LOL-SM-Lion Optimization adapted Spectrum Distribution for Cognitive Radio Enabled Vehicular Ad-Hoc Network

Abstract

The Vehicular Ad-hoc Network (VANET) is massively used in challenging traffic regulatory systems in the recent times. The bulky data being articulated by VANET is the critical part leading to the spectrum limitation issues. Cognitive Radio (CR) technology is a prominent stream to manage uncertainty in the spectrum distribution. The CR chooses the idle path in the entire spectrum and allocate as per the requirement for handling smooth traffic flow. Furthermore,   parameters   such as a multipath fading, primary user static problem and dynamic topology of vehicular communications remains to be challenge for implementing the CR in VANET for an effective and intelligent spectrum distribution. Moreover conventional methods for CR-VANET spectrum allocation is limited yet to handle Considering the higher mobility and uncertainty constraints, a novel deep learning adopted CR-VANET model is proposed. This paper proposes the new deep learning model which works on the principle of Lion Optimized Long Short Term Memory (LOL-SM) models which overcomes the drawbacks of the traditional LSTM and these learning models are implemented in the road side units (RSU) which predicts the vacant models and sends it to the vehicles. Comparing with the existing spectrum sensing strategies, the proposed LOL-SM based -CR VANET model attains a reduced overall transmission delay with minimum loss probability. Also, the false alarm rate is almost nullified in the proposed approach thus enhancing an effective spectrum usage in VANETs