Reconfigurable Intelligent Surface Aided Massive MIMO Systems With Low-Resolution DACs

TLDR: In this paper, the authors investigate a reconfigurable intelligent surface (RIS)-aided multi-user massive MIMO system where low-resolution digital-analog converters (DACs) are configured at the base station (BS) in order to reduce the cost and power consumption.

Abstract: We investigate a reconfigurable intelligent surface (RIS)-aided multi-user massive multiple-input multi-output (MIMO) system where low-resolution digital-analog converters (DACs) are configured at the base station (BS) in order to reduce the cost and power consumption. An approximate analytical expression for the downlink achievable rate is derived based on maximum ratio transmission (MRT) and additive quantization noise model (AQNM), and the rate maximization problem is solved by particle swarm optimization (PSO) method under both continuous phase shifts (CPSs) and discrete phase shifts (DPSs) at the RIS. Simulation results show that the downlink sum achievable rate tends to a constant with the increase of the number of quantization bits of DACs, and four quantization bits are enough to capture a large portion of the performance of the ideal perfect DACs case.

Figures

TABLE I: ρ VERSUS QUANTIZATION BITS b

Fig. 4: The downlink sum achievable rate versus the number of quantization bits of phase shifts at the RIS.

Fig. 3: The downlink sum achievable rate versus the number of quantization bits of DACs.

Fig. 1: System Model

Fig. 2: The downlink sum achievable rate versus P with b = 1 under PSO.

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Q1. What contributions have the authors mentioned in the paper "Reconfigurable intelligent surface aided massive mimo systems with low-resolution dacs" ?

The authors investigate a reconfigurable intelligent surface ( RIS ) -aided multi-user massive multiple-input multi-output ( MIMO ) system where low-resolution digital-analog converters ( DACs ) are configured at the base station ( BS ) in order to reduce the cost and power consumption. An approximate analytical expression for the downlink achievable rate is derived based on maximum ratio transmission ( MRT ) and additive quantization noise model ( AQNM ), and the rate maximization problem is solved by particle swarm optimization ( PSO ) method under both continuous phase shifts ( CPSs ) and discrete phase shifts ( DPSs ) at the RIS.