[en] We investigate the optimal signal detection problem in large-scale multiple-input multiple-output (MIMO) system with the generalized spatial modulation (GSM) scheme, which can be formulated as a closest lattice point search (CLPS). To identify invalid signals, an efficient pruning strategy is needed while searching on the GSM decision tree. However, the existing algorithms have exponential complexity, whereas they are infeasible in large-scale GSM-MIMO systems. In order to tackle this problem, we propose a memory-efficient pruning strategy by leveraging the combinatorial nature of the GSM signal structure. Thus, the required memory size is squared to the number of transmit antennas. We further propose an efficient memory-bounded maximum likelihood (ML) search (EM-MLS) algorithm by jointly employing the proposed pruning strategy and the memory-bounded best-first algorithm. Theoretical and simulation results show that our proposed algorithm can achieve the optimal bit error rate (BER) performance, while its memory size can be bounded. Moreover, the expected time complexity decreases exponentially with increasing the signal-to-noise ratio (SNR) as well as the system’s excess degree of freedom, and it often converges to squared time under practical scenarios.
Electrical & electronics engineering
Author, co-author :
He, Ke ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom ; Guangzhou University > School of Computer Science and Cyber Engineering
He, Le; Guangzhou University > School of Computer Science and Cyber Engineering
Fan, Lisheng; Guangzhou University > School of Computer Science and Cyber Engineering
Lei, Xianfu; Southwest Jiaotong University > School of Information Science and Technology
Deng, Yansha; King’s College London > Department of Engineering
Karagiannidis, George; Aristotle University of Thessaloniki > Department of Electrical Computer Engineering
External co-authors :
Efficient Memory-Bounded Optimal Detection for GSM-MIMO Systems
Publication date :
Journal title :
IEEE Transactions on Communications
Institute of Electrical and Electronics Engineers, United States
M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, "Toward 6G networks: Use cases and technologies," IEEE Commun. Mag., vol. 58, no. 3, pp. 55-61, Mar. 2020.
T.-H. Vu, T.-V. Nguyen, D. B. da Costa, and S. Kim, "Performance analysis and deep learning design of underlay cognitive NOMA-based CDRT networks with imperfect SIC and co-channel interference," IEEE Trans. Commun., vol. 69, no. 12, pp. 8159-8174, Dec. 2021.
L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, "An overview of massive MIMO: Benefits and challenges," IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 742-758, Jun. 2014.
A. S. de Sena, "Massive MIMO-NOMA networks with imperfect SIC: Design and fairness enhancement," IEEE Trans. Wireless Commun., vol. 19, no. 9, pp. 6100-6115, Jun. 2020.
S. Zheng, C. Shen, and X. Chen, "Design and analysis of uplink and downlink communications for federated learning," IEEE J. Sel. Areas Commun., vol. 39, no. 7, pp. 2150-2167, Jul. 2021.
M. D. Renzo, H. Haas, A. Ghrayeb, S. Sugiura, and L. Hanzo, "Spatial modulation for generalized MIMO: Challenges, opportunities, and implementation," Proc. IEEE, vol. 102, no. 1, pp. 56-103, Jan. 2014.
M. Wen et al., "A survey on spatial modulation in emerging wireless systems: Research progresses and applications," IEEE J. Sel. Areas Commun., vol. 37, no. 9, pp. 1949-1972, Sep. 2019.
A. Younis, R. Mesleh, M. D. Renzo, and H. Haas, "Generalised spatial modulation for large-scale MIMO," in [Proc. 22nd Eur. Signal Process. Conf., (EUSIPCO), Lisbon, Portugal, Sep. 2014, pp. 346-350.
J. Liao, J. Zhao, F. Gao, and G. Y. Li, "A model-driven deep learning method for massive MIMO detection," IEEE Commun. Lett., vol. 24, no. 8, pp. 1724-1728, Aug. 2020.
F. Liang, C. Shen, and F. Wu, "An iterative BP-CNN architecture for channel decoding," IEEE J. Sel. Topics Signal Process., vol. 12, no. 1, pp. 144-159, Feb. 2018.
Y. Xiao et al., "Low-complexity signal detection for generalized spatial modulation," IEEE Commun. Lett., vol. 18, no. 3, pp. 403-406, Mar. 2014.
C.-T. Lin, W.-R. Wu, and C.-Y. Liu, "Low-complexity ML detectors for generalized spatial modulation systems," IEEE Trans. Commun., vol. 63, no. 11, pp. 4214-4230, Nov. 2015.
Z. Ma, F. Gao, J. Jiang, and Y.-C. Liang, "Cooperative detection for ambient backscatter assisted generalized spatial modulation," in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2019, pp. 1-6.
L. Liu, S. Huang, and B. M. Kurkoski, "Memory approximate message passing," in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2021, pp. 1379-1384.
L. Liu, C. Yuen, Y. L. Guan, Y. Li, and C. Huang, "Gaussian message passing for overloaded massive MIMO-NOMA," IEEE Trans. Wireless Commun., vol. 18, no. 1, pp. 210-226, Jan. 2019.
C. Wang, P. Cheng, Z. Chen, J. A. Zhang, Y. Xiao, and L. Gui, "Near-ML low-complexity detection for generalized spatial modulation," IEEE Commun. Lett., vol. 20, no. 3, pp. 618-621, Mar. 2016.
L. Xiao et al., "Efficient compressive sensing detectors for generalized spatial modulation systems," IEEE Trans. Veh. Technol., vol. 66, no. 2, pp. 1284-1298, Feb. 2017.
Y. Wu, H. Ying, X.-Q. Jiang, and H. Hai, "A joint data mapping and detection for high performance generalized spatial modulation," IEEE Commun. Lett., vol. 23, no. 11, pp. 2008-2011, Nov. 2019.
H. Albinsaid, K. Singh, S. Biswas, C.-P. Li, and M.-S. Alouini, "Block deep neural network-based signal detector for generalized spatial modulation," IEEE Commun. Lett., vol. 24, no. 12, pp. 2775-2779, Dec. 2020.
J. A. Cal-Braz and R. Sampaio-Neto, "Low-complexity sphere decoding detector for generalized spatial modulation systems," IEEE Commun. Lett., vol. 18, no. 6, pp. 949-952, Jun. 2014.
B. Zheng, X. Wang, M. Wen, and F. Chen, "Soft demodulation algorithms for generalized spatial modulation using deterministic sequential Monte Carlo," IEEE Trans. Wireless Commun., vol. 16, no. 6, pp. 3953-3967, Jun. 2017.
B. Zheng, M. Wen, F. Chen, N. Huang, F. Ji, and H. Yu, "The K-best sphere decoding for soft detection of generalized spatial modulation," IEEE Trans. Commun., vol. 65, no. 11, pp. 4803-4816, Nov. 2017.
T. Q. Tran, S. Sugiura, and K. Lee, "Ordering-and partitioning-aided sphere decoding for generalized spatial modulation," IEEE Trans. Veh. Technol., vol. 67, no. 10, pp. 10087-10091, Oct. 2018.
Y.-M. Chen, W.-C. Cheng, C.-P. Li, and Z. J. Haas, "Low-complexity generalized spatial modulation schemes using codebook-assisted MIMO detectors," IEEE Trans. Veh. Technol., vol. 67, no. 12, pp. 12358-12362, Dec. 2018.
T.-H. Liu, C.-E. Chen, and C.-H. Liu, "Fast maximum likelihood detection of the generalized spatially modulated signals using successive sphere decoding algorithms," IEEE Commun. Lett., vol. 23, no. 4, pp. 656-659, Apr. 2019.
V. M. Garcia-Molla, F. J. Martínez-Zaldívar, M. Angeles Simarro, and A. Gonzalez, "Maximum likelihood low-complexity GSM detection for large MIMO systems," Signal Process., vol. 175, Oct. 2020, Art. no. 107661.
J. Sun, Y. Zhang, J. Xue, and Z. Xu, "Learning to search for MIMO detection," IEEE Trans. Wireless Commun., vol. 19, no. 11, pp. 7571-7584, Nov. 2020.
Y. Dai and Z. Yan, "Memory-constrained tree search detection and new ordering schemes," IEEE J. Sel. Topics Signal Process., vol. 3, no. 6, pp. 1026-1037, Dec. 2009.
R. Y. Chang and W.-H. Chung, "Best-first tree search with probabilistic node ordering for MIMO detection: Generalization and performancecomplexity tradeoff," IEEE Trans. Wireless Commun., vol. 11, no. 2, pp. 780-789, Feb. 2012.
L. He, K. He, L. Fan, X. Lei, A. Nallanathan, and G. K. Karagiannidis, "Towards optimally efficient search with deep learning for large-scale MIMO systems," IEEE Trans. Commun., vol. 70, no. 2, pp. 101-116, May 2022.
A. D. Murugan, H. El Gamal, M. O. Damen, and G. Caire, "A unified framework for tree search decoding: Rediscovering the sequential decoder," IEEE Trans. Inf. Theory, vol. 52, no. 3, pp. 933-953, Mar. 2006.
J. Schalkwijk, "An algorithm for source coding," IEEE Trans. Inf. Theory, vol. IT-18, no. 3, pp. 395-399, May 1972.
P. Kabal. (2018). Combinatorial Coding and Lexicographic Ordering. [Online]. Available: http://www-mmsp.ece.mcgill.ca/Documents/ Reports/2018/KabalR2018.pdf
S. J. Russell, "Efficient memory-bounded search methods," in Proc. Eur. Conf. Artif. Intell. (ECAI), 1992, pp. 1-5.
X. Lai, "Outdated access point selection for mobile edge computing with cochannel interference," IEEE Trans. Vehic. Tech., early access, Apr. 14, 2022, doi: 10.1109/TVT.2022.3167405.
S. Tang, "Dilated convolution based CSI feedback compression for massive MIMO systems," IEEE Trans. Vehic. Tech., vol. 71, no. 5, pp. 211-216, Jun. 2022.
F. Beukers, "Gauss' hypergeometric function," in Arithmetic Geometry Around Hypergeometric Functions. Cham, Switzerland: Springer, 2007, pp. 23-42.
G. Nemes and A. B. Olde Daalhuis, "Large-parameter asymptotic expansions for the legendre and allied functions," SIAM J. Math. Anal., vol. 52, no. 1, pp. 437-470, Jan. 2020.
S. Dragomir, R. Agarwal, and N. Barnett, "Inequalities for beta and gamma functions via some classical and new integral inequalities," J. Inequal. Appl., vol. 2000, no. 2, 2000, Art. no. 504054.
L. Andrews, "Special functions for engineers and applied mathematicians," Appl. Opt., vol. 25, no. 18, p. 3096, 1986.
B. Hassibi and H. Vikalo, "On the sphere-decoding algorithm I. Expected complexity," IEEE Trans. Signal Process., vol. 53, no. 8, pp. 2806-2818, Aug. 2005.