88,254 research outputs found
Intelligent Reward based Data Offloading in Next Generation Vehicular Networks
A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle s data traffic from congested cellular network s licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module
Joint Routing and Energy Optimization for Integrated Access and Backhaul with Open RAN
Energy consumption represents a major part of the operating expenses of
mobile network operators. With the densification foreseen with 5G and beyond,
energy optimization has become a problem of crucial importance. While energy
optimization is widely studied in the literature, there are limited insights
and algorithms for energy-saving techniques for Integrated Access and Backhaul
(IAB), a self-backhauling architecture that ease deployment of dense cellular
networks reducing the number of fiber drops. This paper proposes a novel
optimization model for dynamic joint routing and energy optimization in IAB
networks. We leverage the closed-loop control framework introduced by the Open
Radio Access Network (O-RAN) architecture to minimize the number of active IAB
nodes while maintaining a minimum capacity per User Equipment (UE). The
proposed approach formulates the problem as a binary nonlinear program, which
is transformed into an equivalent binary linear program and solved using the
Gurobi solver. The approach is evaluated on a scenario built upon open data of
two months of traffic collected by network operators in the city of Milan,
Italy. Results show that the proposed optimization model reduces the RAN energy
consumption by 47%, while guaranteeing a minimum capacity for each UE.Comment: 6 pages, Accepted at IEEE GLOBECOM 202
Toward Open Integrated Access and Backhaul with O-RAN
Millimeter wave (mmWave) communications has been recently standardized for use in the fifth generation (5G) of cellular networks, fulfilling the promise of multi-gigabit mobile throughput of current and future mobile radio network generations. In this context, the network densification required to overcome the difficult mmWave propagation will result in increased deployment costs. Integrated Access and Backhaul (IAB) has been proposed as an effective mean of reducing densification costs by deploying a wireless mesh network of base stations, where backhaul and access transmissions share the same radio technology. However, IAB requires sophisticated control mechanisms to operate efficiently and address the increased complexity. The Open Radio Access Network (RAN) paradigm represents the ideal enabler of RAN intelligent control, but its current specifications are not compatible with IAB. In this work, we discuss the challenges of integrating IAB into the Open RAN ecosystem, detailing the required architectural extensions that will enable dynamic control of 5G IAB networks. We implement the proposed integrated architecture into the first publiclyavailable Open-RAN-enabled experimental framework, which allows prototyping and testing Open-RAN-based solutions over end-to-end 5G IAB networks. Finally, we validate the framework with both ideal and realistic deployment scenarios exploiting the large-scale testing capabilities of publicly available experimental platforms
Joint Design of Access and Backhaul in Densely Deployed MmWave Small Cells
With the rapid growth of mobile data traffic, the shortage of radio spectrum
resource has become increasingly prominent. Millimeter wave (mmWave) small
cells can be densely deployed in macro cells to improve network capacity and
spectrum utilization. Such a network architecture is referred to as mmWave
heterogeneous cellular networks (HetNets). Compared with the traditional wired
backhaul, The integrated access and backhaul (IAB) architecture with wireless
backhaul is more flexible and cost-effective for mmWave HetNets. However, the
imbalance of throughput between the access and backhaul links will constrain
the total system throughput. Consequently, it is necessary to jointly design of
radio access and backhaul link. In this paper, we study the joint optimization
of user association and backhaul resource allocation in mmWave HetNets, where
different mmWave bands are adopted by the access and backhaul links.
Considering the non-convex and combinatorial characteristics of the
optimization problem and the dynamic nature of the mmWave link, we propose a
multi-agent deep reinforcement learning (MADRL) based scheme to maximize the
long-term total link throughput of the network. The simulation results show
that the scheme can not only adjust user association and backhaul resource
allocation strategy according to the dynamics in the access link state, but
also effectively improve the link throughput under different system
configurations.Comment: 15 page
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
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