Partitioning of Distributed MIMO Systems based on Overhead Considerations

TL;DR

This paper explores how to optimize data partitioning in distributed MIMO networks to maximize effective data throughput while considering overhead signaling constraints, using a novel Knapsack-based approach.

Contribution

It introduces a constrained orthogonal partitioning method formulated as a Knapsack problem for D-MIMO networks with joint multi-user beamforming.

Findings

Partitioning improves sum-rate under overhead constraints.

Knapsack formulation provides quick, accurate solutions.

Numerical results demonstrate effective sum-rate scaling.

Abstract

Distributed-Multiple Input Multiple Output (DMIMO) networks is a promising enabler to address the challenges of high traffic demand in future wireless networks. A limiting factor that is directly related to the performance of these systems is the overhead signaling required for distributing data and control information among the network elements. In this paper, the concept of orthogonal partitioning is extended to D-MIMO networks employing joint multi-user beamforming, aiming to maximize the effective sum-rate, i.e., the actual transmitted information data. Furthermore, in order to comply with practical requirements, the overhead subframe size is considered to be constrained. In this context, a novel formulation of constrained orthogonal partitioning is introduced as an elegant Knapsack optimization problem, which allows the derivation of quick and accurate solutions. Several numerical results give insight into the capabilities of D-MIMO networks and the actual sum-rate scaling under overhead constraints.