The performance of learning models heavily relies on the availability and adequacy of training data. To address the dataset adequacy issue, researchers have extensively explored data augmentation (DA) as a promising approach. DA generates new data instances through transformations applied to the available data, thereby increasing dataset size and variability. This approach has enhanced model performance and accuracy, particularly in addressing class imbalance problems in classification tasks. However, few studies have explored DA for the Arabic language, relying on traditional approaches such as paraphrasing or noising-based techniques. In this paper, we propose a new Arabic DA method that employs the recent powerful modeling technique, namely the AraGPT-2, for the augmentation process. The generated sentences are evaluated in terms of context, semantics, diversity, and novelty using the Euclidean, cosine, Jaccard, and BLEU distances. Finally, the AraBERT transformer is used on sentiment classification tasks to evaluate the classification performance of the augmented Arabic dataset. The experiments were conducted on four sentiment Arabic datasets: AraSarcasm, ASTD, ATT, and MOVIE. The selected datasets vary in size, label number, and unbalanced classes. The results show that the proposed methodology enhanced the Arabic sentiment text classification on all datasets with an increase in F1 score by 7% in AraSarcasm, 8% in ASTD, 11% in ATT, and 13% in MOVIE.

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. Although we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. This article introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts and then moves on to experimentally validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.

Unmanned aerial vehicle (UAV)-assisted visible light communication (VLC) constitutes a promising approach for providing illumination and communication simultaneously. In this paper, we consider the problem of joint UAV deployment, cell association, and power allocation, to meet the illumination and data rate demands of a set of users in a geographical area. Towards this goal, we develop SOUVLC , a multi-objective s equentially o ptimized U AV-assisted VLC framework that aims at minimizing the required number of UAVs first and minimizing the total transmitted power second . SOUVLC mitigates inter-cell interference and accounts for illumination interference. We derive a mixed-integer linear formulation of SOUVLC , numerically evaluate it under different user demands and distributions, and demonstrate its superiority over several benchmarks.

Recently, tremendous progress has been made in self-interference cancellation (SIC) techniques that enable a wireless device to transmit and receive data simultaneously on the same frequency channel, a.k.a. in-band full-duplex (FD). Although operating in FD mode significantly improves the throughput of a single wireless link, it doubles the number of concurrent transmissions, which limits the potential for coexistence between multiple FD-enabled links. In this paper, we consider the coexistence problem of concurrent transmissions between multiple FD-enabled links with different SIC capabilities; each link can operate in either FD or half-duplex mode. First, we consider two links and formulate the interactions between them as a Bayesian game. In this game, each link tries to maximize its throughput while minimizing the transmission power cost. We derive a closed-form expression for the Bayesian Nash equilibrium and determine the conditions under which no outage occurs at either link. Then, we study the coexistence problem between more than two links, assuming that each link is only affected by its dominant interfering link. We show that under this assumption no more than two links will be involved in a single game. Finally, we corroborate our analytical findings via extensive simulations and numerical results.

The uncertainty in user locations and channel conditions makes the deployment and performance analysis of wireless networks challenging. Stochastic geometry has emerged as a powerful tool for analyzing the performance of wireless networks, assuming certain stochastic models for the distribution of both users and base stations (BSs). In this paper, seeking further precision, we derive the downlink rate coverage probability of a wireless network with deterministically known BS locations. Then, we use this result to optimize the placement of BSs while keeping the downlink rate coverage probability above a certain threshold.

Millimeter wave (mmW) systems typically use beam-forming techniques to compensate for the high pathloss. However, directional communications in the presence of uncertainty in user equipment (UE) locations and channel conditions make maintaining coverage and connectivity challenging. In this context, we propose a joint optimization framework to determine the minimum number of required access points (APs), their optimal locations, their optimal beam directions, and their optimal assignments to individual UEs in order to maintain a network-wide signal-to-noise ratio (SNR) coverage and stable connections. The network deployment decisions (i.e., the required number of APs, their placements, and their beam directions) are static and are taken before UE locations and channel conditions are revealed. The UE assignment decisions are taken under each realization of UE locations and channel conditions considering the availability and stability of the mmW beams. We develop our joint optimization framework following a two-stage chance-constrained stochastic optimization model. Our numerical results demonstrate the gains brought by our proposed framework in terms of reducing cost of network deployment while ensuring a network-wide SNR coverage and stable connections under various UE distributions and system parameters.