450 research outputs found
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
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