In this paper, the recently proposed doubly inverted Gamma-Gamma (IGGG) turbulence channel model is considered to analyze the performance of a free-space optical communication system. To this end, the probability density function (PDF) and cumulative distribution function (CDF) of the irradiance fluctuations of an optical wave propagating through an IGGG turbulence channel under nonzero-boresight pointing errors are derived. Furthermore, the PDF and CDF under zero-boresight pointing errors are obtained as a special case. To study the impact of nonzero-boresight pointing error parameters and turbulence condition on the system performance, exact closed-form expressions for outage probability, bit error rate, and average capacity under intensity modulation/direct detection are derived. Besides, asymptotic analysis is provided which help us to identify the diversity gain of the system. The analytical analyzes are verified through numerical and Monte-Carlo simulation results.

High route loss and line-of-sight requirements are two of the fundamental challenges of millimeter-wave (mm-wave) communications that are mitigated by incorporating sensor technology. Sensing gives the deep reinforcement learning (DRL) agent comprehensive environmental feedback, which helps it better predict channel fluctuations and modify beam patterns accordingly. For multi-user massive multiple-input multiple-output (mMIMO) systems, hybrid precoding requires sophisticated real-time low-complexity power allocation (PA) approaches to achieve near-optimal capacity. This study presents a unique angular-based hybrid precoding (AB-HP) framework that minimizes radio frequency (RF) chain and channel estimation while optimizing energy efficiency (EE) and spectral efficiency (SE). DRL is essential for mm-wave technology to make adaptive and intelligent decision-making possible, which effectively transforms wireless communication systems. DRL optimizes RF chain usage to maintain excellent SE while drastically lowering hardware complexity and energy consumption in an AB-HP architecture by dynamically learning optimal precoding methods using environmental angular information. This article proposes enabling dual optimization of EE and SE while drastically lowering beam training overhead by incorporating maximum reward beam training driven (RBT) in the DRL. The proposed RBT-DRL improves system performance and flexibility by dynamically modifying the number of active RF chains in dynamic network situations. The simulation results show that RBT-DRL-driven beam training guarantees good EE performance for mobile users while increasing SE in mm-wave structures. Even though total power consumption rises by 45%, the SE improves by 39%, increasing from 14 dB to 20 dB, suggesting that this strategy could successfully achieve a balance between performance and EE in upcoming B5G networks.

In this article, we consider a scenario of a decode-and-forward (DaF) wireless system supporting the communication of an unmanned aerial vehicle (UAV) with a ground-control-station (GCS) through an intelligent reflecting surface (IRS). Particularly, the UAV moves according to the three-dimensional (3D) random way point model at low altitude in a complex urban environment. However, a stationary relay-station (RS) decodes and forwards the UAV’s signal over an IRS-aided virtual line-of-sight (LoS) link to a GCS. The highly dynamic and terrain-dependent UAV-to-RS channel follows the Beaulieu-Xie fading model. However, the RS-to-IRS and IRS-to-GCS links enjoy clear LoS; thus, follow the Rice fading model. We derive new closed-form expressions for the probability density functions (PDFs) and the cumulative distribution functions (CDFs). Then based on the derived statistical expressions, several performance metrics including outage probability, average bit error rate, and ergodic channel capacity are derived in closed-forms. Additionally, simple and accurate approximated expressions in the high signal-to-noise ratio regime are also provided. The analytical results are validated through some representative numerical examples and supported by Monte-Carlo simulation results.

In this article, we develop a general framework for the performance analysis of unmanned aerial vehicle (UAV) aided terahertz (THz) communications under generalized geometric loss, which considers the fluctuations of the UAV's position and orientation alongside the non-orthogonality of the THz beam with respect to the detector plane. In particular, we derive general and novel analytical expressions for the probability density function and the cumulative distribution function of the instantaneous signal-to-noise ratio (SNR) under generalized fading channels subject to generalized geometric loss. Consequently, novel, general, and accurate analytical expressions for the outage probability, average symbol error rate, and average channel capacity are derived. Furthermore, approximate asymptotic expressions in the high SNR regime are also derived. Moreover, A very accurate approximate expression for the channel capacity is also provided in the low SNR regime. As an example, the system performance under the $\alpha$ - $\mu$ fading channel model is analyzed in terms of the above-mentioned performance metrics.

This paper derives new and exact closed-form expressions for the average symbol error rate (SER) of square M-ary quadrature amplitude modulation (M-QAM) in wireless communication systems over the α-μ fading channels subject to an additive non-Gaussian noise. The obtained expressions take into account static and mobile wireless receivers. In addition, a closed-form expression for the outage probability in mobile networks is obtained. Please note that all derived expressions in this paper a valid for integer and non-integer values of the fading parameters. Analytical results are presented to study the impact of noise shaping parameter, severity of fading, and mobility on the average SER. Monte-Carlo simulations results are also provided to validate the accuracy of the analytical results.

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