Abstract
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.
Highlights
In recent times, there has been a growing interest in the emergence of IoT as an enabling technology in the realization of an energy efficient communication in the Industry 4.0 paradigm [1,2]
We compare the latency and throughput performances of the Spectrum handoff (SH)+reinforcement learning (RL) scheme with five SH schemes: a reactive SH with sequential spectrum sensing (RSHSS) [16], an SH management scheme with a QoE-driven channel allocation strategy [30], the conventional reactive SH scheme with random selection strategy (RRSS), the conventional proactive SH scheme with random selection strategy (PRSS) respectively
Latency is defined as the period of time between the instant an SH request is initiated and the instant a cognitive radio (CR) node is connected to a new channel
Summary
There has been a growing interest in the emergence of IoT as an enabling technology in the realization of an energy efficient communication in the Industry 4.0 paradigm [1,2]. The stringent QoS requirements of the industrial application scenarios, e.g., reliability, timeliness, and robustness, coupled with some IoT challenges have slowed down the realization of IIoT [5,6,7]. There are some challenges associated with IoT which are inimical to the realization of IIoT like [9,10,11]: (1) spectrum, memory, energy and computational limitations;.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
