42 research outputs found
Spatio-Temporal Motifs for Optimized Vehicle-to-Vehicle (V2V) Communications
Caching popular contents in vehicle-to-vehicle (V2V) communication networks
is expected to play an important role in road traffic management, the
realization of intelligent transportation systems (ITSs), and the delivery of
multimedia content across vehicles. However, for effective caching, the network
must dynamically choose the optimal set of cars that will cache popular content
and disseminate it in the entire network. However, most of the existing prior
art on V2V caching is restricted to cache placement that is solely based on
location and user demands and does not account for the large-scale
spatio-temporal variations in V2V communication networks. In contrast, in this
paper, a novel spatio-temporal caching strategy is proposed based on the notion
of temporal graph motifs that can capture spatio-temporal communication
patterns in V2V networks. It is shown that, by identifying such V2V motifs, the
network can find sub-optimal content placement strategies for effective content
dissemination across a vehicular network. Simulation results using real traces
from the city of Cologne show that the proposed approach can increase the
average data rate by for different network scenarios
Matching Theory for Backhaul Management in Small Cell Networks with mmWave Capabilities
Designing cost-effective and scalable backhaul solutions is one of the main
challenges for emerging wireless small cell networks (SCNs). In this regard,
millimeter wave (mmW) communication technologies have recently emerged as an
attractive solution to realize the vision of a high-speed and reliable wireless
small cell backhaul network (SCBN). In this paper, a novel approach is proposed
for managing the spectral resources of a heterogeneous SCBN that can exploit
simultaneously mmW and conventional frequency bands via carrier aggregation. In
particular, a new SCBN model is proposed in which small cell base stations
(SCBSs) equipped with broadband fiber backhaul allocate their frequency
resources to SCBSs with wireless backhaul, by using aggregated bands. One
unique feature of the studied model is that it jointly accounts for both
wireless channel characteristics and economic factors during resource
allocation. The problem is then formulated as a one-to-many matching game and a
distributed algorithm is proposed to find a stable outcome of the game. The
convergence of the algorithm is proven and the properties of the resulting
matching are studied. Simulation results show that under the constraints of
wireless backhauling, the proposed approach achieves substantial performance
gains, reaching up to compared to a conventional best-effort approach.Comment: In Proc. of the IEEE International Conference on Communications
(ICC), Mobile and Wireless Networks Symposium, London, UK, June 201
Matching theory for priority-based cell association in the downlink of wireless small cell networks
The deployment of small cells, overlaid on existing cellular infrastructure,
is seen as a key feature in next-generation cellular systems. In this paper,
the problem of user association in the downlink of small cell networks (SCNs)
is considered. The problem is formulated as a many-to-one matching game in
which the users and SCBSs rank one another based on utility functions that
account for both the achievable performance, in terms of rate and fairness to
cell edge users, as captured by newly proposed priorities. To solve this game,
a novel distributed algorithm that can reach a stable matching is proposed.
Simulation results show that the proposed approach yields an average utility
gain of up to 65% compared to a common association algorithm that is based on
received signal strength. Compared to the classical deferred acceptance
algorithm, the results also show a 40% utility gain and a more fair utility
distribution among the users.Comment: 5 page
Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN
The next-generation wireless networks are required to satisfy a variety of
services and criteria concurrently. To address upcoming strict criteria, a new
open radio access network (O-RAN) with distinguishing features such as flexible
design, disaggregated virtual and programmable components, and intelligent
closed-loop control was developed. O-RAN slicing is being investigated as a
critical strategy for ensuring network quality of service (QoS) in the face of
changing circumstances. However, distinct network slices must be dynamically
controlled to avoid service level agreement (SLA) variation caused by rapid
changes in the environment. Therefore, this paper introduces a novel framework
able to manage the network slices through provisioned resources intelligently.
Due to diverse heterogeneous environments, intelligent machine learning
approaches require sufficient exploration to handle the harshest situations in
a wireless network and accelerate convergence. To solve this problem, a new
solution is proposed based on evolutionary-based deep reinforcement learning
(EDRL) to accelerate and optimize the slice management learning process in the
radio access network's (RAN) intelligent controller (RIC) modules. To this end,
the O-RAN slicing is represented as a Markov decision process (MDP) which is
then solved optimally for resource allocation to meet service demand using the
EDRL approach. In terms of reaching service demands, simulation results show
that the proposed approach outperforms the DRL baseline by 62.2%.Comment: This paper has been accepted for the 2022 IEEE Globecom Workshops (GC
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Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks
In this paper, the problem of enhancing the virtual reality (VR) experience
for wireless users is investigated by minimizing the occurrence of breaks in
presence (BIP) that can detach the users from their virtual world. To measure
the BIP for wireless VR users, a novel model that jointly considers the VR
application type, transmission delay, VR video quality, and users' awareness of
the virtual environment is proposed. In the developed model, the base stations
(BSs) transmit VR videos to the wireless VR users using directional
transmission links so as to provide high data rates for the VR users, thus,
reducing the number of BIP for each user. Since the body movements of a VR user
may result in a blockage of its wireless link, the location and orientation of
VR users must also be considered when minimizing BIP. The BIP minimization
problem is formulated as an optimization problem which jointly considers the
predictions of users' locations, orientations, and their BS association. To
predict the orientation and locations of VR users, a distributed learning
algorithm based on the machine learning framework of deep (ESNs) is proposed.
The proposed algorithm uses concept from federated learning to enable multiple
BSs to locally train their deep ESNs using their collected data and
cooperatively build a learning model to predict the entire users' locations and
orientations. Using these predictions, the user association policy that
minimizes BIP is derived. Simulation results demonstrate that the developed
algorithm reduces the users' BIP by up to 16% and 26%, respectively, compared
to centralized ESN and deep learning algorithms.Comment: This paper has been accepted by the IEEE Transactions on Wireless
Communication