Data Rate Limit in Low and High SNR Regime for Nakagami-q Fading Wireless Channel

Abstract

Adequate data rate is always desired in wireless communication channels. Previously, few fading models were used to model wireless communication channels and to perform analysis on them. In this paper, analyses of data rate limit of single-input single-output (SISO) wireless communication system over Nakagami-q fading channels are presented. The calculation of capacity has been carried out using small and large limit argument approximations. The analytical solution for channel capacity is presented using small and large limit argument approximations. Where small and large limit argument approximations correspond low and high signal-to-noise ratio (SNR) regime. Behavior of channel capacity with respect to SNR and fading parameter respectively has been investigated deeply. The comparison of the channel capacity behavior for both low SNR and high SNR regime and have also been done and analyzed. It has found that the channel capacity increased with increasing SNR in low SNR regime. The channel capacity also behave in the same manner in high SNR regime as well.

Multi-Parameter Estimation Method and Closed-Form Solution Study for k-µ Channel Model

“…Rayleigh distribution [6] 3 Rician distribution 2 [7] 4 Nakagami-m distribution 2 [8] 5 Hoyt (Nakagami-q) distribution [9] 6 k-µ distribution 1, 2, 3, 4 [10] 7 k-µ shadowed fading 1, 2, 3, 4, 5, 6, 7 [4] In recent years, there have been many studies on channel performance analysis and system performance optimization of typical fading channel models both domestically and internationally, as reported in [1,[11][12][13]. However, there have been relatively few studies on parameter estimation of the k-µ distribution and k-µ shadowing distribution models [14][15][16][17].…”

Design of matched filter for signal‐to‐noise ratio maximization to improve data rate

“…4 The Nakagami-q distribution is used to calculate the capacity of low and high SNR systems for efficient data rates. 5 With fundamental changes in the substrate material, the LED's luminosity is increased. In this way, SNR improves VLC capacity to get high data rate considering ≤3 m transceiver distance.…”

“…Various ML algorithms are support vector machine (SVM), decision tree (DT), random forest (RF), and k‐nearest neighbor (KNN) to improve data rate 4 . The Nakagami‐q distribution is used to calculate the capacity of low and high SNR systems for efficient data rates 5 . With fundamental changes in the substrate material, the LED's luminosity is increased.…”