Dynamic Interference Control in OFDM-Based Cognitive Radio Network Using Genetic Algorithm

Abstract

In OFDM-based cognitive radio networks, minimizing the interference caused to the primary user (PU) by the substantial amount of out-of-band (OOB) emission is a great challenge. In this paper, we propose a dynamic interference control method using the additive signal side lobe reduction technique and genetic algorithm (GA) in CR-OFDM systems. Additive signal side lobe reduction technique is based on adding a complex array to modulated data symbols in the constellation plane for side lobe reduction in OFDM system. In the proposed method, GA generates optimum additive signal which can effectively reduce the OOB signal interference to the primary system. The GA also strives to keep the interference below a predefined tolerable limit and at the same time it maximizes secondary user's transmission opportunity. The results show that the side lobes of the OFDM-based secondary user signal can be reduced by up to 38 dB and the PU interference tolerable limit can be satisfied at the cost of a minor addition in bit error rate (BER). The results further show that the proposed method delivers better performance as compared to non-GA additive signal method in terms of side lobe reduction as well as BER.

Optimization of Cognitive Radio Secondary Information Gathering Station Positioning and Operating Channel Selection for IoT Sensor Networks

“…Ozcan and Gursoy [21] investigated channel selection based on the lowest channel gain or the highest data channel gain. Khan and Yoo [22] proposed a dynamic interference control in OFDM-based cognitive radio network using GA.…”

Techniques for the suppression of sidelobes in a non-contiguous orthogonal frequency division multiplexing framework

“…category are called the frequency domain methods, including subcarrier weightings (SW) (Cosovic et al 2006), advanced subcarrier weightings (ASW) , cancellation carriers (CC) (Brandes et al 2005;Pagadarai et al 2008b), advanced cancellation carriers (ACC) Rady et al 2015), CC using (genetic algorithm) GA and (differential evolution) DE (Elahi et al 2016), active and null cancellation carriers (ANCC) (Lopes and Panaro 2013), constellation expansion (CE) (Pagadarai et al 2008a;Li et al 2009;Selim et al 2012), CE using GA (Khan and Yoo 2015) and generalized sidelobe canceller (GSC) (Elahi et al 2015), etc. where the OOBR elimination process is carried out before the IFFT.…”