Distributed Optimization of Multi-Cell Uplink Co-operation with Backhaul Constraints

TL;DR

This paper proposes a distributed algorithm, LiquidMAAS, for uplink multi-cell cooperation that optimizes network utility under backhaul and antenna processing constraints, demonstrating fast convergence in simulations.

Contribution

It introduces LiquidMAAS, a novel distributed algorithm that efficiently solves a convex relaxation of the cooperative uplink optimization problem with backhaul and antenna constraints.

Findings

Fast convergence in large network simulations

Effective optimization under backhaul constraints

Distributed implementation with demand prices

Abstract

We address the problem of uplink co-operative reception with constraints on both backhaul bandwidth and the receiver aperture, or number of antenna signals that can be processed. The problem is cast as a network utility (weighted sum rate) maximization subject to computational complexity and architectural bandwidth sharing constraints. We show that a relaxed version of the problem is convex, and can be solved via a dual-decomposition. The proposed solution is distributed in that each cell broadcasts a set of {\em demand prices} based on the data sharing requests they receive. Given the demand prices, the algorithm determines an antenna/cell ordering and antenna-selection for each scheduled user in a cell. This algorithm, referred to as {\em LiquidMAAS}, iterates between the preceding two steps. Simulations of realistic network scenarios show that the algorithm exhibits fast convergence even for systems with large number of cells.