Maximizing Achievable Transmission Time in Cognitive Radio Networks Under Sensor-Aided Crowdsourced Spectrum Sensing

2019 ◽  
Vol 62 (10) ◽  
pp. 1477-1489
Author(s):  
Hisham M Almasaeid
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Radio Networks ◽  
Hill Climbing ◽  
Transmission Time ◽  
The Hill ◽  
Cognitive User

Abstract Spectrum-sensing as a service has been proposed and studied by many researchers over the past decade as a promising approach to support the viability of cognitive radio networks (CRNs). A spectrum-sensing service provider (SSP) provides information about spectrum occupancy to its clients that is generally more accurate than what clients can learn on their own. Two approaches are used by SSPs in their operation, the dedicated sensing infrastructure approach (sensor-aided CRN) and the crowdsensing approach. In this work, we assume a hybrid model where a dedicated sensing infrastructure is used along with crowdsensing. We study the tradeoff between sensing time paid by cognitive users to the SSP and their achievable transmission time. Our objective is to maximize the minimum achievable transmission time for any cognitive user in the network by carefully selecting the channels to be used. Two algorithms are proposed, one is based on the hill-climbing search algorithm (abbreviated HCA) and the other is a less optimal but faster greedy selection algorithm (abbreviated GSA). Results show that both HCA and GSA are within 3% of the optimal solution. Results also confirm that GSA is faster than HCA, while HCA outperforms GSA.

Advanced Spectrum Sensing Techniques

2019 ◽  
pp. 133-141
Author(s):  
P. T. V. Bhuvaneswari Mohan ◽  
Bino Jesu Stephen
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Wide Frequency Range ◽  
Radio Network ◽  
Binary Decision ◽  
Wideband Spectrum Sensing ◽  
Channel Bandwidth ◽  
Cognitive User

Spectrum sensing plays an important role in cognitive radio networks. Based on the bandwidth considered for spectrum sensing, it can be classified into narrowband and wideband spectrum sensing. Traditional spectrum sensing methods are devised for narrow band as it focuses on narrow frequency a range that is the channel bandwidth is lesser than the coherence bandwidth of the channel. To provide more spectral opportunity to cognitive user and to increase the throughput, cognitive radio network needs techniques that exploits spectral opportunities over a wide frequency range. Wideband spectrum sensing techniques aim to sense a channel bandwidth that exceeds the coherence bandwidth of the channel. Narrowband sensing techniques cannot be directly employed to perform wideband spectrum sensing as they make a single binary decision. In this chapter, the advanced spectrum sensing techniques and their taxonomy are discussed in detail.

An Energy Efficient Spectrum Aware Routing for Cognitive Radio Networks (CRNs)

2020 ◽  
Vol 10 ◽  
Author(s):  
Dileep Reddy Bolla ◽  
Jijesh J J ◽  
Mahaveer Penna ◽  
Shiva Shankar
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Energy Efficient ◽  
Research Work ◽  
Spectrum Management ◽  
Residual Energy ◽  
Radio Networks ◽  
Traffic Load ◽  
The Impact

Back Ground/ Aims:: Now-a-days in the Wireless Communications some of the spectrum bands are underutilized or unutilized; the spectrum can be utilized properly by using the Cognitive Radio Techniques using the Spectrum Sensing mechanisms. Objectives:: The prime objective of the research work carried out is to achieve the energy efficiency and to use the spectrum effectively by using the spectrum management concept and achieve better throughput, end to end delay etc., Methods:: The detection of the spectrum hole plays a vital role in the routing of Cognitive Radio Networks (CRNs). While detecting the spectrum holes and the routing, sensing is impacted by the hidden node issues and exposed node issues. The impact of sensing is improved by incorporating the Cooperative Spectrum Sensing (CSS) techniques. Along with these issues the spectrum resources changes time to time in the routing. Results:: All the issues are addressed with An Energy Efficient Spectrum aware Routing (EESR) protocol which improves the timeslot and the routing schemes. The overall network life time is improved with the aid of residual energy concepts and the overall network performance is improved. Conclusion:: The proposed protocol (EESR) is an integrated system with spectrum management and the routing is successfully established to communication in the network and further traffic load is observed to be balanced in the protocol based on the residual energy in a node and further it improves the Network Lifetime of the Overall Network and the Individual CR user, along with this the performance of the proposed protocol outperforms the conventional state of art routing protocols.

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