Fundamental Limits of Wideband Localization - Part II: Cooperative Networks

The availability of positional information is of great importance in many commercial, governmental, and military applications. Localization is commonly accomplished through the use of radio communication between mobile devices (agents) and fixed infrastructure (anchors). However, precise determination of agent positions is a challenging task, especially in harsh environments due to radio blockage or limited anchor deployment. In these situations, cooperation among agents can significantly improve localization accuracy and reduce localization outage probabilities. A general framework of analyzing the fundamental limits of wideband localization has been developed in Part I of the paper. Here, we build on this framework and establish the fundamental limits of wideband cooperative location-aware networks. Our analysis is based on the waveforms received at the nodes, in conjunction with Fisher information inequality. We provide a geometrical interpretation of equivalent Fisher information for cooperative networks. This approach allows us to succinctly derive fundamental performance limits and their scaling behaviors, and to treat anchors and agents in a unified way from the perspective of localization accuracy. Our results yield important insights into how and when cooperation is beneficial.Comment: To appear in IEEE Transactions on Information Theor

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Prospects for joint observations of gravitational waves and gamma rays from merging neutron star binaries

The detection of the events GW150914 and GW151226, both consistent with the merger of a binary black hole system (BBH), opened the era of gravitational wave (GW) astronomy. Besides BBHs, the most promising GW sources are the coalescences of binary systems formed by two neutron stars or a neutron star and a black hole. These mergers are thought to be connected with short Gamma Ray Bursts (GRBs), therefore combined observations of GW and electromagnetic (EM) signals could definitively probe this association. We present a detailed study on the expectations for joint GW and high-energy EM observations of coalescences of binary systems of neutron stars with Advanced Virgo and LIGO and with the \emph{Fermi} gamma-ray telescope. To this scope, we designed a dedicated Montecarlo simulation pipeline for the multimessenger emission and detection by GW and gamma-ray instruments, considering the evolution of the GW detector sensitivities. We show that the expected rate of joint detection is low during the Advanced Virgo and Advanced LIGO 2016-2017 run; however, as the interferometers approach their final design sensitivities, the rate will increase by $\sim$ a factor of ten. Future joint observations will help to constrain the association between short GRBs and binary systems and to solve the puzzle of the progenitors of GWs. Comparison of the joint detection rate with the ones predicted in this paper will help to constrain the geometry of the GRB jet.Comment: 24 pages, 4 figure