Maximal ratio combining diversity analysis of underwater acoustic communications subject to к-μ shadowed fading channels

Abstract

In this paper, a novel unified analytical expression for average bit error rates (ABER) and average channel capacity (ACC) is presented for the -shadowed fading model. This model has recently shown to be suitable for underwater acoustic wireless channel modeling using the measurements conducted by the Naval Research Laboratory [1], and is not so well covered in the public literature. Deploying the maximal ratio combining diversity (MRC) receiver, a new simple analytical expression for the probability density function (PDF) of the receiver's output signal-to-noise ratio (SNR) is presented. Based on this new PDF, a novel unified ABER and ACC expression is derived. Moreover, to generalize the ABER analysis, the additive white generalized Gaussian noise model is assumed, which models different noise environments. The new unified expression is accurate, simple and generic, and is suitable for MRC analysis in this generalized shadowed fading and noise environment. Numerical techniques and published results from the literature prove the accuracy of our analysis and the generality of the derived unified expression.

Unified analytical modeling of the error rates and the ergodic channel capacity in η-μ generalized fading channels with integer μ and maximal ratio combining receiver

“…Some different forms of the probability density function of this fading model were also obtained in [11]. This fading was also reported to be a special case of the − shadowed fading model [12] [13].…”

Performance analysis of intelligent reflecting surface‐assisted mobile wireless networks subjected to generalized Gaussian noise

“…The term in above equation can be well approximated as, 43 , eq.18 where and . By capitalizing on Gradshteyn and Ryzhik, 34 , eq.3.381.4 the closed‐form expression of ACC in a static scenario can be expressed as …”

Three‐dimensional sensor network connectivity considering border effects and channel randomness with application to underwater networks

“…Researchers in the past years suggest various statistical models for the characterisation of underwater channels that account for the phenomenon of random effects [9,10,[29][30][31][32][33][34][35][36][37]. The occurrence of fading and shadowing in the ocean is due to the rapidly varying dynamics of ocean surface which causes the signal to fluctuate and form multipath signals with long propagation delays.…”

“…In [29], the authors have stated that the joint probability distribution of Rayleigh and log-normal fading statistics can be well approximated as K distribution. K distribution is found to be the best distribution for the characterisation of underwater acoustic channels [30,31]. The path-loss effect of the acoustic channel is studied in [32] and here, the path-loss exponent value for the underwater environment is chosen between 1 and 2.…”