Comparative Analysis of Blind Detectors in a Cluster-Based Cooperative Spectrum Hole Detection

Prevention of authorized users from interference determine the accurate detection of Spectrum Hole (SH) is of great importance in a Spectrum Shearing Network (SSN). However, multipath fading and shadowing affect the accurate detection of SH resulting in interference. Cluster-Based Cooperative Spectrum Hole Detection (CBCSHD) used to address this problem depends on detector and number of clusters. Hence, comparative analysis of blind detectors in CBCSHD is carried out to evaluate its performance with various blind detectors and number of clusters. The CBCSHD is carried out using six Cognitive Users (CUs) that jointly carry out detection of SH and each of the CUs performs local sensing using Eigenvalue Detector (EVD), Energy Detector (ED) and Cyclostationary Detector (CD). The CUs form clusters to reduce reporting overhead between CUs. The local sensing results from individual user are combined at the Cluster Head (CH) using majority fusion rule. The performance of each of the detectors in CBCSHD is evaluated using Probability of Detection (PD) and Sensing Time (ST). PD values of 0.7661, 0.7160 and 0.6229 are obtained at SNR of 4 dB for ED, CD and EVD, respectively, while ST values of 3.0707, 3.7163 and 4.0907 s are obtained for ED, CD and EVD, respectively. The results obtained show that ED has the highest detection rate, followed by CD, while EVD shows the worst detection rate

Superallocation and Cluster‐Based Cooperative Spectrum Sensing in 5G Cognitive Radio Network

Consequently, the research and development for the 5G systems have already been started. This chapter presents an overview of potential system network architecture and highlights a superallocation technique that could be employed in the 5G cognitive radio network (CRN). A superallocation scheme is proposed to enhance the sensing detection performance by rescheduling the sensing and reporting time slots in the 5G cognitive radio network with a cluster‐based cooperative spectrum sensing (CCSS). In the 4G CCSS scheme, first, all secondary users (SUs) detect the primary user (PU) signal during a rigid sensing time slot to check the availability of the spectrum band. Second, during the SU reporting time slot, the sensing results from the SUs are reported to the corresponding cluster heads (CHs). Finally, during CH reporting time slots, the CHs forward their hard decision to a fusion center (FC) through the common control channels for the global decision. However, the reporting time slots for the SUs and CHs do not contribute to the detection performance. In this chapter, a superallocation scheme that merges the reporting time slots of SUs and CHs by rescheduling the reporting time slots as a nonfixed sensing time slot for SUs to detect the PU signal promptly and more accurately is proposed. In this regard, SUs in each cluster can obtain a nonfixed sensing time slot depending on their reporting time slot order. The effectiveness of the proposed chapter that can achieve better detection performance under –28 to –10 dB environments and thus reduce reporting overhead is shown through simulations

Messhu-gata musen nettowaku ni okeru supekutoramu kenchi to MAC-so purotokoru ni kansuru kenkyu

制度:新 ; 報告番号:甲3458号 ; 学位の種類:博士(国際情報通信学) ; 授与年月日:2011/11/16 ; 早大学位記番号:新578

Distributed Detection and Estimation in Wireless Sensor Networks

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201

Distributed field estimation in wireless sensor networks

This work takes into account the problem of distributed estimation of a physical field of interest through a wireless sesnor networks

Distributed field estimation in wireless sensor networks

This work takes into account the problem of distributed estimation of a physical field of interest through a wireless sesnor networks

DR9.3 Final report of the JRRM and ASM activities

Deliverable del projecte europeu NEWCOM++This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.Postprint (published version