Alfonso Cano

High-Performance Cooperative Demodulation With Decode-and-Forward Relays

Cooperative communication systems using various relay strategies can achieve spatial diversity gains, enhance coverage, and potentially increase capacity. For the practically attractive decode-and-forward (DF) relay strategy, we derive a high-performance low-complexity coherent demodulator at the destination in the form of a weighted combiner. The weights are selected adaptively to account for the quality of both source-relay-destination and source-destination links. Analysis proves that the novel coherent demodulator can achieve the maximum possible diversity, regardless of the underlying constellation. Its error performance tightly bounds that of maximum-likelihood (ML) demodulation, which provably quantifies the diversity gain of ML detection with DF relaying. Simulations corroborate the analysis and compare the performance of the novel decoder with existing diversity-achieving strategies including analog amplify-and-forward and selective-relaying.

Consensus-based distributed linear support vector machines

This paper develops algorithms to train linear support vector machines (SVMs) when training data are distributed across different nodes and their communication to a centralized node is prohibited due to, for example, communication overhead or privacy reasons. To accomplish this goal, the centralized linear SVM problem is cast as the solution of coupled decentralized convex optimization subproblems with consensus constraints on the parameters defining the classifier. Using the method of multipliers, distributed training algorithms are derived that do not exchange elements from the training set among nodes. The communications overhead of the novel approach is fixed and fully determined by the topology of the network instead of being determined by the size of the training sets as it is the case for existing incremental approaches. An online algorithm where data arrive sequentially to the nodes is also developed. Simulated tests illustrate the performance of the algorithms.

Distributed Clustering Using Wireless Sensor Networks

Clustering spatially distributed data is well motivated and especially challenging when communication to a central processing unit is discouraged, e.g., due to power constraints. Distributed clustering schemes are developed in this paper for both deterministic and probabilistic approaches to unsupervised learning. The centralized problem is solved in a distributed fashion by recasting it to a set of smaller local clustering problems with consensus constraints on the cluster parameters. The resulting iterative schemes do not exchange local data among nodes, and rely only on single-hop communications. Performance of the novel algorithms is illustrated with simulated tests on synthetic and real sensor data. Surprisingly, these tests reveal that the distributed algorithms can exhibit improved robustness to initialization than their centralized counterparts.

Distributed In-Network Channel Decoding

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.

Distributed consensus-based demodulation: algorithms and error analysis

This paper deals with distributed demodulation of space-time transmissions of a common message from a multi-antenna 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-to-sensor 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.

Consensus-based distributed expectation-maximization algorithm for density estimation and classification using wireless sensor networks

The present paper develops a decentralized expectation-maximization (EM) algorithm to estimate the parameters of a mixture density model for use in distributed learning tasks performed with data collected at spatially deployed wireless sensors. The E-step in the novel iterative scheme relies on local information available to individual sensors, while during the M-step sensors exchange information only with their one- hop neighbors to reach consensus and eventually percolate the global information needed to estimate the wanted parameters across the wireless sensor network (WSN). Analysis and simulations demonstrate that the resultant consensus-based distributed EM (CB-DEM) algorithm matches well the resource- limited characteristics of WSNs and compares favorably with existing alternatives because it has wider applicability and remains resilient to inter-sensor communication noise.

Distributed feature-based modulation classification using wireless sensor networks

Automatic modulation classification (AMC) is a critical prerequisite for demodulation of communication signals in tactical scenarios. Depending on the number of unknown parameters involved, the complexity of AMC can be prohibitive. Existing maximum-likelihood and feature-based approaches rely on centralized processing. The present paper develops AMC algorithms using spatially distributed sensors, each acquiring relevant features of the received signal. Individual sensors may be unable to extract all relevant features to reach a reliable classification decision. However, the cooperative in-network approach developed enables high classification rates at reduced-overhead, even when features are noisy and/or missing. Simulated tests illustrate the performance of the novel distributed AMC scheme.

Efficient Demodulation in Cooperative Schemes Using Decode-and-Forward Relays

Cooperative communication systems using various relay strategies can achieve spatial diversity gains, enhance coverage and potentially increase capacity. For the practically attractive decode-and-forward (DF) relay strategy, we derive an efficient demodulator at the destination in the form of a weighted combiner. The weights are selected adaptively to account for the quality of both source-relay-destination and source-destination links. Analysis proves that the novel demodulator can achieve the maximum possible diversity, regardless of the underlying constellation. Its error performance tightly bounds that of maximumlikelihood (ML) demodulation which provably quantifies the diversity gain of ML detection with DF relaying. Simulations corroborate our theoretical analyses and compare performance of the novel decoder with existing diversity-achieving strategies including analog amplify-and-forward and selective-relaying.

High-Throughput Multi-Source Cooperation via Complex-Field Network Coding

Physical-layer network coding over wireless networks can provide considerable throughput gains with respect to traditional cooperative relaying strategies at no loss of diversity gain. In this paper, a novel cooperation protocol is developed based on complex-field wireless network coding. Sources transmit efficiently information symbols linearly combined with symbols from other sources. Different from existing wireless network coding protocols, transmissions are not restricted to binary symbols, and do not have to be received simultaneously. In a network with N sources, the developed protocol can achieve throughput up to approximately 1/N symbols per source per channel use, as well as diversity of order N. To deal with decoding errors at sources, selective- and adaptive-forwarding protocols are also developed at no loss of diversity gain. Analytical results corroborated by simulated tests show considerable performance gains with respect to distributed space-time coding, and bit-level network coding protocols.

Link-Adaptive Cooperative Communications Without Channel State Information

Link-adaptive regeneration (LAR) is a novel relaying strategy other than decode-and-forward (DF) or amplify-and-forward (AF) in user cooperative communications. Requiring simple channel state information (CSI) of both the source-relay and the relay-destination links, LAR has been shown to achieve full diversity using coherent modulations. In this paper, we generalize the idea of LAR into differential and non-coherent cooperative transmissions, which do not require CSI at either relays or destination. We prove that full spatial diversity gain can still be achieved in such systems, without incurring the overhead of cyclic redundancy check (CRC) codes. Simulations demonstrate that the proposed scheme is universally applicable to multi-branch and multi-hop cooperation regardless of the constellation size and outperforms existing alternatives.