Optimized BER for channel equalizer using cuckoo search and neural network

Abstract

The digital data transfer faces issues regarding Inter-Symbol Interference (ISI); therefore, the error rate becomes dependent upon channel estimation and its equalization. This paper focuses on the development of a method for optimizing the channel data to improve ISI by utilizing a swarm intelligence series algorithm termed as Cuckoo Search (CS). The adjusted data through CS is cross-validated using Artificial Neural Network (ANN). The data acceptance rate is considered with 0-10% marginal error which varies in the given range with different bit streams. The performance evaluation of the proposed algorithm using the Average Bit Error Rate (A-BER) and Logarithmic Bit Error Rate (L-BER) had shown an overall improvement of 30-50% when compared with the Kalman filter based algorithm.

Improved Channel Equalization using Deep Reinforcement Learning and Optimization

“…In this context, Kari et al had evaluated the effectiveness of the nonlinear channel equalization technique that proved to be highly efficient for acoustic communication with improved MSE [6]. Following this, Katwal et al had taken advantage of adaptive filers for channel equalization that were improved based on Cuckoo Search (CS) optimization strategies followed by neural network architecture [7]. More recently, Majumder also implemented non-linear equalization based on the neural architecture that outperformed the traditional non-linear equalization technique with reduced BER [8].…”

“…In contrast to the DCSK concept, the proposed modulation strategy proved to successfully resolve multipath performance and noise reduction with higher data transmission rates. Simulation analysis in AWGN and similar channels had shown that proposed CSF-M-DCSK outperformed in terms of BER with proven feasibility in real scenarios [21]. Majumder and Giri (2020) had proposed an improvement in the training of neural network-based equalizers using the architecture of Particle Swarm Optimization (PSO).…”

“…The data-optimized through CSO was cross-validated using neural network architecture. Simulation analysis in terms of Average-BER and Logarithmic-BER demonstrated an overall improvement of 30% to 50% as compared with existing approaches [7]. Zhang and Yang (2020) presented a Machine Learning (ML) architecture based channel equalization inspired by the deep learning neural networks.…”

“…Some researchers took advantage of particle swarm architecture using Particle Swarm Optimization (PSO) [28]. While others were inspired by the behavioral characteristics of Firefly [29], Cat [30], and Cuckoo [7]. Whale Optimization Algorithm (WOA) developed by Mirjalili and Lewis has recently stepped into the swarm intelligence category and was inspired by whales' behavior [31].…”

“…The performance of the proposed equalization strategy is first evaluated in the AWGN channel in terms of BER evaluation. Figure 5 shows the comparison of BER exhibited by the proposed work against [21], Kalman filter [41], [42], and [19] 6 shows that the proposed work exhibits higher BER at lower values of 𝐸 𝑏 /𝑁 0 which gets improved with the increase in 𝐸 𝑏 /𝑁 0 . This shows that better BER performance is observed for the proposed work between 10 and 17 𝐸 𝑏 /𝑁 0 while exhibiting an average BER of 0.87 as compared to existing works.…”