Duality and Optimization for Generalized Multi-Hop MIMO Amplify-and-Forward Relay Networks With Linear Constraints

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 co...

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Veröffentlicht in:IEEE transactions on signal processing Jg. 61; H. 9; S. 2356 - 2365
Hauptverfasser: An Liu, Lau, V. K. N., Youjian Liu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.05.2013
Institute of Electrical and Electronics Engineers
Schlagworte:
Algorithm design and analysis
Amplify and forward
Applied sciences
Covariance matrix
Detection, estimation, filtering, equalization, prediction
duality
Exact sciences and technology
Information, signal and communications theory
MIMO
MIMO precoder optimization
multi-hop MIMO networks
Network topology
Optimization
relay
Relays
Signal and communications theory
Signal, noise
Spread spectrum communication
Telecommunications and information theory
MIMO system
Non convex programming
Relay
Relay network
Amplify and forward
Coding circuit
Transmission protocol
multi-hop MIMO networks
Dual transformation
duality
Optimization
Multihop network
Covariance
Signal processing
MIMO precoder optimization
Routing protocols
Fast algorithm
ISSN:1053-587X, 1941-0476
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Beschreibung
Zusammenfassung: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. A unified optimization framework is proposed to find a stationary point for an important class of nonconvex 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 with the existing algorithms, the proposed optimization framework with duality-based primal algorithms can be used to solve more general problems with lower computation cost.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2013.2245126