Software-Defined Collaborative Offloading for Heterogeneous Vehicular Networks

Vehicle-assisted data offloading is envisioned to significantly alleviate the problem of explosive growth of mobile data traffic. However, due to the high mobility of vehicles and the frequent disruption of communication links, it is very challenging to efficiently optimize collaborative offloading from a group of vehicles. In this paper, we leverage the concept of Software-Defined Networking (SDN) and propose a software-defined collaborative offloading (SDCO) solution for heterogeneous vehicular networks. In particular, SDCO can efficiently manage the offloading nodes and paths based on a centralized offloading controller. The offloading controller is equipped with two specific functions: the hybrid awareness path collaboration (HPC) and the graph-based source collaboration (GSC). HPC is in charge of selecting the suitable paths based on the round-trip time, packet loss rate, and path bandwidth, while GSC optimizes the offloading nodes according to the minimum vertex cover for effective offloading. Simulation results are provided to demonstrate that SDCO can achieve better offloading efficiency compared to the state-of-the-art solutions.

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A Fast Link Initialization Protocol for Beam-Steering based Cellular Backhaul Systems

Network Protocols and Algorithms ◽

10.5296/npa.v7i2.7440 ◽

2015 ◽

Vol 7 (2) ◽

pp. 113

Author(s):

Markus Petri ◽

Marcus Ehrig ◽

Markus Günther

Keyword(s):

State Of The Art ◽

Beam Steering ◽

Small Cell ◽

Base Stations ◽

Data Traffic ◽

Mobile Data ◽

User Expectations ◽

Network Topologies ◽

Speed Up ◽

Directive Antennas

<p>To deal with the enormous increase of mobile data traffic, new cellular network topologies are necessary. The reduction of cell area and the usage of light-weighted base stations serving only a handful of users, commonly known as the small cell approach, seems to be a suitable solution addressing changes in user expectations and usage scenarios. This paper is an extended version of [1], where current challenges of small cell deployments were presented from a backhaul perspective. A mesh-type backhaul network topology based on beam-steering millimeter-wave systems was proposed as a future-proof solution. In this paper, we focus on a link initialization protocol for beam-steering with highly directive antennas. Special requirements and problems for link setup are analyzed. Based on that, a fast protocol for link initialization is presented and it is evaluated in terms of the resulting initialization speed-up compared to state-of-the-art solutions. Furthermore, a potential approach for extending the fast link initialization protocol to support point-to-multipoint connections is given.</p>

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Study Of Resilient Packet Ring Based On OPNET

10.32920/ryerson.14645313 ◽

2021 ◽

Author(s):

Xin Zhang

Keyword(s):

Work Group ◽

Control Algorithms ◽

Data Traffic ◽

Round Trip ◽

New Approach ◽

Logic Control ◽

Trip Time ◽

Resilient Packet Ring ◽

Key Parameter ◽

Ieee 802.17

Resilient Packet Ring (RPR) is the next generation layer-2 protocol optimized for transporting data traffic rather than circuit-based traffic. In this thesis, we design and evaluate our own RPR simulation model that is fully compliant with the latest proposal promoted by IEEE 802.17 Work Group. By using this model, we investigate the limitations of the fairness control algorithms proposed by IEEE 802.17 WG. An alternative design, namely, Fuzzy Logic Control, is considered to overcome the shortcomings. Real world scenarious are simulated using this new approach. The simulation results justify the application of this new RPR model, and support its validity. Furthermore, by using this model we also derived an equation to calculate Fairness Round Trip Time (FRTT), which is a key parameter in designing an appropriated size for Secondary Transit Queue (STQ) in RPR. This equation overcomes the limitations proposed by IEEE 802.17 Work Group.

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Study Of Resilient Packet Ring Based On OPNET

10.32920/ryerson.14645313.v1 ◽

2021 ◽

Author(s):

Xin Zhang

Keyword(s):

Work Group ◽

Control Algorithms ◽

Data Traffic ◽

Round Trip ◽

New Approach ◽

Logic Control ◽

Trip Time ◽

Resilient Packet Ring ◽

Key Parameter ◽

Ieee 802.17

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Deep Reinforcement Learning Based Resource Allocation with Radio Remote Head Grouping and Vehicle Clustering in 5G Vehicular Networks

Electronics ◽

10.3390/electronics10233015 ◽

2021 ◽

Vol 10 (23) ◽

pp. 3015

Author(s):

Hyebin Park ◽

Yujin Lim

Keyword(s):

Resource Allocation ◽

Energy Efficiency ◽

Reinforcement Learning ◽

Vehicular Networks ◽

Interference Management ◽

High Mobility ◽

System Capacity ◽

Data Traffic ◽

System Energy

With increasing data traffic requirements in vehicular networks, vehicle-to-everything (V2X) communication has become imperative in improving road safety to guarantee reliable and low latency services. However, V2X communication is highly affected by interference when changing channel states in a high mobility environment in vehicular networks. For optimal interference management in high mobility environments, it is necessary to apply deep reinforcement learning (DRL) to allocate communication resources. In addition, to improve system capacity and reduce system energy consumption from the traffic overheads of periodic messages, a vehicle clustering technique is required. In this paper, a DRL based resource allocation method is proposed with remote radio head grouping and vehicle clustering to maximize system energy efficiency while considering quality of service and reliability. The proposed algorithm is compared with three existing algorithms in terms of performance through simulations, in each case outperforming the existing algorithms in terms of average signal to interference noise ratio, achievable data rate, and system energy efficiency.

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