Performance Enhancement of MIMO-OFDM using Redundant Residue Number System

Abstract

Telecommunication industry requires high capacity networks with high data rates which are achieved through utilization of Multiple-Input-Multiple-Output (MIMO) communication along with Orthogonal Frequency Division Multiplexing (OFDM) system. Still, the communication channel suffers from noise, interference or distortion due to hardware design limitations, and channel environment, and to combat these challenges, and achieve enhanced performance; various error control techniques are implemented to enable the receiver to detect any possible received errors and correct it and thus; for a certain transmitted signal power the system would have lower Bit Error Rate (BER). The provided research focuses on Redundant Residue Number System (RRNS) coding as a Forward Error Correction (FEC) scheme that improves the performance of MIMO-OFDM based wireless communications in comparison with current methods as Low-Density Parity Check (LDPC) coders at the transmitter side or equalizers at receiver side. The Bit Error Rate (BER) performance over the system was measured using MATLAB tool for different simulated channel conditions, including the effect of signal amplitude reduction and multipath delay spreading. Simulation results had shown that RRNS coding scheme provides an enhancement in system performance over conventional error detection and correction coding schemes by utilizing the distinct features of Residue Number System (RNS).

BER-performance evaluation for 5G-PD-NOMA system in multipath communication channels

“…Different wireless channel environments are considered in the proposed simulations; additive noise (AWGN) with flat and frequency selective fading channels. In AWGN channel, a white gaussian noise which has a constant power spectral density (PSD) along the channel bandwidth is added to the transmitted signal, where they are independent of each other as shown in (9).…”

CryptoDL: Predicting Dyslexia Biomarkers from Encrypted Neuroimaging Dataset Using Energy-Efficient Residue Number System and Deep Convolutional Neural Network

“…These features make RNS useful in digital signal processing (DSP) applications including digital filtering, convolution, fast Fourier transform, discrete Fourier transform, image processing, and cryptography, among others [48]. However, ordered significance among the residue digits is less important, meaning that removal of some residue digits has no effect, except dynamic range reduction [33].…”

“…Its carry-free computation and parallelism properties have been exploited in various digital signal processing (DSP) applications such as filtering, Fourier transforms (discrete and fast), and cryptography [29]. It has widely been used either singly [30] or in combination [31] with other methods to encrypt both text-based and digital image datasets [32][33][34]. Using the RNS concept along with deep learning on medical images dataset presents novel research in the machine learning era, as well as an important breakthrough in patients' information privacy preservation in medicine, thereby corroborating the concept of "CryptoDL" described in the previous studies.…”

Performance evaluation of the IEEE 802.11n random topology WLAN with QoS application

“…QoS technology is applied to manage and investigate the performance of the high data traffic load for high number of nodes network model and also in order to perfectly evaluate the performance (throughputs, packet loss and reduce latency) of the network [20][21][22]. Through the data flow management process with priorities for the different services, a certain level of data flow is ensured and as a result 1926 performance is improved.…”