performance of machine-type communications have improved.
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Arcidiacono, Antonio, Daniele Finocchiaro, Riccardo De Gaudenzi, et al. "Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?" Sensors 21, no. 13 (2021): 4290. http://dx.doi.org/10.3390/s21134290.
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Non-orthogonal multiple access (NOMA) technologies are considered key technologies for terrestrial 5G massive machine-type communications (mMTC) applications. It is less known that NOMA techniques were pioneered about ten years ago in the satellite domain to match the growing demand for mMTC services. This paper presents the key features of the first NOMA-based satellite network, presenting not only the underlying technical solutions and measured performance but also the related deployment over the Eutelsat satellite fleet. In particular, we describe the specific ground segment developments for the user terminals and the gateway station. It is shown that the developed solution, based on an Enhanced Spread ALOHA random access technique, achieves an unprecedented throughput, scalability and service cost and is well matched to several mMTC satellite use cases. The ongoing R&D lines covering both the ground segment capabilities enhancement and the extension to satellite on-board packet demodulation are also outlined. These pioneering NOMA satellite technology developments and in-the-field deployments open up the possibility of developing and exploiting 5G mMTC satellite- and terrestrial-based systems in a synergic and interoperable architecture.
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Belhadj, Salima, Abdelmounaim Moulay Lakhdar, and Ridha Ilyas Bendjillali. "Performance comparison of channel coding schemes for 5G massive machine type communications." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 902–8. https://doi.org/10.11591/ijeecs.v22.i2.pp902-908.
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Channel coding for the fifth generation (5G) mobile communication is currently facing new challenges as it needs to uphold diverse emerging applications and scenarios. Massive machine-type communication (mMTC) constitute one of the main usage scenarios in 5G systems, which promise to provide low data rate services to a large number of low power and low complexity devices. Research on efficient coding schemes for such use case is still ongoing and no decision has been made yet. Therefore, This paper compares the performance of different coding schemes, namely: tail-biting convolutional code (TBCC), low density parity check codes (LDPC), Turbo code and Polar codes, in order to select the appropriate channel coding technique for 5G-mMTC scenario. The considered codes are evaluated in terms of bit error rate (BER) and block error rate (BLER) for short information block lengths (K ≤ 256). We further investigate their Algorithmic complexity in terms of the number of basic operations. The Simulation results indicate that polar code with CRC-aided successive cancelation list decoder has better performance compared with other coding schemes for 5G-mMTC scenario.
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Miao, Junfeng, Zhaoshun Wang, Mei Wang, Xiao Feng, Nan Xiao, and Xiaoxue Sun. "Security Authentication Protocol for Massive Machine Type Communication in 5G Networks." Wireless Communications and Mobile Computing 2023 (April 6, 2023): 1–10. http://dx.doi.org/10.1155/2023/6086686.
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As one of the three major applications of 5G, massive machine type communication (mMTC) is mainly oriented to network access scenarios for massive devices. mMTC focuses on solving the problem that traditional mobile communication cannot well support the Internet of Things and vertical industry applications. According to the current 3GPP standard, these massive devices still use the traditional authentication process to realize mutual authentication with 5G core network, which brings a lot of communication and computing overhead. In addition, privacy protection will also be threatened in the authentication process. In order to alleviate the signaling congestion during authentication and solve the insecurity in authentication, this paper proposes a group authentication scheme for mMTC. Due to the characteristics of low power consumption and massive connection, the scheme mainly adopts lightweight encryption operation to avoid the computational burden of equipment and server. We verify the security of our scheme by using BAN logic to formally analyze the scheme. Then, through informal analysis, our proposed scheme can not only avoid signaling blocking and provide mutual authentication but also resist various possible attacks. Through performance evaluation, it is proved that our scheme has better efficiency.
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Belhadj, Salima, Abdelmounaim Moulay Lakhdar, and Ridha Ilyas Bendjillali. "Performance comparison of channel coding schemes for 5G massive machine type communications." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 902. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp902-908.
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<p><span>Channel coding for the fifth generation (5G) mobile communication is currently facing new challenges as it needs to uphold diverse emerging applications and scenarios. Massive machine-type communication (mMTC) constitute one of the main usage scenarios in 5G systems, which promise to provide low data rate services to a large number of low power and low complexity devices. Research on efficient coding schemes for such use case is still ongoing and no decision has been made yet. Therefore, This paper compares the performance of different coding schemes, namely: tail-biting convolutional code (TBCC), low density parity check codes (LDPC), Turbo code and Polar codes, in order to select the appropriate channel coding technique for 5G-mMTC scenario. The considered codes are evaluated in terms of bit error rate (BER) and block error rate (BLER) for short information block lengths (K ≤ 256). We further investigate their Algorithmic complexity in terms of the number of basic operations. The Simulation results indicate that polar code with CRC-aided successive cancelation list decoder has better performance compared with other coding schemes for 5G-mMTC scenario.</span></p>
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Leyva-Mayorga, Israel, Miguel Rodriguez-Hernandez, Vicent Pla, and Jorge Martinez-Bauset. "Filtering Methods for Efficient Dynamic Access Control in 5G Massive Machine-Type Communication Scenarios." Electronics 8, no. 1 (2018): 27. http://dx.doi.org/10.3390/electronics8010027.
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One of the three main use cases of the fifth generation of mobile networks (5G) is massive machine-type communications (mMTC). The latter refers to the highly synchronized accesses to the cellular base stations from a great number of wireless devices, as a product of the automated exchange of small amounts of data. Clearly, an efficient mMTC is required to support the Internet-of-Things (IoT). Nevertheless, the method to change from idle to connected mode, known as the random access procedure (RAP), of 4G has been directly inherited by 5G, at least, until the first phase of standardization. Research has demonstrated the RAP is inefficient to support mMTC, hence, access control schemes are needed to obtain an adequate performance. In this paper, we compare the benefits of using different filtering methods to configure an access control scheme included in the 5G standards: the access class barring (ACB), according to the intensity of access requests. These filtering methods are a key component of our proposed ACB configuration scheme, which can lead to more than a three-fold increase in the probability of successfully completing the random access procedure under the most typical network configuration and mMTC scenario.
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Hou, Wenjun, Song Li, Yanjing Sun, Jiasi Zhou, Xiao Yun, and Nannan Lu. "Interference-Aware Subcarrier Allocation for Massive Machine-Type Communication in 5G-Enabled Internet of Things." Sensors 19, no. 20 (2019): 4530. http://dx.doi.org/10.3390/s19204530.
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Massive machine-type communication (mMTC) is investigated as one of three typical scenes of the 5th-generation (5G) network. In this paper, we propose a 5G-enabled internet of things (IoT) in which some enhanced mobile broadband devices transmit video stream to a centralized controller and some mMTC devices exchange short packet data with adjacent devices via D2D communication to promote inter-device cooperation. Since massive MTC devices have data transmission requirements in 5G-enabled IoT with limited spectrum resources, the subcarrier allocation problem is investigated to maximize the connectivity of mMTC devices subject to the quality of service (QoS) requirement of enhanced Mobile Broadband (eMBB) devices and mMTC devices. To solve the formulated mixed-integer non-linear programming (MINLP) problem, which is NP-hard, an interference-aware subcarrier allocation algorithm for mMTC communication (IASA) is developed to maximize the number of active mMTC devices. Finally, the performance of the proposed algorithm is evaluated by simulation. Numerical results demonstrate that the proposed algorithm outperforms the three traditional benchmark methods, which significantly improves the utilization of the uplink spectrum. This indicates that the proposed IASA algorithm provides a better solution for IoT application.
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Hussain, Sajjad, Aftab Hussain, Muhmmad Waleed Aftab, et al. "Massive Connectivity and Low-Latency for Next-Generation Internet of Things: A Filtered OFDM-based Deep Learning Approach." Journal of Smart Internet of Things 2023, no. 2 (2023): 115–21. http://dx.doi.org/10.2478/jsiot-2023-0014.
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Abstract The Internet of Things (IoT) is one of the numerous services offered by sixth-generation (6G) mobile communications. It is necessary to meet heterogeneous criteria for large connectivity and low latency to serve the various kinds of IoT applications. In order to concurrently provide enormous connectivity and meet the low-latency conditions in the uplink IoT network, we proposed filtered orthogonal frequency division multiplexing (FOFDM) based on a service group adopting the deep learning (DL) technique. The proposed FOFDM-DL platform focuses on two key areas: first, it works for the concurrence of different time-frequency granularity appropriate for distinct service-based grouping, and secondly, it facilitates low latency and massive connectivity to deliver dependable communications. The suggested FOFDM-DL architecture may accommodate the needs of massive machine-type communication referred as mMTC and ultra-reliable and low-latency communications known as uRLLC concurrently for the next-generation communication systems. However, the uRLLC and mMTC requirements can only be supported separately by the new radio (NR)-5G, beyond 5G (or 6G). In comparison to the traditional scheme, simulation results demonstrate that the suggested FOFDM-DL platform works surprisingly well in the next-generation IoT network.
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Feliana, Ferlinda, Ruki Harwahyu, and Marlinda Vasty Overbeek. "Multichannel Slotted ALOHA Simulator Design for Massive Machine-Type Communication (mMTC) on 5G Network." International Journal of Electrical, Computer, and Biomedical Engineering 1, no. 2 (2023): 1–31. http://dx.doi.org/10.62146/ijecbe.v1i2.8.
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Massive Machine-type Communication (mMTC) is one of the main service scenarios in 5G. At the time of initializing the connection to the base station, the MTC machines will make a connection request via the random access procedure. One of the schemes of random access procedure for handling this connection request is similar to how multichannel slotted ALOHA works. Multichannel slotted ALOHA itself is a development of the slotted ALOHA scheme which originally has only a single channel. At the initial state of mMTC, there will be an explosion of the number of demands to the available channels. Given the number of machines that will be connected, the likelihood of a collision on the same channel increases. As a result, the probability of failure also increases. The system's configuration has an impact on the likelihood of success and the time it takes to achieve it. The number of channels influences the likelihood of collisions, the backoff window influences the transmission distribution in each slot, and the maximum transmission limits the ability of device retransmission. These three arrangements have an impact on one another. The simulator build in this research is expected to make it easier for researchers to optimize multichannel slotted ALOHA configurations in 5G to handle the surge in access demands from mMTC devices.
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Alam, Mehmood, and Qi Zhang. "A Survey: Nonorthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC." Wireless Communications and Mobile Computing 2021 (October 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/8840519.
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One objective of the 5G communication system and beyond is to support massive machine type of communication (mMTC) to propel the fast growth of diverse Internet of Things use cases. The mMTC is aimed at providing connectivity to tens of billions of sensor nodes. The dramatic increase of sensor devices and massive connectivity impose critical challenges for the network to handle the enormous control signaling overhead with limited radio resources. Nonorthogonal multiple access (NOMA) is a new paradigm shift in the design of multiple user detection and multiple access. NOMA with compressive sensing-based multiuser detection is one of the promising candidates to address the challenges of mMTC. The survey article is aimed at providing an overview of the current state-of-art research work in various compressive sensing-based techniques that enable NOMA. We present characteristics of different algorithms and compare their pros and cons, thereby providing useful insights for researchers to make further contributions in NOMA using compressive sensing techniques. Nonorthogonal CDMA massive connectivity grant free medium access compressive sensing multiuser detection
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Paik, Mincheol, and Haneul Ko. "Distributed Group Location Update Algorithm for Massive Machine Type Communication." Sensors 20, no. 24 (2020): 7336. http://dx.doi.org/10.3390/s20247336.
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Frequent location updates of individual Internet of Things (IoT) devices can cause several problems (e.g., signaling overhead in networks and energy depletion of IoT devices) in massive machine type communication (mMTC) systems. To alleviate these problems, we design a distributed group location update algorithm (DGLU) in which geographically proximate IoT devices determine whether to conduct the location update in a distributed manner. To maximize the accuracy of the locations of IoT devices while maintaining a sufficiently small energy outage probability, we formulate a constrained stochastic game model. We then introduce a best response dynamics-based algorithm to obtain a multi-policy constrained Nash equilibrium. From the evaluation results, it is demonstrated that DGLU can achieve an accuracy of location information that is comparable with that of the individual location update scheme, with a sufficiently small energy outage probability.
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Chaudhary, Urvashi, Mohammad Furqan Ali, Samikkannu Rajkumar, and Dushantha Nalin K. Jayakody. "Sensing and Secure NOMA-Assisted mMTC Wireless Networks." Electronics 12, no. 10 (2023): 2322. http://dx.doi.org/10.3390/electronics12102322.
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Throughout this study, a novel network model for massive machine-type communications (mMTC) is proposed using a compressive sensing (CS) algorithm and a non-orthogonal multiple access (NOMA) scheme. Further, physical-layer security (PLS) is applied in this network to provide secure communication. We first assume that all the legitimate nodes operate in full-duplex mode; then, an artificial noise (AN) signal is emitted while receiving the signal from the head node to confuse eavesdroppers (Eve). A convex optimization tool is used to detect the active number of nodes in the proposed network using a sparsity-aware maximum a posteriori (S-MAP) detection algorithm. The sensing-aided secrecy sum rate of the proposed network is analyzed and compared with the sum rate of the network without sensing, and the closed-form expression of the secrecy outage probability of the proposed mMTC network is derived. Finally, our numerical results demonstrate the impact of an active sensing algorithm in the proposed mMTC network; improvement in the secrecy outage of the proposed network is achieved through increasing the distance of the Eve node.
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Moreno-Cardenas, Edison, and Luis Guijarro. "Market and Sharing Alternatives for the Provision of Massive Machine-Type and Ultra-Reliable Low-Latency Communications Services over a 5G Network." Electronics 12, no. 24 (2023): 4994. http://dx.doi.org/10.3390/electronics12244994.
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The objective of this paper is to analyze economic alternatives for the provision of ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC) services over a fifth-generation (5G) network. Two business models, a monopoly and a duopoly, are studied and two 5G network scenarios are analyzed: a 5G network where the network resources are shared between the two services without service priority, and a 5G network with network slicing that allows for URLLC traffic to have a higher priority. Microeconomics is used to model the behavior of users and operators, and game theory is used to model the strategic interaction between users and operators. The results show that a monopoly over a 5G network with network slicing is the most efficient way to provide both URLLC and mMTC services.
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Mahyastuty, Veronica Windha, Iskandar Iskandar, Hendrawan Hendrawan, and Mohammad Sigit Arifianto. "Medium Access Control Protocol for High Altitude Platform Based Massive Machine Type Communication." Journal of ICT Research and Applications 16, no. 2 (2022): 123–37. http://dx.doi.org/10.5614/itbj.ict.res.appl.2022.16.2.2.
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Massive Machine Type Communication (mMTC) can be used to connect a large number of sensors over a wide coverage area. One of the places where mMTC can be applied is in wireless sensor networks (WSNs). A WSN consists of several sensor nodes that send their sensing information to the cluster head (CH), which can then be forwarded to a high altitude platform (HAP) station. Sensing information can be sent by the sensor nodes at the same time through the same medium, which means collision can occur. When this happens, the sensor node must re-send the sensing information, which causes energy wastage in the WSN. In this paper, we propose a Medium Access Control (MAC) protocol to control access from several sensor nodes during data transmission to avoid collision. The sensor nodes send Round Robin, Interrupt and Query data every eight hours. The initial slot for transmission of the Round Robin data can be either randomized or reserved. Analysis performance was done to see the efficiency of the network with the proposed MAC protocol. Based on the series of simulations that was conducted, the proposed MAC protocol can support a WSN system-based HAP for monitoring every eight hours. The proposed MAC protocol with an initial slot that is reserved for transmission of Round Robin data has greater network efficiency than a randomized slot.
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Boisguene, Rubbens, Ibrahim Althamary, and Chih-Wei Huang. "A Learning-Based Energy-Efficient Device Grouping Mechanism for Massive Machine-Type Communication in the Context of Beyond 5G Networks." Journal of Sensor and Actuator Networks 13, no. 3 (2024): 33. http://dx.doi.org/10.3390/jsan13030033.
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With the increasing demand for high data rates, low delay, and extended battery life, managing massive machine-type communication (mMTC) in the beyond 5G (B5G) context is challenging. MMTC devices, which play a role in developing the Internet of Things (IoT) and smart cities, need to transmit short amounts of data periodically within a specific time frame. Although blockchain technology is utilized for secure data storage and transfer while digital twin technology provides real-time monitoring and management of the devices, issues such as constrained time delays and network congestion persist. Without a proper data transmission strategy, most devices would fail to transmit in time, thus defying their relevance and purpose. This work investigates the problem of massive random access channel (RACH) attempts while emphasizing the energy efficiency and access latency for mMTC devices with critical missions in B5G networks. Using machine learning techniques, we propose an attention-based reinforcement learning model that orchestrates the device grouping strategy to optimize device placement. Thus, the model guarantees a higher probability of success for the devices during data transmission access, eventually leading to more efficient energy consumption. Through thorough quantitative simulations, we demonstrate that the proposed learning-based approach significantly outperforms the other baseline grouping methods.
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El Hassan, Adil Abou, Abdelmalek El Mehdi, and Mohammed Saber. "NB-IoT and LTE-M towards massive MTC: Complete performance evaluation for 5G mMTC." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (2021): 308. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp308-320.
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Since the emerging 5G wireless network is expected to significantly revolutionize thefield of communication, its standardization and design should regard the internet ofthings (IoT) among the main orientations. Also, emerging IoT applications introducenew requirements other than throughput to support massive machine-type commu-nication (mMTC) where small data packets are occasionally sent. Therefore, moreimportance is attached to coverage, latency, power consumption, and connection den-sity. For this purpose, the third generation partnership project (3GPP) has introducedtwo novel cellular IoT technologies supporting mMTC, known as NB-IoT and LTE-M. This paper aims to determine the system configuration and deployment required forNB-IoT and LTE-M technologies to fully meet the 5G mMTC requirements in termsof coverage, throughput, latency, battery life, and connection density. An overview ofthese technologies and their design principles is also described. A complete evalua-tion of NB-IoT and LTE-M performance against 5G mMTC requirements is presented,and it is shown that these requirements can be met but only under certain conditionsregarding system configuration and deployment. This is followed by a performancecomparative analysis, which is mainly conducted to determine the limits and suitableuse cases of each technology.
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Adel Aly, Ahmed, Hussein M. ELAttar, Hesham ElBadawy, and Wael Abbas. "Aggregated Throughput Prediction for Collated Massive Machine-Type Communications in 5G Wireless Networks." Sensors 19, no. 17 (2019): 3651. http://dx.doi.org/10.3390/s19173651.
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The demand for extensive data rates in dense-traffic wireless networks has expanded and needs proper controlling schemes. The fifth generation of mobile communications (5G) will accommodate these massive communications, such as massive Machine Type Communications (mMTC), which is considered to be one of its top services. To achieve optimal throughput, which is considered a mandatory quality of service (QoS) metric, the carrier sense multiple access (CSMA) transmission attempt rate needs optimization. As the gradient descent algorithms consume a long time to converge, an approximation technique that distributes a dense global network into local neighborhoods that are less complex than the global ones is presented in this paper. Newton’s method of optimization was used to achieve fast convergence rates, thus, obtaining optimal throughput. The convergence rate depended only on the size of the local networks instead of global dense ones. Additionally, polynomial interpolation was used to estimate the average throughput of the network as a function of the number of nodes and target service rates. Three-dimensional planes of the average throughput were presented to give a profound description to network’s performance. The fast convergence time of the proposed model and its lower complexity are more practical than the previous gradient descent algorithm.
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Youn, Jiseung, Joohan Park, Joohyun Oh, et al. "CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC." Sensors 22, no. 20 (2022): 7959. http://dx.doi.org/10.3390/s22207959.
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With the growing interest in the Internet of Things (IoT), research on massive machine-type communication (mMTC) services is being actively promoted. Because mMTC services are required to serve a large number of devices simultaneously, a lack of resources during initial access can be a significant problem when providing mMTC services in cellular networks. Various studies on efficient preamble transmission have been conducted to solve the random access problem of mMTC services. However, supporting a large number of devices simultaneously with limited resources is a challenging problem. In this study, we investigate code-expanded random access (CeRA), which extends the limited preamble resources to the code domain to decrease the high collision rate. To solve the existing CeRA phantom codeword and physical uplink shared channel (PUSCH) resource shortage problems, we propose an optimal preamble codeword set selection algorithm based on mathematical analysis. The simulation results indicate that the proposed code-expanded random access scheme to enhance success probability (CeRA-eSP) achieves a higher random access success rate with a lower access delay compared to the existing random access schemes.
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Sun, Mengjiang, and Peng Chen. "Neural Network Based AMP Method for Multi-User Detection in Massive Machine-Type Communication." Electronics 9, no. 8 (2020): 1286. http://dx.doi.org/10.3390/electronics9081286.
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In massive machine-type communications (mMTC) scenarios, grant-free non-orthogonal multiple access becomes crucial due to the small transmission latency, limited signaling overhead and the ability to support massive connectivity. In a multi-user detection (MUD) problem, the base station (BS) is unaware of the active users and needs to detect active devices. With sporadic devices transmitting signals at any moment, the MUD problem can be formulated as a multiple measurement vector (MMV) sparse recovery problem. Through the Khatri–Rao product, we prove that the MMV problem is transformed into a single measurement vector (SMV) problem. Based on the basis pursuit de-noising approximate message passing (BPDN-AMP) algorithm, a novel learning AMP network (LAMPnet) algorithm is proposed, which is designed to reduce the false alarm probability when the required detection probability is high. Simulation results show that when the required detection probablity is high, the AMP algorithm based on LAMPnet noticeably outperforms the traditional algorithms with acceptable computational complexity.
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Math, Sa, Prohim Tam, Dae-Young Kim, and Seokhoon Kim. "Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications." Security and Communication Networks 2022 (April 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/4289216.
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The heterogeneous novelty applications present in the 5th generation (5G) era, including machine-type communication (mMTC), enhanced mobile broadband (eMBB) communication, and ultra-reliable low latency communication (URLLC), which required mobile edge computing (MEC) for local computation and services. The next-generation radio networking (NGRN) will rely on new radio (NR) with the millimeter-wavelength (mmWave) technologies that enable ultra-dense connectivities of the deployed heterogeneous mobile terminal gateways (MTG). However, the mission-critical mMTC applications will suffer from inadequate radio resource management and orchestration (MANO), which will diminish end-to-end (E2E) communication reliability in edge areas. This paper proposed optimal MTG selections and resource allocation (RA) based on the complementary between MTG loading prediction based on recurrent neural network-based long short-term memory (RNN-LSTM) and MTG loading adjustment based on the applied deep reinforcement learning (DRL) approaches, respectively. Furthermore, the RNN-LSTM enhances offloading and handover decisions with discrete-time predictions, while the DRL plays an essential role in adjusting the determined MTG during congestion situations. The proposed method contributed to remarkable outcomes in crucial performance metrics over reference approaches regarding computation and communication quality of service (QoS).
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Shen, Qingguo, Fan Luo, Ruiqi Qiu, and Zulei Hou. "Optimizating Sporadic Data Transmission for mMTC in 5G Networks." Advances in Engineering Technology Research 12, no. 1 (2024): 222. https://doi.org/10.56028/aetr.12.1.222.2024.
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In response to the explosive growth of massive Machine-Type Communication (mMTC) terminals in 5G networks, this paper proposes an optimization method for sporadic mMTC data transmission in 5G based on grant-free radio bearers. This method achieves efficient access and resource sharing among mMTC terminals through sharing pre-configured radio bearers. Additionally, an uplink grant-free random access mechanism is introduced to ensure that terminals are only activated for necessary data transmissions, then automatically entering a low-power sleep state. By optimizing the design of the air interface protocol stack, transmission delay and operating cost of the system are significantly reduced, and data transmission efficiency is further improved. The paper also explores the application of this method in satellite networks, demonstrating in detail its capacity to substantially reduce signaling overhead and enhance network performance for mMTC data transmission. This method has broad application prospects in 5G and satellite communication networks.
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Hassan, Adil Abou El, Abdelmalek El Mehdi, and Mohammed Saber. "NB-IoT and LTE-M towards massive MTC: Complete performance evaluation for 5G mMTC." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (2021): 308–20. https://doi.org/10.11591/ijeecs.v23.i1.pp308-320.
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Since the emerging 5G wireless network is expected to significantly revolutionize the field of communication, its standardization and design should regard the internet of things (IoT) among the main orientations. Also, emerging IoT applications introduce new requirements other than throughput to support massive machine-type communication (mMTC) where small data packets are occasionally sent. Therefore, more importance is attached to coverage, latency, power consumption, and connection density. For this purpose, the third generation partnership project (3GPP) has introduced two novel cellular IoT technologies supporting mMTC, known as NB-IoT and LTEM. This paper aims to determine the system configuration and deployment required for NB-IoT and LTE-M technologies to fully meet the 5G mMTC requirements in terms of coverage, throughput, latency, battery life, and connection density. An overview of these technologies and their design principles are also described. A complete evaluation of NB-IoT and LTE-M performance against 5G mMTC requirements is presented, and it is shown that these requirements can be met but only under certain conditions regarding system configuration and deployment. This is followed by a performance comparative analysis, which is mainly conducted to determine the limits and suitable use cases of each technology.
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Llobet, M., M. Cabrera-Bean, J. Vidal, and A. Agustin. "Optimizing Access Demand for mMTC Traffic Using Neural Networks." IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 72, no. 12 (2023): 16834–38. https://doi.org/10.1109/TVT.2023.3294724.
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Machine-type communications show unique spatial and temporal correlation properties that often lead to bursty access demand profiles. With the expected large-scale deployment of the Internet of Things (IoT), next-generation mobile networks should be redesigned to manage massive, highly synchronized arrivals of access requests by employing efficient access barring schemes. In this work, we first derived the analytical expression of the optimal Access Class Barring (ACB) parameter as standardized by the Third Generation Partnership Project (3GPP). Secondly, we predict the type and number of accessing devices from measurements acquired by the Base Station (BS) by employing Neural Networks (NNs). These estimates are used to effectively implement the optimal barring scheme, achieving performance results close to the theoretical bound. © 1967-2012 IEEE.
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Rodriguez Medel, Abel, and Jose Marcos C. Brito. "Random-Access Accelerator (RAA): A Framework to Speed Up the Random-Access Procedure in 5G New Radio for IoT mMTC by Enabling Device-To-Device Communications." Sensors 20, no. 19 (2020): 5485. http://dx.doi.org/10.3390/s20195485.
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Mobile networks have a great challenge by serving the expected billions of Internet of Things (IoT) devices in the upcoming years. Due to the limited simultaneous access in the mobile networks, the devices should compete between each other for resource allocation during a Random-Access procedure. This contention provokes a non-depreciable delay during the device’s registration because of the great number of collisions experienced. To overcome such a problem, a framework called Random-Access Accelerator (RAA) is proposed in this work, in order to speed up network access in massive Machine Type Communication (mMTC). RAA exploits Device-To-Device (D2D) communications, where devices with already assigned resources act like relays for the rest of devices trying to gain access in the network. The simulation results show an acceleration in the registration procedure of 99%, and a freed space of the allocated spectrum until 74% in comparison with the conventional Random-Access procedure. Besides, it preserves the same device’s energy consumption compared with legacy networks by using a custom version of Bluetooth as a wireless technology for D2D communications. The proposed framework can be taken into account for the standardization of mMTC in Fifth-Generation-New Radio (5G NR).
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Pratik, Jangale. "Integration of Edge Computing in 5G RAN: Deploying Low-Latency and High-Efficiency Networks." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 7, no. 5 (2019): 1–11. https://doi.org/10.5281/zenodo.14535487.
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The advent of 5G has ushered in an era of unprecedented connectivity, driven by enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). However, achieving the low latency and computational efficiency required by these services necessitates a fundamental shift in network architecture. Edge Computing in 5G Radio Access Networks (RAN) addresses these challenges by decentralizing processing capabilities, bringing computation closer to end users. This paper explores the integration of Edge Computing in 5G RAN, detailing its architecture, enabling technologies, deployment strategies, and the associated challenges. Key use cases and their implications for future networks are discussed, underpinned by technical insights and industry standards.
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Mikhaylov, Konstantin, Vitaly Petrov, Rohit Gupta, et al. "Energy Efficiency of Multi-Radio Massive Machine-Type Communication (MR-MMTC): Applications, Challenges, and Solutions." IEEE Communications Magazine 57, no. 6 (2019): 100–106. http://dx.doi.org/10.1109/mcom.2019.1800394.
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Kiran, Vinayak Shanbhag, and Sathish Dayakshini. "Low complexity physical layer security approach for 5G internet of things." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6466–75. https://doi.org/10.11591/ijece.v13i6.pp6466-6475.
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Fifth-generation (5G) massive machine-type communication (mMTC) is expected to support the cellular adaptation of internet of things (IoT) applications for massive connectivity. Due to the massive access nature, IoT is prone to high interception probability and the use of conventional cryptographic techniques in these scenarios is not practical considering the limited computational capabilities of the IoT devices and their power budget. This calls for a lightweight physical layer security scheme which will provide security without much computational overhead and/or strengthen the existing security measures. Here a shift based physical layer security approach is proposed which will provide a low complexity security without much changes in baseline orthogonal frequency division multiple access (OFDMA) architecture as per the low power requirements of IoT by systematically rearranging the subcarriers. While the scheme is compatible with most fast Fourier transform (FFT) based waveform contenders which are being proposed in 5G especially in mMTC and ultra-reliable low latency communication (URLLC), it can also add an additional layer of security at physical layer to enhanced mobile broadband (eMBB).
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Fayad, Abdulhalim, and Tibor Cinkler. "Optimal Slicing of mmWave Micro Base Stations for 5G and Beyond." Journal of Networking and Network Applications 3, no. 3 (2023): 99–108. http://dx.doi.org/10.33969/j-nana.2023.030301.
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5G and beyond 5G mobile networks are expected to cater to diverse needs by efficiently allocating network resources based on demand. Network slicing is a fundamental approach that involves segregating and allocating network resources distinctly to a group of users based on their individual needs, and it is widely recognized as an essential concept that caters to various requirements. Allocating such slices will encounter conflicting requests, and effectively implementing network slicing presents multiple challenges. Effective network slicing necessitates efficient management of priority levels among diverse slices. Network slicing necessitates efficient management of priority levels across various slices, specifically focusing on three distinctive categories: Ultra-Reliable Low Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine Type Communications (mMTC). This paper proposes an optimization framework utilizing a Mixed Integer Linear Program (MILP) to allocate network resources for multiple slices efficiently. Our framework aims to maximize user satisfaction while ensuring that the specific requirements of each slice are met. We categorize the slices into three priority levels: the URLLC slice holds the highest priority, followed by the eMBB slice, and finally, the mMTC slice receives the least priority. By leveraging our proposed MILP-based approach, we dynamically assign network resources to different slices, considering their priority levels. This allocation strategy enables us to optimize resource utilization and effectively meet the diverse demands of users across various slices. Our framework provides a balance between meeting the stringent requirements of the URLLC slice, delivering high-quality services to the eMBB slice, and accommodating the massive connectivity needs of the mMTC slice.
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Papidas, Andreas G., and George C. Polyzos. "Self-Organizing Networks for 5G and Beyond: A View from the Top." Future Internet 14, no. 3 (2022): 95. http://dx.doi.org/10.3390/fi14030095.
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We describe self-organizing network (SON) concepts and architectures and their potential to play a central role in 5G deployment and next-generation networks. Our focus is on the basic SON use case applied to radio access networks (RAN), which is self-optimization. We analyze SON applications’ rationale and operation, the design and dimensioning of SON systems, possible deficiencies and conflicts that occur through the parallel operation of functions, and describe the strong reliance on machine learning (ML) and artificial intelligence (AI). Moreover, we present and comment on very recent proposals for SON deployment in 5G networks. Typical examples include the binding of SON systems with techniques such as Network Function Virtualization (NFV), Cloud RAN (C-RAN), Ultra-Reliable Low Latency Communications (URLLC), massive Machine-Type Communication (mMTC) for IoT, and automated backhauling, which lead the way towards the adoption of SON techniques in Beyond 5G (B5G) networks.
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Tanveer, Jawad, Amir Haider, Rashid Ali, and Ajung Kim. "An Overview of Reinforcement Learning Algorithms for Handover Management in 5G Ultra-Dense Small Cell Networks." Applied Sciences 12, no. 1 (2022): 426. http://dx.doi.org/10.3390/app12010426.
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The fifth generation (5G) wireless technology emerged with marvelous effort to state, design, deployment and standardize the upcoming wireless network generation. Artificial intelligence (AI) and machine learning (ML) techniques are well capable to support 5G latest technologies that are expected to deliver high data rate to upcoming use cases and services such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). These services will surely help Gbps of data within the latency of few milliseconds in Internet of Things paradigm. This survey presented 5G mobility management in ultra-dense small cells networks using reinforcement learning techniques. First, we discussed existing surveys then we are focused on handover (HO) management in ultra-dense small cells (UDSC) scenario. Following, this study also discussed how machine learning algorithms can help in different HO scenarios. Nevertheless, future directions and challenges for 5G UDSC networks were concisely addressed.
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AlQahtani, Salman A. "Cooperative-Aware Radio Resource Allocation Scheme for 5G Network Slicing in Cloud Radio Access Networks." Sensors 23, no. 11 (2023): 5111. http://dx.doi.org/10.3390/s23115111.
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The 5G network is designed to serve three main use cases: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low-latency communications (uRLLC). There are many new technological enablers, including the cloud radio access network (C-RAN) and network slicing, that can support 5G and meet its requirements. The C-RAN combines both network virtualization and based band unit (BBU) centralization. Using the network slicing concept, the C-RAN BBU pool can be virtually sliced into three different slices. 5G slices require a number of Quality of service (QoS) metrics, such as average response time and resource utilization. In order to enhance the C-RAN BBUs utilization while protecting the minimum QoS of the coexisting three slices, a priority-based resource allocation with queuing model is proposed. The uRLLC is given the highest priority, while eMBB has a higher priority than mMTC services. The proposed model allows the eMBB and mMTC to be queued and the interrupted mMTC to be restored in its queue to increase its chance to reattempt the service later. The proposed model’s performance measures are defined and derived using a continuous-time Markov chain (CTMC) model and evaluated and compared using different methodologies. Based on the results, the proposed scheme can increase C-RAN resource utilization without degrading the QoS of the highest-priority uRLLC slice. Additionally, it can reduce the forced termination priority of the interrupted mMTC slice by allowing it to re-join its queue. Therefore, the comparison of the results shows that the proposed scheme outperforms the other states of the art in terms of improving the C-RAN utilization and enhancing the QoS of eMBB and mMTC slices without degrading the QoS of the highest priority use case.
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Vo, Ta-Hoang, Zhi Ding, Quoc-Viet Pham, and Won-Joo Hwang. "Access Control and Pilot Allocation for Machine-Type Communications in Crowded Massive MIMO Systems." Symmetry 11, no. 10 (2019): 1272. http://dx.doi.org/10.3390/sym11101272.
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Massive machine-type communication (mMTC) in 5G New Radio (5G-NR) or the Internet of Things (IoT) is a network of physical devices such as vehicles, smart meters, sensors, and smart appliances, which can communicate and interact in real time without human intervention. In IoT systems, the number of networked devices is expected to be in the tens of billions, while radio resources remain scarce. To connect the massive number of devices with limited bandwidth, it is crucial to develop new access solutions that can improve resource efficiency and reduce control overhead as well as access delay. The key idea is controlling the number of arrival devices that want to access the system, and then allowing only the strongest device (that has the largest channel gain and each device is able to check whether it is the strongest device) be able to transit to BS. In this paper, we consider a random access problem in massive MIMO context for the collision resolution, in which the access class barring (ACB) factor is dynamically adjusted in each time slot to maximize access success rate for the strongest-user collision resolution (SUCRe) protocol. We propose the dynamic ACB scheme to find optimal ACB factor in the next time slot and then apply SUCRe protocol to achieve a good performance. This method is called dynamic access class barring combined strongest-user collision resolution (DACB-SUCR). In addition, we investigate two different ACB schemes that consist of the fixed ACB and the traffic-aware ACB to compare with the proposed dynamic ACB. Analysis and simulation results demonstrate that, compared with SUCRe protocol, the proposed DACB-SUCR method can remarkably reduce pilot collision, and increase access success rate. It is also shown that the dynamic ACB gives better performance than the fixed ACB and the traffic-aware ACB.
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Gao, Zihang. "Research on 5G Network Slicing Strategy for Urban Complex Environment." Wireless Communications and Mobile Computing 2023 (June 30, 2023): 1–11. http://dx.doi.org/10.1155/2023/2820966.
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Focusing on the layout of the 5G mobile communication base station in the city center, we design a 5G city network slicing strategy for the three typical application scenarios with enhanced mobile broadband (eMBB), ultrareliable low-latency communications (URLLC), and massive machine type communications (mMTC). The strategy considers multiple important network performance indicators, including user guaranteed bandwidth, maximum bandwidth limit, QoS (quality of service), link delay tolerance, and slicing throughput. The slicing strategy can greatly increase the connections of base station clients and the utilization of network resources, and effectively reduce block radio and handover radio. The simulation experiments adopt the 5G base station dataset of a coastal city layout in Zhejiang province. Our tests show that the 5G network slicing strategy has certain advantages in network transmission performance in urban complex environment. The research can provide an effective reference for 5G infrastructure construction in other cities.
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Cao, Jin, Hui Li, Maode Ma, and Fenghua Li. "UPPGHA: Uniform Privacy Preservation Group Handover Authentication Mechanism for mMTC in LTE-A Networks." Security and Communication Networks 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/6854612.
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Machine Type Communication (MTC), as one of the most important wireless communication technologies in the future wireless communication, has become the new business growth point of mobile communication network. It is a key point to achieve seamless handovers within Evolved-Universal Terrestrial Radio Access Network (E-UTRAN) for massive MTC (mMTC) devices in order to support mobility in the Long Term Evolution-Advanced (LTE-A) networks. When mMTC devices simultaneously roam from a base station to a new base station, the current handover mechanisms suggested by the Third-Generation Partnership Project (3GPP) require several handover signaling interactions, which could cause the signaling load over the access network and the core network. Besides, several distinct handover procedures are proposed for different mobility scenarios, which will increase the system complexity. In this paper, we propose a simple and secure uniform group-based handover authentication scheme for mMTC devices based on the multisignature and aggregate message authentication code (AMAC) techniques, which is to fit in with all of the mobility scenarios in the LTE-A networks. Compared with the current 3GPP standards, our scheme can achieve a simple authentication process with robust security protection including privacy preservation and thus avoid signaling congestion. The correctness of the proposed group handover authentication protocol is formally proved in the Canetti-Krawczyk (CK) model and verified based on the AVISPA and SPAN.
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Liesegang, Sergi, Antonio Pascual-Iserte, and Olga Muñoz. "Approximations of the Aggregated Interference Statistics for Outage Analysis in Massive MTC." Sensors 19, no. 24 (2019): 5448. http://dx.doi.org/10.3390/s19245448.
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This paper presents several analytic closed-form approximations of the aggregated interference statistics within the framework of uplink massive machine-type-communications (mMTC), taking into account the random activity of the sensors. Given its discrete nature and the large number of devices involved, a continuous approximation based on the Gram–Charlier series expansion of a truncated Gaussian kernel is proposed. We use this approximation to derive an analytic closed-form expression for the outage probability, corresponding to the event of the signal-to-interference-and-noise ratio being below a detection threshold. This metric is useful since it can be used for evaluating the performance of mMTC systems. We analyze, as an illustrative application of the previous approximation, a scenario with several multi-antenna collector nodes, each equipped with a set of predefined spatial beams. We consider two setups, namely single- and multiple-resource, in reference to the number of resources that are allocated to each beam. A graph-based approach that minimizes the average outage probability, and that is based on the statistics approximation, is used as allocation strategy. Finally, we describe an access protocol where the resource identifiers are broadcast (distributed) through the beams. Numerical simulations prove the accuracy of the approximations and the benefits of the allocation strategy.
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Wu, Yan-Jing, Wen-Shyang Hwang, Chih-Yi Shen, and Yu-Yen Chen. "Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches." Electronics 11, no. 14 (2022): 2111. http://dx.doi.org/10.3390/electronics11142111.
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Massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) are two key services in fifth-generation (5G) mobile wireless networks. These networks have been developed with extremely high service quality requirements: scalability for mMTC and reliability with low latency for URLLC. Fifth-generation network slicing will play a key role in supporting the distinct requirements of various services. Software-defined networking (SDN), a promising technology for network softwarization, physically separates the network control plane from the data plane by centrally controlling switches with an SDN controller. However, control channel bottleneck and processing delays due to this centralized control may reduce the scalability, reliability, and security of SDN. This paper proposes an SDN framework with programming protocol–independent packet processor (P4) switches (SDNPS), and defines a packet format containing in-band network telemetry data to simultaneously support heavy Internet of Things and URLLC traffic in 5G network slices. The method both satisfies the requirements of mMTC and URLLC and alleviates the load on the SDN controller. P4 is an advanced switch interface technology that provides enhanced stateful forwarding and reveals a persistent state on the SDN data plane. To demonstrate the superiority of SDNPS, simulations are performed on conventional SDNs and SDNPS. SDNPS outperforms the other schemes in terms of average throughput, packet loss ratio, and packet delay for both the mMTC and URLLC network slices.
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Shanbhag, Kiran Vinayak, and Dayakshini Sathish. "Low complexity physical layer security approach for 5G internet of things." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6466. http://dx.doi.org/10.11591/ijece.v13i6.pp6466-6475.
Full textAbstract:
<span lang="EN-US">Fifth-generation (5G) massive machine-type communication (mMTC) is expected to support the cellular adaptation of internet of things (IoT) applications for massive connectivity. Due to the massive access nature, IoT is prone to high interception probability and the use of conventional cryptographic techniques in these scenarios is not practical considering the limited computational capabilities of the IoT devices and their power budget. This calls for a lightweight physical layer security scheme which will provide security without much computational overhead and/or strengthen the existing security measures. Here a shift based physical layer security approach is proposed which will provide a low complexity security without much changes in baseline orthogonal frequency division multiple access (OFDMA) architecture as per the low power requirements of IoT by systematically rearranging the subcarriers. While the scheme is compatible with most fast Fourier transform (FFT) based waveform contenders which are being proposed in 5G especially in mMTC and ultra-reliable low latency communication (URLLC), it can also add an additional layer of security at physical layer to enhanced mobile broadband (eMBB).</span>
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Sharma, Vipin, Krishna Pandey, Rachit Patel, and Kamal Kumar Gaur. "ROBUST CHANNEL ENCODING FOR MMTC NODE IN 5G (NR)." International Journal of Advanced Research 8, no. 10 (2020): 234–39. http://dx.doi.org/10.21474/ijar01/11848.
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The paper is focused on robust channel encoding for Massive machine type communication (mMTC) communication in 5G (NR). The performance evaluation of channel encoding is obtained at 5G New Radio (NR) PHY. The results show that reliable bit error rate (BER) against the poor channel condition or random fluctuated channel applied. Channel encoding algorithm as a forward error correction code (FEC) is applied on packet to packet basis to improve the BER performance against inter symbol interference. The concept of adaptation of code rate is valuable to reduce the payload effect and provide optimum solution between BER and throughput. Adaptive code rate selection is based on impact of earlier transmitted packet bit using feedback indicator.
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Nawaz, Farhan, Hemant Kumar, Syed Ali Hassan, and Haejoon Jung. "Opportunistic Large Array Propagation Models: A Comprehensive Survey." Sensors 21, no. 12 (2021): 4206. http://dx.doi.org/10.3390/s21124206.
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Enabled by the fifth-generation (5G) and beyond 5G communications, large-scale deployments of Internet-of-Things (IoT) networks are expected in various application fields to handle massive machine-type communication (mMTC) services. Device-to-device (D2D) communications can be an effective solution in massive IoT networks to overcome the inherent hardware limitations of small devices. In such D2D scenarios, given that a receiver can benefit from the signal-to-noise-ratio (SNR) advantage through diversity and array gains, cooperative transmission (CT) can be employed, so that multiple IoT nodes can create a virtual antenna array. In particular, Opportunistic Large Array (OLA), which is one type of CT technique, is known to provide fast, energy-efficient, and reliable broadcasting and unicasting without prior coordination, which can be exploited in future mMTC applications. However, OLA-based protocol design and operation are subject to network models to characterize the propagation behavior and evaluate the performance. Further, it has been shown through some experimental studies that the most widely-used model in prior studies on OLA is not accurate for networks with networks with low node density . Therefore, stochastic models using quasi-stationary Markov chain are introduced, which are more complex but more exact to estimate the key performance metrics of the OLA transmissions in practice. Considering the fact that such propagation models should be selected carefully depending on system parameters such as network topology and channel environments, we provide a comprehensive survey on the analytical models and framework of the OLA propagation in the literature, which is not available in the existing survey papers on OLA protocols. In addition, we introduce energy-efficient OLA techniques, which are of paramount importance in energy-limited IoT networks. Furthermore, we discuss future research directions to combine OLA with emerging technologies.
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Jadhav, Dr. Ramchandra T., and Ketan P. Salunkhe. "5G technology and beyond (Innovation in wireless communication)." International Journal of Advance and Applied Research 6, no. 25(C) (2025): 228–31. https://doi.org/10.5281/zenodo.15331788.
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Abstract: The rapid evolution of wireless communication technologies has driven the Development of 5G networks, promising high-speed data transmission, low latency, And the seamless integration of the Internet of Things (IoT). 5G technology marks a significant leap from its predecessors, facilitating advancements in sectors such as Autonomous vehicles, smart cities, healthcare, and industrial automation. This Research explores the core principles behind 5G technology, its architecture, and its Applications, along with challenges such as spectrum management, network Infrastructure, and security concerns. Furthermore, the study investigates the future Trajectory of wireless communication, focusing on beyond-5G (B5G) and 6G Technologies. B5G aims to enhance connectivity with ultra-reliable, low-latency Communication (URLLC), massive machine-type communications (mMTC), and Network intelligence powered by AI. The abstract highlights key innovations Expected in the evolution of 5G, including terahertz communications, advanced AI- Driven networks, and the integration of satellite networks. As global demands for Faster, more reliable networks continue to grow, the ongoing research into 5G and beyond is crucial for meeting the needs of future technological landscapes.
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Wang, Zhen, Dantao Han, Yanjie Gong, and Yanling Zhao. "Research on the convergence architecture of 5G and industrial communication." MATEC Web of Conferences 336 (2021): 04013. http://dx.doi.org/10.1051/matecconf/202133604013.
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As the next generation of mobile communication technology, 5G has the characteristics of enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communication (URLLC), which are critical technologies for the future development of industrie 4.0. So far, these technologies have been explored and applied in industry, however, there is still a lack of general architectures. This paper presents a 5G and industrial communication convergence architecture and elaborates on the implementation methods from the aspects of device access technology and network configuration strategies, and discusses the transparent transmission and mapping methods adopted by 5G terminal access technology as well.
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Burkov, Artem. "Signal power and energy-per-bit optimization problems in systems mMTC." Information and Control Systems, no. 5 (October 26, 2021): 51–58. http://dx.doi.org/10.31799/1684-8853-2021-5-51-58.
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Introduction: Currently, the issues of Internet of Things technology are being actively studied. The operation of a large number of various self-powered sensors is within the framework of a massive machine-type communication scenario, using random access methods. Topical issues in this type of communication are how to reduce the transmission signal power and to increase the device lifetime by reducing the consumed energy per bit. Purpose: Formulation and analysis of the problems of minimizing the transmission power and consumed energy per bit in systems with or without retransmissions in order to obtain the achievability bounds. Results: A model of the system is described, within which four problems are formulated and described, concerning the signal power minimization and energy consumption for given parameters (the number of information bits, the spectral efficiency of the system, and the Packet Delivery Ratio). The numerical results of solving these optimization problems are presented. They make it possible to obtain the achievability bounds for the considered characteristics in systems with or without losses. The lower bounds obtained by the Shannon formula are also presented, assuming that the message length is not limited. The obtained results showed that solving the minimization problem with respect to one of the parameters (signal power or consumed energy per bit) does not minimize the second parameter. This difference is most significant for information messages of a small length, which is common in IoT scenarios. Practical relevance: The results obtained allow you to assess the potential for minimizing the transmission signal power and consumed energy per bit in random multiple access systems with massive machine-type communication scenarios. Discussion: The presented problems were solved without taking into account the average delay of message transmission; the introduction of such a limitation should increase the transmitted signal power and consumed energy per bit.
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Siddiqi, Yu, and Joung. "5G Ultra-Reliable Low-Latency Communication Implementation Challenges and Operational Issues with IoT Devices." Electronics 8, no. 9 (2019): 981. http://dx.doi.org/10.3390/electronics8090981.
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To meet the diverse industrial and market demands, the International Telecommunication Union (ITU) has classified the fifth-generation (5G) into ultra-reliable low latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC). Researchers conducted studies to achieve the implementation of the mentioned distributions efficiently, within the available spectrum. This paper aims to highlight the importance of URLLC in accordance with the approaching era of technology and industry requirements. While highlighting a few implementation issues of URLLC, concerns for the Internet of things (IoT) devices that depend on the low latency and reliable communications of URLLC are also addressed. In this paper, the recent progress of 3rd Generation Partnership Project (3GPP) standardization and the implementation of URLLC are included. Finally, the research areas that are open for further investigation in URLLC implementation are highlighted, and efficient implementation of URLLC is discussed.
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Cardoso, Caio M. M., Fabrício J. B. Barros, Joel A. R. Carvalho, et al. "SNR Prediction with ANN for UAV Applications in IoT Networks Based on Measurements." Sensors 22, no. 14 (2022): 5233. http://dx.doi.org/10.3390/s22145233.
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The 5G deployment brings forth the usage of Unmanned Aerial Vehicles (UAV) to assist wireless networks by providing improved signal coverage, acting as relays or base-stations. Another technology that could help achieve 5G massive machine-type communications (mMtc) goals is the Long Range Wide-Area Network (LoRaWAN) communication protocol. This paper studied these complementary technologies, LoRa and UAV, through measurement campaigns in suburban, densely forested environments. Downlink and uplink communication at different heights and spreading factors (SF) demonstrate distinct behavior through our analysis. Moreover, a neural network was trained to predict the measured signal-to-noise ratio (SNR) behavior and results compared with SNR regression models. For the downlink scenario, the neural network results show a root mean square error (RMSE) variation between 1.2322–1.6623 dB, with an error standard deviation (SD) less than 1.6730 dB. For the uplink, the RMSE variation was between 0.8714–1.3891 dB, with an error SD less than 1.1706 dB.
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Han, Jingyuan, Tao Liu, Jingye Ma, Yi Zhou, Xin Zeng, and Ying Xu. "Anomaly Detection and Early Warning Model for Latency in Private 5G Networks." Applied Sciences 12, no. 23 (2022): 12472. http://dx.doi.org/10.3390/app122312472.
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Different from previous generations of communication technology, 5G has tailored several modes especially for industrial applications, such as Ultra-Reliable Low-Latency Communications (URLLC) and Massive Machine Type Communications (mMTC). The industrial private 5G networks require high performance of latency, bandwidth, and reliability, while the deployment environment is usually complicated, causing network problems difficult to identify. This poses a challenge to the operation and maintenance (O&M) of private 5G networks. It is needed to quickly diagnose or predict faults based on high-dimensional data of networks and services to reduce the impact of network faults on services. This paper proposes a ConvAE-Latency model for anomaly detection, which enhances the correlation between target indicators and hidden features by multi-target learning. Meanwhile, transfer learning is applied for anomaly prediction in the proposed LstmAE-TL model to solve the problem of unbalanced samples. Based on the China Telecom data platform, the proposed models are deployed and tested in an Automated Guided Vehicles (AGVs) application scenario. The results have been improved compared to existing research.
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Jang, Changuk, Juhong Han, Akshita Maradapu Vera Venkata Sai, Yingshu Li, and Okyeon Yi. "A Study on Scalar Multiplication Parallel Processing for X25519 Decryption of 5G Core Network SIDF Function for mMTC IoT Environment." Wireless Communications and Mobile Computing 2022 (June 6, 2022): 1–17. http://dx.doi.org/10.1155/2022/4087816.
Full textAbstract:
When 5G telecommunication becomes a standardized and widely used communication medium, it must be implemented in coherence with certain 5G network standards and requirements. One such requirement is a Subscription Concealed Identifier called SUCI. SUCI prevents the exposure of international mobile subscriber identity (IMSI), which was a vulnerability in previous generation mobile telecommunication networks. Unlike IMSI, SUCI is encrypted and transmitted using a symmetric key cryptographic algorithm, to prevent the aforementioned vulnerabilities. However, for the first terminal to be encrypted, it is necessary to exchange a key with the home network, and this key exchange for SUCI encryption is performed through the Elliptic Curve Integrated Encryption Scheme (ECIES) key exchange algorithm, which is a public-key encryption scheme. However, ECIES uses more computing resources compared to a symmetric key cryptographic algorithm. Additionally, for 5G Subscriber Identity Deconcealing Function (SIDF) to satisfy the massive machine-type communication (mMTC) requirements of 5G, it is necessary to decrypt at least a million SUCIs within a short time. This puts a great burden on the 5G home network to provide the mMTC service for IoT. Therefore, in this paper, we propose a method of constructing 5G SIDF in an mMTC IoT environment. A key method of the proposed 5G SIDF configuration is the use of GPUs. This proposal was aimed at reducing the load in the mMTC environment by performing parallel processing of all cryptographic operations performed in the SIDF using a GPU. In particular, we focused on parallelization of public-key encryption algorithms. In addition, we also compared the method proposed in this paper through a survey of various 5G security products.
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Bueno, Felipe Augusto Dutra, Alessandro Goedtel, Taufik Abrão, and José Carlos Marinello. "A Multi-Agent Reinforcement Learning-Based Grant-Free Random Access Protocol for mMTC Massive MIMO Networks." Journal of Sensor and Actuator Networks 13, no. 3 (2024): 30. http://dx.doi.org/10.3390/jsan13030030.
Full textAbstract:
The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols.