MAC Layer Spectrum Sensing in Cognitive Radio Networks

2013 ◽  
pp. 210-230
Author(s):  
Mohamed Hamid ◽  
Abbas Mohammed
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Performance Metrics ◽  
Computational Cost ◽  
Radio Spectrum ◽  
Radio Networks ◽  
Superior Performance ◽  
Mac Layer ◽  
Spectrum Utilization ◽  
Network Nodes

Efficient use of the available licensed radio spectrum is becoming increasingly difficult as the demand and usage of the radio spectrum increases. This usage of the spectrum is not uniform within the licensed band but concentrated in certain frequencies of the spectrum while other parts of the spectrum are inefficiently utilized. In cognitive radio environments, the primary users are allocated licensed frequency bands while secondary cognitive users can dynamically allocate the empty frequencies within the licensed frequency band, according to their requested quality of service specifications. In this chapter, the authors investigate and assess the performance of MAC layer sensing schemes in cognitive radio networks. Two performance metrics are used to assess the performance of the sensing schemes: the available spectrum utilization and the idle channel search delay for reactive and proactive sensing schemes. In proactive sensing, the adapted and non-adapted sensing period schemes are also assessed. Simulation results show that proactive sensing with adapted periods provides superior performance at the expense of higher computational cost performed by network nodes.

scholarly journals Improved Buffer-Aided Multi-Hop Relaying with Reduced Outage and Packet Delay in Cognitive Radio Networks

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 895
Author(s):  
Shakeel Alvi ◽  
Riaz Hussain ◽  
Qadeer Hasan ◽  
Shahzad Malik
Keyword(s):  
Cognitive Radio ◽  
Outage Probability ◽  
Cognitive Radio Networks ◽  
Communication Systems ◽  
Performance Metrics ◽  
Packet Delay ◽  
Radio Spectrum ◽  
Radio Networks ◽  
Cooperative Relaying ◽  
End To End

Cognitive radio networks have emerged to exploit optimally the scarcely-available radio spectrum resources to enable evolving 5G wireless communication systems. These networks tend to cater to the ever-increasing demands of higher data rates, lower latencies and ubiquitous coverage. By using the buffer-aided cooperative relaying, a cognitive radio network can enhance both the spectral efficiency and the range of the network; although, this could incur additional end-to-end delays. To mitigate this possible limitation of the buffer-aided relaying in the underlay cognitive network, a virtual duplex multi-hop scheme, referred as buffer-aided multi-hop relaying, is proposed, which improves throughput and reduces end-to-end delays while keeping the outage probability to a minimum as well. This scheme simultaneously takes into account the inter-relay interference and the interference to the primary network. The proposed scheme is modeled as a Markov chain, and Monte Carlo simulations under various scenarios are conducted to evaluate several key performance metrics such as throughput, outage probability, and average packet delay. The results show that the proposed scheme outperforms many non-buffer-aided relaying schemes in terms of outage performance. When compared with other buffer-aided relaying schemes such as max-max, max-link, and buffer-aided relay selection with reduced packet delay, the proposed scheme demonstrated better interference mitigation without compromising the delay performance as well.

scholarly journals Optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Haiyan Ye ◽  
Jiabao Jiang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Spectrum Utilization ◽  
Channel Characteristics ◽  
Optimal Linear ◽  
Static Division ◽  
Cooperative Nodes

AbstractThe lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

Role of Artificial Intelligence in Cognitive Radio Networks

2019 ◽  
pp. 258-279 ◽  
Author(s):  
Shikha Singhal ◽  
Shashank Gupta ◽  
Adwitiya Sinha
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Evolutionary Process ◽  
Radio Spectrum ◽  
Radio Networks ◽  
Training Dataset ◽  
Wireless Medium ◽  
Computing Machines

The role of artificial intelligence techniques and its impact in context of cognitive radio networks has become immeasurable. Artificial intelligence redefines and empowers the decision making and logical capability of computing machines through the evolutionary process of leaning, adapting, and upgrading its knowledge bank accordingly. Significant functionalities of artificial intelligence include sensing, collaborating, learning, evolving, training, dataset, and performing tasks. Cognitive radio enables learning and evolving through contextual data perceived from its immediate surrounding. Cognitive science aims at acquiring knowledge by observing and recording externalities of environment. It allows self-programming and self-learning with added intelligence and enhanced communicational capabilities over wireless medium. Equipped with cognitive technology, the vision of artificial intelligence gets broadened towards optimizing usage of radio spectrum by accessing spectrum availability, thereby reducing channel interferences while communication among licensed and non-licensed users.

scholarly journals Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3800
Author(s):  
Xiang Xiao ◽  
Fanzi Zeng ◽  
Zhenzhen Hu ◽  
Lei Jiao
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Dynamic Spectrum Access ◽  
Blocking Probability ◽  
Network Capacity ◽  
Radio Networks ◽  
Forced Termination ◽  
Spectrum Utilization ◽  
Dynamic Flow ◽  
Spectrum Leasing

Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the SUs suffer from frequent spectrum handoffs and transmission limitations. In this paper, considering the quality of service (QoS) requirements of PUs and SUs, we propose a novel dynamic flow-adaptive spectrum leasing with channel aggregation. Specifically, we design an adaptive leasing algorithm, which adaptively adjusts the portion of leased channels based on the number of ongoing and buffered PU flows. Furthermore, in the leased spectrum band, the SU flows with access priority employ dynamic spectrum access of channel aggregation, which enables one flow to occupy multiple channels for transmission in a dynamically changing environment. For performance evaluation, the continuous time Markov chain (CTMC) is developed to model our proposed strategy and conduct theoretical analyses. Numerical results demonstrate that the proposed strategy effectively improves the spectrum utilization and network capacity, while significantly reducing the forced termination probability and blocking probability of SU flows.

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