Modified-PBIL Based User Selection for Multi-user Massive MIMO Systems with Massive Connectivity

Inspired by the big data processing capability of the machine learning, we propose a user selection algorithm based on modified population-based incremental learning (MPBIL) for multi-user massive MIMO systems with massive connectivity. With the objective of enhancing the algorithm efficiency, the proposed algorithm evolves the population exploiting both the superior individuals and the best individual. In further, we design the orthogonal permutation to increase individual diversity and avoid the overfit of the classical population-based incremental learning. Simulation results demonstrate that the performance of the proposed algorithm is far better than the classical greedy based user selection method while maintaining low complexity, especially for the large number of MU-MIMO users and candidates.

[1]  Rich Caruana,et al.  Removing the Genetics from the Standard Genetic Algorithm , 1995, ICML.

[2]  Guosen Yue,et al.  User Grouping for Massive MIMO in FDD Systems: New Design Methods and Analysis , 2014, IEEE Access.

[3]  Geoffrey Ye Li,et al.  Agglomerative user clustering and downlink group scheduling for FDD massive MIMO systems , 2017, 2017 IEEE International Conference on Communications (ICC).

[4]  Tiejun Lv,et al.  Low-Complexity Downlink User Selection for Massive MIMO Systems , 2017, IEEE Systems Journal.

[5]  Andrea J. Goldsmith,et al.  On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming , 2006, IEEE Journal on Selected Areas in Communications.

[6]  Jeffrey G. Andrews,et al.  Low Complexity User Selection Algorithms for Multiuser MIMO Systems with Block Diagonalization , 2006, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[7]  Mohamad Assaad,et al.  On Optimal Scheduling for Joint Spatial Division and Multiplexing Approach in FDD Massive MIMO , 2018, IEEE Transactions on Signal Processing.

[8]  Tho Le-Ngoc,et al.  Angular-based Massive-MIMO User Clustering and Precoding , 2019, 2019 IEEE Wireless Communications and Networking Conference (WCNC).

[9]  Giuseppe Caire,et al.  Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling , 2014, IEEE Journal of Selected Topics in Signal Processing.

[10]  Fakhri Karray,et al.  Approaching evolutionary robotics through population-based incremental learning , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[11]  Xuezhi Tan,et al.  Overlapping user grouping in IoT oriented massive MIMO systems , 2017, 2017 International Conference on Computing, Networking and Communications (ICNC).

[12]  Emil Björnson,et al.  Massive MIMO: ten myths and one critical question , 2015, IEEE Communications Magazine.

[13]  Yue Yuan,et al.  Multi-User Hybrid Precoding for Dynamic Subarrays in mmWave Massive MIMO Systems , 2019, IEEE Access.

[14]  Robert W. Heath,et al.  Spatially Sparse Precoding in Millimeter Wave MIMO Systems , 2013, IEEE Transactions on Wireless Communications.

[15]  H. Tullberg,et al.  The Foundation of the Mobile and Wireless Communications System for 2020 and Beyond: Challenges, Enablers and Technology Solutions , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).

[16]  K.A. Folly.  Design of power system stabilizer: a comparison between genetic algorithms (GAs) and population-based incremental learning (PBIL) , 2006, 2006 IEEE Power Engineering Society General Meeting.

[17]  Long Bao Le,et al.  User scheduling for massive MIMO OFDMA systems with hybrid analog-digital beamforming , 2015, 2015 IEEE International Conference on Communications (ICC).

[18]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .