Elements of information theory
Elements of information theory
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Convex Optimization
Distributed MIMO receiver: achievable rates and upper bounds
IEEE Transactions on Information Theory
Optimized signaling for MIMO interference systems with feedback
IEEE Transactions on Signal Processing
Overcoming interference in spatial multiplexing MIMO cellular networks
IEEE Wireless Communications
Gaussian multiterminal source coding
IEEE Transactions on Information Theory
Iterative water-filling for Gaussian vector multiple-access channels
IEEE Transactions on Information Theory
The Wyner-Ziv problem with multiple sources
IEEE Transactions on Information Theory
The Distributed Karhunen–Loève Transform
IEEE Transactions on Information Theory
Successive Wyner–Ziv Coding Scheme and Its Application to the Quadratic Gaussian CEO Problem
IEEE Transactions on Information Theory
Communication Via Decentralized Processing
IEEE Transactions on Information Theory
A tutorial on decomposition methods for network utility maximization
IEEE Journal on Selected Areas in Communications
Achievable rates for the AWGN channel with multiple parallel relays
IEEE Transactions on Wireless Communications
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We consider the uplink of a backhaul-constrained, MIMO coordinated network. That is, a single-frequency network with N + 1 multi-antenna base stations (BSs) that cooperate in order to decode the users' data, and that are linked by means of a common lossless backhaul, of limited capacity R. To implement the receive cooperation, we propose distributed compression: N BSs, upon receiving their signals, compress them using a multi-source lossy compression code. Then, they send the compressed vectors to a central BS, which performs users' decoding. Distributed Wyner-Ziv coding is proposed to be used, and is designed in thiswork. The first part of the paper is devoted to a network with a unique multi-antenna user, that transmits a predefined Gaussian space-time codeword. For such a scenario, the "compression noise" covariance at the BSs is optimized, considering the user's achievable rate as the performance metric. In particular, for N = 1 the optimum covariance is derived in closed form, while for N 1 an iterative algorithm is devised. The second part of the contribution focusses on the multi-user scenario. For it, the achievable rate region is obtained by means of the optimum "compression noise" covariances for sum-rate and weighted sum-rate, respectively.