Reverse Compute and Forward: A Low-Complexity Architecture for Downlink Distributed Antenna Systems

TL;DR

This paper introduces Reverse Quantized Compute and Forward, a low-complexity downlink precoding scheme for distributed antenna systems, demonstrating improved achievable rates and benefits from user selection in realistic fading environments.

Contribution

The paper proposes a novel downlink precoding scheme called Reverse Quantized Compute and Forward (RQCoF) for distributed antenna systems, with analysis and simulation showing its advantages.

Findings

RQCoF achieves competitive rates compared to existing methods.

Channel-based user selection significantly improves system performance.

User selection mitigates rank deficiency issues in the system matrix.

Abstract

We consider a distributed antenna system where $L$ antenna terminals (ATs) are connected to a Central Processor (CP) via digital error-free links of finite capacity $R_0$, and serve $L$ user terminals (UTs). This system model has been widely investigated both for the uplink and the downlink, which are instances of the general multiple-access relay and broadcast relay networks. In this work we focus on the downlink, and propose a novel downlink precoding scheme nicknamed "Reverse Quantized Compute and Forward" (RQCoF). For this scheme we obtain achievable rates and compare with the state of the art available in the literature. We also provide simulation results for a realistic network with fading and pathloss with $K > L$ UTs, and show that channel-based user selection produces large benefits and essentially removes the problem of rank deficiency in the system matrix.