Noncoherent Joint Transmission Beamforming for Dense Small Cell Networks: Global Optimality, Efficient Solution and Distributed Implementation

TL;DR

This paper develops globally optimal and efficient locally optimal beamforming algorithms for noncoherent joint transmission in dense small cell networks, demonstrating improved performance and distributed implementation feasibility.

Contribution

It introduces a globally optimal solution, an efficient local approximation method, and a distributed algorithm for noncoherent joint transmission beamforming.

Findings

Noncoherent JT outperforms coordinated beamforming.

The algorithms achieve near-optimal performance.

Distributed implementation is feasible with the proposed method.

Abstract

We investigate the coordinated multi-point noncoherent joint transmission (JT) in dense small cell networks. The goal is to design beamforming vectors for macro cell and small cell base stations (BSs) such that the weighted sum rate of the system is maximized, subject to a total transmit power at individual BSs. The optimization problem is inherently nonconvex and intractable, making it difficult to explore the full potential performance of the scheme. To this end, we first propose an algorithm to find a globally optimal solution based on the generic monotonic branch reduce and bound optimization framework. Then, for a more computationally efficient method, we adopt the inner approximation (InAp) technique to efficiently derive a locally optimal solution, which is numerically shown to achieve near-optimal performance. In addition, for decentralized networks such as those comprising of multi-access edge computing servers, we develop an algorithm based on the alternating direction method of multipliers, which distributively implements the InAp-based solution. Our main conclusion is that the noncoherent JT is a promising transmission scheme for dense small cell networks, since it can exploit the densitification gain, outperforms the coordinated beamforming, and is amenable to distributed implementation.