Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity for the users in such a scenario. The main contributions of this work include a less complex approach to optimally position the UAVs and to assign user equipment (UE) to each ABS, such that the total spectral efficiency (TSE) of the network is maximized, while maintaining a minimum QoS requirement for the UEs. The main advantage of the proposed approach is that it only requires the knowledge of UE and ABS locations and statistical channel state information. The optimal 2-dimensional (2D) positions of the ABSs and the UE assignments are found using K-means clustering and a stable marriage approach, considering the characteristics of the air-to-ground propagation channels, the impact of co-channel interference from other ABSs, and the energy constraints of the ABSs. Two approaches are proposed to find the optimal altitudes of the ABSs, using search space constrained exhaustive search and particle swarm optimization (PSO). The numerical results show that the PSO-based approach results in higher TSE compared to the exhaustive search-based approach in dense networks, consuming similar amount of energy for ABS movements. Both approaches lead up to approximately 8-fold energy savings compared to ABS placement using naive exhaustive search.

Metaverse is the latest technological hype in the modern world due to its potential for revolutionizing the digital visual perspective. With the COVID-19 pandemic, most industries have moved towards digitization, and the metaverse is identified as one of the most promising platforms for such a transition, as it provides a three-dimensional (3D) immersive experience for the users. Currently, most digital service providers and organizations are actively working on metaverse- based applications. In addition, there has been a rapid increase on research work involving metaverse realization. Launching a large scale metaverse in the real world is a challenging task. However, fifth-generation (5G) and beyond 5G (B5G) technologies are envisioned to improve the feasibility of pragmatic deployments. Although, there are several conceptual designs available, actual adaptations of the concepts are still limited. This survey focuses on providing a practical approach for metaverse realization using 5G and B5G technologies. Specifically, We discuss the importance of network slicing (NS) and multi-access edge computing (MEC) as emerging 5G technologies for enabling the realization of the metaverse. We first introduce the motivation behind metaverse for future envisaged technologies. Next, we present a holistic high-level framework for metaverse realization based on network slicing and edge computing. Moreover, we discuss the futuristic metaverse applications, their technical requirements, and methods to satisfy the requirements. Finally, we highlight the deployment challenges and possible approaches to overcome them for an actual metaverse realization.

This letter investigates the exploitation of an intelligent reflecting surface (IRS) to communicate securely in a two-way network consisting of an untrusted user. In particular, the transmit powers and the phase shift at each element of the IRS are optimized to maximize the sum-secrecy rate, such that the IRS-reflected and non-IRS-reflected signals are added destructively at the untrusted user. The proposed iterative algorithm converges rapidly to a feasible solution of high accuracy with a few iterations. Numerical results demonstrate sum-secrecy rate gains up to 120% compared to naive or partially optimized schemes.

The acquisition of channel state information is an essential step in enabling intelligent reflecting surfaces (IRSs) for wireless communications. In this letter, we introduce a novel procedure to estimate the cascaded channel between the base station (BS), IRS, and users in a multi-user multiple-input single-output system. The common BS-IRS channel, over which all users transmit, is leveraged to decompose the cascaded channel into a series of rank-1 matrices. Low-rank matrix recovery methods are utilized to improve upon the linear minimum mean-squared error estimate of the cascaded channel. A theoretical upper bound for the mean-square error (MSE) of the proposed estimator is derived. Numerical results reveal that the proposed techniques outperform the existing counterparts in terms of the MSE and scale with the number of BS antennas.

The paper considers physical layer security (PLS) of an untrusted unmanned aerial vehicle (UAV) network, where a multitude of UAVs communicate in full-duplex (FD) mode. A source-based jamming (SBJ) scheme is exploited for secure communication without utilizing any external jammers. Firstly, the optimal power allocation between the confidential signal and the jamming signal is derived to maximize the secrecy rate of each link between the source and the destination. Then, the best UAV selection scheme is proposed to maximize the overall secrecy rate of the network. The corresponding secrecy outage probability (SOP) and the average secrecy rate (ASR) of the network are analyzed based on the proposed UAV selection and the optimal power allocation schemes. Asymptotic results are also obtained to derive the achievable diversity order. Results are validated through numerical evaluations while providing useful network design insights such as locations and altitudes of the UAVs.

Growing concerns over privacy invasion due to video camera based monitoring systems have made way to non-invasive Wi-Fi signal sensing based alternatives. This paper introduces a novel end-to-end deep learning framework that utilizes the changes in orthogonal frequency division multiplexing (OFDM) sub-carrier amplitude information to simultaneously predict the identity, activity and the trajectory of a user and create a user profile that is of similar utility to a one made through a video camera based approach. The novelty of the proposed solution is that the system is fully autonomous and requires zero user intervention unlike systems that require user originated initialization, or a user held transmitting device to facilitate the prediction. Experimental results demonstrate over 95% accuracy for user identification and activity recognition, while the user localization results exhibit a ±12cm error, which is a significant improvement over the existing user tracking methods that utilize passive Wi-Fi signals.