Abstract
Abstract This paper analyzes multi-step-ahead spectrum prediction for Cognitive Radio (CR) systems using several future states. A slot-based scenario is used, and prediction is based on the Support Vector Machine (SVM) algorithm. The aim is to determine whether multi-step-ahead spectrum prediction has gains in terms of reduced channel-switching and increased network throughput compared with short-term prediction. The system model is simulated in software using an exponential on-off distribution for primary-user traffic. A classical energy detector is used to perform sensing. With the help of simplifications, we present new closed-form expressions for the detection probability under AWGN and Rayleigh fading channels which allows the appropriate number of samples for these scenarios to be found. The performance of the proposed predictor is thoroughly assessed in these scenarios. The SVM algorithm had low prediction error rates, and multi-step-ahead idle-channel scheduling resulted in a reduction in channel switching by the SU of up to 51%. An increase in throughput of approximately 4% was observed for multi-step-ahead prediction with three future states. The results also show channel-switching savings can be achieved in a CR network with the proposed approach.
Highlights
Since the publication of the seminal work by Mitola [1], Cognitive Radio (CR) has become an effective technology for solving the challenges generated by the growing demand for wireless communications
Prediction in the context of CR can be performed through different techniques described in the literature including Machine Learning (ML) algorithms
The energy-detector sensing technique was analyzed for the Rayleigh fading scenario, and a new detection-probability equation for low-signal-tonoise ratio (SNR) scenarios was derived
Summary
Since the publication of the seminal work by Mitola [1], Cognitive Radio (CR) has become an effective technology for solving the challenges generated by the growing demand for wireless communications. The main contributions of this work are: The multi-step-ahead joint prediction sensing analysis for the purpose of resourceful channel usage in CR network This scheme allows channel-scheduling to be implemented as the SU can choose the channel with the largest number of consecutive time slots predicted as idle allowing for a lower number of channels switching. The prediction stage, which occurs only in the initial frame, is performed with an ML algorithm In this stage, the SU estimates the state of channels based on historical data and selects only those that are considered idle to perform sensing. If the predicted channel is the same as that already occupied by the SU, this stage is not counted, increasing the effective information transmission time This model is more realistic than previous schemes because it considers the channel-switching latency and does not repeat the prediction stages. Its consideration is very important as this work presents an algorithm that tries to avoid channel switching as much as possible
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