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Journal articles on the topic 'Spatial precoding'

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Author: Grafiati

Published: 4 June 2021

Last updated: 28 January 2023

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1

Wu, Feilong, Lie-Liang Yang, Wenjie Wang, and Zhengmin Kong. "Secret Precoding-Aided Spatial Modulation." IEEE Communications Letters 19, no. 9 (September 2015): 1544–47. http://dx.doi.org/10.1109/lcomm.2015.2453313.

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2

Li, Jun, Miaowen Wen, Xiang Cheng, Yier Yan, Sangseob Song, and Moon Ho Lee. "Generalized Precoding-Aided Quadrature Spatial Modulation." IEEE Transactions on Vehicular Technology 66, no. 2 (February 2017): 1881–86. http://dx.doi.org/10.1109/tvt.2016.2565618.

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3

Luo, Junshan, Fanggang Wang, and Shilian Wang. "Signaling–Spatial Constellation Tradeoff in Precoding-Aided Spatial Modulation." IEEE Transactions on Vehicular Technology 68, no. 10 (October 2019): 10301–5. http://dx.doi.org/10.1109/tvt.2019.2934735.

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4

Shailender, Shelej Khera, Sajjan Singh, and Jyoti. "Estimation of Channel for Millimeter-Wave Hybrid Massive MIMO Systems using Orthogonal Matching Pursuit (OMP)." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012040. http://dx.doi.org/10.1088/1742-6596/2327/1/012040.

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Abstract In a millimeter-wave (mm-Wave) large MIMO system, hybrid precoding is a critical component for lowering radio-frequency hardware costs as compared to the traditional full-digital precoding strategy. Knowledge of channels is required for hybrid precoding. Though, estimation of the channel is problematic for the mm-Wave system because of the usage of a massive array of antenna and hybrid architecture with analog precoding. Due to the extremely directed nature of wireless propagation, wireless channels have spatial sparsity. In this paper, we exploit this spatial sparsity nature of wireless channels to develop orthogonal matching pursuit technique-based estimation of channels for hybrid millimeter-wave (mm-Wave) wireless systems. Genie (Ideal) channel estimation is also performed in which we presume that actual angle of departures (AoD) and angle of arrivals (AoA) are known to us. Finally, the simulation results reveal that suggested OMP algorithm-based channel estimation has a significant advantage over conventional approaches like least-squares channel estimation.

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5

Ibrahim, Sarmad Khaleel, and Saif A. Abdulhussien. "Performance evaluation of precoding system for massive multiple-input multiple-output." Bulletin of Electrical Engineering and Informatics 11, no. 4 (August 1, 2022): 2054–61. http://dx.doi.org/10.11591/eei.v11i4.3877.

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Low latency, high data speeds, and a higher degree of perceived service quality for consumers and base station capacity are only some of the advantages of fifth generation (5G) mobile communications. This paper focuses on the design of a precoding system for downlink transmission of multi-user multiple-input multiple-output (MU-MIMO). For MU-MIMO systems, the traditional precoding techniques investigated are difficult since the transmitter precoding matrices created by singular value decomposition (SVD) are calculated twice. This paper implements different techniques of precoding with channel coding. Two advanced precoding, zero forcing (ZF) and maximum ratio transmitter (MRT) systems will be evaluated to find the best between them. Three different coding channels (turbo, low-density parity-check (LDPC), and polar) are used in this paper. The results indicate that the ZF-MU-MIMO with turbo coding outperforms MRT precoding, and more spatial diversity gain may be gained, in terms of throughput, number of users supported, and lower error rate in downlink and uplink massive MIMO.

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6

Rezaei Aghdam, Sina, and Thomas Eriksson. "Low Spatial Peak-to-Average Power Ratio Transmission for Improved Energy Efficiency in Massive MIMO Systems." Sensors 21, no. 16 (August 17, 2021): 5534. http://dx.doi.org/10.3390/s21165534.

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A significant portion of the operating power of a base station is consumed by power amplifiers (PAs). Much of this power is dissipated in the form of heat, as the overall efficiency of currently deployed PAs is typically very low. This is because the structure of conventional precoding techniques typically results in a relatively high variation in output power at different antennas in the array, and many PAs are operated well below saturation to avoid distortion of the transmitted signals. In this work, we use a realistic model for power consumption in PAs and study the impact of power variation across antennas in the array on the energy efficiency of a massive MIMO downlink system. We introduce a family of linear precoding matrices that allow us to control the spatial peak-to-average power ratio by projecting a fraction of the transmitted power onto the null space of the channel. These precoding matrices preserve the structure of conventional precoders; e.g., they suppress multiuser interference when used together with zeroforcing precoding and bring advantages over these precoders by operating PAs in a more power-efficient region and reducing the total radiated distortion. Our numerical results show that by controlling the power variations between antennas in the array and incorporating the nonlinearity properties of PA into the precoder optimization, significant gains in energy efficiency can be achieved over conventional precoding techniques.

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7

LONG, Hang, Wenbo WANG, Fangxiang WANG, and Kan ZHENG. "Distributed Spatial-Temporal Precoding with Limited Feedback." IEICE Transactions on Communications E93-B, no. 2 (2010): 407–10. http://dx.doi.org/10.1587/transcom.e93.b.407.

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8

Zhang, Bijun, Guangxi Zhu, and Yingzhuang Liu. "Precoding for multiuser spatial multiplexing MIMO downlink." Tsinghua Science and Technology 11, no. 5 (October 2006): 597–605. http://dx.doi.org/10.1016/s1007-0214(06)70239-5.

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9

Medra, Ahmed, and Timothy N. Davidson. "Spatial Reuse Precoding for Scalable Downlink Networks." IEEE Transactions on Signal Processing 63, no. 22 (November 2015): 5976–89. http://dx.doi.org/10.1109/tsp.2015.2461511.

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10

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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11

Zhang, Dong-Fang, Hong-Yi Yu, Yi-Jun Zhu, and Zheng-Guo Sun. "Optimal Linear Precodings for Multi-Color, Multi-User Visible Light Communication System with Fairness Considerations." Crystals 8, no. 11 (October 25, 2018): 404. http://dx.doi.org/10.3390/cryst8110404.

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With fairness consideration, optimal linear precoding designs for multi-color, multi-user visible light communication (VLC) systems are presented in this paper. Utilizing both the spatial and multi-color resources, the precoding designs are proposed to mitigate the impact of the multi-user interference (MUI) and the multi-color crosstalk. With the constraints of chromaticity, luminance, and signal range, the precoding designs are formulated to achieve the max-min fairness and the maximum sum-rate. Since the closed-form expression for the capacities is not available, the lower and upper bounds are chosen as the performance criterions. To make the optimization problems be easy to be solved by using standard optimization packages, the non-convex problems are cast into convex ones. Subsequently, algorithms are developed to find optimal solutions. Extensive simulation results indicate that the proposed precoding schemes outperform the conventional pseudo inverse method.

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12

Iqbal, Zahid, Fei Ji, and Yun Liu. "Virtual Spatial Channel Number and Index Modulation." Wireless Communications and Mobile Computing 2021 (September 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/2982226.

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This paper proposes a novel precoding-aided and efficient data transmission scheme called virtual spatial channel number and index modulation (VS-CNIM), which conveys extra data by changing both the number and index of active virtual parallel channels of multiple-input multiple-output (MIMO) channels, obtained through the singular value decomposition (SVD) in each time slot. Unlike the conventional virtual spatial modulation (VSM), where extra data bits are transmitted only using index of active virtual parallel channels, the VS-CNIM scheme, depending on incoming information bits, transmits extra bits utilizing both the number and indices of active parallel channels along the bits carried by M -ary constellation symbols. Therefore, VS-CNIM provides significantly superior spectral efficiency (SE) compared to VSM. Considering the influence of imperfect channel estimation, a closed-form upper bound is derived on average bit error probability (ABEP). The asymptotic performance is also analyzed, which gives the coding gain and diversity order and describes error floor under the consideration of perfect and imperfect channel estimation, respectively. Monte Carlo simulations exhibit that the VS-CNIM scheme achieves considerably better error performance and high SE than precoding-aided SM (PSM) and VSM schemes.

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13

Love, D. J., and R. W. Heath. "Limited Feedback Unitary Precoding for Spatial Multiplexing Systems." IEEE Transactions on Information Theory 51, no. 8 (August 2005): 2967–76. http://dx.doi.org/10.1109/tit.2005.850152.

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14

Kim, Sangchoon. "Transmit Antenna Selection for Precoding-Aided Spatial Modulation." IEEE Access 8 (2020): 40723–31. http://dx.doi.org/10.1109/access.2020.2976732.

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15

Cheng, Peng, Zhuo Chen, J. Andrew Zhang, Yonghui Li, and Branka Vucetic. "A Unified Precoding Scheme for Generalized Spatial Modulation." IEEE Transactions on Communications 66, no. 6 (June 2018): 2502–14. http://dx.doi.org/10.1109/tcomm.2018.2796605.

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16

Koca, Mutlu, and Hikmet Sari. "Precoding for Spatial Modulation Against Correlated Fading Channels." IEEE Transactions on Wireless Communications 17, no. 9 (September 2018): 5857–70. http://dx.doi.org/10.1109/twc.2018.2850771.

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17

Yang, Ping, Mohammed El-Hajjar, Yue Xiao, Shaoqian Li, Lajos Hanzo, and Bo Zhang. "Phase rotation-based precoding for spatial modulation systems." IET Communications 9, no. 10 (July 2, 2015): 1315–23. http://dx.doi.org/10.1049/iet-com.2014.1065.

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18

Wu, Feilong, Rong Zhang, Lie-Liang Yang, and Wenjie Wang. "Transmitter Precoding-Aided Spatial Modulation for Secrecy Communications." IEEE Transactions on Vehicular Technology 65, no. 1 (January 2016): 467–71. http://dx.doi.org/10.1109/tvt.2015.2395457.

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19

Chen, Chiao-En. "Constructive Interference-Based Symbol-Level Precoding for Generalized Precoding-Aided Spatial Modulation With PSK Signaling." IEEE Communications Letters 24, no. 8 (August 2020): 1816–20. http://dx.doi.org/10.1109/lcomm.2020.2989212.

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20

Medvedev, A. S., and V. V. Ivanov. "Throughput modeling of cellular network systems with spatial precoding." Scientific and Technical Journal of Information Technologies, Mechanics and Optics 22, no. 2 (April 1, 2022): 392–400. http://dx.doi.org/10.17586/2226-1494-2022-22-2-392-400.

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21

Luo, Junshan, Shilian Wang, Fanggang Wang, and Wei Zhang. "Generalized Precoding-Aided Spatial Modulation via Receive Antenna Transition." IEEE Wireless Communications Letters 8, no. 3 (June 2019): 733–36. http://dx.doi.org/10.1109/lwc.2018.2889857.

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22

Cao, Yuwen, and Tomoaki Ohtsuki. "Orthogonality Structure Designs for Generalized Precoding Aided Spatial Modulation." IEEE Wireless Communications Letters 8, no. 5 (October 2019): 1406–9. http://dx.doi.org/10.1109/lwc.2019.2919571.

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23

Huang, Yu, Miaowen Wen, Beixiong Zheng, Xiang Cheng, Liuqing Yang, and Fei Ji. "Secure Precoding Aided Spatial Modulation via Transmit Antenna Selection." IEEE Transactions on Vehicular Technology 68, no. 9 (September 2019): 8893–905. http://dx.doi.org/10.1109/tvt.2019.2930071.

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24

Costa, Bruno Felipe, and Taufik Abrão. "MIMO Precoding for Correlated Fading Channels." Journal of Circuits, Systems and Computers 25, no. 05 (February 25, 2016): 1650041. http://dx.doi.org/10.1142/s0218126616500419.

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This contribution proposes a precoder-decoder design aiming to improve the performance of multiple-input–multiple-output (MIMO) detectors under correlated fading channels. The MIMO detection principle namely minimum mean squared error (MMSE) detector is analyzed under such channel condition. The proposed approach deploys the channel state information (CSI) aiming to estimate the level of spatial correlation channel, namely normalized correlation index [Formula: see text] and uses this information to improve the MIMO system performance. Furthermore, the impact of the [Formula: see text] estimation errors on the performance, as well the performance degradation for different levels of correlation have been analyzed and compared with the classical MMSE-MIMO detector operating under uncorrelated channels and perfect channel estimation.

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25

Li, Hongwei, Bo Zhao, Jipeng Ni, and Wei Gao. "Tailoring spatial structure of Brillouin spectra via spiral phase precoding." Photonics Research 9, no. 4 (March 31, 2021): 637. http://dx.doi.org/10.1364/prj.416308.

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26

Jang, Jungyup, and Dong Ho Kim. "Open-Loop Precoding for Spatial Multiplexing Systems in Correlated Channels." Journal of Korean Institute of Communications and Information Sciences 40, no. 1 (January 30, 2015): 58–60. http://dx.doi.org/10.7840/kics.2015.40.1.58.

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27

Pechetti, Sasi Vinay, and Ranjan Bose. "Precoding-Aided Spatial Modulation Assisted Joint Two-Tier Downlink Reception." IEEE Transactions on Wireless Communications 19, no. 5 (May 2020): 3332–45. http://dx.doi.org/10.1109/twc.2020.2972377.

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28

Zhang, Huiliang, Meng Ma, and Ziyu Shao. "Multi-User Linear Precoding for Downlink Generalized Spatial Modulation Systems." IEEE Communications Letters 24, no. 1 (January 2020): 212–16. http://dx.doi.org/10.1109/lcomm.2019.2947451.

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29

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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30

Koca, Mutlu, and Hikmet Sari. "Precoding-Aided Spatial Modulation With Increased Robustness to Channel Correlations." IEEE Communications Letters 21, no. 11 (November 2017): 2388–91. http://dx.doi.org/10.1109/lcomm.2017.2733540.

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31

Cao, Yuwen, Tomoaki Ohtsuki, and Xue-Qin Jiang. "Precoding Aided Generalized Spatial Modulation With an Iterative Greedy Algorithm." IEEE Access 6 (2018): 72449–57. http://dx.doi.org/10.1109/access.2018.2880844.

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32

GENG, Xuan, Chen HE, Zuo-lei SUN, and Kun LIU. "Tomlinson-Harashima precoding minimizing total MSE with transmitter spatial correlation." Journal of China Universities of Posts and Telecommunications 19, no. 6 (December 2012): 19–24. http://dx.doi.org/10.1016/s1005-8885(11)60313-0.

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33

Zhang, Lu, Mingyang Sun, Zeliang Ou, Chongjun Ouyang, and Hongwen Yang. "A Secure Receive Spatial Modulation Scheme Based on Random Precoding." IEEE Access 7 (2019): 122367–77. http://dx.doi.org/10.1109/access.2019.2937962.

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34

Dinh, Van Khoi. "LINEAR GROUP PRECODING FOR MASSIVE MIMO SYSTEMS UNDER EXPONENTIAL SPATIAL CORRELATION." Journal of Science and Technique 15, no. 1 (April 6, 2020): 56–71. http://dx.doi.org/10.56651/lqdtu.jst.v15.n01.91.

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In this paper, a low-complexity linear group precoding algorithm in the exponential correlation channel model is proposed for massive MIMO systems. The proposed precoder consists of two components: The first one minimizes the interferences among neighboring user groups; The second one improves the system performance by utilizing the ELR-SLB technique. Numerical and simulation results show that the proposed precoder has remarkably lower computational complexity than its LC-RBD-LR-ZF counterpart, while its bit error rate (BER) performance is asymptotic to that of the LC-RBD-LR-ZF precoder as the number of groups increases.

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35

Gao, Junjun, Jianhua Zhang, Yanwei Xiong, Yanliang Sun, and Xiaofeng Tao. "Performance Evaluation of Closed-Loop Spatial Multiplexing Codebook Based on Indoor MIMO Channel Measurement." International Journal of Antennas and Propagation 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/701985.

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Closed-loop MIMO technique standardized in LTE can support different layer transmissions through precoding operation to match the channel multiplexing capability. However, the performance of the limited size codebook still needs to be evaluated in real channel environment for further insights. Based on the wideband MIMO channel measurement in a typical indoor scenario, capacity loss (CL) of the limited size codebook relative to perfect precoding is studied first in two extreme channel conditions. The results show that current codebook design for single layer transmission is nearly capacity lossless, and the CL will increase with the number of transmitted layers. Furthermore, the capacity improvement of better codebook selection criterions is very limited compared to CL. Then we define the maximum capacity boost achieved by frequency domain layer adaption (FDLA) and investigate its sensitivity to SNR and channel condition. To survey the effect of frequency domain channel variation on MIMO-OFDM system, we define a function to measure the fluctuation levels of the key channel metrics within a subband and reveal the inherent relationship between them. Finally, a capacity floor resulted as the feedback interval increases in frequency domain.

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36

Wang, Yong, Xiyuan Wang, Qiao Liu, and Hui Li. "Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension." Remote Sensing 14, no. 18 (September 8, 2022): 4474. http://dx.doi.org/10.3390/rs14184474.

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With the increasingly obvious trend of satellite communication and network integration, especially the emergence of inter satellite links, the interconnection between space nodes has become the development trend and inevitable requirement of future space communication. However, problems, such as long communication distance, large transmission delay and loss, limited network resources and frequent switching of transmission links, limit the ability of spatial information transmission. Firstly, based on the idea of ring planning and design, this paper uses the joint design of beamforming between nodes to design the physical layer network coding in the relay to realize the reliable transmission of information. Secondly, according to the diversity of relay signal space resources and network node information exchange, a joint design method of relay compression matrix and node precoding vector is studied, which breaks through the existing configuration constraints. In this scheme, the computational complexity is reduced by compressing and precoding the matrix to ensure reliable decoding while obtaining spatial alignment gain and degrees of freedom. Simulated and real data results demonstrate the superiority and effectiveness of the proposed method.

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37

Peng, Shixin, Xiaohui Chen, Wei Lu, Chao Deng, and Jingying Chen. "Spatial Interference Alignment with Limited Precoding Matrix Feedback in a Wireless Multi-User Interference Channel for Smart Grids." Energies 15, no. 5 (March 1, 2022): 1820. http://dx.doi.org/10.3390/en15051820.

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Cellular communication provides an efficient, flexible, long-lived, and reliable communication technology for smart grids to improve the automated analysis, demand response, adoptive control, and coordination between the generator and consumers. With the expansion of wireless networks and the increase of access devices, interference has become a major problem that limits the performance of cellular wireless communication systems for smart grids. Spatial interference alignment (IA) is an effective method to eliminate interference and improve the capacity of wireless communication networks. This paper provides the sufficient conditions of spatial interference alignment operating with limited precoding matrix feedback for a K-user MIMO interference channel. Each receiver feeds the matrix index of the transmitting precoder back to the corresponding transmitter through an interference-free and error-free link. We calculated the number of feedback bits required to achieve the maximum theoretical multiplexing gain for the spatial interference alignment schemes considered and demonstrate the feasibility of spatial interference alignment under the limited feedback constraint investigated. It is shown that in order to maintain the same spatial multiplexing gain as that of the idealized scheme relying on perfect channel state information, the number of feedback bits per receiver scales as Nd≥di(M−di)log2SNR, where M and di denote the number of transmit (receive) antennas and the number of data steams for user i. Finally, the analytical results were verified by simulations for practical interference alignment schemes relying on limited precoding matrix feedback indices.

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38

Chen, Na, Songlin Sun, Michel Kadoch, and Bo Rong. "SDN Controlled mmWave Massive MIMO Hybrid Precoding for 5G Heterogeneous Mobile Systems." Mobile Information Systems 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9767065.

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In 5G mobile network, millimeter wave (mmWave) and heterogeneous networks (Hetnets) are significant techniques to sustain coverage and spectral efficiency. In this paper, we utilize the hybrid precoding to overcome hardware constraints on the analog-only beamforming in mmWave systems. Particularly, we identify the complicated antenna coordination and vast spatial domain information as the outstanding challenges in mmWave Hetnets. In our work, we employ software defined network (SDN) to accomplish radio resource management (RRM) and achieve flexible spacial coordination in mmWave Hetnets. In our proposed scheme, SDN controller is responsible for collecting the user channel state information (CSI) and applying hybrid precoding based on the calculated null-space of victim users. Simulation results show that our design can effectively reduce the interference to victim users and support high quality of service.

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39

Kim, Sangchoon. "Decoupled Transmit and Receive Antenna Selection for Precoding-Aided Spatial Modulation." IEEE Access 9 (2021): 57829–40. http://dx.doi.org/10.1109/access.2021.3072428.

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40

Bakulin, M. G., V. B. Kreindelin, and A. A. Reznev. "Orthogonal Precoding for Systems with Spatial Multiplexing with a Linear Receiver." Journal of Communications Technology and Electronics 66, no. 12 (December 2021): 1346–53. http://dx.doi.org/10.1134/s1064226921120020.

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41

Wang, Hailin, Wen-Qin Wang, and Shilong Ji. "Joint Precoding Spatial and Rotating Symbol Modulation for Physical-Layer Security." IEEE Communications Letters 23, no. 12 (December 2019): 2150–53. http://dx.doi.org/10.1109/lcomm.2019.2944910.

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42

Zhang, Kai, Xue-Qin Jiang, Miaowen Wen, Runhe Qiu, and Huayong Ge. "Precoding-Aided Spatial Modulation With Dual-Polarized Antennas Over Correlated Channels." IEEE Communications Letters 24, no. 3 (March 2020): 676–80. http://dx.doi.org/10.1109/lcomm.2019.2963660.

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43

Cao, Yuwen, Tomoaki Ohtsuki, and Tony Q. S. Quek. "Dual-Ascent Inspired Transmit Precoding for Evolving Multiple-Access Spatial Modulation." IEEE Transactions on Communications 68, no. 11 (November 2020): 6945–61. http://dx.doi.org/10.1109/tcomm.2020.3013030.

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44

Mun, Cheol. "Quantized Principal Component Selection Precoding for Spatial Multiplexing with Limited Feedback." IEEE Transactions on Communications 56, no. 5 (May 2008): 838–46. http://dx.doi.org/10.1109/tcomm.2008.060228.

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45

Liu, Chaowen, Lie-Liang Yang, and Wenjie Wang. "Transmitter-Precoding-Aided Spatial Modulation Achieving Both Transmit and Receive Diversity." IEEE Transactions on Vehicular Technology 67, no. 2 (February 2018): 1375–88. http://dx.doi.org/10.1109/tvt.2017.2757403.

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46

Kim, S. "Diversity order of precoding‐aided spatial modulation using receive antenna selection." Electronics Letters 56, no. 5 (March 2020): 260–62. http://dx.doi.org/10.1049/el.2019.3224.

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47

Bjornson, Emil, Eduard Jorswieck, and Bjorn Ottersten. "Impact of Spatial Correlation and Precoding Design in OSTBC MIMO Systems." IEEE Transactions on Wireless Communications 9, no. 11 (November 2010): 3578–89. http://dx.doi.org/10.1109/twc.2010.100110.091176.

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48

Park, Sungwoo, Jeonghun Park, Ali Yazdan, and Robert W. Heath. "Exploiting Spatial Channel Covariance for Hybrid Precoding in Massive MIMO Systems." IEEE Transactions on Signal Processing 65, no. 14 (July 15, 2017): 3818–32. http://dx.doi.org/10.1109/tsp.2017.2701321.

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49

Kim, Young-Tae, Heunchul Lee, Seokhwan Park, and Inkyu Lee. "Optimal precoding for orthogonalized spatial multiplexing in closed-loop MIMO systems." IEEE Journal on Selected Areas in Communications 26, no. 8 (October 2008): 1556–66. http://dx.doi.org/10.1109/jsac.2008.081021.

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50

He, Longzhuang, Jintao Wang, and Jian Song. "Spectral-Efficient Analog Precoding for Generalized Spatial Modulation Aided MmWave MIMO." IEEE Transactions on Vehicular Technology 66, no. 10 (October 2017): 9598–602. http://dx.doi.org/10.1109/tvt.2017.2702591.

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