A New Closed-Form Expressions of Channel Capacity with MRC, EGC and SC Over Lognormal Fading Channel

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In this paper, we present an analytical framework to analyse the error probability and the channel capacity of the inverse gamma (I-Gamma) shadowed fading channel. First, the work discusses the utility of the I-Gamma over log-normal (LN) and gamma fading models where the closeness of I-Gamma with other existing shadowing models is carried out. Utilising the probability density function (PDF) of the I-Gamma shadowed fading channel, various metrics of the communication system, namely, the average symbol error probability (SEP), the channel capacity under optimal rate adaptation (ORA), channel inversion with fixed rate (CIFR), and truncated CIFR (TIFR) are derived. Further, the work is extended to derive a novel selection combining (SC) PDF, and the analytical results for the SEP and the channel capacity of SC diversity are presented. Furthermore, we also provide simpler asymptotic expressions for the average SEP. In addition, the simplified high and low signal-to-noise-ratio (SNR) solutions to channel capacity are also provided. The derived mathematical formulations have been endorsed by comparing with Monte Carlo simulations. KEYWORDSBessel K, bit error rate, capacity, distribution, fading Int J Commun Syst. 2019;32:e4083.wileyonlinelibrary.com/journal/dac

Section: Optimum Rate Adaptationmentioning

confidence: 99%

“…16,42 The analytical expression for the CIFR channel capacity is defined as follows [42,Eq. As a result, the Tx and Rx assume fading channel as the time-invariant AWGN channel.…”

Section: Channel Inversion With Fixed Ratementioning

confidence: 99%

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Error probability and channel capacity analysis of wireless system over inverse gamma shadowed fading channel with selection diversity

Pant

Chauhan

Soni

2019

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“…These assumptions allow the Tx to adopt its power and rate according to the actual channel state. The channel capacity under this policy is defined as

C_{o p r a} = B true \int_{γ_{0}}^{\infty} \log_{2} ((), \frac{γ}{γ_{0}}) f_{Y} false(γ false) d γ,

where B is the channel bandwidth and γ 0 is the optimal cut‐off SNR below which transmission is suspended. The optimal cut‐off SNR must satisfy the relation p ( γ 0 )=0, with p (.)…”

Section: Performance Analysismentioning

confidence: 99%

“…The ergodic capacity is derived by averaging the AWGN capacity over the instantaneous fading SNR, eq. (11)

C_{o r a} = B true \int_{0}^{\infty} \log_{2} false(1 + γ false) f_{Y} false(γ false) d γ .

…”

Section: Performance Analysismentioning

confidence: 99%

Performance analysis of wireless communication system over nonidentical cascaded generalised gamma fading channels

Chauhan¹,

Rana

Kumar

et al. 2019

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Summary Multiplicative fading statistics usually encountered in different radio propagation environments. In this context, we evaluate and analyse the performance of a wireless communication system over the nonidentical cascaded generalised Gamma Fading Channels, also known as generalised Bessel‐K fading channel. To this end, the closed‐form expressions for the amount of fading (AOF), the outage probability (OP), the average symbol error probability (SEP), and the channel capacity are derived. In addition approximate expressions for the average SEP with maximal ratio combining (MRC) diversity are also provided. The low‐ and high‐power solutions for the channel capacity are also provided. Furthermore, simplified asymptotic average SEP expressions for MRC and selection combining (SC) are presented to gain the system performance with diversity. The proposed methodologies provide more flexibility to accommodate different radio propagation scenarios. To examine the accuracy of the proposed solutions, numerical and simulation results are compared and shown to fit for variety of fading parameters.

Development of analytical model for energy detectors with SC and MRC diversity over Inverse‐Gamma fading

Maurya

Srivastava

Chauhan

et al. 2020

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Summary An ever‐escalating demand for wireless applications has caused great concern for the proper exploitation of the accessible radio spectrum. Cognitive radio materializes as an auspicious remedy to the present‐day crisis of spectral congestion, by detecting the licensed primary user (PU). This is accomplished with the assistance of the spectrum sensing technique, which provides an indication of the presence of PU over the spectrum. Energy detection is one of the prevailing spectrum sensing techniques due to its low implementation complexity. In the present work, the performance of an energy detector (ED) over Inverse‐Gamma (I‐Gamma) fading distribution is examined. Initially, a closed‐form expression of the probability density function for I‐Gamma distribution with maximal ratio combining diversity reception is derived. Following it, an investigation of an ED‐based cognitive radio device is carried out in the form of the average probability of detection (PD) and average area under the receiver operating characteristic curve (AUC). In addition, we also present a performance analysis of an ED with selection combining diversity. Optimization of the detection threshold is also executed alongside the low signal‐to‐noise ratio analysis. In the end, the resulting expression of the PD is exploited to examine the functioning of cooperative spectrum sensing within the erroneous environment. The validation of derived mathematical forms has been confirmed by comparing it with the Monte‐Carlo simulation and exact numerical results.