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Analysis of spatial correlation of precoding indoor MIMO visible light communication system
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Abstract
In order to solve the problem of multi-user interference and the subchannel strength generated by the block diagonalization (BD) algorithm in multi-user MIMO (MU-MIMO) indoor visible light communication, the bit error rate of the indoor MU-MIMO visible light communication system is optimized by using the substream selected BD algorithm. This paper establishes the channel model for MU-MIMO indoor visible light communication and compares the channel spatial correlation between the 4×4 MIMO and 8×8 MIMO in different indoor system layout modes by using the control variable method and taking different parameters of LED and PD distance, the system capacity and bit error rate performance of substream selected BD algorithm and BD algorithm are compared and analyzed. The results show that with the continuous enhancement of spatial correlation, the bit error rate performance decreases, and the substream selected BD algorithm can bring a gain of more than 4 dB compared with BD algorithm. -
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References
[1] Tran T A, O'Brien D C. Performance metrics for Multi-Input Multi-Output (MIMO) visible light communications[C]//Proceedings of 2012 International Workshop on Optical Wireless Communications (IWOW), 2012. [2] Wang Y G, Zhang M L, Wang Y Q, et al. Experimental demonstration of visible light communication based on sub-carrier multiplexing of multiple-input-single-output OFDM[C]//Proceedings of the 2012 17th Opto-Electronics and Communications Conference, 2012: 745-746. [3] 赵黎, 朱彤, 刘智港, 等.一种兼顾照明与通信的环形光源布局模型[J].光电工程, 2018, 45(7): 170503. doi: 10.12086/oee.2018.170503 Zhao L, Zhu T, Liu Z G, et al. An annular light source layout model for both lighting and communication reliability[J]. Opto-Electronic Engineering, 2018, 45(7): 170503. doi: 10.12086/oee.2018.170503 [4] Ishikawa N, Sugiura S. Maximizing constrained capacity of power-imbalanced optical wireless MIMO communications using spatial modulation[J]. Journal of Lightwave Technology, 2015, 33(2): 519-527. doi: 10.1109/JLT.2015.2391198 [5] 薛家豪, 李琪, 徐胜男, 等.室内LED可见光通信的分集接收系统[J].物理实验, 2016, 36(9): 15-18, 22. doi: 10.3969/j.issn.1005-4642.2016.09.005 Xue J H, Li Q, Xu S N, et al. Diversity reception system for indoor LED visible light communication[J]. Physics Experimentation, 2016, 36(9): 15-18, 22. doi: 10.3969/j.issn.1005-4642.2016.09.005 [6] Huang N, Wang X, Chen M. Transceiver Design for MIMO VLC Systems With Integer-Forcing Receivers[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(1): 66-77. doi: 10.1109/JSAC.2017.2774458 [7] Narmanlioglu O, Kizilirmak R C, Miramirkhani F, et al. Effect of Wiring and Cabling Topologies on the Performance of Distributed MIMO OFDM VLC Systems[J]. IEEE Access, 2019: 52743-52754. [8] Chen J X, Wang Q, Wang Z C. Leakage-based precoding for MU-MIMO VLC systems under optical power constraint[J]. Optics Communications, 2017, 382: 348-353. doi: 10.1016/j.optcom.2016.08.014 [9] 洪阳, 陈健, 王子雄.基于BD预编码的多用户MIMO室内可见光通信系统[J].光子学报, 2013, 42(11): 1277-1282. Hong Y, Chen J, Wang Z X. Multi-user MIMO indoor visible light communication system based on BD precoding algorithm[J]. Acta Photonica Sinica, 2013, 42(11): 1277-1282. [10] Liu Q F, Xiao S F, Huang K Z, et al. A SVD-based optical MIMO precoding scheme in indoor visible light communication[J]. International Journal of Future Computer and Communication, 2014, 3(6): 421-426. doi: 10.7763/IJFCC.2014.V3.340 [11] 祝锴, 王丽, 胡捍英.基于BD的改进多用户MIMO预编码算法[J].信息工程大学学报, 2010, 11(1): 7-10. doi: 10.3969/j.issn.1671-0673.2010.01.002 Zhu K, Wang L, Hu H Y. Improved BD precoding algorithms for multi-user MIMO systems[J]. Journal of Information Engineering University, 2010, 11(1): 7-10. doi: 10.3969/j.issn.1671-0673.2010.01.002 [12] Ding J P, Huang Z T, Ji Y F. Evolutionary algorithm based power coverage optimization for visible light communications[J]. IEEE Communications Letters, 2012, 16(4): 439-441. doi: 10.1109/LCOMM.2012.030512.112020 -
Overview
Overview: Visible light communication is a kind of wireless communication, which can solve the problem of serious electromagnetic radiation or limited spectrum resources in hospitals, mines, military, etc. It's a new communication technology with green, high efficiency and energy saving. In the actual application scenario, there are multiple users and multiple sets of LEDs in the room, which can effectively reduce the communication link interruption caused by indoor displays and personnel walking. There are many researches on indoor MIMO visible light communication, most of which are mainly to solve the problem of indoor layout, diversity reception and so on. However, the receiver can receive interference from others. Precoding technology is mainly used to reduce the inter-user interference now, but the spatial correlation research of precoding for visible light communication is relatively rare. In this paper, two indoor MIMO visible light communication system models are established, namely 4×4 and 8×8. The substream selected BD algorithm is applied to the indoor MIMO visible light communication system. By optimizing the singular value of the singular value decomposition caused by the BD algorithm in the equivalent channel matrix, the purpose of reducing inter-user interference is realized. At the same time, under different indoor system models and the distribution of LED and PD, the channel capacity and bit error rate performance of BD algorithm and substream selected BD algorithm are studied. The indoor MIMO visible light communication system with substream selected BD algorithm in the above figure mainly includes three parts: the transmitter, the receiver and channel. The transmitter mainly performs serial-to-parallel conversion of data, controls the parallel data modulation by on-off keying modulation and processes substream selected BD algorithm, and to add the DC offset and use the high frequency flickering characteristic of the LED to perform data transmission; at the receiver, the received signal is decoded and demodulated by the optical detector to restore the original data and complete the information transmission. The simulation results show that in terms of channel capacity, the spatial correlation of the channel is stronger and the channel capacity is increased. Meanwhile, under the indoor channel of 4×4, the channel capacity of the BD algorithm is higher than the substream selected BD algorithm under different spatial correlations, but under the indoor 8×8 channel, BD algorithm and substream selected BD algorithm have little difference in capacity under different spatial correlation; in terms of the bit error rate of the system, the bit error rate of the substream selected BD algorithm can bring a gain of more than 4 dB compared with the BD algorithm, the spatial correlation is continuously enhanced, and the system error rate performance is degraded.
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Figure 1.
Substream selected BD algorithm MU-MIMO indoor VLC system model
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Figure 2.
Indoor MIMO VLC system spatial distribution of 4×4.
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Figure 3.
Indoor MIMO VLC system spatial distribution of 8×8.
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Figure 4.
Curve between SNR and channel capacity under four spatial correlation.
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Figure 5.
Curve between SNR and BER under different channel correlation.
