Physically-Consistent Multi-Band Massive MIMO Systems: A Radio Resource Management Model

Massive multiple-input multiple-output (mMIMO) antenna systems and inter-band carrier aggregation (CA)-enabled multi-band communication are two key technologies to achieve very high data rates in beyond fifth generation (B5G) wireless systems. We propose a joint optimization framework for such systems where the mMIMO antenna spacing selection, pre-coder optimization, optimum subcarrier selection and optimum power allocation are carried out simultaneously. We harness the bandwidth gain existing in a tightly coupled base station mMIMO antenna system to avoid sophisticated, non-practical antenna systems for multi-band operation. In particular, we analyze a multi-band communication system using a circuit-theoretic model to consider physical characteristics of a tightly coupled antenna array, and formulate a joint optimization problem to maximize the sum-rate. As part of the optimization, we also propose a novel block iterative water-filling-based subcarrier selection and power allocation optimization algorithm for the multi-band mMIMO system. A novel subcarrier windowing-based subcarrier selection scheme is also proposed which considers the physical constraints (hardware limitation) at the mobile user devices. We carry out the optimizations in two ways: (i) to optimize the antenna spacing selection in an offline manner, and (ii) to select antenna elements from a dense array dynamically. Via computer simulations, we illustrate superior bandwidth gains present in the tightly-coupled colinear and rectangular planar antenna arrays, compared to the loosely-coupled or tightly-coupled parallel arrays. We compare the optimum sum-rate performance of the proposed optimizationbased framework under various power allocation schemes and various user capability scenarios. Also, we show that the proposed optimization framework is superior to the existing joint optimization frameworks in terms of sum-rate performance and we verify the convergence of the proposed iterative optimization algorithms.