Truly Distributed Multicell Multi-Band Multiuser MIMO by Synergizing Game Theory and Deep Learning

Abstract

Dynamic frequency allocation (DFA) with massive multiple-input multiple-output (MIMO) is a promising candidate for multicell communications where massive MIMO is adopted to maximize the per-cell capacity whereas the inter-cell interference (ICI) is tackled by DFA. Realizing this approach in a distributed fashion is however very difficult due to the lack of global channel state available at the base stations (BSs) in the cell level. We utilize a forward-looking game to automate reconciliation for DFA in a distributed manner between cells while zero-forcing (ZF) is used at each cell to maximize the multiplexing gain. To maximize the network capacity, multi-agent deep reinforcement learning (DRL) using offline centralized training is leveraged to train the BSs to master their game-theoretic reconciliation strategies. The result is a trained neural network for each BS, empowering it with rich experience of reconciliation with other BSs for converging to a network-efficient equilibrium. The online algorithm is distributed with the BSs competing as expert players to start the negotiation process using their trained actions. Simulation results show that the proposed synergized deep-learning game-theoretic algorithm outperforms significantly the DRL-only and game-theoretic only methods, and other benchmarks for multicell MIMO.

Distributed Learning for Wireless Communications: Methods, Applications and Challenges

“…On the other hand, it reduces the computational complexity by more than 70% compared to the linear minimum mean squared error (LMMSE) method. Also distributed algorithms can be applied to solve dynamic resource allocation problems in multi-cell MIMO, and in [77] the authors use algorithms for collaborative deep learning and game theory to train the base stations and master their reconciliation strategies. The algorithm automatically optimizes the spectrum allocation of all base station users and later translates into a power allocation problem.…”

Hybrid Self-Organizing Networks: Evolution, Standardization Trends, and a 6G Architecture Vision

“…For instance, the whole SON can be modelled based on a game-theoretic approach and then deep reinforcement learning techniques can be applied to converge to the optimal action for each SONF. This combination has been successfully explored in [15] for the case of inter-cell interference avoidance. However, research still needs to deal with the slow convergence speed of evolutionary methods and neural networks (e.g., via offline training), and minimize the amount of data locally needed to achieve the global benefit.…”

Multiuser Scheduling with Enhanced Greedy Techniques for Multicell and Cell-Free Massive MIMO Systems

“…Multicell multiuser MIMO (MU-MIMO) systems have been widely used in wireless networks to improve the transmission rate, spectral and energy efficiencies using antenna arrays at each base station (BS), which can serve several user terminals simultaneously in each cell [1], [2], [3]. In these systems, different beamforming or precoding techniques such as zero forcing (ZF) or minimum mean squared error (MMSE) [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23] are used to improve the system performance by maximizing the received signal power or by resorting interference cancellation at the receiver side [24], [25], [26].…”