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Journal articles on the topic 'MIMO ; Spatial Modulation ; experimental'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Guo, Xinyue, Keer Zhang, and Xufa Huang. "Switching MIMO System with Adaptive OFDM Modulation for Indoor Visible Light Communication." Advances in Condensed Matter Physics 2018 (September 2, 2018): 1–7. http://dx.doi.org/10.1155/2018/5694196.

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In this paper, we propose and experimentally demonstrate a switching multiple input and multiple output (MIMO) system combining with adaptive orthogonal frequency division multiplexing (OFDM) modulation for high-speed indoor visible light communications. The adaptive OFDM modulation, which is realized by power and bit allocation on OFDM subchannels, is utilized to achieve the maximum channel capacity under a given target bit error rate (BER). Meanwhile, the MIMO mode switches between spatial multiplexing and transmit diversity adapting to the channel correlation, where the modulation order solved by adaptive OFDM modulation is chosen as the switching criterion. Experimental results validate data rates improvement over the pure spatial multiplexing and the pure transmit diversity system, where BERs are all below the 7% preforward error correction (pre-FEC) threshold of 3.8 × 10−3 in experiments.

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2

Zhang, Xinhe, Yuehua Zhang, Chang Liu, and Hanzhong Jia. "Low-Complexity Detection Algorithms for Spatial Modulation MIMO Systems." Journal of Electrical and Computer Engineering 2018 (November 15, 2018): 1–7. http://dx.doi.org/10.1155/2018/4034625.

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In this paper, the authors propose three low-complexity detection schemes for spatial modulation (SM) systems based on the modified beam search (MBS) detection. The MBS detector, which splits the search tree into some subtrees, can reduce the computational complexity by decreasing the nodes retained in each layer. However, the MBS detector does not take into account the effect of subtree search order on computational complexity, and it does not consider the effect of layers search order on the bit-error-rate (BER) performance. The ost-MBS detector starts the search from the subtree where the optimal solution is most likely to be located, which can reduce total searches of nodes in the subsequent subtrees. Thus, it can decrease the computational complexity. When the number of the retained nodes is fixed, which nodes are retained is very important. That is, the different search orders of layers have a direct influence on BER. Based on this, we propose the oy-MBS detector. The ost-oy-MBS detector combines the detection order of ost-MBS and oy-MBS together. The algorithm analysis and experimental results show that the proposed detectors outstrip MBS with respect to the BER performance and the computational complexity.

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3

Yamaguchi, Kazuhiro, Takaharu Nagahashi, Takuya Akiyama, Hideaki Matsue, Kunio Uekado, Takakazu Namera, Hiroshi Fukui, and Satoshi Nanamatsu. "Computer Simulation and Field Experiment for Downlink Multiuser MIMO in Mobile WiMAX System." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/481676.

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The transmission performance for a downlink mobile WiMAX system with multiuser multiple-input multiple-output (MU-MIMO) systems in a computer simulation and field experiment is described. In computer simulation, a MU-MIMO transmission system can be realized by using the block diagonalization (BD) algorithm, and each user can receive signals without any signal interference from other users. The bit error rate (BER) performance and channel capacity in accordance with modulation schemes and the number of streams were simulated in a spatially correlated multipath fading environment. Furthermore, we propose a method for evaluating the transmission performance for this downlink mobile WiMAX system in this environment by using the computer simulation. In the field experiment, the received power and downlink throughput in the UDP layer were measured on an experimental mobile WiMAX system developed in Azumino City in Japan. In comparison with the simulated and experimented results, the measured maximum throughput performance in the downlink had almost the same performance as the simulated throughput. It was confirmed that the experimental mobile WiMAX system for MU-MIMO transmission successfully increased the total channel capacity of the system.

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4

Tuan, Le Minh, Le Hoang Son, Hoang Viet Long, L. Rajaretnam Priya, K. Ruba Soundar, Y. Harold Robinson, and Raghvendra Kumar. "ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET." Sensors 20, no. 12 (June 16, 2020): 3394. http://dx.doi.org/10.3390/s20123394.

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One of the crucial problems in Industry 4.0 is how to strengthen the performance of mobile communication within mobile ad-hoc networks (MANETs) and mobile computational grids (MCGs). In communication, Industry 4.0 needs dynamic network connectivity with higher amounts of speed and bandwidth. In order to support multiple users for video calling or conferencing with high-speed transmission rates and low packet loss, 4G technology was introduced by the 3G Partnership Program (3GPP). 4G LTE is a type of 4G technology in which LTE stands for Long Term Evolution, followed to achieve 4G speeds. 4G LTE supports multiple users for downlink with higher-order modulation up to 64 quadrature amplitude modulation (QAM). With wide coverage, high reliability and large capacity, LTE networks are widely used in Industry 4.0. However, there are many kinds of equipment with different quality of service (QoS) requirements. In the existing LTE scheduling methods, the scheduler in frequency domain packet scheduling exploits the spatial, frequency, and multi-user diversity to achieve larger MIMO for the required QoS level. On the contrary, time-frequency LTE scheduling pays attention to temporal and utility fairness. It is desirable to have a new solution that combines both the time and frequency domains for real-time applications with fairness among users. In this paper, we propose a channel-aware Integrated Time and Frequency-based Downlink LTE Scheduling (ITFDS) algorithm, which is suitable for both real-time and non-real-time applications. Firstly, it calculates the channel capacity and quality using the channel quality indicator (CQI). Additionally, data broadcasting is maintained by using the dynamic class-based establishment (DCE). In the time domain, we calculate the queue length before transmitting the next packets. In the frequency domain, we use the largest weight delay first (LWDF) scheduling algorithm to allocate resources to all users. All the allocations would be taken placed in the same transmission time interval (TTI). The new method is compared against the largest weighted delay first (LWDF), proportional fair (PF), maximum throughput (MT), and exponential/proportional fair (EXP/PF) methods. Experimental results show that the performance improves by around 12% compared with those other algorithms.

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5

Cogalan, T., H. Haas, and E. Panayirci. "Optical spatial modulation design." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2169 (March 2, 2020): 20190195. http://dx.doi.org/10.1098/rsta.2019.0195.

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Visible light communication (VLC) systems are inherently signal-to-noise ratio (SNR) limited due to link budget constraints. One favourable method to overcome this limitation is to focus on the pre-log factors of the channel capacity. Multiple-input multiple-output (MIMO) techniques are therefore a promising avenue of research. However, inter-channel interference in MIMO limits the achievable capacity. Spatial modulation (SM) avoids this limitation. Furthermore, the performance of MIMO systems in VLC is limited by the similarities among spatial channels. This limitation becomes particularly severe in intensity modulation/direct detection (IM/DD) systems because of the lack of phase information. The motivation of this paper is to propose a system that results in a multi-channel transmission system that enables reliable multi-user optical MIMO SM transmission without the need for a precoder, power allocation algorithm or additional optics at the receiver. A general bit error performance model for the SM system is developed for an arbitrary number of light-emitting diodes (LEDs) in conjunction with pulse amplitude modulation. Based on this model, an LED array structure is designed to result in spatially separated multiple channels by manipulating the transmitter geometry. This article is part of the theme issue ‘Optical wireless communication’.

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6

Choi, Jiwook, Yunseo Nam, and Namyoon Lee. "Spatial Lattice Modulation for MIMO Systems." IEEE Transactions on Signal Processing 66, no. 12 (June 15, 2018): 3185–98. http://dx.doi.org/10.1109/tsp.2018.2827325.

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7

Basnayaka, Dushyantha A., and Harald Haas. "MIMO Interference Channel Between Spatial Multiplexing and Spatial Modulation." IEEE Transactions on Communications 64, no. 8 (August 2016): 3369–81. http://dx.doi.org/10.1109/tcomm.2016.2580146.

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8

Solanki, KS, and Abhilasha Singh. "MULTIPLE ACTIVE SPATIAL MODULATION IN MIMO SYSTEMS." International Journal of Advanced Research 5, no. 9 (September 30, 2017): 391–93. http://dx.doi.org/10.21474/ijar01/5340.

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9

Anto., Nicy. "MULTIPLE ACTIVE SPATIAL MODULATION IN MIMO SYSTEMS." International Journal of Advanced Research 4, no. 9 (September 30, 2016): 547–51. http://dx.doi.org/10.21474/ijar01/1516.

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10

Park, Myung Chul, and Dong Seog Han. "A Golden Coded-Spatial Modulation MIMO System." Journal of the Institute of Electronics and Information Engineers 50, no. 10 (October 25, 2013): 31–40. http://dx.doi.org/10.5573/ieek.2013.50.10.031.

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11

Abu-Hudrouss, Ammar M., M. T. O. El Astal, Alaa H. Al Habbash, and Sonia Aissa. "Signed Quadrature Spatial Modulation for MIMO Systems." IEEE Transactions on Vehicular Technology 69, no. 3 (March 2020): 2740–46. http://dx.doi.org/10.1109/tvt.2020.2964118.

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12

v. Nair, Karthika, and Poorna R. Prabhu. "Multiple Active Spatial Modulation in MIMO Systems." International Journal of Engineering Trends and Technology 10, no. 10 (April 25, 2014): 489–91. http://dx.doi.org/10.14445/22315381/ijett-v10p295.

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13

Sarieddeen, Hadi, Mohamed-Slim Alouini, and Tareq Y. Al-Naffouri. "Terahertz-Band Ultra-Massive Spatial Modulation MIMO." IEEE Journal on Selected Areas in Communications 37, no. 9 (September 2019): 2040–52. http://dx.doi.org/10.1109/jsac.2019.2929455.

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14

Soria, F. R. C., J. S. Garcia, V. I. R. Abdala, and R. P. Michel. "Multiuser MIMO Downlink Transmission using Spatial Modulation." IEEE Latin America Transactions 13, no. 11 (November 2015): 3568–72. http://dx.doi.org/10.1109/tla.2015.7387932.

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15

Kumari, Prabha. "A Survey on Spatial Modulation and MIMO System for Emerging Wireless Communication." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3059–62. http://dx.doi.org/10.22214/ijraset.2021.35590.

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In this paper we have studied about Spatial Modulation (SM) in MIMO system. Spatial modulation is a unique and newly proposed technique. Spatial modulation is a multiple input multiple output technique which provides higher throughput and gain as compared to Quadrature Amplitude Modulation. Spatial modulation is a technique which enhances the performance of MIMO system. Spatial modulation and MIMO technique are used to attracted research for its high energy and spectral efficiency because it is working on single RF chain. This paper has considered the advantages of spatial modulation and MIMO systems, using different technique to improve the bandwidth efficiency. Some of such MIMO systems applications are discussed wherein become a requirement for an emerging wireless communication system.

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16

Li, Qiang, Miaowen Wen, and Marco Di Renzo. "Single-RF MIMO: From Spatial Modulation to Metasurface-Based Modulation." IEEE Wireless Communications 28, no. 4 (August 2021): 88–95. http://dx.doi.org/10.1109/mwc.021.2000376.

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17

Mohaisen, Manar. "Generalized Complex Quadrature Spatial Modulation." Wireless Communications and Mobile Computing 2019 (April 28, 2019): 1–12. http://dx.doi.org/10.1155/2019/3137927.

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Spatial modulation (SM) is a multiple-input multiple-output (MIMO) system that achieves a MIMO high spectral efficiency while maintaining the transmitter computational complexity and requirements as low as those of the single-input systems. The complex quadrature spatial modulation (CQSM) builds on the QSM scheme and improves the spectral efficiency by transmitting two signal symbols at each channel use. In this paper, we propose two generalizations of CQSM, namely, generalized CQSM with unique combinations (GCQSM-UC) and with permuted combinations (GCQSM-PC). These two generalizations perform close to CQSM or outperform it, depending on the system parameters. Also, the proposed schemes require much less transmit antennas to achieve the same spectral efficiency of CQSM, for instance, assuming 16-QAM, GCQSM-PC, and GCQSM-UC require 10 and 15 transmit antennas, respectively, to achieve the same spectral of CQSM which is equipped with 32 antennas.

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18

Rachedi, Kammel, Abdelwaheb Ourir, Dinh-Thuy Phan-Huy, and Julien De Rosny. "Reconfigurable Compact Antenna for Spatial Modulation MIMO Communications." International Journal on Communications Antenna and Propagation (IRECAP) 9, no. 3 (June 30, 2019): 218. http://dx.doi.org/10.15866/irecap.v9i3.17051.

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19

Zhang, Yao Yuan, Zi Sheng Zhang, Ya Fei Lian, and Qian Ding. "A Novel Spatial Modulation Scheme for MIMO System." Applied Mechanics and Materials 198-199 (September 2012): 1573–77. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1573.

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A novel spatial modulation scheme based on Space-Shift Keying (SSK) modulation is introduced for MIMO system. The proposed scheme doubles the spectrum efficiency of SSK by assigning unique symbols to different combinations of antenna indexes per symbol duration, while retaining the SSK’s inherent advantages. The optimal detector is utilized at the receiver simultaneously. Analytical and simulation results show: 1) in Rayleigh fading channel scenario，the obtained gains in SNR for the same spectral efficiency of the proposed scheme over amplitude/phase modulation (APM), vertical Bell Labs layered structure (V-BLAST) and spatial modulation (SM) systems are obvious; 2) under the correlated channel assumption, the new one still outperforms SM and V-BLAST clearly, which indicates the new one is more robust in the presence of channel correlation.

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20

Castillo-Soria, F. R., Joaquin Cortez, C. A. Gutiérrez, M. Luna-Rivera, and A. Garcia-Barrientos. "Extended quadrature spatial modulation for MIMO wireless communications." Physical Communication 32 (February 2019): 88–95. http://dx.doi.org/10.1016/j.phycom.2018.11.006.

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21

QU, Qian-qian, An-guo WANG, Zhong-er NIE, and Jian-feng ZHENG. "Block mapping spatial modulation scheme for MIMO systems." Journal of China Universities of Posts and Telecommunications 18, no. 5 (October 2011): 30–36. http://dx.doi.org/10.1016/s1005-8885(10)60099-4.

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22

Yang, Ping, Yue Xiao, Yi Yu, and Shaoqian Li. "Adaptive Spatial Modulation for Wireless MIMO Transmission Systems." IEEE Communications Letters 15, no. 6 (June 2011): 602–4. http://dx.doi.org/10.1109/lcomm.2011.040711.110014.

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23

G, Laxminarayanan, and Jayanthi S. "MIMO Schemes With Spatial Modulation in Wireless Communication." International Journal of Computer Trends and Technology 7, no. 4 (January 25, 2014): 183–87. http://dx.doi.org/10.14445/22312803/ijctt-v7p150.

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24

Humadi, Khaled M., Ahmed Iyanda Sulyman, and Abdulhameed Alsanie. "Spatial Modulation Concept for Massive Multiuser MIMO Systems." International Journal of Antennas and Propagation 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/563273.

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This paper presents the concept of spatial modulation (SM) scheme for massive multiuser MIMO (MU-MIMO) system. We consider a MU-MIMO system whereKusers, each equipped with multiple antennas, are jointly serviced by a multiantenna base station transmitter (BSTx) using appropriate precoding scheme at the BSTx. The main idea introduced here is the utilization of the user’s subchannel index corresponding to the precoding matrix used at the BSTx, to convey extra useful information. This idea has not been explored, and it provides significant throughput enhancements in a multiuser system with large number of users. We examine the performance of the proposed scheme by numerical simulations. The results show that as the number of users and the receiving antennas for each user increase, the overall system throughput gets better, albeit at the cost of some degradation in the BER performance due to interantenna interference (IAI) experienced at the receiver. We then explore zero-padding approach that helps to remove these IAI, in order to alleviate the BER degradations.

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25

Yang, Ping, Yue Xiao, Ming Xiao, Yong Liang Guan, Shaoqian Li, and Wei Xiang. "Adaptive Spatial Modulation MIMO Based on Machine Learning." IEEE Journal on Selected Areas in Communications 37, no. 9 (September 2019): 2117–31. http://dx.doi.org/10.1109/jsac.2019.2929404.

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26

Castillo-Soria, Francisco Ruben, Ertugrul Basar, Joaquín Cortez, and Marco Cardenas-Juarez. "Quadrature spatial modulation based multiuser MIMO transmission system." IET Communications 14, no. 7 (April 22, 2020): 1147–54. http://dx.doi.org/10.1049/iet-com.2019.0573.

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27

Rajani Kumari, P., K. Chenna Kesava Reddy, and K. S. Ramesh. "Hybrid Low Complex near Optimal Detector for Spatial Modulation." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 2 (April 1, 2017): 818. http://dx.doi.org/10.11591/ijece.v7i2.pp818-822.

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In our previous work maximum throughput in multi stream MIMO is analyzed by overcoming the inter antenna interference. To mitigate the Inter antenna interference spatial modulation can be used. Spatial Modulation(SM) aided MIMO systems are the emerging MIMO systems which are low complex and energy efficient. These systems additionally use spatial dimensions for transmitting information. In this paper a low complex detector based on matched filter is proposed for spatial modulation to achieve near maximum likelihood performance while avoiding exhaustive ML search since MF based detector exhibits a considerable reduced complexity since activated transmitting antenna and modulated amplitude phase modulation constellation are estimated separately. Simulation results show the performance of the proposed method with optimal ML detector, MRC and conventional matched filter methods.

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28

Abdelaziz, Amr, and Ashraf Elbayoumy. "Spatial Signature Modulation: A Novel Secure Modulation Approach for MIMO Wiretap Channel." Journal of Engineering Science and Military Technologies 1, no. 2 (October 1, 2017): 96–107. http://dx.doi.org/10.21608/ejmtc.2017.1438.1055.

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29

Aydin, Erdogan, Fatih Cogen, and Ertugrul Basar. "Code-Index Modulation Aided Quadrature Spatial Modulation for High-Rate MIMO Systems." IEEE Transactions on Vehicular Technology 68, no. 10 (October 2019): 10257–61. http://dx.doi.org/10.1109/tvt.2019.2928378.

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30

González-Pérez, M. G., J. M. Luna-Rivera, and D. U. Campos-Delgado. "Pre-equalization for MIMO Wireless Systems Using Spatial Modulation." Procedia Technology 3 (2012): 1–8. http://dx.doi.org/10.1016/j.protcy.2012.03.001.

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31

Lee, Ming-Chun, Wei-Ho Chung, and Ta-Sung Lee. "BER Analysis for Spatial Modulation in Multicast MIMO Systems." IEEE Transactions on Communications 64, no. 7 (July 2016): 2939–51. http://dx.doi.org/10.1109/tcomm.2016.2555912.

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32

Yang, Ping, Yue Xiao, Ming Xiao, and Zheng Ma. "NOMA-Aided Precoded Spatial Modulation for Downlink MIMO Transmissions." IEEE Journal of Selected Topics in Signal Processing 13, no. 3 (June 2019): 729–38. http://dx.doi.org/10.1109/jstsp.2019.2901131.

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33

Lakshmi Narasimhan, T., P. Raviteja, and A. Chockalingam. "Generalized Spatial Modulation in Large-Scale Multiuser MIMO Systems." IEEE Transactions on Wireless Communications 14, no. 7 (July 2015): 3764–79. http://dx.doi.org/10.1109/twc.2015.2411651.

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34

Di Renzo, Marco, Harald Haas, Ali Ghrayeb, Shinya Sugiura, and Lajos Hanzo. "Spatial Modulation for Generalized MIMO: Challenges, Opportunities, and Implementation." Proceedings of the IEEE 102, no. 1 (January 2014): 56–103. http://dx.doi.org/10.1109/jproc.2013.2287851.

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35

Yang, Ping, Yue Xiao, Yi Yu, Lei Li, Qian Tang, and Shaoqian Li. "Simplified Adaptive Spatial Modulation for Limited-Feedback MIMO Systems." IEEE Transactions on Vehicular Technology 62, no. 6 (July 2013): 2656–66. http://dx.doi.org/10.1109/tvt.2013.2242502.

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36

Sanila, K. S., and Neelakandan Rajamohan. "Enhanced Transmit-Receive Spatial Modulation for Massive MIMO Systems." IEEE Communications Letters 25, no. 7 (July 2021): 2300–2304. http://dx.doi.org/10.1109/lcomm.2021.3067881.

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37

Kumari, Prabha. "Performance Analysis of Spectrally Efficient Adaptive Spatial Modulation in MIMO System by using QAM." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1128–32. http://dx.doi.org/10.22214/ijraset.2021.38144.

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Abstract: In this article, we proposed a multiple input multiple outputs (MIMO) technique such as spectrally efficient adaptive quadrature spatial modulation (SEAQSM) which is based on space modulation techniques (SMTs). SMTs are logarithmically proportional to transmitting antenna & this technique fulfills the requirement of high data rate in the MIMO system. The Spatial position of the transmitting antenna improves the performance of the MIMO system. In space modulation technique spectral efficiency is logarithmically proportional to transmit antenna, if we increase the antenna at the transmitter end then the bandwidth efficiency significantly improved. We have to improve the performance MIMO system, minimize the latency and low power consumption. The proposed technique performance is explored over Rayleigh fading channel for a particular MIMO. These techniques underestimate the transmit antennas with less RF chain. In this paper, we analyzed the performance of our proposed scheme with conventional SM and QSM by using MONTE CARLO Simulation in term of BER with distinct order of QAM symbol. SE acquired for varying SNR at a BER of 10−3are obtained for uncorrelated Rayleigh channel. Keywords: Spatial Modulation(SM), MIMO, Spectral efficiency, Energy efficiency, Quadrature Spatial Modulation (QAM), Maximum Likelihood (ML) detector.

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38

He, Longzhuang, Jintao Wang, and Jian Song. "Spatial Modulation for More Spatial Multiplexing: RF-Chain-Limited Generalized Spatial Modulation Aided MM-Wave MIMO With Hybrid Precoding." IEEE Transactions on Communications 66, no. 3 (March 2018): 986–98. http://dx.doi.org/10.1109/tcomm.2017.2773543.

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39

Xiao, Yue, Qian Tang, Lisha Gong, Ping Yang, and Zongfei Yang. "Power Scaling for Spatial Modulation with Limited Feedback." International Journal of Antennas and Propagation 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/718482.

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Spatial modulation (SM) is a recently developed multiple-input multiple-output (MIMO) technique which offers a new tradeoff between spatial diversity and spectrum efficiency, by introducing the indices of transmit antennas as a means of information modulation. Due to the special structure of SM-MIMO, in the receiver, maximum likelihood (ML) detector can be combined with low complexity. For further improving the system performance with limited feedback, in this paper, a novel power scaling spatial modulation (PS-SM) scheme is proposed. The main idea is based on the introduction of scaling factor (SF) for weighting the modulated symbols on each transmit antenna of SM, so as to enlarge the minimal Euclidean distance of modulated constellations and improve the system performance. Simulation results show that the proposed PS-SM outperforms the conventional adaptive spatial modulation (ASM) with the same feedback amount and similar computational complexity.

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40

Junejo, Naveed Ur Rehman, Hamada Esmaiel, Haixin Sun, Zeyad A. H. Qasem, and Junfeng Wang. "Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies." Symmetry 11, no. 5 (May 24, 2019): 711. http://dx.doi.org/10.3390/sym11050711.

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Spatial Modulation Technologies (SMTs) are schemes that reduce inter-carrier interference (ICI), inter-channel interference, inter-antenna synchronization (IAS), and system complexity for multiple-input multiple-output (MIMO) communication systems. Moreover, high spectral and energy efficiency have rendered SMTs attractive to underwater acoustic (UWA) MIMO communication systems. Consequently, this paper focuses on SMTs such as spatial modulation (SM), generalized spatial modulation (GSM), and fully generalized spatial modulation (FGSM) in which one constant number and one multiple number of antennas are active to transmit data symbols in any time interval for underwater acoustic communication (UWAC). In SMTs, the receiver requires perfect channel state information (P-CSI) for accurate data detection. However, it is impractical that the perfect channel knowledge is available at the receiver. Therefore, channel estimation is of critical importance to obtain the CSI. This paper proposes the pilot-based recursive least-square (RLS) adaptive channel estimation method over the underwater time-varying MIMO channel. Furthermore, maximum likelihood (ML) decoder is used to detect the transmitted data and antennas indices from the received signal and the estimated UWA-MIMO channel. The numerical computation of mean square error (MSE) and bit error rate (BER) performance are computed for different SMTs like SM, GSM and FSGM using Monte Carlo iterations. Simulation results demonstrate that the RLS channel estimation method achieves the nearly same BER performance as P-CSI.

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41

Park, Myung Chul, and Dong Seog Han. "Analysis of Spatial Modulation MIMO Reception Performance for UHDTV Broadcasting." Journal of Broadcast Engineering 20, no. 6 (November 30, 2015): 837–47. http://dx.doi.org/10.5909/jbe.2015.20.6.837.

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42

Anoh, K. O. O., R. A. Abd-Alhameed, G. N. Okorafor, J. M. Noras, J. Rodriguez, and S. M. R. Jones. "Performance Evaluation of Spatial Modulation and QOSTBC for MIMO Systems." ICST Transactions on Mobile Communications and Applications 2, no. 6 (August 11, 2015): 150094. http://dx.doi.org/10.4108/eai.11-8-2015.150094.

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43

Wang, Sixin, Wei Li, and Jing Lei. "Physical-layer encryption in massive MIMO systems with spatial modulation." China Communications 15, no. 10 (October 2018): 159–71. http://dx.doi.org/10.1109/cc.2018.8485478.

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44

Maleki, Marjan, Kamal Mohamed-Pour, and Mojtaba Soltanalian. "Receive Spatial Modulation in Correlated Massive MIMO With Partial CSI." IEEE Transactions on Signal Processing 67, no. 5 (March 1, 2019): 1237–50. http://dx.doi.org/10.1109/tsp.2018.2890063.

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45

Qu, Weilin, Meng Zhang, Xiang Cheng, and Peizhong Ju. "Generalized Spatial Modulation With Transmit Antenna Grouping for Massive MIMO." IEEE Access 5 (2017): 26798–807. http://dx.doi.org/10.1109/access.2017.2775281.

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46

Cal-Braz, Joao, and Raimundo Sampaio-Neto. "Projection-Based List Detection in Generalized Spatial Modulation MIMO Systems." IEEE Communications Letters 19, no. 7 (July 2015): 1145–48. http://dx.doi.org/10.1109/lcomm.2015.2435007.

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47

Lee, Ming-Chun, Wei-Ho Chung, and Ta-Sung Lee. "Limited Feedback Precoder Design for Spatial Modulation in MIMO Systems." IEEE Communications Letters 19, no. 11 (November 2015): 1909–12. http://dx.doi.org/10.1109/lcomm.2015.2475265.

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Chen, Yingyang, Li Wang, Zijun Zhao, Meng Ma, and Bingli Jiao. "Secure Multiuser MIMO Downlink Transmission Via Precoding-Aided Spatial Modulation." IEEE Communications Letters 20, no. 6 (June 2016): 1116–19. http://dx.doi.org/10.1109/lcomm.2016.2549014.

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Kumar, C. Rajesh, and R. K. Jeyachitra. "Dual-Mode Generalized Spatial Modulation MIMO for Visible Light Communications." IEEE Communications Letters 22, no. 2 (February 2018): 280–83. http://dx.doi.org/10.1109/lcomm.2017.2774263.

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Li, Cheng-Han, Yen-Lin Chen, Wan-Nong Hu, Chiao-En Chen, and Yuan-Hao Huang. "A $4\times64$ MIMO Detector for Generalized Spatial Modulation Systems." IEEE Transactions on Circuits and Systems I: Regular Papers 66, no. 9 (September 2019): 3585–97. http://dx.doi.org/10.1109/tcsi.2019.2925069.

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