Adaptive signal processing algorithms: stability and performance
Adaptive signal processing algorithms: stability and performance
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Linear Systems
Modern Coding Theory
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
Using linear programming to Decode Binary linear codes
IEEE Transactions on Information Theory
Consensus-based distributed linear support vector machines
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Distributed consensus-based demodulation: algorithms and error analysis
IEEE Transactions on Wireless Communications
Consensus-Based Distributed Support Vector Machines
The Journal of Machine Learning Research
Distributed sparse linear regression
IEEE Transactions on Signal Processing
Foundations and Trends® in Machine Learning
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Average log-likelihood ratios (LLRs) constitute sufficient statistics for centralized maximum-likelihood block decoding as well as for a posteriori probability evaluation which enables bit-wise (possibly iterative) decoding. By acquiring such average LLRs per sensor it becomes possible to perform these decoding tasks in a low-complexity distributed fashion using wireless sensor networks. At affordable communication overhead, the resultant distributed decoders rely on local message exchanges among single-hop neighboring sensors to achieve iteratively consensus on the average LLRs per sensor. Furthermore, the decoders exhibit robustness to non-ideal inter-sensor links affected by additive noise and random link failures. Pairwise error probability bounds benchmark the decoding performance as a function of the number of consensus iterations. Interestingly, simulated tests corroborating the analytical findings demonstrate that only a few consensus iterations suffice for the novel distributed decoders to approach the performance of their centralized counterparts.