Matrix computations (3rd ed.)
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Space Time Coding for Broadband Wireless Communications
Space Time Coding for Broadband Wireless Communications
Distributed in-network channel decoding
IEEE Transactions on Signal Processing
Distributed Detection in Sensor Networks With Packet Losses and Finite Capacity Links
IEEE Transactions on Signal Processing
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
Multi-Tier Cooperative Broadcasting with Hierarchical Modulations
IEEE Transactions on Wireless Communications
Cooperative diversity in wireless networks: Efficient protocols and outage behavior
IEEE Transactions on Information Theory
Broadcast Channels With Cooperating Decoders
IEEE Transactions on Information Theory
Rate Regions for Relay Broadcast Channels
IEEE Transactions on Information Theory
Foundations and Trends® in Machine Learning
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This paper deals with distributed demodulation of space-time transmissions of a common message from a multiantenna access point (AP) to a wireless sensor network. Based on local message exchanges with single-hop neighboring sensors, two algorithms are developed for distributed demodulation. In the first algorithm, sensors consent on the estimated symbols. By relaxing the finite-alphabet constraints on the symbols, the demodulation task is formulated as a distributed convex optimization problem that is solved iteratively using the method of multipliers. Distributed versions of the centralized zero-forcing (ZF) and minimum mean-square error (MMSE) demodulators follow as special cases. In the second algorithm, sensors iteratively reach consensus on the average (cross-) covariances of locally available per-sensor data vectors with the corresponding AP-tosensor channel matrices, which constitute sufficient statistics for maximum likelihood demodulation. Distributed versions of the sphere decoding algorithm and the ZF/MMSE demodulators are also developed. These algorithms offer distinct merits in terms of error performance and resilience to non-ideal inter-sensor links. In both cases, the per-iteration error performance is analyzed, and the approximate number of iterations needed to attain a prescribed error rate are quantified. Simulated tests verify the analytical claims. Interestingly, only a few consensus iterations (roughly as many as the number of sensors), suffice for the distributed demodulators to approach the performance of their centralized counterparts.