MIMO-OFDM Based Energy Harvesting Cooperative Communications Using Coalitional Game Algorithm

This document is the Accepted Manuscript version. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we consider the problem of cooperative communication between relays and base station in an advanced MIMO-OFDM framework, under the assumption that the relays are supplied by electric power drawn from energy harvesting (EH) sources. In particular, we focus on the relay selection, with the goal to guarantee the required performance in terms of capacity. In order to maximize the data throughput under the EH constraint, we model the transmission scheme as a non-transferable coalition formation game, with characteristic function based on an approximated capacity expression. Then, we introduce a powerful mathematical tool inherent to coalitional game theory, namely: the Shapley value (Sv) to provide a reliable solution concept to the game. The selected relays will form a virtual dynamically-configuredMIMO network that is able to transmit data to destination using efficient space-time coding techniques. Numerical results, obtained by simulating the EH-powered cooperativeMIMO-OFDMtransmission with Algebraic Space-Time Coding (ASTC), prove that the proposed coalitional game-based relay selection allows to achieve performance very close to that obtained by the same system operated by guaranteed power supply. The proposed methodology is finally compared with some recent related state-of-the-art techniques showing clear advantages in terms of link performance and goodput.Peer reviewe

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing.  The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing.  This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties.  The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing

Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence

Inferring events of interest by fusing data from multiple heterogeneous sources has been an interesting and important topic in recent years. Several issues related to inference using heterogeneous data with complex and nonlinear dependence are investigated in this dissertation. We apply copula theory to characterize the dependence among heterogeneous data. In centralized detection, where sensor observations are available at the fusion center (FC), we study copula-based fusion. We design detection algorithms based on sample-wise copula selection and mixture of copulas model in different scenarios of the true dependence. The proposed approaches are theoretically justified and perform well when applied to fuse acoustic and seismic sensor data for personnel detection. Besides traditional sensors, the access to the massive amount of social media data provides a unique opportunity for extracting information about unfolding events. We further study how sensor networks and social media complement each other in facilitating the data-to-decision making process. We propose a copula-based joint characterization of multiple dependent time series from sensors and social media. As a proof-of-concept, this model is applied to the fusion of Google Trends (GT) data and stock/flu data for prediction, where the stock/flu data serves as a surrogate for sensor data. In energy constrained networks, local observations are compressed before they are transmitted to the FC. In these cases, conditional dependence and heterogeneity complicate the system design particularly. We consider the classification of discrete random signals in Wireless Sensor Networks (WSNs), where, for communication efficiency, only local decisions are transmitted. We derive the necessary conditions for the optimal decision rules at the sensors and the FC by introducing a hidden random variable. An iterative algorithm is designed to search for the optimal decision rules. Its convergence and asymptotical optimality are also proved. The performance of the proposed scheme is illustrated for the distributed Automatic Modulation Classification (AMC) problem. Censoring is another communication efficient strategy, in which sensors transmit only informative observations to the FC, and censor those deemed uninformative . We design the detectors that take into account the spatial dependence among observations. Fusion rules for censored data are proposed with continuous and discrete local messages, respectively. Their computationally efficient counterparts based on the key idea of injecting controlled noise at the FC before fusion are also investigated. In this thesis, with heterogeneous and dependent sensor observations, we consider not only inference in parallel frameworks but also the problem of collaborative inference where collaboration exists among local sensors. Each sensor forms coalition with other sensors and shares information within the coalition, to maximize its inference performance. The collaboration strategy is investigated under a communication constraint. To characterize the influence of inter-sensor dependence on inference performance and thus collaboration strategy, we quantify the gain and loss in forming a coalition by introducing the copula-based definitions of diversity gain and redundancy loss for both estimation and detection problems. A coalition formation game is proposed for the distributed inference problem, through which the information contained in the inter-sensor dependence is fully explored and utilized for improved inference performance

Cooperative Game Theory and Its Application in Localization Algorithms

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Chapter Cooperative Game Theory and Its Application in Localization Algorithms

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Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches