A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks

Abstract

Cognitive radio sensor networks are one of the kinds of application where cognitive techniques can be adopted and have many potential applications, challenges and future research trends. According to the research surveys, dynamic spectrum access is an important and necessary technology for future cognitive sensor networks. Traditional methods of dynamic spectrum access are based on spectrum holes and they have some drawbacks, such as low accessibility and high interruptibility, which negatively affect the transmission performance of the sensor networks. To address this problem, in this paper a new initialization mechanism is proposed to establish a communication link and set up a sensor network without adopting spectrum holes to convey control information. Specifically, firstly a transmission channel model for analyzing the maximum accessible capacity for three different polices in a fading environment is discussed. Secondly, a hybrid spectrum access algorithm based on a reinforcement learning model is proposed for the power allocation problem of both the transmission channel and the control channel. Finally, extensive simulations have been conducted and simulation results show that this new algorithm provides a significant improvement in terms of the tradeoff between the control channel reliability and the efficiency of the transmission channel.

Calculation Method of Short Term Flicker Severity Pst for Power System based on Atomic Decomposition and Real-Coded Quantum Evolutionary Algorithm

“…So, we use a real-coded quantum evolutionary algorithm to optimize the matching algorithm. Finally, simulation experiments show that the proposed method can greatly improve the calculation accuracy of the short time flicker severity Pst [18,19,20].…”

Optimal Power Allocation of Relay Sensor Node Capable of Energy Harvesting in Cooperative Cognitive Radio Network

“…A licensed primary network (PN) can use the frequency band at any time for signal transmission, whereas the secondary network (SN) senses the frequency band to make opportunistic transmissions. An underlay protocol was also proposed [4,5,6]. With this protocol, the SN can share the frequency band and the operation time with the PN, as long as its signal does not affect the signal of the PN.…”

Optimal Channel Selection Based on Online Decision and Offline Learning in Multichannel Wireless Sensor Networks

“…Kakalou et al [28] and Saleem et al [29] use cluster-based architectures instead of the central entity, in which cluster head observes the traffic of primary user (PU) to avoid collisions while keeping other member nodes synchronized. In [30], Lin et al have investigated a novel dynamic spectrum access framework with control information exchange through beacons. In [31], a novel distributed -learning algorithm with heuristically accelerated scheme has been shown to be a powerful approach to solve dynamic spectrum access problem.…”

“…Subsequently, more and more investigations are performed by combining it with the other mechanisms [29][30][31][32][33] in cognitive networks. Teng et al [27] have discussed a scheme which adopts a -learning-based auction game to help nodes compete channel access opportunity.…”

“…They are different from conventional channel selection strategies by taking advantage of the sense information around external environment to make a decision in the channel selection process. The intelligent methods can be broadly divided into two categories: the game-based category that focuses on applying game theoretical tools to make a real-time decision [5][6][7][12][13][14][15][16][17][18][19][20][21][22][23] and the learning-based category that focuses on introducing reinforcement learning techniques to select optimized resource [8][9][10][11][24][25][26][27][28][29][30][31].…”