scholarly journals BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 932
Author(s):  
Kaiwen Xia ◽  
Jing Feng ◽  
Chao Yan ◽  
Chaofan Duan
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Reinforcement Learning ◽  
Resource Utilization ◽  
Communication System ◽  
Transmission Loss ◽  
Short Message ◽  
Resource Allocation Algorithm ◽  
Processing Resources

The comprehensively completed BDS-3 short-message communication system, known as the short-message satellite communication system (SMSCS), will be widely used in traditional blind communication areas in the future. However, short-message processing resources for short-message satellites are relatively scarce. To improve the resource utilization of satellite systems and ensure the service quality of the short-message terminal is adequate, it is necessary to allocate and schedule short-message satellite processing resources in a multi-satellite coverage area. In order to solve the above problems, a short-message satellite resource allocation algorithm based on deep reinforcement learning (DRL-SRA) is proposed. First of all, using the characteristics of the SMSCS, a multi-objective joint optimization satellite resource allocation model is established to reduce short-message terminal path transmission loss, and achieve satellite load balancing and an adequate quality of service. Then, the number of input data dimensions is reduced using the region division strategy and a feature extraction network. The continuous spatial state is parameterized with a deep reinforcement learning algorithm based on the deep deterministic policy gradient (DDPG) framework. The simulation results show that the proposed algorithm can reduce the transmission loss of the short-message terminal path, improve the quality of service, and increase the resource utilization efficiency of the short-message satellite system while ensuring an appropriate satellite load balance.

scholarly journals Quality of Service Class Identifier (QCI) radio resource allocation algorithm for LTE downlink

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0210310 ◽  
Author(s):  
Maharazu Mamman ◽  
Zurina Mohd Hanapi ◽  
Azizol Abdullah ◽  
Abdullah Muhammed
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Radio Resource Allocation ◽  
Service Class ◽  
Radio Resource ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm

scholarly journals Energy Efficient Resource Allocation for M2M Devices in 5G

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1830 ◽  
Author(s):  
Anum Ali ◽  
Ghalib A. Shah ◽  
Junaid Arshad
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Threshold Value ◽  
Second Phase ◽  
Resource Allocation Algorithm ◽  
Power Profile ◽  
5G Network ◽  
Efficient Resource ◽  
Energy Efficient Resource Allocation

Resource allocation for machine-type communication (MTC) devices is one of the keys challenges in the 5G network as it affects the lifetime of battery powered devices and also the quality of service of the applications. MTC devices are battery restrained and cannot afford a lot of power consumption due to spectrum usage. In this paper, we propose a novel resource allocation algorithm termed threshold controlled access (TCA) protocol. We propose a novel technique of uplink resource allocation in which the devices make a decision of resource allocation blocks based on their battery status and related application’s power profile that eventually leads to required quality of service (QoS) metric. The first phase of the TCA algorithm selects the number of carriers to be allocated to a certain device for the better lifetime of low power MTC devices. In the second phase, the efficient solution is implemented through inducing a threshold value. A certain value of the threshold is selected through a mapping based on a QoS metric. The threshold enhances the selection of subcarriers for less powered devices, such as small e-health sensors. The algorithm is simulated for the physical layer of the 5G network. Simulation results show that the proposed algorithm is less complex and achieves better performance when compared to existing solutions in the literature.

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.

A quantitative analysis of the Quality of Service of Short Message Service in the Philippines

2010 ◽  
Author(s):  
Marnelli Canlas ◽  
Kristoffer Paolo Cruz ◽  
Ma. Kristle Dimarucut ◽  
Patrick Uyengco ◽  
Gregory Tangonan ◽  
...  
Keyword(s):  
Quality Of Service ◽  
Quantitative Analysis ◽  
Short Message Service ◽  
The Philippines ◽  
Short Message ◽  
Message Service

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.

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