Instead of conventional knowledge-driven modeling, this study introduced data-driven channel modeling methods based on deep learning (DL) in a free-space optical (FSO) communication system to achieve low complexity, less modeling difficulty, high fidelity, and differentiability. The FSO channel was modeled via three DL algorithms: generative adversarial network (GAN), bidirectional long short-term memory (BiLSTM), and Bayesian neural network (BNN). Consequently, their performance was comprehensively compared in terms of optical amplitude waveforms, Kullback-Leibler (KL) divergence, 95% confidence interval, data distribution, and fitted probability density functions. The results indicated that the GAN-based channel exhibited optimal performance, implying its suitability for learning the stochastic distribution of the FSO channel. In addition, the performance of GAN-based channels was examined under different conditions: turbulence intensities (weak, moderate, and strong), transmission distances (3, 4, and 5 km), launch powers (0, 10, and 20 dBm), modulation formats (on-off keying, pulse position modulation, and pulse amplitude modulation 4), and the adaptability to channel variation. The test results were satisfactory and confirmed the ability of the GAN to learn the approximate distribution function of the FSO channel. Thus, the primary aim was to prove that for FSO channels with statistical distribution characteristics, a data-driven DL model can not only serve as an effective surrogate model for specific scenarios but also provide a potential tool for future modeling methods.

This paper investigates resource allocation design for intelligent reflecting surface (IRS)-aided joint processing coordinated multipoint (JP-CoMP) downlink cellular networks with underlaying device-to-device (D2D) communications. In particular, an IRS is employed to establish favorable communication channel conditions and to mitigate the malignant interference caused by D2D devices. We aim to maximize the system sum-rate by jointly designing the cellular user (CU) association, the active beamforming at the base stations (BSs), the passive beamforming at the IRS, and the transmit power of each D2D transmitter (DT). The resource allocation design is formulated as a non-convex optimization problem while taking into account the quality of service (QoS) requirement of CUs, the power allocations for both CUs and D2D pairs, and the limited backhaul capacity. To handle the non-convex optimization problem, we propose a computationally efficient iterative algorithm exploiting the big-M formulation, the penalty method, and the successive convex approximation, which is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) solution. Simulation results demonstrate that the proposed scheme can increase the system sum-rate by 70% and 20% compared with the schemes with no IRS and random phase shifts, respectively, when the minimum required SINR of CUs is 5 dB. Additionally, our results confirm the superiority of introducing IRS for harnessing interference in wireless communication systems.

Unmanned Aerial Vehicle (UAV)-assisted free-space optical (FSO) communication has become a promising solution in both civil and military applications. Nevertheless, hovering UAV-assisted FSO links suffer from several losses due to atmospheric turbulence, pointing errors (PEs), angle-of-arrival (AOA) fluctuations, and link attenuation related to various weather conditions. In this paper, we optimize the theoretical channel model for hovering UAV-assisted FSO links considering all the above destructive channel factors. Our optimized theoretical channel model agrees with the Monte-Carlo simulation results much better under the full range of channel coefficients compared to previous published works. Based on our optimized theoretical channel model, theoretical expressions of the link outage probability, ergodic and outage capacity, and bit error rate are derived. The impact of various parameters, such as Rytov variance, root-mean-squares (RMS) of PEs and AOA fluctuations, angle of field-of-view, rain rate, visibility, optical beam divergence angle, and transmitted power on the above performance metrics is analytically studied and corroborated by Monte-Carlo simulations. Simulation results also show that for a given receiving aperture, transmit power, and link length, the performance of the hovering UAV-assisted FSO links can be optimized by carefully designing the ratio of optical beam divergence angle to RMS of PEs.

To further improve the transmission efficiency and link stability for free space optical (FSO)-based satellite communication (SatCom) systems when transmitting large-scale remote sensing images, a scheme based on the integration of FSO and semantic communication (FSO-SC) is proposed, which employs a vector quantized variational autoencoder with spatial normalization to extract essential semantic features of images while preserving intricate details. Additionally, the Máalaga distribution model is utilized to simulate FSO channels with diverse turbulence conditions. Moreover, a comparative evaluation between the FSO-SC and traditional systems is conducted through 28 GBaud satellite-ground simulation with three modulation formats considering various effects. Compared to the traditional systems, without incurring additional bits for error corrections, the FSO-SC system achieves a power gain of over 3 dB while enabling transmission at zenith angles over 60°. Moreover, it achieves performance on par with state-of-the-art 4-receiver spatial diversity technology, while offering superior hardware and transmission efficiency. Furthermore, we conduct 10 Gbps real-time satellite-ground equivalent experiments to validate the practicality of the FSO-SC, where it achieves a 60% reduction in communication overhead compared to existing solutions while maintaining comparable received image quality and can reach a minimum receiver sensitivity gain of 4 dB. Simulation and experimental results demonstrate that the proposed FSO-SC scheme achieves high system efficiency and stability, holding promise as a viable solution for future SatCom.

This paper studies the robust resource allocation design for secure intelligent reflecting surface (IRS)-aided wireless powered communication networks (WPCN). In particular, deploying an IRS can establish favorable end-to-end radio propagation environment for achieving the desired performance gain in secure wireless-powered systems. We aim to minimize the total hybrid base station (HBS) transmit power by jointly designing the active transmitting and receiving beamforming at the HBS, the passive beamforming at the IRS, and the transmit power of each wireless-powered device (WD) and jammer node (JN). We formulate a non-convex optimization problem for the robust resource allocation design taking into account the secrecy rate requirement of the WDs and the power budgets for both the WDs and the JNs. To handle this intractable problem, we propose a computationally efficient iterative suboptimal algorithm exploiting the block coordinate descent approach, the successive convex approximation, and the penalty method, which attains a Karush-Kuhn-Tucker (KKT) solution of the transformed problem. Also, we reveal that the optimal energy beamforming matrices are rank-one sharing the same spatial direction. Simulation results unveil that the proposed scheme is able to dramatically reduce the HBS transmit power over various baseline schemes adopting existing solutions. Besides, our results show the superiority of introducing IRS for secure communication in wireless-powered systems.

In future elastic optical networks, hardware-efficient and multi-format frequency offset estimation (FOE) is critical for digital coherent receivers (DCRs) to alleviate the requirement of wavelength matching while reducing the power consumption as well as the footprint of digital signal processing units. Here, we propose a blind feedforward and low-complexity FOE algorithm based on the techniques of extended QPSK partitioning and quasi-linear approximation. With processing in polar coordinates, a simple novel error function is first defined to measure the phase deviation induced by frequency offset so that robust FOE can be directly determined by the quasi-linear relationship between the phase deviation and the corresponding offset distance. Besides, a multi-timespan estimation method is also introduced to further mitigate the phase noise impact on FOE. Not only the simulation results of 4/16/32/64-quadrature amplitude modulation (QAM) demonstrate its feasibility and versatility, but also the complexity analyses show that hardware complexities are significantly reduced. The bit error rate versus OSNR performance is experimentally investigated in double-polarization 4/16/32-QAM measurements. The superiority of the proposed FOE makes it highly desirable for application in practical multi-format DCRs.