scholarly journals A Two-Layer, Energy-Efficient Approach for Joint Power Control and Uplink–Downlink Channel Allocation in D2D Communication

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3285 ◽  
Author(s):  
Li Zhou ◽  
Yucheng Wu ◽  
Haifei Yu
Keyword(s):  
Power Control ◽  
Polynomial Time ◽  
Performance Indicator ◽  
Channel Allocation ◽  
D2d Communication ◽  
Mixed Integer ◽  
Lambert W Function ◽  
Allocation Scheme ◽  
Joint Power ◽  
Battery Capacity

Energy efficiency (EE) is a critical performance indicator for the device-to-device (D2D) communication underlaying cellular networks due to limited battery capacity and serious interference between user equipment. In this study, we proposed a power control and channel allocation scheme for the EE maximization of the D2D pairs, while jointly reusing uplink–downlink resources and guaranteeing the cellular users’ (CUs) quality of service (QoS). The formulated problem was a mixed-integer nonlinear programming (MINLP) problem, which is generally an unsolved non-deterministic polynomial-time hardness (NP-hard) problem within polynomial time. To make it tractable to solve, the original problem was divided into two sub-problems: power control and channel allocation. A power control algorithm based on the Lambert W function was proposed to maximize the EE of the individual D2D pair. Assigning either an uplink or downlink resource to reuse, the EE of each D2D pair was calculated using the power control results. A channel allocation scheme based on the Kuhn–Munkres algorithm utilized the EE weights to optimize the overall EE of the D2D pairs. The simulation results verified the theoretical analysis and proved that the proposed algorithm could remarkably improve the EE of D2D pairs while guaranteeing the QoS of the CUs.

scholarly journals A Two-Stage Energy-Efficient Approach for Joint Power Control and Channel Allocation in D2D Communication

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 16940-16951 ◽  
Author(s):  
Sihan Liu ◽  
Yucheng Wu ◽  
Liang Li ◽  
Xiaocui Liu ◽  
Weiyang Xu
Keyword(s):  
Power Control ◽  
Energy Efficient ◽  
Channel Allocation ◽  
D2d Communication ◽  
Two Stage ◽  
Joint Power ◽  
Efficient Approach ◽  
Joint Power Control

scholarly journals Joint Power Control and Channel Allocation for Interference Mitigation Based on Reinforcement Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 177254-177265 ◽  
Author(s):  
Guofeng Zhao ◽  
Yong Li ◽  
Chuan Xu ◽  
Zhenzhen Han ◽  
Yuan Xing ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Power Control ◽  
Interference Mitigation ◽  
Channel Allocation ◽  
Joint Power ◽  
Joint Power Control

Joint power and channel allocation scheme for IEEE 802.11af based smart grid communication network

2019 ◽  
Vol 95 ◽  
pp. 694-712 ◽  
Author(s):  
Sheraz Alam ◽  
Naveed Aqdas ◽  
I.M. Qureshi ◽  
Sajjad A. Ghauri ◽  
Mubashar Sarfraz
Keyword(s):  
Communication Network ◽  
Smart Grid ◽  
Channel Allocation ◽  
Allocation Scheme ◽  
Joint Power ◽  
Smart Grid Communication

scholarly journals A Joint Channel Allocation and Power Control Scheme for D2D Communication in UAV-Based Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Enchang Sun ◽  
Hanxing Qu ◽  
Yongyi Yuan ◽  
Meng Li ◽  
Zhuwei Wang ◽  
...  
Keyword(s):  
Power Control ◽  
Learning Strategy ◽  
Channel Allocation ◽  
Pso Algorithm ◽  
Population Variation ◽  
Base Stations ◽  
D2d Communication ◽  
Adaptive Mutation ◽  
Kmeans Algorithm

With the increasing application of unmanned aerial vehicles (UAVs), UAV-based base stations (BSs) have been widely used. In some situations when there is no ground BSs, such as mountainous areas and isolated islands, or BSs being out of service, like disaster areas, UAV-based networks may be rapidly deployed. In this paper, we propose a framework for UAV deployment, power control, and channel allocation for device-to-device (D2D) users, which is used for the underlying D2D communication in UAV-based networks. Firstly, the number and location of UAVs are iteratively optimized by the particle swarm optimization- (PSO-) Kmeans algorithm. After UAV deployment, this study maximizes the energy efficiency (EE) of D2D pairs while ensuring the quality of service (QoS). To solve this optimization problem, the adaptive mutation salp swarm algorithm (AMSSA) is proposed, which adopts the population variation strategy, the dynamic leader-follower numbers, and position update, as well as Q -learning strategy. Finally, simulation results show that the PSO-Kmeans algorithm can achieve better communication quality of cellular users (CUEs) with fewer UAVs compared with the PSO algorithm. The AMSSA has excellent global searching ability and local mining ability, which is not only superior to other benchmark schemes but also closer to the optimal performance of D2D pairs in terms of EE.

scholarly journals Joint Channel Allocation and Power Control for Uplink NOMA-Assisted Multi-UAV Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shaojie Wen ◽  
Lianbing Deng ◽  
Zengliang Liu
Keyword(s):  
Power Control ◽  
Network Performance ◽  
Channel Allocation ◽  
Integral Form ◽  
Mixed Integer ◽  
Time Interval ◽  
Integer Programming Problem ◽  
Power Control Algorithm ◽  
Initial Power ◽  
Multi Uav

The explosive growth of data leads to that the traditional wireless networks cannot enable various quality of service (QoS) communication for cellular-connected multi-UAV (unmanned aerial vehicle) networks. To overcome this obstacle, we solve the joint optimization problem of channel allocation and power control for uplink NOMA-assisted multi-UAV networks. Firstly, we design a mixed integer nonlinear programming framework, where the channel gains are characterized with integral form in time interval and sorted in nondescending order as the priority index of the decoded signal. In order to propose a feasible algorithm, the initial power levels of UAVs are obtained and integrated into the original problem which is reduced to integer programming problem. Then, the UAVs whose channel gain differences satisfy the constraints will be divided into a group to share the same channel, while the initial power levels of UAVs are adjusted to get a more satisfactory initial solution for power control. Combining the solution of channel allocation and the initial power levels, we solve power control problem with asynchronous update mechanism until the power levels of UAVs remain unchanged. Finally, we propose a channel allocation algorithm and a power control algorithm with the asynchronous optimization mechanism, respectively. Simulation results show that the proposed algorithms can effectively improve the network performance in terms of the aggregated rate.

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