Cooperative Spectrum Sensing With Heterogeneous Devices: Hard Combining Versus Soft Combining

Ejaz, Waleed; Hattab, Ghaith; Cherif, Nesrine; Ibnkahla, Mohamed; Abdelkefi, Fatma; Siala, Mohamed

doi:10.1109/jsyst.2016.2582647

Cited by 44 publications

(44 citation statements)

References 34 publications

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“…For soft combining, an optimal fusion rule is derived for a given quantization scheme. Likewise, the performance comparison of hard and soft combining cooperative spectrum sensing with heterogeneous devices is presented in [24]. For hard combining, k-out-of-N fusion rule is used for the final decision at the FC.…”

Section: A Related Workmentioning

confidence: 99%

Joint Quantization and Confidence-Based Generalized Combining Scheme for Cooperative Spectrum Sensing

Ejaz

Hattab

Attia

et al. 2018

IEEE Systems Journal

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Cooperative spectrum sensing can effectively protect licensed users from harmful interference and satisfy the qualityof-service requirements of secondary users (SUs) in cognitive radio networks. In this paper, a novel joint quantization and confidencebased generalized (JQCG) combining scheme is proposed. A centralized approach is considered for cooperative spectrum sensing with SUs using the energy detector as a local spectrum sensing scheme. The confidence level is calculated using a fuzzy logic membership function for each cooperative SU. In the proposed JQCG combining scheme, each cooperative SU transmits multiple bits instead of transmitting one bit (hard combining) or the complete test statistic (soft combining) to report local sensing results to the fusion center (FC). We also derive optimal weights (in Neyman-Pearson sense) for each quantized level at the FC to maximize the probability of detection for a given false alarm probability. The proposed JQCG combining scheme is validated by extensive simulation results showing that it has a comparable performance to the soft combining scheme with less overhead. Extensive computer simulations show that the JQCG combining scheme significantly outperforms the hard combining and existing quantized schemes for cooperative spectrum sensing.Index Terms-Cognitive radio (CR), cooperative spectrum sensing, fuzzy logic, hard combining, quantization, soft combining. 1937-9234

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Section: A Related Workmentioning

confidence: 99%

Joint Quantization and Confidence-Based Generalized Combining Scheme for Cooperative Spectrum Sensing

Ejaz

Hattab

Attia

et al. 2018

IEEE Systems Journal

Self Cite

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“…The SUs must estimate the channel occupancy of the PUs across the network based on local measurements. In principle, these measurements can be collected at a fusion center [9]- [11], but centralized estimation may incur unacceptable delays and overhead [8], [12]. To reduce this cost and provide a form of coordination, neighboring cells may inform each other of spectrum they are occupying [13]; however, this scheme cannot manage interference beyond the cell neighborhood, which may be significant in dense topologies.…”

Section: Introductionmentioning

confidence: 99%

“…The optimal design of decision threshold, local estimators, fusion rules, are outside the scope of this paper and can be found in other prior work, such as[9]-[11], for the case of a single-cell.December 7, 2018 DRAFT…”

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confidence: 99%

Multi-Scale Spectrum Sensing in Dense Multi-Cell Cognitive Networks

Michelusi

Nokleby

Mitra

et al. 2019

IEEE Trans. Commun.

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Multi-scale spectrum sensing is proposed to overcome the cost of full network state information on the spectrum occupancy of primary users (PUs) in dense multi-cell cognitive networks. Secondary users (SUs) estimate the local spectrum occupancies and aggregate them hierarchically to estimate spectrum occupancy at multiple spatial scales. Thus, SUs obtain fine-grained estimates of spectrum occupancies of nearby cells, more relevant to scheduling tasks, and coarse-grained estimates of those of distant cells. An agglomerative clustering algorithm is proposed to design a cost-effective aggregation tree, matched to the structure of interference, robust to local estimation errors and delays. Given these multi-scale estimates, the SU traffic is adapted in a decentralized fashion in each cell, to optimize the trade-off among SU cell throughput, interference caused to PUs, and mutual SU interference. Numerical evaluations demonstrate a small degradation in SU cell throughput (up to 15% for a 0dB interference-to-noise ratio experienced at PUs) compared to a scheme with full network state information, using only one-third of the cost incurred in the exchange of spectrum estimates. The proposed interference-matched design is shown to significantly outperform a random tree design, by providing more relevant information for network control, and a state-of-the-art consensus-based algorithm, which does not leverage the spatio-temporal structure of interference across the network. December 7, 2018 DRAFT arXiv:1802.08378v3 [cs.IT] 6 Dec 2018 greatly facilitates the estimation of the interference caused to PUs (Lemma 3). 3) We address the design of the hierarchical aggregation tree under a constraint on the aggregation cost based on agglomerative clustering [16, Ch. 14] (Algorithm 1).Our analysis demonstrates that multi-scale spectrum estimation using hierarchical aggregation matched to the structure of interference is a much more cost-effective solution than fine-grained network state estimation, and provides more valuable information for network control. Additionally, it demonstrates the importance of leveraging the spatial and temporal dynamics of interference arising in dense multi-cell systems, made possible by our multi-scale strategy; in contrast, consensus-based strategies, which average out the spectrum estimate over multiple cells and over time, are unable to achieve this goal and perform poorly in dense multi-cell systems.This paper is organized as follows. In Sec. II, we present the system model. In Sec. III, we present the proposed local and multi-scale estimation algorithms, whose performance is analyzed in Sec. IV. In Sec. V, we address the tree design. In Sec. VI, we present numerical results and, in Sec. VII, we conclude this paper. The main proofs are provided in the Appendix. Table I provides the main parameters and metrics. II. SYSTEM MODELNetwork Model: We consider the network depicted in Fig. 1, composed of a multi-cell network of PUs with N C cells operating in downlink, indexed by C≡{1, 2, . . . , N C }, and...

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“…Further, authors propose an adaptive spectrum sensing strategy, which uses PSSS or random spectrum sensing strategy (RSSS) to select channels to be sensed. In [37], a heterogeneous centralized system using k-out-of-K rule is analyzed. Centralized and distributed consensus learning-based heterogeneous CRNs are compared in terms of CRN throughput analysis in [6].…”

Section: Related Workmentioning

confidence: 99%

“…Centralized cognitive IoT systems use specific rules to decide on the availability of channels. Traditionally, there are four different centralized rules, which include OR, k-out-of-K, majority, and AND rules [6,37]. In case of the OR rule, the results from all cooperative SUs are obtained according to the OR logic, which implies by itself at least one SU to detect the PU signal to be present.…”

Section: Centralized Rules / Algorithmsmentioning

confidence: 99%

Distributed Learning-Based Cooperative Spectrum Sensing for Cognitive Internet of Things Systems

Gharib¹

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The emerge of Internet of Things (IoT) brings up revolutionary changes to the field of wireless communications. Providing connection to billions of IoT devices together with challenging IoT environments lead to the need of new technologies in order to fulfill IoT spectrum demands. Cognitive radio (CR) technology can be seen as one of the prominent solutions to the spectrum scarcity issue in IoT, where multiband cooperative spectrum sensing (CSS) is the key. In this thesis, we consider heterogeneous distributed learning-based CR networks (CRNs). Focus is given to learning approaches named as incremental, consensus, and diffusion. We perform a comparative study to investigate which one of them best fits cognitive IoT demands. Simulation results are performed illustrating potentials of diffusion and consensus algorithms and disadvantages of incremental for cognitive IoT systems. Lack of centralized control, increase in number of devices, and dynamic environments place a room for lots of challenges in the CSS process. Conventional CSS techniques have to be improved in order to fulfill sophisticated IoT requirements. One of the main challenges is cooperative secondary users' (SUs') scheduling to sense a subset of channels. To overcome the aforementioned challenge, in this thesis, we propose a novel multi-band CSS scheme, named as heterogeneous multi-band multiuser CSS (HM2CSS). The proposed scheme allows heterogeneous SUs to sense multiple channels and works by selecting cooperative SUs in two stages. Only SUs owning different information about channels are chosen to be cooperative. This is done by selecting leaders for each channel in the first stage and corresponding cooperative SUs in the second stage. Careful choice of leaders reflects the selection of cooperative SUs and hence, improves system performance. Then, diffusion learning algorithm is used to exchange locally sensed information among cooperative SUs for all channels. Further, the decision on the availability of channels is made. Extensive simulation results illustrate that the proposed HM2CSS scheme satisfies the IEEE 802.22 detection performance standard. Detection performance and CRN To my beloved family v Table of Contents vii List of Tables x List of Figures xi Nomenclature xiv List of Tables 4.1 Comparison of incremental, consensus, and diffusion-based learning approaches' characteristics. .

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Cooperative Spectrum Sensing With Heterogeneous Devices: Hard Combining Versus Soft Combining

Cited by 44 publications

References 34 publications

Joint Quantization and Confidence-Based Generalized Combining Scheme for Cooperative Spectrum Sensing

Joint Quantization and Confidence-Based Generalized Combining Scheme for Cooperative Spectrum Sensing

Multi-Scale Spectrum Sensing in Dense Multi-Cell Cognitive Networks

Distributed Learning-Based Cooperative Spectrum Sensing for Cognitive Internet of Things Systems

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