Duality and Optimization for Generalized Multi-hop MIMO Amplify-and-Forward Relay Networks with Linear Constraints

TL;DR

This paper introduces duality principles and an optimization framework for multi-hop MIMO amplify-and-forward relay networks, enabling efficient design of precoders under linear constraints with improved computational efficiency.

Contribution

It generalizes duality results for multi-hop MIMO AF relay networks and proposes a unified, low-cost optimization method leveraging duality and polite water-filling structures.

Findings

Duality transformations are established for generalized multi-hop MIMO AF networks.

A unified optimization framework using a local Lagrange dual method is proposed.

The framework achieves faster algorithms with lower computational cost.

Abstract

We consider a generalized multi-hop MIMO amplify-and-forward (AF) relay network with multiple sources/destinations and arbitrarily number of relays. We establish two dualities and the corresponding dual transformations between such a network and its dual, respectively under single network linear constraint and per-hop linear constraint. The result is a generalization of the previous dualities under different special cases and is proved using new techniques which reveal more insight on the duality structure that can be exploited to optimize MIMO precoders. A unified optimization framework is proposed to find a stationary point for an important class of non-convex optimization problems of AF relay networks based on a local Lagrange dual method, where the primal algorithm only finds a stationary point for the inner loop problem of maximizing the Lagrangian w.r.t. the primal variables. The input covariance matrices are shown to satisfy a polite water-filling structure at a stationary point of the inner loop problem. The duality and polite water-filling are exploited to design fast primal algorithms. Compared to the existing algorithms, the proposed optimization framework with duality-based primal algorithms can be used to solve more general problems with lower computation cost.