Decentralized Algorithms for Out-of-System Interference Suppression in Distributed MIMO
Zakir Hussain Shaik, Erik G. Larsson
TL;DR
OoS interference in distributed MIMO operating in unlicensed or spectrum-sharing scenarios is unknown and must be canceled without prior OoS statistics. The paper introduces two distributed OoS-estimation schemes—the sequential Procrustes rotation/averaging and the sequential accumulation of Gramians—that enable coherent suppression in the uplink and directional nulling in the downlink using only local processing and moderate fronthaul. Results show that the Procrustes method achieves near-centralized IRC with fronthaul load scaling roughly with $O(K)$, while the Gramian-based method matches centralized performance at higher fronthaul cost, with MMSE variants preserving the trends. These techniques provide scalable OoS management for cell-free MIMO in shared spectra and offer guidance on choosing the method based on the number of OoS sources relative to $N$.
Abstract
Out-of-system (OoS) interference is a potential limitation for distributed networks that operate in unlicensed spectrum or in a spectrum sharing scenario. The OoS interference differs from the in-system interference in that OoS signals and their associated channels (or even their statistics) are completely unknown. In this paper, we propose a novel distributed algorithm that can mitigate OoS interference in the uplink and suppress the signal transmission in the OoS direction in the downlink. To estimate the OoS interference, each access point (AP), upon receiving an estimate of OoS interference from a previous AP, computes a better estimate of OoS interference by rotate-and-average using Procrustes method and forwards the estimates to the next AP. This process continues until the central processing unit (CPU) receives the final estimate. Our method has comparable performance to that of a fully centralized interference rejection combining algorithm and has much lower fronthaul load requirements.