Performance analysis of superior selective reporting-based energy efficient cooperative spectrum sensing in cognitive radio networks

Kishore, Rajalekshmi; Ramesha, C. K.; Gurugopinath, Sanjeev; Anupama, K. R.

doi:10.1016/j.adhoc.2017.07.010

Cited by 10 publications

(15 citation statements)

References 32 publications

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“…Some of the major benefits of CRs have been consolidated with regard to green communications in the work of Gur and Alagoz . Energy efficiency in CRs has become a topic of great interest among researchers in the wireless domain recently . In CRs with an interweave architecture, EE gains were achieved by optimising with respect to sensing parameters, viz, sensing duration and transmission duration, as demonstrated in other works .…”

Section: Introductionmentioning

confidence: 90%

Energy efficient design using statistical interference constraints for cognitive 5G networks under PU mobility

Moorthy¹,

Pillai²,

Shiny³

2018

Trans Emerging Tel Tech

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Energy efficiency (EE) has now become one of the essential parameters in the design of 5G and other next‐generation communication networks mainly due to economic and environmental concerns. In this paper, we develop an EE maximisation algorithm for the orthogonal frequency‐division multiplexing (OFDM)–based cognitive radio (CR) systems where the PU activity is highly dynamic. The problem is formulated as a joint optimisation problem in 3 variables: sensing duration, transmission duration, and transmit power on the subcarrier, with statistical constraint on the primary user (PU) interference. The statistical interference constraint frees the CR transmitter from the additional burden of obtaining the instantaneous channel quality feedback from the PU receivers. Furthermore, PU mobility, which is the main consideration of this work, closely portrays a real and practical CR network. We analyse how the mobility of the PUs affect their Quality of Service, particularly in terms of the interference impinged on PUs by the SUs. Moreover, the mobility of the PUs has been modelled, and PU interference constraints are reformulated by considering collisions between the PUs and SUs during their overlap period. Moreover, closed‐form expressions for secondary throughput and EE have been derived. The original problem, which is mathematically intractable due to its nonconvexity, is reformulated in terms of 3 alternately iterating subproblems. Numerical results show that with the proposed optimal algorithm, CR users can achieve significantly higher EE compared with existing algorithms. The suboptimal algorithm guarantees reduced complexity with a performance gap close to zero with the original optimal problem.

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Section: Introductionmentioning

confidence: 90%

Energy efficient design using statistical interference constraints for cognitive 5G networks under PU mobility

Moorthy¹,

Pillai²,

Shiny³

2018

Trans Emerging Tel Tech

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“…There have been many researches for the "selective energy detection". Such methods select the "optimal" sensor to do the sensing based on different criterias [41,[90][91][92][95][96][97][98].…”

Section: Data Fusionmentioning

confidence: 99%

Dynamic Spectrum Management

Liang

2020

Signals and Communication Technology

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This series is devoted to fundamentals and applications of modern methods of signal processing and cutting-edge communication technologies. The main topics are information and signal theory, acoustical signal processing, image processing and multimedia systems, mobile and wireless communications, and computer and communication networks. Volumes in the series address researchers in academia and industrial R&D departments. The series is application-oriented. The level of presentation of each individual volume, however, depends on the subject and can range from practical to scientific.

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“…, K, simultaneously sense the presence of the PU for a time t s , and one among these M (k) sensors is selected based on its SNR to report its decision to the sink during reporting time slot t r , corresponding to each channel. The advantage of employing the SSR scheme is that it increases the throughput and reduces the sensing overhead when compared to the conventional cooperative sensing (CCS) scheme using the OR rule [36]. Meanwhile, the data sensors collect information and when the sink identifies all the available channels, the data sensors transmit data by utilizing all the available channels in the data transmission phase for a duration T T otal − τ s .…”

Section: A Network Architecturementioning

confidence: 99%

“…2) SSR Scheme: We follow the method of choosing the superior SU and calculating the received SNR as described in [22], [36]. The probabilities of false-alarm, G SSR f (k), and signal detection, G SSR d (k), at the FC are given, respectively, as [22]…”

Section: Performance Analysis With Energy Detectionmentioning

confidence: 99%

Sensing-Throughput Tradeoff for Superior Selective Reporting-Based Spectrum Sensing in Energy Harvesting HCRNs

Kishore

Gurugopinath

Muhaidat

et al. 2019

IEEE Trans. Cogn. Commun. Netw.

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In this paper, we investigate the performance of conventional cooperative sensing (CCS) and superior selective reporting (SSR)-based cooperative sensing in an energy harvestingenabled heterogeneous cognitive radio network (HCRN). In particular, we derive expressions for the achievable throughput of both schemes and formulate nonlinear integer programming problems, in order to find the throughput-optimal set of spectrum sensors scheduled to sense a particular channel, given primary user (PU) interference and energy harvesting constraints. Furthermore, we present novel solutions for the underlying optimization problems based on the cross-entropy (CE) method, and compare the performance with exhaustive search and greedy algorithms. Finally, we discuss the tradeoff between the average achievable throughput of the SSR and CCS schemes, and highlight the regime where the SSR scheme outperforms the CCS scheme. Notably, we show that there is an inherent tradeoff between the channel available time and the detection accuracy. Our numerical results show that, as the number of spectrum sensors increases, the channel available time gains a higher priority in an HCRN, as opposed to detection accuracy.

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Performance analysis of superior selective reporting-based energy efficient cooperative spectrum sensing in cognitive radio networks

Cited by 10 publications

References 32 publications

Energy efficient design using statistical interference constraints for cognitive 5G networks under PU mobility

Energy efficient design using statistical interference constraints for cognitive 5G networks under PU mobility

Dynamic Spectrum Management

Sensing-Throughput Tradeoff for Superior Selective Reporting-Based Spectrum Sensing in Energy Harvesting HCRNs

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