A Low Complexity Algorithm for Intelligent Reflective Surface-assisted Tera Hertz Channel Estimation
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摘要: 信道估计是智能反射表面(IRS)辅助太赫兹(THz)通信中的一大挑战。为了减少由收发端天线和IRS反射元件数增加引起的信道估计导频开销过大的问题，该文提出一种基于正则平行因子(CP)分解的信道估计算法。首先在分析信道特点的基础上对IRS阵元进行分组设计，然后将IRS辅助的无线通信信道表示为统一数学表达式，接着利用多天线THz信道固有的低秩结构将信号接收矩阵构建成一个3维张量，并且利用基于正则平行因子分解算法对张量进行分解，最后利用相关性对信道参数进行估计。通过蒙特卡罗仿真表明，该算法在信道传输条件相同的情况下相对于基准算法有4.28 dB和7.12 dB左右的性能提升，并且具有更低的计算复杂度。Abstract: Channel estimation is a major challenge in Tera Hertz (THz) communication assisted by Intelligent Reflecting Surfaces (IRS). To reduce the problem of large channel estimation pilot overhead caused by an increase in the number of receiving/transmitting antennas and IRS reflecting elements, a channel estimation algorithm based on Canonical decomposition/Parallel factors (CP) decomposition is proposed. Firstly, based on the analysis of the channel characteristics, the IRS elements are designed by grouping. Then, the wireless communication channel assisted by IRS is expressed in a unified mathematical expression. Subsequently, the received signal matrix is constructed into a three-dimensional tensor by utilizing the inherent low-rank structure of the THz channel with multiple antennas. The tensor is decomposed using the CP decomposition algorithm, and the channel parameters are estimated using correlation. Monte Carlo simulations show that the proposed algorithm has a performance improvement of around 4.28 dB and 7.12 dB compared to the benchmark algorithm under the same channel transmission conditions, and has a lower computational complexity.
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[1] YOU Xiaohu, WANG Chengxiang, HUANG Jie, et al. Towards 6G wireless communication networks: Vision, enabling technologies, and new paradigm shifts[J]. Science China Information Sciences, 2021, 64(1): 110301. doi: 10.1007/s11432-020-2955-6 [2] AKYILDIZ I F, JORNET J M, and HAN Chong. Terahertz band: Next frontier for wireless communications[J]. Physical Communication, 2014, 12: 16–32. doi: 10.1016/j.phycom.2014.01.006 [3] PRIEBE S and KURNER T. Stochastic modeling of THz indoor radio channels[J]. IEEE Transactions on Wireless Communications, 2013, 12(9): 4445–4455. doi: 10.1109/TWC.2013.072313.121581 [4] WU Qingqing and ZHANG Rui. Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network[J]. IEEE Communications Magazine, 2020, 58(1): 106–112. doi: 10.1109/MCOM.001.1900107 [5] WU Qingqing, ZHANG Shuowen, ZHENG Beixiong, et al. Intelligent reflecting surface-aided wireless communications: A tutorial[J]. IEEE Transactions on Communications, 2021, 69(5): 3313–3351. doi: 10.1109/TCOMM.2021.3051897 [6] WU Qingqing and ZHANG Rui. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming[J]. IEEE Transactions on Wireless Communications, 2019, 18(11): 5394–5409. doi: 10.1109/TWC.2019.2936025 [7] PAN Cunhua, REN Hong, WANG Kezhi, et al. Multicell MIMO communications relying on intelligent reflecting surfaces[J]. IEEE Transactions on Wireless Communications, 2020, 19(8): 5218–5233. doi: 10.1109/TWC.2020.2990766 [8] MA Xiaoyan, GUO Shuaishuai, ZHANG Haixia, et al. Joint beamforming and reflecting design in reconfigurable intelligent surface-aided multi-user communication systems[J]. IEEE Transactions on Wireless Communications, 2021, 20(5): 3269–3283. doi: 10.1109/TWC.2020.3048780 [9] 张祖凡, 张迪. 智能反射表面辅助无线能量传输的保密率最大化算法[J]. 电子与信息学报, 2022, 44(7): 2366–2373. doi: 10.11999/JEIT211255ZHANG Zufan and ZHANG Di. Secrecy rate maximization algorithm for intelligent reflecting surface assisted wireless energy transmission[J]. Journal of Electronics &Information Technology, 2022, 44(7): 2366–2373. doi: 10.11999/JEIT211255 [10] MA Xinying, CHEN Zhi, CHI Yaojia, et al. Channel estimation for intelligent reflecting surface enabled terahertz MIMO systems[C]. 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, 2020: 1–6. [11] ZHOU Lei, DAI Jisheng, XU Weichao, et al. Sparse channel estimation for intelligent reflecting surface assisted massive MIMO systems[J]. IEEE Transactions on Green Communications and Networking, 2022, 6(1): 208–220. doi: 10.1109/TGCN.2022.3146188 [12] NING Boyu, CHEN Zhi, CHEN Wenrong, et al. Terahertz multi-user massive MIMO with intelligent reflecting surface: Beam training and hybrid beamforming[J]. IEEE Transactions on Vehicular Technology, 2021, 70(2): 1376–1393. doi: 10.1109/TVT.2021.3052074 [13] LIN Cen and LI G Y. Indoor terahertz communications: How many antenna arrays are needed?[J]. IEEE Transactions on Wireless Communications, 2015, 14(6): 3097–3107. doi: 10.1109/TWC.2015.2401560 [14] PRIEBE S, KANNICHT M, JACOB M, et al. Ultra broadband indoor channel measurements and calibrated ray tracing propagation modeling at THz frequencies[J]. Journal of Communications and Networks, 2013, 15(6): 547–558. doi: 10.1109/JCN.2013.000103 [15] TANG Wankai, CHEN Mingzheng, CHEN Xiangyu, et al. Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement[J]. IEEE Transactions on Wireless Communications, 2021, 20(1): 421–439. doi: 10.1109/TWC.2020.3024887 [16] HITCHCOCK F L. The expression of a tensor or a polyadic as a sum of products[J]. Journal of Mathematics and Physics, 1927, 6(1/4): 164–189. doi: 10.1002/sapm192761164 [17] KOLDA T G and BADER B W. Tensor decompositions and applications[J]. SIAM Review, 2009, 51(3): 455–500. doi: 10.1137/07070111X [18] KRUSKAL J B. Three-way arrays: Rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics[J]. Linear Algebra and its Applications, 1977, 18(2): 95–138. doi: 10.1016/0024-3795(77)90069-6 [19] ZHOU Zhou, FANG Jun, YANG Linxiao, et al. Low-rank tensor decomposition-aided channel estimation for millimeter wave MIMO-OFDM systems[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(7): 1524–1538. doi: 10.1109/JSAC.2017.2699338 [20] DE ARAÚJO G T and DE ALMEIDA A L F. PARAFAC-based channel estimation for intelligent reflective surface assisted MIMO system[C]. 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), Hangzhou, China, 2020: 1–5. [21] WEI Li, HUANG Chongwen, ALEXANDROPOULOS G C, et al. Channel estimation for RIS-empowered multi-user MISO wireless communications[J]. IEEE Transactions on Communications, 2021, 69(6): 4144–4157. doi: 10.1109/TCOMM.2021.3063236 -
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