QoS-guaranteed channel selection scheme for cognitive radio networks with variable channel bandwidths

Author(s):  
Samer T. Talat ◽  
Li-Chun Wang
2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Saleem Aslam ◽  
Adnan Shahid ◽  
Kyung Geun Lee

This paper presents a centralized control-channel selection scheme for cognitive radio networks (CRNs) by exploiting the variation in the spectrum across capacity, occupancy, and error rate. We address the fundamental challenges in the design of the control-channel for CRNs: (1) random licensed users (LUs) activity and (2) the economical and vulnerability concerns for a dedicated control-channel. We develop a knapsack problem (KP) based reliable, efficient, and power optimized (REPO) control-channel selection scheme with an optimal data rate, bit error rate (BER), and idle time. Moreover, we introduce the concept of the backup channels in the context of control-channel selection, which assists the CRs to quickly move on to the next stable channel in order to cater for the sudden appearance of LUs. Based on the KP and its dynamic programming solution, simulation results show that the proposed scheme is highly adaptable and resilient to random LU activity. The REPO scheme reduces collisions with the LUs, minimizes the alternate channel selection time, and reduces the bit error rate (BER). Moreover, it reduces the power consumed during channel switching and provides a performance, that is, competitive with those schemes that are using a static control-channel for the management of control traffic in CRNs.


2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Fanzi Zeng ◽  
Xinwang Shen

This paper proposes a channel selection scheme for the multiuser, multichannel cognitive radio networks. This scheme formulates the channel selection as the multiarmed bandit problem, where cognitive radio users are compared to the players and channels to the arms. By simulation negotiation we can achieve the potential reward on each channel after it is selected for transmission; then the channel with the maximum accumulated rewards is formally chosen. To further improve the performance, the trust model is proposed and combined with multi-armed bandit to address the channel selection problem. Simulation results validate the proposed scheme.


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