Scalable Multiuser Immersive Communications with Multi-numerology and Mini-slot

This paper studies multiuser immersive communications networks in which different user equipment may demand various extended reality (XR) services. In such heterogeneous networks, time-frequency resource allocation needs to be more adaptive since XR services are usually multi-modal and latency-sensitive. To this end, we develop a scalable time-frequency resource allocation method based on multi-numerology and mini-slot. To appropriately determining the discrete parameters of multi-numerology and mini-slot for multiuser immersive communications, the proposed method first presents a novel flexible time-frequency resource block configuration, then it leverages the deep reinforcement learning to maximize the total quality-of-experience (QoE) under different users' QoE constraints. The results confirm the efficiency and scalability of the proposed time-frequency resource allocation method

Five Facets of 6G: Research Challenges and Opportunities

Whilst the fifth-generation (5G) systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage we survey five main research facets of this field, namely {\em Facet~1: next-generation architectures, spectrum and services, Facet~2: next-generation networking, Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing, as well as Facet~5: applications of deep learning in 6G networks.} In this paper, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we have listed a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm-shift that has taken place from pure single-component bandwidth-efficiency, power-efficiency or delay-optimization towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

QoS-aware adaptive call admission control in multiuser OFDM wireless network.

Yu, Xi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 46-49).Abstracts in English and Chinese.Acknowledgement --- p.iAbstract --- p.iiChapter Chapter 1 --- Introduction and Background --- p.1Chapter 1.1 --- Background --- p.3Chapter 1.1.1 --- Brief Review of CAC --- p.3Chapter 1.1.2 --- Dynamic Sub-carrier Allocation in Multi-user OFDM Wireless Network --- p.6Chapter 1.2 --- Problem Statement --- p.11Chapter 1.3 --- The Organization of The Thesis --- p.12Chapter Chapter2 --- System Model and Call Admission Control Framework --- p.13Chapter 2.1 --- System setup --- p.13Chapter 2.2 --- The CAC Strategy Framework --- p.14Chapter Chapter 3 --- QoS-aware Adaptive Call Admission Control´ؤStep One: The QoS-Provisioning CAC --- p.18Chapter 3.1 --- Problem Formulation --- p.19Chapter 3.2 --- Optimal Condition Analysis --- p.21Chapter 3.3 --- Throughput Estimation Algorithm --- p.22Chapter 3.4 --- QoS-Provisioning CAC --- p.25Chapter 3.5 --- Performance Evaluation --- p.26Chapter Chapter 4 --- QoS-aware Adaptive Call Admission Control´ؤStep Two: Average Revenue Maximization CAC --- p.30Chapter 4.1 --- Semi-Markov Decision Process --- p.30Chapter 4.2 --- Investigation of Algorithms for SMDP --- p.34Chapter 4.3 --- The Average Revenue Maximum CAC --- p.37Chapter 4.4 --- Performance Evaluation --- p.40Chapter Chapter 5 --- Conclusion and Future Work --- p.44Bibliography --- p.4