Optimizing Resolution-Adaptive Massive MIMO Networks

We consider the uplink of a cellular network wherein each base-station (BS) simultaneously communicates with multiple users. Each BS is equipped with a large number of antenna elements and a limited number of RF chains. Each RF chain (on each BS) houses an analog-to-digital converter (ADC) whose bit resolution can be configured. We seek to jointly optimize user transmit powers and ADC bit resolutions in order to maximize the network spectral efficiency, subject to power budget constraints at each user and BS. This joint optimization becomes intractable if we insist on exactly modeling the nonlinear quantization operation performed at each ADC. On the other hand, simplistic approximations made for tractability need not be meaningful. In this work, we propose a methodology based on a constrained worst-case quantization noise formulation, along with another one that assumes quantization noise covariance to be diagonal. In each case, using a series of effective mathematical re-formulations we are able to express our problem in a form that is well-suited for alternating optimization, in which each sub-problem can be efficiently and optimally solved. Through a detailed performance analysis, we demonstrate that the optimized transmit powers and bit resolutions can yield very significant improvements in achievable spectral efficiency, at a reduced sum power consumption and an affordable complexity.