Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems with Resolution-Adaptive ADCs

TL;DR

This paper proposes a joint optimization framework for maximizing uplink achievable rate in RIS-assisted mmWave systems with adaptive ADCs, addressing hardware cost and power constraints.

Contribution

It introduces a novel BCD-based algorithm to optimize ADC resolution, RIS phase shifts, and beam selection, improving performance under hardware limitations.

Findings

RIS mitigates hardware loss from RADCs

Proposed algorithm outperforms existing methods

Adaptive ADCs balance performance and power consumption

Abstract

In this letter, we investigate the uplink of a reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multi-user system. In the considered system, however, problems with hardware cost and power consumption arise when massive antenna arrays coupled with power-demanding analog-to-digital converters (ADCs) are employed. To account for practical hardware complexity, we consider that the access point (AP) is equipped with resolution-adaptive analog-to-digital converters (RADCs). We maximize the achievable rate under hardware constraints by jointly optimizing the ADC quantization bits, the RIS phase shifts, and the beam selection matrix. Due to the non-convexity of the feasible set and objective function, the formulated problem is non-convex and difficult to solve. To efficiently tackle this problem, a block coordinated descent (BCD)-based algorithm is proposed. Simulations demonstrate that an RIS can mitigate the hardware loss due to use of RADCs, and that the proposed BCD-based algorithm outperforms state-of-the-art algorithms.