Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360-Degree Video Streaming

360-degree videos require significant bandwidth to provide an immersive viewing experience. Wireless systems using terahertz (THz) frequency band can meet this high data rate demand. However, self-blockage is a challenge in such systems. To ensure reliable transmission, this paper explores THz-enabled 360-degree video streaming through multiple multi-antenna access points (APs). Guaranteeing users' quality of experience (QoE) requires accurate viewport prediction to determine which video tiles to send, followed by asynchronous bitrate selection for those tiles and beamforming design at the APs. To address users' privacy and data heterogeneity, we propose a content-based viewport prediction framework, wherein users' head movement prediction models are trained using a personalized federated learning algorithm. To address asynchronous decision-making for tile bitrates and dynamic THz link connections, we formulate the optimization of bitrate selection and beamforming as a macro-action decentralized partially observable Markov decision process (MacDec-POMDP) problem. To efficiently tackle this problem for multiple users, we develop two deep reinforcement learning (DRL) algorithms based on multi-agent actor-critic methods and propose a hierarchical learning framework to train the actor and critic networks. Experimental results show that our proposed approach provides a higher QoE when compared with three benchmark algorithms.

Multiple Access Techniques for Intelligent and Multi-Functional 6G: Tutorial, Survey, and Outlook

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions to serve multiple users/devices/machines/services, ideally in the most efficient way. Given the needs of multi-functional wireless networks for integrated communications, sensing, localization, computing, coupled with the surge of machine learning / artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this paper, we provide a tutorial, survey and outlook of past, emerging and future MA techniques and pay a particular attention to how wireless network intelligence and multi-functionality will lead to a re-thinking of those techniques. The paper starts with an overview of orthogonal, physical layer multicasting, space domain, power domain, ratesplitting, code domain MAs, and other domains, and highlight the importance of researching universal multiple access to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, receiver designs, user behavior predictions, and MA for AI such as federated learning/edge intelligence and over the air computation. We then discuss MA for network multi-functionality and the interplay between MA and integrated sensing, localization, and communications. We finish with studying MA for emerging intelligent applications before presenting a roadmap toward 6G standardization. We also point out numerous directions that are promising for future research.

Joint User Pairing and Association for Multicell NOMA: A Pointer Network-based Approach

In this paper, we investigate the joint user pairing and association problem for multicell non-orthogonal multiple access (NOMA) systems. We consider a scenario where the user equipments (UEs) are located in a multicell network equipped with multiple base stations. Each base station has multiple orthogonal physical resource blocks (PRBs). Each PRB can be allocated to a pair of UEs using NOMA. Each UE has the additional freedom to be served by any one of the base stations, which further increases the complexity of the joint user pairing and association algorithm design. Leveraging the recent success on using machine learning to solve numerical optimization problems, we formulate the joint user pairing and association problem as a combinatorial optimization problem. The solution is found using an emerging deep learning architecture called Pointer Network (PtrNet), which has a lower computational complexity compared to solutions based on iterative algorithms and has been proven to achieve near-optimal performance. The training phase of the PtrNet is based on deep reinforcement learning (DRL), and does not require the use of the optimal solution of the formulated problem as training labels. Simulation results show that the proposed joint user pairing and association scheme achieves near-optimal performance in terms of the aggregate data rate, and outperforms the random user pairing and association heuristic by up to 30%.

On Identification of Distribution Grids

Large-scale integration of distributed energy resources into residential distribution feeders necessitates careful control of their operation through power flow analysis. While the knowledge of the distribution system model is crucial for this type of analysis, it is often unavailable or outdated. The recent introduction of synchrophasor technology in low-voltage distribution grids has created an unprecedented opportunity to learn this model from high-precision, time-synchronized measurements of voltage and current phasors at various locations. This paper focuses on joint estimation of model parameters (admittance values) and operational structure of a poly-phase distribution network from the available telemetry data via the lasso, a method for regression shrinkage and selection. We propose tractable convex programs capable of tackling the low rank structure of the distribution system and develop an online algorithm for early detection and localization of critical events that induce a change in the admittance matrix. The efficacy of these techniques is corroborated through power flow studies on four three-phase radial distribution systems serving real household demands.