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

2021 ◽  
Author(s):  
Enchang Sun ◽  
Hanxing Qu ◽  
Yongyi Yuan ◽  
Meng Li ◽  
Zhuwei Wang ◽  
...  
Keyword(s):  
Power Control ◽  
Channel Allocation ◽  
D2d Communication ◽  
Control Scheme ◽  
Joint Channel

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 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 Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1606
Author(s):  
Donghyeon Kim ◽  
In-Ho Lee
Keyword(s):  
Deep Learning ◽  
Power Control ◽  
Spectral Efficiency ◽  
Communication Systems ◽  
Data Rate ◽  
D2d Communication ◽  
Transmit Power ◽  
Transmit Power Control ◽  
Control Scheme ◽  
Control Schemes

The proximity-based device-to-device (D2D) communication allows for internet of things, public safety, and data offloading services. Because of these advantages, D2D communication has been applied to wireless communication networks. In wireless networks using D2D communication, there are challenging problems of the data rate shortage and coverage limitation due to co-channel interference in the proximity communication. To resolve the problems, transmit power control schemes that are based on deep learning have been presented in network-assisted D2D communication systems. The power control schemes have focused on enhancing spectral efficiency and energy efficiency in the presence of interference. However, the data-rate fairness performance may be a key performance metric in D2D communications, because devices in proximity can expect fair quality of service in the system. Hence, in this paper, a transmit power control scheme using a deep-learning algorithm based on convolutional neural network (CNN) is proposed to consider the data-rate fairness performance in network-assisted D2D communication systems, where the wireless channels are modelled by path loss and Nakagami fading. In the proposed scheme, the batch normalization (BN) scheme is introduced in order to further enhance the spectral efficiency of the conventional deep-learning transmit power control scheme. In addition, a loss function for the deep-learning optimization is defined in order to consider both the data-rate fairness and spectral efficiency. Through simulation, we show that the proposed scheme can achieve extremely high fairness performance while improving the spectral efficiency of the conventional schemes. It is also shown that the improvement in the fairness and spectral efficiency is achieved for different Nakagami fading conditions and sizes of area containing the devices.

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.

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