Academic literature on the topic 'User equipment (UE) - to - network relaying'

With the advancement of smart phone technologies cellular communication has come to a stage where user bandwidth has surpassed the available bandwidth. In addition, the well-organized but stubborn architecture of cellular networks sometimes creates hindrance to the optimal usage of the network resources. Due to this, a User Equipment (UE) experiencing a poor channel to the Base Station (BTS) or evolved NodeB (eNB) or any other Access Point (AP) retransmits the data. In such scenarios, Device-to-Device (D2D) communication and offload/relay underlying the cellular networks or the access networks provides a unique solution where the affected UE can find a close proximity offloader UE to relay its data to eNB. Delay Tolerant Networks (DTN) is another framework which has potential usage in low-connectivity zones like cell edge and/or remote locations in cellular networks. This chapter investigates various possibilities where D2D and DTN can be jointly used to improve teledensity as well delayed but guaranteed services to poor or no connectivity areas.

3GPP Long Term Evolution-Advanced (LTE-A) defines a wireless network standard for high packet transmission rate and low packet latency provisions. When an user equipment(UE) moves away from signal range of serving cell may cause the received signal strength weakened, even if disconnected. To solve this problem, it is necessary to execute handover procedure. When executed handover procedure, it is may fail because to early handover, to late handover or handover to wrong cell and then occur packet loss or communication interruption problems. In this thesis, we propose predicted handover mechanism. User equipments provide history information for eNBs to estimate the user equipment's location and velocity. Based on history information, our proposed mechanism can achieve to handover the appropriate target cell. Through selecting the target cell efficiently, we can reduce the rate of handover failure and Ping-Pong handover. Finally, improving the UE's QoS. And conducted a simulation integration, combined with the user equipment velocity prediction mechanism and trigger time adjustment mechanism. Experimental results show that compared with the standard user equipment handover process can significantly reduce the rate of handover failure and Ping-Pong handover.

In the current 3G systems and the upcoming 4G wireless systems, missing neighbor pilot refers to the condition of receiving a high-level pilot signal from a Base Station (BS) that is not listed in the mobile receiver’s neighbor list (LCC International, 2004; Agilent Technologies, 2005). This pilot signal interferes with the existing ongoing call, causing the call to be possibly dropped and increasing the handoff call dropping probability. Figure 1 describes the missing pilot scenario where BS1 provides the highest pilot signal compared to BS1 and BS2’s signals. Unfortunately, this pilot is not listed in the mobile user’s active list. The horizontal and vertical handoff algorithms are based on continuous measurements made by the user equipment (UE) on the Primary Scrambling Code of the Common Pilot Channel (CPICH). In 3G systems, UE attempts to measure the quality of all received CPICH pilots using the Ec/Io and picks a dominant one from a cellular system (Chiung & Wu, 2001; El-Said, Kumar, & Elmaghraby, 2003). The UE interacts with any of the available radio access networks based on its memorization to the neighboring BSs. As the UE moves throughout the network, the serving BS must constantly update it with neighbor lists, which tell the UE which CPICH pilots it should be measuring for handoff purposes. In 4G systems, CPICH pilots would be generated from any wireless system including the 3G systems (Bhashyam, Sayeed, & Aazhang, 2000). Due to the complex heterogeneity of the 4G radio access network environment, the UE is expected to suffer from various carrier interoperability problems. Among these problems, the missing neighbor pilot is considered to be the most dangerous one that faces the 4G industry. The wireless industry responded to this problem by using an inefficient traditional solution relying on using antenna downtilt such as given in Figure 2. This solution requires shifting the antenna’s radiation pattern using a mechanical adjustment, which is very expensive for the cellular carrier. In addition, this solution is permanent and is not adaptive to the cellular network status (Agilent Technologies, 2005; Metawave, 2005).

A revolutionary advance in wireless communication technology has been illustrated through the development of fifth generation (5G) network architecture and standards. In addition, 5G is intended to provide unequalled speed, minimal latency, and widespread device connection in contrast to its predecessors. The user equipment (UE) core network (CN) and radio access network (RAN) are the three main parts of the architecture. By using technologies like beamforming and Massive MIMO the RAN maximizes spectral efficiency. Deploying an open-minded, cloud-based design that utilizes software-defined networking (SDN) and network function virtualization (NFV) to improve scalability and efficiency of resources implies a significant shift in the CN. Further, new radio (NR) protocol supports a variety of applications thereby high data rates is crucial to the development of 5G. Ultimately the requirements of (MMTC), ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (EMBB) are all addressed by this advancement.

In this chapter, the authors explore a cost model and the come about cost-minimization client booking issue in multi-level mist figuring organizations. For an average multi-level haze figuring network comprising of one haze control hub (FCN), different fog access nodes (FANs), and user equipment (UE), how to model the cost paid to FANs for propelling assets sharing and how to adequately plan UEs to limit the cost for FCN are still issues to be settled. To unravel these issues, multi-level cost model, including the administration delay and a straight backwards request dynamic installment conspire, is proposed, and a cost-minimization client planning issue is defined. Further, the client planning issue is reformulated as an expected game and demonstrated to have a Nash equilibrium (NE) arrangement.

In the current 3G systems and the upcoming 4G wireless systems, missing neighbor pilot refers to the condition of receiving a high-level pilot signal from a Base Station (BS) that is not listed in the mobile receiver’s neighbor list (LCC International, 2004; Agilent Technologies, 2005). This pilot signal interferes with the existing ongoing call, causing the call to be possibly dropped and increasing the handoff call dropping probability. Figure 1 describes the missing pilot scenario where BS1 provides the highest pilot signal compared to BS1 and BS2’s signals. Unfortunately, this pilot is not listed in the mobile user’s active list. The horizontal and vertical handoff algorithms are based on continuous measurements made by the user equipment (UE) on the Primary Scrambling Code of the Common Pilot Channel (CPICH). In 3G systems, UE attempts to measure the quality of all received CPICH pilots using the Ec/Io and picks a dominant one from a cellular system (Chiung & Wu, 2001; El-Said, Kumar, & Elmaghraby, 2003). The UE interacts with any of the available radio access networks based on its memorization to the neighboring BSs. As the UE moves throughout the network, the serving BS must constantly update it with neighbor lists, which tell the UE which CPICH pilots it should be measuring for handoff purposes. In 4G systems, CPICH pilots would be generated from any wireless system including the 3G systems (Bhashyam, Sayeed, & Aazhang, 2000). Due to the complex heterogeneity of the 4G radio access network environment, the UE is expected to suffer from various carrier interoperability problems. Among these problems, the missing neighbor pilot is considered to be the most dangerous one that faces the 4G industry. The wireless industry responded to this problem by using an inefficient traditional solution relying on using antenna downtilt such as given in Figure 2. This solution requires shifting the antenna’s radiation pattern using a mechanical adjustment, which is very expensive for the cellular carrier. In addition, this solution is permanent and is not adaptive to the cellular network status (Agilent Technologies, 2005; Metawave, 2005).Therefore, a self-managing solution approach is necessary to solve this critical problem. Whisnant, Kalbarczyk, and Iyer (2003) introduced a system model for dynamically reconfiguring application software. Their model relies on considering the application’s static structure and run-time behaviors to construct a workable version of reconfiguration software application. Self-managing applications are hard to test and validate because they increase systems complexity (Clancy, 2002). The ability to reconfigure a software application requires the ability to deploy a dynamically hardware infrastructure in systems in general and in cellular systems in particular (Jann, Browning, & Burugula, 2003).