scholarly journals Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1884
Author(s):  
Shuai Liu ◽  
Jing He ◽  
Jiayun Wu
Keyword(s):  
Reinforcement Learning ◽  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Prior Information ◽  
Cooperative Spectrum Sensing ◽  
Spectrum Access ◽  
Spectrum Utilization ◽  
Channel Strategy ◽  
Large State Space ◽  
Network Utility

Dynamic spectrum access (DSA) has been considered as a promising technology to address spectrum scarcity and improve spectrum utilization. Normally, the channels are related to each other. Meanwhile, collisions will be inevitably caused by communicating between multiple PUs or multiple SUs in a real DSA environment. Considering these factors, the deep multi-user reinforcement learning (DMRL) is proposed by introducing the cooperative strategy into dueling deep Q network (DDQN). With no demand of prior information about the system dynamics, DDQN can efficiently learn the correlations between channels, and reduce the computational complexity in the large state space of the multi-user environment. To reduce the conflicts and further maximize the network utility, cooperative channel strategy is explored by utilizing the acknowledge (ACK) signals without exchanging spectrum information. In each time slot, each user selects a channel and transmits a packet with a certain probability. After sending, ACK signals are utilized to judge whether the transmission is successful or not. Compared with other popular models, the simulation results show that the proposed DMRL can achieve better performance on effectively enhancing spectrum utilization and reducing conflict rate in the dynamic cooperative spectrum sensing.

scholarly journals Development and Demonstration of Graphical User Interface Spectrum Sensing Algorithm using some Wireless Systems in South Africa

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Jide Julius Popoola ◽  
Rex van Olst
Keyword(s):  
Cognitive Radio ◽  
User Interface ◽  
Spectrum Sensing ◽  
Graphical User Interface ◽  
Dynamic Spectrum Access ◽  
Cooperative Spectrum Sensing ◽  
Radio Spectrum ◽  
Spectrum Utilization ◽  
Application Program ◽  
Radio Technology

The wireless communication industry using radio spectrum is recently going through major innovations and advancements. With this transformation, the demand for and usage of radio spectrum has increased exponentially making radio spectrum indeed a scarce natural resource. In order to solve this problem, the possibility of opening up the unused portions of licensed spectrum by sharing using cognitive radio technology has been in the spotlight for maximizing radio spectrum utilization as well to as ensure sufficient radio spectrum availability for future wireless services and applications. With this objective in mind, this paper looks at the principles and technologies of cooperative spectrum sensing in cognitive radio environment in improving radio spectrum utilization. The paper provides a comprehensive review on spectrum sensing as a key functional requirement for cognitive radio technology by focusing on its application on dynamic spectrum access that enables unused portions of licensed spectrum to be used in an opportunistic manner as long as the operation of the unlicensed user will not affect that of the licensed user. In satisfying this dynamic spectrum access requirement, a friendly interactive graphical user interface (GUI) spectrum sensing application program was developed. The detail activities involve in the development of the application program, also known as spectrum sensing and detection algorithm (SSADA), was fully documented and presented in the paper. The developed graphical user interface application program after successfully developed was evaluated. The performance evaluations of developed graphical user interface sensing algorithm show that the algorithm performs favourably well. The program overall evaluation results provide bedrock information on how to improve cooperative spectrum sensing gain without incurring a cooperative overhead.

scholarly journals An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1395 ◽  
Author(s):  
Stephen Oyewobi ◽  
Gerhard Hancke ◽  
Adnan Abu-Mahfouz ◽  
Adeiza Onumanyi
Keyword(s):  
Reinforcement Learning ◽  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Network Performance ◽  
Channel Selection ◽  
Selection Strategy ◽  
Spectrum Access ◽  
Network Resources ◽  
Spectrum Handoff ◽  
Industrial Internet

The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches.

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.

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