1,539 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
IRS-aided UAV for Future Wireless Communications: A Survey and Research Opportunities
Both unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces
(IRS) are gaining traction as transformative technologies for upcoming wireless
networks. The IRS-aided UAV communication, which introduces IRSs into UAV
communications, has emerged in an effort to improve the system performance
while also overcoming UAV communication constraints and issues. The purpose of
this paper is to provide a comprehensive overview of IRSassisted UAV
communications. First, we provide five examples of how IRSs and UAVs can be
combined to achieve unrivaled potential in difficult situations. The
technological features of the most recent relevant researches on IRS-aided UAV
communications from the perspective of the main performance criteria, i.e.,
energy efficiency, security, spectral efficiency, etc. Additionally, previous
research studies on technology adoption as machine learning algorithms. Lastly,
some promising research directions and open challenges for IRS-aided UAV
communication are presented
Reconfigurable Intelligent Surface Assisted High-Speed Train Communications: Coverage Performance Analysis and Placement Optimization
Reconfigurable intelligent surface (RIS) emerges as an efficient and
promising technology for the next wireless generation networks and has
attracted a lot of attention owing to the capability of extending wireless
coverage by reflecting signals toward targeted receivers. In this paper, we
consider a RIS-assisted high-speed train (HST) communication system to enhance
wireless coverage and improve coverage probability. First, coverage performance
of the downlink single-input-single-output system is investigated, and the
closed-form expression of coverage probability is derived. Moreover, travel
distance maximization problem is formulated to facilitate RIS discrete phase
design and RIS placement optimization, which is subject to coverage probability
constraint. Simulation results validate that better coverage performance and
higher travel distance can be achieved with deployment of RIS. The impacts of
some key system parameters including transmission power, signal-to-noise ratio
threshold, number of RIS elements, number of RIS quantization bits, horizontal
distance between base station and RIS, and speed of HST on system performance
are investigated. In addition, it is found that RIS can well improve coverage
probability with limited power consumption for HST communications.Comment: 14 figures, accepted by IEEE Transactions on Vehicular Technolog
RIS-assisted Scheduling for High-Speed Railway Secure Communications
With the rapid development of high-speed railway systems and railway wireless
communication, the application of ultra-wideband millimeter wave band is an
inevitable trend. However, the millimeter wave channel has large propagation
loss and is easy to be blocked. Moreover, there are many problems such as
eavesdropping between the base station (BS) and the train. As an emerging
technology, reconfigurable intelligent surface (RIS) can achieve the effect of
passive beamforming by controlling the propagation of the incident
electromagnetic wave in the desired direction.We propose a RIS-assisted
scheduling scheme for scheduling interrupted transmission and improving quality
of service (QoS).In the propsed scheme, an RIS is deployed between the BS and
multiple mobile relays (MRs). By jointly optimizing the beamforming vector and
the discrete phase shift of the RIS, the constructive interference between
direct link signals and indirect link signals can be achieved, and the channel
capacity of eavesdroppers is guaranteed to be within a controllable range.
Finally, the purpose of maximizing the number of successfully scheduled tasks
and satisfying their QoS requirements can be practically realized. Extensive
simulations demonstrate that the proposed scheme has superior performance
regarding the number of completed tasks and the system secrecy capacity over
four baseline schemes in literature.Comment: 15 pages, 10 figures, to appear in IEEE Transactions on Vehicular
Technolog
Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile Communications
Unmanned Aerial Vehicles (UAVs) are increasingly used as aerial base stations
to provide ad hoc communications infrastructure. Building upon prior research
efforts which consider either static nodes, 2D trajectories or single UAV
systems, this paper focuses on the use of multiple UAVs for providing wireless
communication to mobile users in the absence of terrestrial communications
infrastructure. In particular, we jointly optimize UAV 3D trajectory and NOMA
power allocation to maximize system throughput. Firstly, a weighted
K-means-based clustering algorithm establishes UAV-user associations at regular
intervals. The efficacy of training a novel Shared Deep Q-Network (SDQN) with
action masking is then explored. Unlike training each UAV separately using DQN,
the SDQN reduces training time by using the experiences of multiple UAVs
instead of a single agent. We also show that SDQN can be used to train a
multi-agent system with differing action spaces. Simulation results confirm
that: 1) training a shared DQN outperforms a conventional DQN in terms of
maximum system throughput (+20%) and training time (-10%); 2) it can converge
for agents with different action spaces, yielding a 9% increase in throughput
compared to mutual learning algorithms; and 3) combining NOMA with an SDQN
architecture enables the network to achieve a better sum rate compared with
existing baseline schemes
Artificial Intelligence Driven UAV-NOMA-MEC in Next Generation Wireless Networks
Driven by the unprecedented high throughput and low latency requirements in
next-generation wireless networks, this paper introduces an artificial
intelligence (AI) enabled framework in which unmanned aerial vehicles (UAVs)
use non-orthogonal multiple access (NOMA) and mobile edge computing (MEC)
techniques to service terrestrial mobile users (MUs). The proposed framework
enables the terrestrial MUs to offload their computational tasks
simultaneously, intelligently, and flexibly, thus enhancing their connectivity
as well as reducing their transmission latency and their energy consumption. To
this end, the fundamentals of this framework are first introduced. Then, a
number of communication and AI techniques are proposed to improve the quality
of experiences of terrestrial MUs. To this end, federated learning and
reinforcement learning are introduced for intelligent task offloading and
computing resource allocation. For each learning technique, motivations,
challenges, and representative results are introduced. Finally, several key
technical challenges and open research issues of the proposed framework are
summarized.Comment: 14 pages, 5 figure
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