Approximations of the aggregated interference statistics for outage analysis in massive MTC

This paper presents several analytic closed-form approximations of the aggregated interference statistics within the framework of uplink massive machine-type-communications (mMTC), taking into account the random activity of the sensors. Given its discrete nature and the large number of devices involved, a continuous approximation based on the Gram–Charlier series expansion of a truncated Gaussian kernel is proposed. We use this approximation to derive an analytic closed-form expression for the outage probability, corresponding to the event of the signal-to-interference-and-noise ratio being below a detection threshold. This metric is useful since it can be used for evaluating the performance of mMTC systems. We analyze, as an illustrative application of the previous approximation, a scenario with several multi-antenna collector nodes, each equipped with a set of predefined spatial beams. We consider two setups, namely single- and multiple-resource, in reference to the number of resources that are allocated to each beam. A graph-based approach that minimizes the average outage probability, and that is based on the statistics approximation, is used as allocation strategy. Finally, we describe an access protocol where the resource identifiers are broadcast (distributed) through the beams. Numerical simulations prove the accuracy of the approximations and the benefits of the allocation strategy.Peer ReviewedPostprint (published version

Parallel Opportunistic Routing in Wireless Networks

We study benefits of opportunistic routing in a large wireless ad hoc network by examining how the power, delay, and total throughput scale as the number of source- destination pairs increases up to the operating maximum. Our opportunistic routing is novel in a sense that it is massively parallel, i.e., it is performed by many nodes simultaneously to maximize the opportunistic gain while controlling the inter-user interference. The scaling behavior of conventional multi-hop transmission that does not employ opportunistic routing is also examined for comparison. Our results indicate that our opportunistic routing can exhibit a net improvement in overall power--delay trade-off over the conventional routing by providing up to a logarithmic boost in the scaling law. Such a gain is possible since the receivers can tolerate more interference due to the increased received signal power provided by the multi-user diversity gain, which means that having more simultaneous transmissions is possible.Comment: 18 pages, 7 figures, Under Review for Possible Publication in IEEE Transactions on Information Theor

Distributed Clustering and Learning Over Networks

Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when agents share a common objective. However, in many applications agents may belong to different clusters that pursue different objectives. Then, indiscriminate cooperation will lead to undesired results. In this work, we propose an adaptive clustering and learning scheme that allows agents to learn which neighbors they should cooperate with and which other neighbors they should ignore. In doing so, the resulting algorithm enables the agents to identify their clusters and to attain improved learning and estimation accuracy over networks. We carry out a detailed mean-square analysis and assess the error probabilities of Types I and II, i.e., false alarm and mis-detection, for the clustering mechanism. Among other results, we establish that these probabilities decay exponentially with the step-sizes so that the probability of correct clustering can be made arbitrarily close to one.Comment: 47 pages, 6 figure

Distributed space–time cooperative schemes for underwater acoustic communications

Author Posting. © IEEE, 2008. This article is posted here by permission of IEEE for personal use, not for redistribution. The definitive version was published in IEEE Journal of Oceanic Engineering 33 (2008): 489-50, doi:10.1109/JOE.2008.2005338.In resource limited, large scale underwater sensor networks, cooperative communication over multiple hops offers opportunities to save power. Intermediate nodes between source and destination act as cooperative relays. Herein, protocols coupled with space-time block code (STBC) strategies are proposed and analyzed for distributed cooperative communication. Amplify-and-forward-type protocols are considered, in which intermediate relays do not attempt to decode the information. The Alamouti-based cooperative scheme proposed by Hua (2003) for flat-fading channels is generalized to work in the presence of multipath, thus addressing a main characteristic of underwater acoustic channels. A time-reversal distributed space-time block code (TR-DSTBC) is proposed, which extends the dual-antenna TR-STBC (time-reversal space-time block code) approach from Lindskog and Paulraj (2000) to a cooperative communication scenario for signaling in multipath. It is first shown that, just as in the dual-antenna STBC case, TR along with the orthogonality of the DSTBC essentially allows for decoupling of the vector intersymbol interference (ISI) detection problem into separate scalar problems, and thus yields strong performance (compared with single-hop communication) and with substantially reduced complexity over nonorthogonal schemes. Furthermore, a performance analysis of the proposed scheme is carried out to provide insight on the performance gains, which are further confirmed via numerical results based on computer simulations and field data experiments

Compressed Sensing based Low-Power Multi-View Video Coding and Transmission in Wireless Multi-Path Multi-Hop Networks

Wireless Multimedia Sensor Network (WMSN) is increasingly being deployed for surveillance, monitoring and Internet-of-Things (IoT) sensing applications where a set of cameras capture and compress local images and then transmit the data to a remote controller. Such captured local images may also be compressed in a multi-view fashion to reduce the redundancy among overlapping views. In this paper, we present a novel paradigm for compressed-sensing-enabled multi-view coding and streaming in WMSN. We first propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and independent decoders. The proposed architecture leverages the properties of CS to overcome many limitations of traditional encoding techniques, specifically massive storage requirements and high computational complexity. Then, we present a modeling framework that exploits the aforementioned coding architecture. The proposed mathematical problem minimizes the power consumption by jointly determining the encoding rate and multi-path rate allocation subject to distortion and energy constraints. Extensive performance evaluation results show that the proposed framework is able to transmit multi-view streams with guaranteed video quality at lower power consumption