Generalized spatial modulation detector assisted by reconfigurable intelligent surface based on deep learning^✩

Abstract

Reconfigurable Intelligent Surface (RIS) is regarded as a cutting-edge technology for the development of future wireless communication networks with improved frequency efficiency and reduced energy consumption. This paper proposes an architecture by combining RIS with Generalized Spatial Modulation (GSM) and then presents a Multi-Residual Deep Neural Network (MR-DNN) scheme, where the active antennas and their transmitted constellation symbols are detected by sub-DNNs in the detection block. Simulation results demonstrate that the proposed MR-DNN detection algorithm performs considerably better than the traditional Zero-Forcing (ZF) and the Minimum Mean Squared Error (MMSE) detection algorithms in terms of Bit Error Rate (BER). Moreover, the MR-DNN detection algorithm has less time complexity than the traditional detection algorithms.

Keywords

Generalized spatial modulation / Multiple input multiple output / Reconfigurable intelligent surface / Deep learning

References

[1] L. Lu, G.Y. Li, A.L. Swindlehurst, A. Ashikhmin, R. Zhang, An overview of mas-sive mimo: benefits and challenges, IEEE J. Sel. Top. Signal Process. 8 (5) (2014) 742-758.

[2] R.Y. Mesleh, H. Haas, S. Sinanovic, C.W. Ahn, S. Yun, Spatial modulation, IEEE Trans. Veh. Technol. 57 (4) (2008) 2228-2241.

[3] M. Di Renzo, H. Haas, A. Ghrayeb, S. Sugiura, L. Hanzo, Spatial modulation for gen-eralized mimo: challenges, opportunities, and implementation, Proc. IEEE 102 (1) (2014) 56-103.

[4] A. Younis, N. Serafimovski, R. Mesleh, H. Haas, Generalised spatial modulation, in: 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Sys-tems and Computers, 2010, pp. 1498-1502.

[5] C. Pan, H. Ren, K. Wang, J.F. Kolb, M. Elkashlan, M. Chen, M. Di Renzo, Y. Hao, J. Wang, A.L. Swindlehurst, X. You, L. Hanzo, Reconfigurable intelligent surfaces for 6g systems: principles, applications, and research directions, IEEE Commun. Mag. 59 (6) (2021) 14-20.

[6] Y. Pan, K. Wang, C. Pan, H. Zhu, J. Wang, Uav-assisted and intelligent reflecting surfaces-supported terahertz communications, IEEE Wirel. Commun. Lett. 10 (6) (2021) 1256-1260.

[7] C. Zhang, Y. Huang, C. He, C. Pan, K. Wang, Energy optimization for irs-aided swipt under imperfect cascaded channels, IEEE Trans. Veh. Technol. 72 (9) (2023) 11631-11643.

[8] Q. Wu, R. Zhang, Towards smart and reconfigurable environment: intelligent reflect-ing surface aided wireless network, IEEE Commun. Mag. 58 (1) (2020) 106-112.

[9] Z. Wei, Y. Cai, Z. Sun, D.W.K. Ng, J. Yuan, M. Zhou, L. Sun, Sum-rate maximiza-tion for irs-assisted uav ofdma communication systems, IEEE Trans. Wirel.commun. 20 (4) (2021) 2530-2550.

[10] Q. Wu, R. Zhang, Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming, IEEE Trans. Wirel.commun. 18 (11) (2019) 5394-5409.

[11] P. Wang, J. Fang, H. Duan, H. Li, Compressed channel estimation for intelligent reflecting surface-assisted millimeter wave systems, IEEE Signal Process. Lett. 27 (2020) 905-909.

[12] N. Agrawal, A. Bansal, K. Singh, C.-P. Li, S. Mumtaz, Finite block length analysis of ris-assisted uav-based multiuser iot communication system with non-linear eh, IEEE Trans. Commun. 70 (5) (2022) 3542-3557.

[13] D. Wang, M. Wu, Z. Wei, K. Yu, L. Min, S. Mumtaz, Uplink secrecy performance of ris-based rf/fso three-dimension heterogeneous networks, IEEE Trans. Wirel.commun. 23 (3) (2024) 1798-1809.

[14] F. Bayar, O. Salan, H. Ilhan, E. Aydin, Space-time block coded reconfigurable intel-ligent surface-based received spatial modulation, IEEE Trans. Mob.comput. 23 (12) (2024) 11564-11575.

[15] H. Albinsaid, K. Singh, A. Bansal, S. Biswas, C.-P. Li, Z.J. Haas, Multiple antenna selection and successive signal detection for sm-based irs-aided communication, IEEE Signal Process. Lett. 28 (2021) 813-817.

[16] C. Zhang, X. Li, C. He, X. Li, D. Lin, Trajectory optimization for uav-enabled relay-ing with reinforcement learning, in: Digital Communications and Networks, 2023, pp. 2352-8648.

[17] C. Zhang, Z. Li, C. He, K. Wang, C. Pan, Deep reinforcement learning based trajectory design and resource allocation for uav-assisted communications, IEEE Commun. Lett. 27 (9) (2023) 2398-2402.

[18] C. Liu, X. Liu, D.W. Kwan Ng, J. Yuan,Deep residual network empowered channel estimation for irs-assisted multi-user communication systems, in: ICC 2021 -IEEE International Conference on Communications, 2021, pp. 1-7.

[19] H. Albinsaid, K. Singh, S. Biswas, C.-P. Li, M.-S. Alouini, Block deep neural network-based signal detector for generalized spatial modulation, IEEE Commun. Lett. 24 (12) (2020) 2775-2779.

[20] N. Van Huynh, G.Y. Li, Transfer learning for signal detection in wireless networks, IEEE Wirel. Commun. Lett. 11 (11) (2022) 2325-2329.

[21] H. Jiang, L. Dai, M. Hao, R. MacKenzie, End-to-end learning for ris-aided communi-cation systems, IEEE Trans. Veh. Technol. 71 (6) (2022) 6778-6783.

[22] C. Huang, R. Mo, C. Yuen, Reconfigurable intelligent surface assisted multiuser miso systems exploiting deep reinforcement learning, IEEE J. Sel. Areas Commun. 38 (8) (2020) 1839-1850.

[23] Z. Yigit, E. Basar, I. Altunbas, Low complexity adaptation for reconfigurable intelli-gent surface-based mimo systems, IEEE Commun. Lett. 24 (12) (2020) 2946-2950.

[24] Q. Mao, F. Hu, Q. Hao, Deep learning for intelligent wireless networks: a compre-hensive survey, IEEE Commun. Surv. Tutor. 20 (4) (2018) 2595-2621.