139 research outputs found
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
Improved Handover Through Dual Connectivity in 5G mmWave Mobile Networks
The millimeter wave (mmWave) bands offer the possibility of orders of
magnitude greater throughput for fifth generation (5G) cellular systems.
However, since mmWave signals are highly susceptible to blockage, channel
quality on any one mmWave link can be extremely intermittent. This paper
implements a novel dual connectivity protocol that enables mobile user
equipment (UE) devices to maintain physical layer connections to 4G and 5G
cells simultaneously. A novel uplink control signaling system combined with a
local coordinator enables rapid path switching in the event of failures on any
one link. This paper provides the first comprehensive end-to-end evaluation of
handover mechanisms in mmWave cellular systems. The simulation framework
includes detailed measurement-based channel models to realistically capture
spatial dynamics of blocking events, as well as the full details of MAC, RLC
and transport protocols. Compared to conventional handover mechanisms, the
study reveals significant benefits of the proposed method under several
metrics.Comment: 16 pages, 13 figures, to appear on the 2017 IEEE JSAC Special Issue
on Millimeter Wave Communications for Future Mobile Network
An Efficient Uplink Multi-Connectivity Scheme for 5G mmWave Control Plane Applications
The millimeter wave (mmWave) frequencies offer the potential of orders of
magnitude increases in capacity for next-generation cellular systems. However,
links in mmWave networks are susceptible to blockage and may suffer from rapid
variations in quality. Connectivity to multiple cells - at mmWave and/or
traditional frequencies - is considered essential for robust communication. One
of the challenges in supporting multi-connectivity in mmWaves is the
requirement for the network to track the direction of each link in addition to
its power and timing. To address this challenge, we implement a novel uplink
measurement system that, with the joint help of a local coordinator operating
in the legacy band, guarantees continuous monitoring of the channel propagation
conditions and allows for the design of efficient control plane applications,
including handover, beam tracking and initial access. We show that an
uplink-based multi-connectivity approach enables less consuming, better
performing, faster and more stable cell selection and scheduling decisions with
respect to a traditional downlink-based standalone scheme. Moreover, we argue
that the presented framework guarantees (i) efficient tracking of the user in
the presence of the channel dynamics expected at mmWaves, and (ii) fast
reaction to situations in which the primary propagation path is blocked or not
available.Comment: Submitted for publication in IEEE Transactions on Wireless
Communications (TWC
Throughput Maximization for Intelligent Refracting Surface Assisted mmWave High-Speed Train Communications
With the increasing demands from passengers for data-intensive services,
millimeter-wave (mmWave) communication is considered as an effective technique
to release the transmission pressure on high speed train (HST) networks.
However, mmWave signals ncounter severe losses when passing through the
carriage, which decreases the quality of services on board. In this paper, we
investigate an intelligent refracting surface (IRS)-assisted HST communication
system. Herein, an IRS is deployed on the train window to dynamically
reconfigure the propagation environment, and a hybrid time division multiple
access-nonorthogonal multiple access scheme is leveraged for interference
mitigation. We aim to maximize the overall throughput while taking into account
the constraints imposed by base station beamforming, IRS discrete phase shifts
and transmit power. To obtain a practical solution, we employ an alternating
optimization method and propose a two-stage algorithm. In the first stage, the
successive convex approximation method and branch and bound algorithm are
leveraged for IRS phase shift design. In the second stage, the Lagrangian
multiplier method is utilized for power allocation. Simulation results
demonstrate the benefits of IRS adoption and power allocation for throughput
improvement in mmWave HST networks.Comment: 13 pages, 7 figures, IEEE Internet of Things Journa
Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance
The pervasive nature of wireless telecommunication has made it the foundation
for mainstream technologies like automation, smart vehicles, virtual reality,
and unmanned aerial vehicles. As these technologies experience widespread
adoption in our daily lives, ensuring the reliable performance of cellular
networks in mobile scenarios has become a paramount challenge. Beamforming, an
integral component of modern mobile networks, enables spatial selectivity and
improves network quality. However, many beamforming techniques are iterative,
introducing unwanted latency to the system. In recent times, there has been a
growing interest in leveraging mobile users' location information to expedite
beamforming processes. This paper explores the concept of contextual
beamforming, discussing its advantages, disadvantages and implications.
Notably, the study presents an impressive 53% improvement in signal-to-noise
ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared
to scenarios without beamforming. It further elucidates how MRT contributes to
contextual beamforming. The importance of localization in implementing
contextual beamforming is also examined. Additionally, the paper delves into
the use of artificial intelligence schemes, including machine learning and deep
learning, in implementing contextual beamforming techniques that leverage user
location information. Based on the comprehensive review, the results suggest
that the combination of MRT and Zero forcing (ZF) techniques, alongside deep
neural networks (DNN) employing Bayesian Optimization (BO), represents the most
promising approach for contextual beamforming. Furthermore, the study discusses
the future potential of programmable switches, such as Tofino, in enabling
location-aware beamforming
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