614 research outputs found
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Reconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities
The concept of Smart Cities has been introduced as a way to benefit from the
digitization of various ecosystems at a city level. To support this concept,
future communication networks need to be carefully designed with respect to the
city infrastructure and utilization of resources. Recently, the idea of 'smart'
environment, which takes advantage of the infrastructure for better performance
of wireless networks, has been proposed. This idea is aligned with the recent
advances in design of reconfigurable intelligent surfaces (RISs), which are
planar structures with the capability to reflect impinging electromagnetic
waves toward preferred directions. Thus, RISs are expected to provide the
necessary flexibility for the design of the 'smart' communication environment,
which can be optimally shaped to enable cost- and energy-efficient signal
transmissions where needed. Upon deployment of RISs, the ecosystem of the Smart
Cities would become even more controllable and adaptable, which would
subsequently ease the implementation of future communication networks in urban
areas and boost the interconnection among private households and public
services. In this paper, we describe our vision of the application of RISs in
future Smart Cities. In particular, the research challenges and opportunities
are addressed. The contribution paves the road to a systematic design of
RIS-assisted communication networks for Smart Cities in the years to come.Comment: Submitted for possible publication in IEEE Open Journal of the
Communications Societ
Energy Optimization of Smart Water Systems using UAV Enabled Zero-Power Wireless Communication Networks
Real-time energy consumption is a crucial consideration when assessing the effectiveness and efficiency of communication using energy hungry devices. Utilizing new technologies such as UAV-enabled wireless powered communication networks (WPCN) and 3D beamforming, and then a combination of static and dynamic optimization methodologies are combined to improve energy usage in water distribution systems (WDS).
A proposed static optimization technique termed the Dome packing method and dynamic optimization methods such as extremum seeking are employed to generate optimum placement and trajectories of the UAV with respect to the ground nodes (GN) in a WDS.
In this thesis, a wireless communication network powered by a UAV serves as a hybrid access point to manage many GNsin WDS. The GNs are water quality sensors that collect radio frequency (RF) energy from the RF signals delivered by the UAV and utilise this energy to relay information via an uplink. Optimum strategies are demonstrated to efficiently handle this process as part of a zero-power system: removing the need for manual battery charging of devices, while at the same time optimizing energy and data transfer over WPCN.
Since static optimization does not account for the UAV's dynamics, dynamic optimization techniques are also necessary. By developing an efficient trajectory, the suggested technique also reduces the overall flying duration and, therefore, the UAV's energy consumption. This combination of techniques also drastically reduces the complexity and calculation overhead of purely high order static optimizations.
To test and validate the efficacy of the extremum seeking implementation, comparison with the optimal sliding mode technique is also undertaken. These approaches are applied to ten distinct case studies by randomly relocating the GNs to various positions. The findings from a random sample of four of these is presented, which reveal that the proposed strategy reduces the UAV's energy usage significantly by about 16 percent compared to existing methods.
The (hybrid) static and dynamic zero-power optimization strategies demonstrated here are readily extendable to the control of water quality and pollution in natural freshwater resources and this will be discussed at the end of this thesis
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