基于最優(yōu)索引廣義正交匹配追蹤的非正交多址系統(tǒng)多用戶檢測(cè)
doi: 10.11999/JEIT190270
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重慶郵電大學(xué)通移動(dòng)通信重點(diǎn)實(shí)驗(yàn)室 重慶 400065
An Optimal Number of Indices Aided gOMP Algorithm for Multi-user Detection in NOMA System
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Key Laboratory of Mobile Communications, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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摘要:
作為5G的關(guān)鍵技術(shù)之一，非正交多址(NOMA)通過非正交方式訪問無(wú)線通信資源，以實(shí)現(xiàn)提高頻譜利用率、增加用戶連接數(shù)的目的。該文提出將壓縮感知(CS)及廣義正交匹配追蹤(gOMP)算法引入上行免調(diào)度NOMA系統(tǒng)，從而增強(qiáng)NOMA系統(tǒng)活躍用戶檢測(cè)及數(shù)據(jù)接收的性能。通過每次迭代識(shí)別多個(gè)索引，gOMP算法實(shí)際上是傳統(tǒng)的正交匹配追蹤(OMP)算法的擴(kuò)展。為了獲得最優(yōu)性能，研究分析了在gOMP算法信號(hào)重構(gòu)的每次迭代中所應(yīng)選擇的最優(yōu)索引數(shù)目。仿真結(jié)果表明：與其它的貪婪追蹤算法及梯度投影稀疏重構(gòu)(GPSR)算法相比，最優(yōu)索引gOMP算法具有更優(yōu)異的信號(hào)重構(gòu)性能；并且，對(duì)于不同的活躍用戶數(shù)或過載率等參數(shù)配置的NOMA系統(tǒng)，均表現(xiàn)出最優(yōu)的多用戶檢測(cè)性能。
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關(guān)鍵詞:
- 多用戶檢測(cè) /
- 免調(diào)度非正交多址 /
- 壓縮感知 /
- 廣義正交匹配追蹤 /
- 最優(yōu)索引數(shù)目
Abstract:As one of the key 5G technologies, Non-Orthogonal Multiple Access (NOMA) can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. In the uplink grant-free NOMA system, the Compressive Sensing (CS) and generalized Orthogonal Matching Pursuit (gOMP) algorithm are introduced in active user and data detection, to enhance the system performance. The gOMP algorithm is literally generalized version of the Orthogonal Matching Pursuit (OMP) algorithm, in the sense that multiple indices are identified per iteration. Meanwhile, the optimal number of indices selected per iteration in the gOMP algorithm is addressed to obtain the optimal performance. Simulations verify that the gOMP algorithm with optimal number of indices has better recovery performance, compared with the greedy pursuit algorithms and the Gradient Projection Sparse Reconstruction (GPSR) algorithm. In addition, given different system configurations in terms of the number of active users and subcarriers, the proposed gOMP with optimal number of indices also exhibits better performance than that of the other algorithms mentioned in this paper.
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表 1 最優(yōu)索引gOMP檢測(cè)算法
算法1 最優(yōu)索引gOMP檢測(cè)算法 輸入 ${{y}}$, ${{H}}$, $S$, ${C_{{\rm{opt}}}}$. 初始化：${{{r}}^0} = {{y}}$,${{{\varGamma}} ^0} = \varnothing $,$t = 0$. (1) While ${\left\| {{{{r}}^t}} \right\|_2} > e$ 且$t \le S$ do (2) $t = t + 1$;
(3) $\eta {\rm{(}}i{\rm{)}} = \mathop {{\rm{argmax}}}\limits_{j:j \in \varOmega \backslash {\rm{\{ }}\eta {\rm{(}}i - 1{\rm{)}}, \cdots ,\eta {\rm{(2)}},\eta {\rm{(}}1{\rm{)\} }}} \left| { < {{{r}}^{t - 1}},{{{\varphi}} _j} > } \right|$;(4) ${{{\varGamma}} ^t} = {{{\varGamma}} ^{t - 1}} \cup {\rm{\{ }}\eta {\rm{(1),}}\eta {\rm{(2),}} ··· ,\eta {\rm{(}}{C_{{\rm{opt}}}}{\rm{)\} }}$; (5) ${\hat {{x} }_{ { {{\varGamma} } ^t} } } = \mathop { {\rm{argmin} } }\limits_{ u} {\left\| { {{y} } - { {{H} }_{ { {{\varGamma} } ^t} } }{{u} } } \right\|_2} = {{H} }_{ { {{\varGamma} } ^t} }^{\rm{? } }{{y} }$; (6) ${{{r}}^t} = {{y}} - {{{H}}_{{{{\varGamma}} ^t}}}{\hat {{x}}_{{{{\varGamma}} ^t}}}$ end while
輸出 ${\hat {{x} }_{ { {{\varGamma} } ^t} } } = \mathop { {\rm{argmin} } }\limits_{ u} {\left\| { {{y} } - { {{H} }_{ { {{\varGamma} } ^t} } }{{u} } } \right\|_2}$
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OSSEIRAN A, BOCCARDI F, BRAUN V, et al. Scenarios for 5G mobile and wireless communications: The vision of the METIS project[J]. IEEE Communications Magazine, 2014, 52(5): 26–35. doi: 10.1109/MCOM.2014.6815890 DAI Linglong, WANG Bichai, DING Zhiguo, et al. A survey of non-orthogonal multiple access for 5G[J]. IEEE Communications Surveys & Tutorials, 2018, 20(3): 2294–2323. doi: 10.1109/COMST.2018.2835558 DAI Linglong, WANG Bichai, YUAN Yifei, et al. Non-orthogonal multiple access for 5G: Solutions, challenges, opportunities, and future research trends[J]. IEEE Communications Magazine, 2015, 53(9): 74–81. doi: 10.1109/MCOM.2015.7263349 ISLAM S M R, AVAZOV N, DOBRE O A, et al. Power-domain non-orthogonal multiple access (NOMA) in 5G systems: Potentials and challenges[J]. IEEE Communications Surveys & Tutorials, 2017, 19(2): 721–742. doi: 10.1109/COMST.2016.2621116 HONG J P, CHOI W, and RAO B D. Sparsity controlled random multiple access with compressed sensing[J]. IEEE Transactions on Wireless Communications, 2015, 14(2): 998–1010. doi: 10.1109/TWC.2014.2363165 CHOI J W, SHIM B, DING Yacong, et al. Compressed sensing for wireless communications: Useful tips and tricks[J]. IEEE Communications Surveys & Tutorials, 2017, 19(3): 1527–1550. doi: 10.1109/COMST.2017.2664421 李燕龍, 陳曉, 詹德滿, 等. 非正交多址接入中稀疏多用戶檢測(cè)方法[J]. 西安電子科技大學(xué)學(xué)報(bào): 自然科學(xué)版, 2017, 44(3): 151–156. doi: 10.3969/j.issn.1001-2400.2017.03.026LI Yanlong, CHEN Xiao, ZHEN Deman, et al. Method of sparse multi-user detection in non-orthogonal multiple access[J]. Journal of Xidian University, 2017, 44(3): 151–156. doi: 10.3969/j.issn.1001-2400.2017.03.026 SHIM B and SONG B. Multiuser detection via compressive sensing[J]. IEEE Communications Letters, 2012, 16(7): 972–974. doi: 10.1109/LCOMM.2012.050112.111980 TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655–4666. doi: 10.1109/TIT.2007.909108 NEEDELL D and VERSHYNIN R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 310–316. doi: 10.1109/JSTSP.2010.2042412 DAI Wei and MILENKOVIC O. Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Transactions on Information Theory, 2009, 55(5): 2230–2249. doi: 10.1109/TIT.2009.2016006 NEEDELL D and TROPP J A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples[J]. Applied and Computational Harmonic Analysis, 2009, 26(3): 301–321. doi: 10.1016/j.acha.2008.07.002 WANG Jian, KWON S, and SHIM B. Generalized orthogonal matching pursuit[J]. IEEE Transactions on Signal Processing, 2012, 60(12): 6202–6216. doi: 10.1109/TSP.2012.2218810 WANG Bichai, DAI Linglong, ZHANG Yuan, et al. Dynamic compressive sensing-based multi-user detection for uplink grant-free NOMA[J]. IEEE Communications Letters, 2016, 20(11): 2320–2323. doi: 10.1109/LCOMM.2016.2602264 FIGUEIREDO M A T, NOWAK R D, and WRIGHT S J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586–597. doi: 10.1109/JSTSP.2007.910281 WANG Bichai, DAI Linglong, MIR T, et al. Joint user activity and data detection based on structured compressive sensing for NOMA[J]. IEEE Communications Letters, 2016, 20(7): 1473–1476. doi: 10.1109/LCOMM.2016.2560180 WEI Chao, LIU Huaping, ZHANG Zaichen, et al. Approximate message passing-based joint user activity and data detection for NOMA[J]. IEEE Communications Letters, 2017, 21(3): 640–643. doi: 10.1109/LCOMM.2016.2624297 CIRIK A C, BALASUBRAMANYA N M, and LAMPE L. Multi-user detection using ADMM-based compressive sensing for uplink grant-free NOMA[J]. IEEE Wireless Communications Letters, 2018, 7(1): 46–49. doi: 10.1109/LWC.2017.2752165 ZHU Hao and GIANNAKIS G B. Exploiting sparse user activity in multiuser detection[J]. IEEE Transactions on Communications, 2011, 59(2): 454–465. doi: 10.1109/TCOMM.2011.121410.090570 WANG Jian and SHIM B. Exact recovery of sparse signals using orthogonal matching pursuit: How many iterations do we need?[J]. IEEE Transactions on Signal Processing, 2016, 64(16): 4194–4202. doi: 10.1109/TSP.2016.2568162 孫娜, 劉繼文, 肖東亮. 基于BFGS擬牛頓法的壓縮感知SL0重構(gòu)算法[J]. 電子與信息學(xué)報(bào), 2018, 40(10): 2408–2414. doi: 10.11999/JEIT170813SUN Na, LIU Jiwen, and XIAO Dongliang. SL0 reconstruction algorithm for compressive sensing based on BFGS quasi newton method[J]. Journal of Electronics &Information Technology, 2018, 40(10): 2408–2414. doi: 10.11999/JEIT170813 -
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