Performance improvement in MIMO-OFDM systems based on adaptive whale elephant herd optimization algorithm

Abstract

Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) is the leading air interface for 4G and 5G broadband wireless communications. Cognitive Radio (CR) is the trending technology, which facilitates spectrum sensing to determine the inadequate bands in the spectrum. Though there are so many spectrum sensing techniques, the existing methods suffered a lot in the communication environment. In the traditional CR network, data transmission is constrained to the secondary user within the available bands. With the aim to increase spectrum efficiency and throughput, this paper proposed the hybrid mixture model using Adaptive Whale Elephant Herd Optimization (Adaptive WEHO) algorithm for spectrum sensing. Adaptive WEHO is the integration of Elephant-Herd Optimization (EHO), and Whale Optimization Algorithm (WOA), with the adaptive concept. The signal received from the OFDM antenna is used to analyze the availability of spectrum based on the signal energy and Eigen statistics. The CR searches the availability of channel and makes the connection when it determines a free channel. Here, the spectrum sensing is achieved by the Gaussian Mixture Model (GMM), which is trained by the proposed Adaptive WEHO algorithm. The proposed Adaptive WEHO algorithm uses the foraging behavior of whales and the herding behavior of elephants, which is applied in the spectrum sensing technique to perform the optimal sensing. The proposed Adaptive WEHO attained better performance with the metrics of probability of detection as 1.0238, and the probability of false alarm as 0.01075, respectively. The proposed method ensures the effective communication in CR without any interference.