454 research outputs found
Aerial Access and Backhaul in mmWave B5G Systems: Performance Dynamics and Optimization
The use of unmanned aerial vehicle (UAV)-based communication in
millimeter-wave (mmWave) frequencies to provide on-demand radio access is a
promising approach to improve capacity and coverage in beyond-5G (B5G) systems.
There are several design aspects to be addressed when optimizing for the
deployment of such UAV base stations. As traffic demand of mobile users varies
across time and space, dynamic algorithms that correspondingly adjust the UAV
locations are essential to maximize performance. In addition to careful
tracking of spatio-temporal user/traffic activity, such optimization needs to
account for realistic backhaul constraints. In this work, we first review the
latest 3GPP activities behind integrated access and backhaul system design,
support for UAV base stations, and mmWave radio relaying functionality. We then
compare static and mobile UAV-based communication options under practical
assumptions on the mmWave system layout, mobility and clusterization of users,
antenna array geometry, and dynamic backhauling. We demonstrate that leveraging
the UAV mobility to serve moving users may improve the overall system
performance even in the presence of backhaul capacity limitations.Comment: 7 pages, 5 figures. This work has been accepted to IEEE
Communications Magazine, 201
Self-Evolving Integrated Vertical Heterogeneous Networks
6G and beyond networks tend towards fully intelligent and adaptive design in
order to provide better operational agility in maintaining universal wireless
access and supporting a wide range of services and use cases while dealing with
network complexity efficiently. Such enhanced network agility will require
developing a self-evolving capability in designing both the network
architecture and resource management to intelligently utilize resources, reduce
operational costs, and achieve the coveted quality of service (QoS). To enable
this capability, the necessity of considering an integrated vertical
heterogeneous network (VHetNet) architecture appears to be inevitable due to
its high inherent agility. Moreover, employing an intelligent framework is
another crucial requirement for self-evolving networks to deal with real-time
network optimization problems. Hence, in this work, to provide a better insight
on network architecture design in support of self-evolving networks, we
highlight the merits of integrated VHetNet architecture while proposing an
intelligent framework for self-evolving integrated vertical heterogeneous
networks (SEI-VHetNets). The impact of the challenges associated with
SEI-VHetNet architecture, on network management is also studied considering a
generalized network model. Furthermore, the current literature on network
management of integrated VHetNets along with the recent advancements in
artificial intelligence (AI)/machine learning (ML) solutions are discussed.
Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are
identified. Finally, the potential future research directions for advancing the
autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table
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