Multilevel Redundant Discrete Wavelet Transform (ML-RDWT) and Optimal Red Deer Algorithm (ORDA) Centred Approach to Mitigate the Effect of ICI, BER and CIR in a MIMO-OFDM System

Abstract

Nowadays, there is a great demand for ultra-high data rate (UHDR) transmission on most 5 th generation wireless networks. In this concern, the multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) scheme is used on a large scale to achieve UHDR transmission with reduced inter-symbol interference (ISI) and inter-carrier interference (ICI). Discrete wavelet transform-based OFDM (DWT-OFDM) provides better orthogonality due to presence of orthogonal wavelets, which mitigates the effects caused by ISI and ICI. Also, it has extended bandwidth than the traditional OFDM systems. But a major drawback in this system is that it suffers from down sampling. The down-sampling effect reduces the actual size of the input bit streams. As a result, the system performance is degraded. For solving this problem, a multilevel redundant discrete wavelet transform (ML-RDWT) is used instead of DWT to achieve improved spectral performance. Here, complex down-sampling operation is eliminated. From the simulation outcomes, it is clearly viewed that effects caused by ICI, ISI and BER are mitigated by improving the performance of CIR. The proposed method employs optimal red deer algorithm (ORDA) to locate the optimized weights for the ICI cancellation system. This algorithm enhances the spectral efficiency by achieving high CIR with reduced BER, ISI and ICI. The BER in the proposed MIMO-ML-RDWT-OFDM-ORDA method is 68%, 76%, 38% and 75%, which is very low when compared to the BER in the existing techniques like MIMO-DWT-OFDM-RDA, MIMO-RNS-OFDM-PNMA, MIMO-OFDM-BMA and MIMO-OFDM-ICIMA. The ISI in the proposed method is 94%, 91%, 95% low when compared to the ISI in the existing techniques. The ICI in the proposed work is 71%, 57%, 73% and 86% low when compared to the ICI in the existing techniques. Therefore, the general performance of the proposed MIMO-ML-RDWT-OFDM-ORDA method is improved in an efficient way with less complexity, error rate and processing delay.

A novel Block Bi‐diagonalization based pre‐coding scheme for bit error reduction in mutiple input multiple output‐orthogonal frequency division multiplexing

“…This method obtains a very BER of 2.65 for 14‐dB SNR. Another study reported in Nagarajan and Sophia 23 introduces space time block codes (STBCs) into the MIMO‐OFDM model that can effectively reduce the frequency of selective fading without increasing the BW consumption. The BER obtained by this method was about 1.5 at an SNR of 20 dB.…”

“…Nagarajan and Sophia 23 define the multilevel RDWT and ORDA strategies for reducing the effects of ICI, BER, and CIR in a MIMO‐OFDM system. In this method, a multilevel RDWT technique was contemplated to enhance the spectral efficiency; thus, the downsampling operation was prevented.…”

Deep Convolutional Spiking Neural Network Optimized with Coyote Chimp Optimization Algorithm for Imperfect Channel Estimation in MIMO-f-OFDM/FQAM Based 5G Network

“…In 2021, Nagarajan and Sophia [24] had suggested multilevel redundant discrete wavelet transform (ML-RDWT) to overcome the effects of down-sampling which degrades the system performance. Further, optimal red deer method (ORDM) is used to optimize the weight to achieve better ICI cancellation.…”

Error Correction Using Hybrid Coding Algorithm for BER Reduction in MIMO-OFDM

“…On the receiver side, the cyclic prefix is neglected and symbols are again converted into the frequency domain using the fast fourier transform (FFT) as shown in Eq. (3).…”

“…MIMO is the reliable approach that can handle the increasing wireless data traffic of the future generation of a wireless communication network [2]. Moreover, the connection of multiple transmitters and receivers at both ends of the MIMO system is used to increase the channel capacity [3,4]. OFDM is the essential approach for the mobile communication network because of its higher spectral efficiency [5,6].…”