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

Electronics ◽  
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.

scholarly journals D2D Assisted Cellular Networks in Licensed and Unlicensed Spectrum: Matching-Iteration-Based Joint User Access and Resource Allocation

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 80
Author(s):  
Qiuqi Han ◽  
Guangyuan Zheng ◽  
Chen Xu
Keyword(s):  
Resource Allocation ◽  
Cellular Networks ◽  
System Capacity ◽  
System Throughput ◽  
Unlicensed Spectrum ◽  
Resource Allocation Algorithm ◽  
User Access ◽  
Limited Spectrum

Device-to-Device (D2D) communications, which enable direct communication between nearby user devices over the licensed spectrum, have been considered a key technique to improve spectral efficiency and system throughput in cellular networks (CNs). However, the limited spectrum resources cannot be sufficient to support more cellular users (CUs) and D2D users to meet the growth of the traffic data in future wireless networks. Therefore, Long-Term Evolution-Unlicensed (LTE-U) and D2D-Unlicensed (D2D-U) technologies have been proposed to further enhance system capacity by extending the CUs and D2D users on the unlicensed spectrum for communications. In this paper, we consider an LTE network where the CUs and D2D users are allowed to share the unlicensed spectrum with Wi-Fi users. To maximize the sum rate of all users while guaranteeing each user’s quality of service (QoS), we jointly consider user access and resource allocation. To tackle the formulated problem, we propose a matching-iteration-based joint user access and resource allocation algorithm. Simulation results show that the proposed algorithm can significantly improve system throughput compared to the other benchmark algorithms.

scholarly journals A Resource Allocation Algorithm for Ultra-Dense Networks Based on Deep Reinforcement Learning

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Huashuai Zhang ◽  
Tingmei Wang ◽  
Haiwei Shen
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Data Traffic ◽  
Wireless Data ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Q Learning ◽  
Dense Networks ◽  
Target Network ◽  
Wireless Resource Allocation

The resource optimization of ultra-dense networks (UDNs) is critical to meet the huge demand of users for wireless data traffic. But the mainstream optimization algorithms have many problems, such as the poor optimization effect, and high computing load. This paper puts forward a wireless resource allocation algorithm based on deep reinforcement learning (DRL), which aims to maximize the total throughput of the entire network and transform the resource allocation problem into a deep Q-learning process. To effectively allocate resources in UDNs, the DRL algorithm was introduced to improve the allocation efficiency of wireless resources; the authors adopted the resource allocation strategy of the deep Q-network (DQN), and employed empirical repetition and target network to overcome the instability and divergence of the results caused by the previous network state, and to solve the overestimation of the Q value. Simulation results show that the proposed algorithm can maximize the total throughput of the network, while making the network more energy-efficient and stable. Thus, it is very meaningful to introduce the DRL to the research of UDN resource allocation.

Quality of Service in UMTS Mobile Systems

2010 ◽  
pp. 14-51
Author(s):  
Jahangir Dadkhah Chimeh
Keyword(s):  
Quality Of Service ◽  
Mobile Systems ◽  
System Capacity ◽  
Data Traffic ◽  
Air Interface ◽  
Layer 2 ◽  
Qos Parameters ◽  
Erlang Capacity ◽  
Transport Channels

Mobile systems and particularly UMTS are growing fast. These systems convey data based services in addition to customary voice services. Quality of service is a function of data rate, delay and signal to noise plus interference ratio in these systems. In this Chapter first the author pays attention to UMTS and its QoS architecture, then to service categorization due to QoS. Afterwards he reviews some QoS parameters. Then he studies Layer 2 QoS parameters and general concepts about Transport channels. Then he review TCP effects on the throughput in the air interface. he introduces HSDPA in the next section. Finally he pays attention to data traffic models and their effects on the system capacity and Erlang capacity and delay in the system.

scholarly journals Software-Defined Collaborative Offloading for Heterogeneous Vehicular Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Quan ◽  
Kai Wang ◽  
Yana Liu ◽  
Nan Cheng ◽  
Hongke Zhang ◽  
...  
Keyword(s):  
Vehicular Networks ◽  
Loss Rate ◽  
State Of The Art ◽  
Vertex Cover ◽  
High Mobility ◽  
Data Traffic ◽  
Round Trip ◽  
Mobile Data ◽  
Trip Time ◽  
Communication Links

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.

scholarly journals FORESAM—FOG Paradigm-Based Resource Allocation Mechanism for Vehicular Clouds

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5028
Author(s):  
Rickson Pereira ◽  
Azzedine Boukerche ◽  
Marco A. C. da Silva ◽  
Luis H. V. Nakamura ◽  
Heitor Freitas ◽  
...  
Keyword(s):  
Resource Allocation ◽  
High Mobility ◽  
Transport Systems ◽  
Management Problem ◽  
Allocation Mechanism ◽  
Network Characteristics ◽  
Intelligent Transport ◽  
Multiple Attribute ◽  
Objective Quality

The Intelligent Transport Systems (ITS) has the objective quality of transportation improvement through transportation system monitoring and management and makes the trip more comfortable and safer for drivers and passengers. The mobile clouds can assist the ITS in handling the resource management problem. However, resource allocation management in an ITS is challenging due to vehicular network characteristics, such as high mobility and dynamic topology. With that in mind, we propose the FORESAM, a mechanism for resources management and allocation based on a set of FOGs which control vehicular cloud resources in the urban environment. The mechanism is based on a more accurate mathematical model (Multiple Attribute Decision), which aims to assist the allocation decision of resources set that meets the period requested service. The simulation results have shown that the proposed solution allows a higher number of services, reducing the number of locks of services with its accuracy. Furthermore, its resource allocation is more balanced the provided a smaller amount of discarded services.

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