A Deep Neural Networks Assisted Channel Estimation and Decoding for MIMO
Keywords:
Multiple Input Multiple Output (MIMO), Deep Neural Networks, Maximum Ratio Combining (MRC), Zero Forcing Equalizer, Minimum Mean Square Error (MSE), Bit Error Rate (BER), Spectral EfficiencyAbstract
Present day communication systems are facing some critical issues which are increased number of users, the amount of bandwidth availability to be used by the users and the need for ever increasing data rates. The major concern regarding all the problems is the high capacity expectation from wireless channels. However, wireless channels are often random in nature with frequency selective nature at the basest. The limitation in the bandwidth support by any channel makes the data rate support to be limited. In this paper, a deep neural network assisted massive MIMO system has been designed and has been employed to commonly existing diverse channel conditions. To increase the spectral efficiency and simultaneously reduce the BER of the system, the Maximum Ratio Comining (MRC) approach has been used along with MMSE and ZFE equalization techniques. The proposed system has been simulated on Matlab. The performance of the system has been evaluated in terms of the Bit Error Rate and Spectral Efficiency of the system.
References
J. Guo, T. Chen, S. Jin, G.Y. Li, X. Wang, X. Hou, “Deep learning for joint channel estimation and feedback in massive MIMO systems”, Digital Communications and Networks, Elsevier 2023.
M. Arvinte and J. I. Tamir, "MIMO Channel Estimation Using Score-Based Generative Models," in IEEE Transactions on Wireless Communications, 2023 vol. 22, no. 6, pp. 3698-3713.
M. Liu, X. Li, B. Ning, C. Huang, S. Sun and C. Yuen, "Deep Learning-Based Channel Estimation for Double-RIS Aided Massive MIMO System," in IEEE Wireless Communications Letters, 2022, vol. 12, no. 1, pp. 70-74.
M. B. Mashhadi and D. Gündüz, "Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems," in IEEE Transactions on Wireless Communications, 2021, vol. 20, no. 10, pp. 6315-6328
B. Lin, F. Gao, S. Zhang, T. Zhou and A. Alkhateeb, "Deep Learning-Based Antenna Selection and CSI Extrapolation in Massive MIMO Systems," in IEEE Transactions on Wireless Communications, 2021, vol. 20, no. 11, pp. 7669-7681
E. Balevi, A. Doshi and J. G. Andrews, "Massive MIMO Channel Estimation With an Untrained Deep Neural Network," in IEEE Transactions on Wireless Communications, vol. 19, no. 3, pp. 2079-2090, March 2020
M. -S. Baek, S. Kwak, J. -Y. Jung, H. M. Kim and D. -J. Choi, "Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters," in IEEE Transactions on Broadcasting, vol. 65, no. 3, pp. 636-642, Sept. 2019.
David Borges, Paulo Montezuma, Afonso Ferreira, Rui Dinis, “Two Low Complexity MRC and EGC Based Receivers for SC-FDE Modulations with Massive MIMO Schemes”, Journal of Signal Processing Systems, volume 90, pp:1357–1367, Springer 2018.
[2] C Thrampoulidis, W Xu, Babak Hassibi, “Symbol error rate performance of box-relaxation decoders in massive mimo”, Volume: 66 , Issue: 3, pp: 3377-3392, IEEE Transactions on Signal Processing 2018.
Prabina Pattanayak, Vinay Kumar Trivedi, Sayan Chakraborty, Preetam Kumar, “BER Performance of Multi User Scheduling for MIMO-STBC and MIMO-OFDM Broadcast Network with Imperfect CSI”, Proceedings in 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE 2017.
Y Li, C Tao, G Seco-Granados, “Channel estimation and performance analysis of one-bit massive MIMO systems”, Volume: 65 , Issue: 15, pp:4075-4089, IEEE Transactions on Signal Processing, 2017.
S Park, A Alkhateeb, RW Heath, “Dynamic subarrays for hybrid precoding in wideband mmWave MIMO systems”, Volume: 16 , Issue: 5, pp: 2907-2920, IEEE Transactions on Wireless Communications 2017.
C Chen, Z Liu, K Xie, Y Liu, Y Zhang, “Adaptive fuzzy asymptotic control of MIMO systems with unknown input coefficients via a robust Nussbaum gain-based approach”, Volume: 25 , Issue: 5, pp:1252-1263, IEEE Transactions on Fuzzy Systems 2017.
X Gao, L Dai, S Han, I Chih-Lin, “Energy-efficient hybrid analog and digital precoding for mmWave MIMO systems with large antenna arrays”, Volume: 34 , Issue: 4, pp: 998-1009, IEEE Journal on Selected Areas in Communications 2016.
X Yu, JC Shen, J Zhang, “Alternating Minimization Algorithms for Hybrid Precoding in Millimeter Wave MIMO Systems”, IEEE 2016
J Mo, RW Heath, “Capacity analysis of one-bit quantized MIMO systems with transmitter channel state information”, Volume: 10 , Issue: 3, pp: 485-500, IEEE Journal of Selected Topics in Signal Processing 2015.
A Pitarokoilis, SK Mohammed, “Uplink performance of time-reversal MRC in massive MIMO systems subject to phase noise”, Volume: 14 , Issue: 2, pp: 711-723, IEEE Transactions on Wireless Communications 2015.
EG Larsson, O Edfors, F Tufvesson, “Massive MIMO for next generation wireless systems”, Volume: 52 , Issue: 2, pp: 186-195, IEEE Communications Magazine 2014
E Björnson, J Hoydis, M Kountouris, “Massive MIMO systems with non-ideal hardware: Energy efficiency, estimation, and capacity limits”, Volume: 60 , Issue: 11, pp: 7112-7139, IEEE Transactions on Information Theory 2014
HQ Ngo, EG Larsson, “Energy and spectral efficiency of very large multiuser MIMO systems”, Volume: 61 , Issue: 4, pp: 1436 – 1449, IEEE Transactions on Communications 2013
X Chen, X Wang, X Chen, “Energy-efficient optimization for wireless information and power transfer in large-scale MIMO systems employing energy beam-forming”, Volume: 2 , Issue: 6 , pp:667-670, IEEE Wireless Communications Letters 2013
O El Ayach, RW Heath, S Abu-Surra, “Low complexity precoding for large millimeter wave MIMO systems”, Proceedings in 2012 IEEE International Conference on Communications (ICC)
O El Ayach, RW Heath, S Abu-Surra, “The capacity optimality of beam steering in large millimeter wave MIMO systems”, Proceeding in 2012 IEEE 13th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).
M.F. Mesiya, “Cotemporary Communication Systems”, 1st Edition, McGraw Hill.
