Improving Sum-Rate of Cell-Free Massive MIMO with Expanded Compute-and-Forward

TL;DR

This paper enhances the sum-rate of cell-free massive MIMO systems by adopting an expanded compute-and-forward framework with novel power control, AP selection, and decoding strategies, outperforming traditional methods.

Contribution

It introduces a new ECF framework with specific algorithms and schemes to mitigate inter-user interference and improve sum-rate in cell-free massive MIMO.

Findings

ECF outperforms conventional CF and MRC in sum-rate.

Power control algorithms effectively exploit performance gains.

AP selection and decoding order strategies enhance system performance.

Abstract

Cell-free massive multiple-input multiple-output (MIMO) employs a large number of distributed access points (APs) to serve a small number of user equipments (UEs) via the same time/frequency resource. Due to the strong macro diversity gain, cell-free massive MIMO can considerably improve the achievable sum-rate compared to conventional cellular massive MIMO. However, the performance of cell-free massive MIMO is upper limited by inter-user interference (IUI) when employing simple maximum ratio combining (MRC) at receivers. To harness IUI, the expanded compute-and-forward (ECF) framework is adopted. In particular, we propose power control algorithms for the parallel computation and successive computation in the ECF framework, respectively, to exploit the performance gain and then improve the system performance. Furthermore, we propose an AP selection scheme and the application of different decoding orders for the successive computation. Finally, numerical results demonstrate that ECF frameworks outperform the conventional CF and MRC frameworks in terms of achievable sum-rate.