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Effective Throughput Analysis of α - η - κ - μ Fading Channels

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TLDR
It is found that the effective throughput improves with the increase of channel nonlinearity and number of multipath clusters, and the high-SNR slope is only dependent on the channel non linearity and the number of multiclass clusters present in the physical channel.
Abstract
The $\alpha $ - $\eta $ - $\kappa $ - $\mu $ fading model is a very useful instrument to accurately describe various radio wave propagation scenarios. In this paper, we study the effective throughput performance of communication systems over the $\alpha $ - $\eta $ - $\kappa $ - $\mu $ fading channels. Novel and exact expressions for the effective throughput over $\alpha $ - $\eta $ - $\kappa $ - $\mu $ channels are derived, and the effective throughput of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) systems over some widely used small-scale fading models are presented based on the derived results. To obtain more understandings on the impact of physical channel characteristics and system configuration on the effective throughput, closed-form expressions for the asymptotic effective throughput at high signal-to-noise ratio (SNR) regimes are also obtained. The results reveal the underlying connections between different physical channel parameters (e.g., scattering level, phase correlation, channel nonlinearity, multipath clustering, and channel imbalance) and the effective throughput. It is found that the effective throughput improves with the increase of channel nonlinearity and number of multipath clusters, and the high-SNR slope is only dependent on the channel nonlinearity and the number of multipath clusters present in the physical channel.

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Citations
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On the performance of DF-based multi-hop system over α − κ − μ and α − κ − μ-extreme fading channels

TL;DR: An integrated performance evaluation of a decode-and-forward (DF) multi-hop wireless communication system is undertaken over the non-linear generalized α − κ − μ and α −δ − μ -Extreme fading models, resulting in novel closed-form expressions for the amount of fading, channel capacity under various adaptive transmission techniques.
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Effective rate analysis of MISO over Beaulieu-Xie fading channel

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References
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Journal ArticleDOI

Using Two-Dimensional Markov Models and the Effective-Capacity Approach for Cross-Layer Design in AMC/ARQ-Based Wireless Networks

TL;DR: This paper proposes a novel framework for the cross-layer analysis and design of wireless networks combining adaptive modulation and coding at the physical layer with an automatic repeat request (ARQ) protocol at the data-link layer using a physical-layer first-order 2-D Markov model.
Journal ArticleDOI

Unified approaches based effective capacity analysis over composite α − η − μ /gamma fading channels

TL;DR: The effective capacity of a communications system using unified models is analysed and a comparison between the simulated and numerical results using both distributions over composite α − η − μ /gamma fading channels have been provided.
Journal ArticleDOI

QoS-Aware Power Allocations for Maximizing Effective Capacity Over Virtual-MIMO Wireless Networks

TL;DR: This paper proposes the optimal power allocation schemes with statistical QoS provisionings to maximize the effective capacity of non-collaborative/Collaborative V-MIMO wireless networks, respectively and derives the QoS-driven collaborative power allocation scheme for both the existing and the newly added mobile users.
Journal ArticleDOI

On the effective capacity of Fisher–Snedecor fading channels

TL;DR: In this article, the performance of the effective capacity metric under Fisher-Snedecor F fading channels is investigated, and a novel exact closed-form expression for effective capacity is derived.
Journal ArticleDOI

Delay Guarantee and Effective Capacity of Downlink NOMA Fading Channels

TL;DR: This paper establishes closed-form upper bounds for the delay violation probability of downlink Nakagami-$m$ and Rician NOMA channels, by exploiting stochastic network calculus (SNC) and applying the min–max and max–min rules.
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