Green cooperative spectrum sensing and scheduling in heterogeneous cognitive radio networks

The motivation behind the cognitive radio networks (CRNs) is rooted in scarcity of the radio spectrum and inefficiency of its management to meet the ever increasing high quality of service demands. Furthermore, information and communication technologies have limited and/or expensive energy resources and contribute significantly to the global carbon footprint. To alleviate these issues, energy efficient and energy harvesting (EEH) CRNs can harvest the required energy from ambient renewable sources while collecting the necessary bandwidth by discovering free spectrum for a minimized energy cost. Therefore, EEH-CRNs have potential to achieve green communications by enabling spectrum and energy self-sustaining networks. In this thesis, green cooperative spectrum sensing (CSS) policies are considered for large scale heterogeneous CRNs which consist of multiple primary channels (PCs) and a large number of secondary users (SUs) with heterogeneous sensing and reporting channel qualities. Firstly, a multi-objective clustering optimization (MOCO) problem is formulated from macro and micro perspectives; Macro perspective partitions SUs into clusters with the objectives: 1) Intra-cluster energy minimization of each cluster, 2) Intra-cluster throughput maximization of each cluster, and 3) Inter-cluster energy and throughput fairness. A multi-objective genetic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is adopted and demonstrated how to solve the MOCO. The micro perspective, on the other hand, works as a sub-procedure on cluster formations given by macro perspective. For the micro perspective, a multihop reporting based CH selection procedure is proposed to find: 1) The best CH which gives the minimum total multi-hop error rate, and 2) the optimal routing paths from SUs to the CHs using Dijkstra\u27s algorithm. Using Poisson-Binomial distribution, a novel and generalized K-out-of-N voting rule is developed for heterogeneous CRNs to allow SUs to have different levels of local detection performance. Then, a convex optimization framework is established to minimize the intra-cluster energy cost subject to collision and spectrum utilization constraints.Likewise, instead of a common fixed sample size test, a weighted sample size test is considered for quantized soft decision fusion to obtain a more EE regime under heterogeneity. Secondly, an energy and spectrum efficient CSS scheduling (CSSS) problem is investigated to minimize the energy cost per achieved data rate subject to collision and spectrum utilization constraints. The total energy cost is calculated as the sum of energy expenditures resulting from sensing, reporting and channel switching operations. Then, a mixed integer non-linear programming problem is formulated to determine: 1) The optimal scheduling subset of a large number of PCs which cannot be sensed at the same time, 2) The SU assignment set for each scheduled PC, and 3) Optimal sensing parameters of SUs on each PC. Thereafter, an equivalent convex framework is developed for specific instances of above combinatorial problem. For the comparison, optimal detection and sensing thresholds are also derived analytically under the homogeneity assumption. Based on these, a prioritized ordering heuristic is developed to order channels under the spectrum, energy and spectrum-energy limited regimes. After that, a scheduling and assignment heuristic is proposed and shown to have a very close performance to the exhaustive optimal solution. Finally, the behavior of the CRN is numerically analyzed under these regimes with respect to different numbers of SUs, PCs and sensing qualities. Lastly, a single channel energy harvesting CSS scheme is considered with SUs experiencing different energy arrival rates, sensing, and reporting qualities. In order to alleviate the half- duplex EH constraint, which precludes from charging and discharging at the same time, and to harvest energy from both renewable sources and ambient radio signals, a full-duplex hybrid energy harvesting (EH) model is developed. After formulating the energy state evolution of half and full duplex systems under stochastic energy arrivals, a convex optimization framework is established to jointly obtain the optimal harvesting ratio, sensing duration and detection threshold of each SU to find an optimal myopic EH policy subject to collision and energy- causality constraints

Censor-based cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels

The present contribution proposes a spectrally efficient censor-based cooperative spectrum sensing (C-CSS) approach in a sustainable cognitive radio network that consists of multiple antenna nodes and experiences imperfect sensing and reporting channels. In this context, exact analytic expressions are first derived for the corre- sponding probability of detection, probability of false alarm and sec- ondary throughput, assuming that each secondary user (SU) sends its detection outcome to a fusion center only when it has detected a primary signal. Capitalizing on the findings of the analysis, the effects of critical measures, such as the detection threshold, the number of SUs and the number of employed antennas, on the overall system performance are also quantified. In addition, the optimal detection threshold for each antenna based on the Neyman-Pearson criterion is derived and useful insights are developed on how to maximize the system throughput with a reduced number of SUs. It is shown that the C-CSS approach provides two distinct benefits compared with the conventional sensing approach, i.e., without censoring: i) the sensing tail problem, which exists in imperfect sensing environments, can be mitigated; ii) less SUs are ultimately required to obtain higher secondary throughput, rendering the system more sustainable

Sensing and transmission strategies in wireless cognitive radio systems

The main challenge in any cognitive radio system is to maximize secondary users throughput while limiting interference imposed on licensed users. In this regard, finding the optimal sensing and transmission timing strategies and accurate sensing techniques are of great importance in a cognitive radio network. In this thesis, we study a sensing-transmission scheme for secondary user in a cognitive radio system where the secondary user senses every primary channel independently and transmits a signal for a fixed duration if it finds the channel empty and stays idle for another fixed duration if it senses the channel busy. We obtain optimal idle and transmission durations which maximize access opportunity of the secondary user while keeping the interference ratios on the primary channels below some thresholds. Our results show that unless we have an error free perfect channel sensing, adding the idle duration improves the performance of the system. We also study a cooperative spectrum sensing scheme for cognitive radio systems where each sensor transmits multi-bit quantized information to a fusion center where the decision about the availability or occupancy of the channel is made. We compare iii the performance of our proposed multi-bit combining scheme with hard and soft combining schemes and show that with transmission of a few bits of information from each sensor , the system can achieve an error rate very close to the optimal soft combining scheme

Censor-Based Cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels

The present contribution proposes a spectrally efficient censor-based cooperative spectrum sensing (C-CSS) approach in a sustainable cognitive radio network that consists of multiple antenna nodes and experiences imperfect sensing and reporting channels. In this context, exact analytic expressions are first derived for the corresponding probability of detection, probability of false alarm, and secondary throughput, assuming that each secondary user (SU) sends its detection outcome to a fusion center only when it has detected a primary signal. Capitalizing on the findings of the analysis, the effects of critical measures, such as the detection threshold, the number of SUs, and the number of employed antennas, on the overall system performance are also quantified. In addition, the optimal detection threshold for each antenna based on the Neyman-Pearson criterion is derived and useful insights are developed on how to maximize the system throughput with a reduced number of SUs. It is shown that the C-CSS approach provides two distinct benefits compared with the conventional sensing approach, i.e., without censoring: i) the sensing tail problem, which exists in imperfect sensing environments, can be mitigated; and ii) less SUs are ultimately required to obtain higher secondary throughput, rendering the system more sustainable.acceptedVersionPeer reviewe

An Exponentially-Tight Approximate Factorization of the Joint PDF of Statistical Dependent Measurements in Wireless Sensor Networks

We consider the distributed detection problem of a temporally correlated random radio source signal using a wireless sensor network capable of measuring the energy of the received signals. It is well-known that optimal tests in the Neyman-Pearson setting are based on likelihood ratio tests (LRT), which, in this set-up, evaluate the quotient between the probability density functions (PDF) of the measurements when the source signal is present and absent. When the source is present, the computation of the joint PDF of the energy measurements at the nodes is a challenging problem. This is due to the statistical dependence introduced to the received signals by the radio source propagated through fading channels. We deal with this problem using the characteristic function of the (intractable) joint PDF, and proposing an approximation to it. We derive bounds for the approximation error in two wireless propagation scenarios, slow and fast fading, and show that the proposed approximation is exponentially tight with the number of nodes when the time-bandwidth product is sufficiently high. The approximation is used as a substitute of the exact joint PDF for building an approximate LRT, which performs better than other well-known detectors, as verified by Monte Carlo simulations.Comment: This is a pre-print version of an article submitted to IEEE Transactions on Wireless Communications and it is under revie

Sequential Statistical Signal Processing with Applications to Distributed Systems

Detection and estimation, two classical statistical signal processing problems with wellestablished theories, are traditionally studied under the fixed-sample-size and centralized setups, e.g., Neyman-Pearson target detection, and Bayesian parameter estimation. Recently, they appear in more challenging setups with stringent constraints on critical resources, e.g., time, energy, and bandwidth, in emerging technologies, such as wireless sensor networks, cognitive radio, smart grid, cyber-physical systems (CPS), internet of things (IoT), and networked control systems. These emerging systems have applications in a wide range of areas, such as communications, energy, the military, transportation, health care, and infrastructure. Sequential (i.e., online) methods suit much better to the ever-increasing demand on time-efficiency, and latency constraints than the conventional fixed-sample-size (i.e., offline) methods. Furthermore, as a result of decreasing device sizes and tendency to connect more and more devices, there are stringent energy and bandwidth constraints on devices (i.e., nodes) in a distributed system (i.e., network), requiring decentralized operation with low transmission rates. Hence, for statistical inference (e.g., detection and/or estimation) problems in distributed systems, today's challenge is achieving high performance (e.g., time efficiency) while satisfying resource (e.g., energy and bandwidth) constraints. In this thesis, we address this challenge by (i) first finding optimum (centralized) sequential schemes for detection, estimation, and joint detection and estimation if not available in the literature, (ii) and then developing their asymptotically optimal decentralized versions through an adaptive non-uniform sampling technique called level-triggered sampling. We propose and rigorously analyze decentralized detection, estimation, and joint detection and estimation schemes based on level-triggered sampling, resulting in a systematic theory of event-based statistical signal processing. We also show both analytically and numerically that the proposed schemes significantly outperform their counterparts based on conventional uniform sampling in terms of time efficiency. Moreover, they are compatible with the existing hardware as they work with discrete-time observations produced by conventional A/D converters. We apply the developed schemes to several problems, namely spectrum sensing and dynamic spectrum access in cognitive radio, state estimation and outage detection in smart grid, and target detection in multi-input multi-output (MIMO) wireless sensor networks