PAPR reduction and spectrum sensing in MIMO systems with optimized model

Abstract

Cognitive radio is trending domain, which provides strong solution for addressing spectrum scarcity issues. Many cognitive radios standards suffer from high peak to average power ratio (PAPR), which may distort transmitted signal. This paper proposes a technique for spectrum sensing based on optimization enabled PAPR using hybrid Gaussian mixture model (GMM). The Eigen statistics, energy, and PAPR reduction block is adapted by hybrid mixture model for predicting the availability of spectrum. In order to model network with PAPR, the newly designed optimization algorithm, namely elephant-sunflower optimization (ESO) is adapted. The proposed ESO technique is combination of elephant herd optimization and sunflower optimization. The GMM is enabled using Eigen statistics, energy along with PAPR. The GMM is adjusted with an optimization algorithm, namely Whale elephant-herd optimization. The PAPR is reduced by optimally adjusting the parameters using proposed ESO. The channel availability is evaluated by providing energy, Eigen statistics and PAPR as input. The effectiveness of proposed ESO is illustrated with maximal probability of detection of 1.00, minimal PAPR of 7.534, and minimal bit error rate of 0.000 respectively.