Massive multiple-input multiple-output (M-MIMO) is one of the main 5G-enabling technologies that promise to increase cell throughput and reduce multiuser interference. However, these abilities rely on exploiting the channel state information (CSI) feedback at base stations (BSs). One critical challenge is that the user equipment (UE) needs to return a large amount of channel information to the base station, creating a large signaling overhead. In this paper, we propose a framework based on deep learning, which is able to efficiently compress and recover the feedback CSI. The encoder learns the most suitable compressed codeword corresponding to the CSI. The decoder decompresses this codeword at the receiving BS end using a Generative Adversarial Network (GAN). A novel objective function is proposed and used to train the Deep Convolutional Generative Adversarial Network (DCGAN) to improve the performance of our proposed framework. Simulation results demonstrate that the proposed framework outperforms traditional compressive sensing-based methods and provides remarkably robust performance for the outdoor channels.

Recently, there have been significant advances in image super-resolution based on generative adversarial networks (GANs) to achieve breakthroughs in generating more images with high subjective quality. However, there are remaining challenges needs to be met, such as simultaneously recovering the finer texture details for large upscaling factors and mitigating the geometric transformation effects. In this paper, we propose a novel robust super-resolution GAN (i.e. namely RSR-GAN) which can simultaneously perform both the geometric transformation and recovering the finer texture details. Specifically, since the performance of the generator depends on the discreminator, we propose a novel discriminator design by incorporating the spatial transformer module with residual learning to improve the discrimination of fake and true images through removing the geometric noise, in order to enhance the super-resolution of geometric corrected images. Finally, to further improve the perceptual quality, we introduce an additional DCT loss term into the existing loss function. Extensive experiments, measured by both PSNR and SSIM measurements, show that our proposed method achieves a high level of robustness against a number of geometric transformations, including rotation, translation, a combination of rotation and scaling effects, and a cobmination of rotaion, transalation and scaling effects. Benchmarked by the existing state-of-the-arts SR methods, our proposed delivers superior performances on a wide range of datasets which are publicly available and widely adopted across research communities.

This paper presents accurate approximation expressions for the outage and secrecy outage probabilities of relay-assisted free-space optical (FSO) communication links utilizing unmanned aerial vehicles (UAVs). We consider the effects of weather attenuation and random fluctuations of UAVs’ orientations and positions. The obtained expressions are applicable to different types of intensity modulation direct detection techniques. We utilized L-ary multipulse pulse-position modulation (LMPPM) scheme and optimized link performance by adjusting the LMPPM settings using our derived expressions. We conduct numerical analyses to evaluate the impact of system settings, including beam divergence and modulation settings, on outage and secrecy outage probabilities. Our results demonstrate that adaptive LMPPM can improve average spectral efficiency, particularly in time-varying UAV-based FSO channels, while maintaining the same outage and secrecy outage probabilities as ordinary MPPM. Moreover, we investigate the effect of an eavesdropper’s location on the probability of a secure channel between legitimate users. We validate the accuracy of our expressions by comparing them with Monte Carlo (MC) simulation results under different channel conditions. Derived expressions provide efficient and accurate evaluation results to optimize the performance of UAV-based relay-assisted FSO communication systems.

Audio compression has become one of the basic multimedia technologies. Choosing an efficient compression scheme that is capable of preserving the signal quality while providing a high compression ratio is desirable in the different standards worldwide. In this paper we study the application of two highly acclaimed sparse signal processing algorithms, namely, Compressed Sensing (CS) and Sparse Fart Fourier transform, to audio compression. In addition, we present a Sparse Fast Fourier transform (SFFT)-based framework to compress audio signal. This scheme embeds the K-largest frequencies indices as part of the transmitted signal and thus saves in the bandwidth required for transmission.