scholarly journals Deep Reinforcement Learning for Channel Selection and Power Allocation in D2D Communications

2021 ◽  
Vol 2082 (1) ◽  
pp. 012003
Author(s):  
Jun Zhou
Keyword(s):  
Reinforcement Learning ◽  
Power Allocation ◽  
Dynamic Environment ◽  
Channel Selection ◽  
Spectrum Efficiency ◽  
D2d Communication ◽  
Maximization Problem ◽  
Multi Agent ◽  
Sum Rate ◽  
Joint Channel

Abstract Device-to-device (D2D) communication is regarded as a key technical component of the fifth-generation (5G), D2D communication usually reuses spectrum resources with cellular users (CUs). To mitigate interference to cellular links and improve spectrum efficiency, this paper investigates a sum-rate maximization problem in the underlay of D2D communication. Particularly, a joint channel selection and power allocation framework based on multi-agent deep reinforcement learning is proposed, named Double Deep Q-Network (DDQN). It can adeptly select the channel and allocate power in a dynamic environment. The proposed scheme only requires local information and some outdated nonlocal information, which reduces signaling overheads significantly. Simulation results show that the proposed scheme can improve the D2D sum rate and ensure quality-of-service (QoS) of CUs compared with other benchmarks.

scholarly journals Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 100480-100490 ◽  
Author(s):  
Khoi Khac Nguyen ◽  
Trung Q. Duong ◽  
Ngo Anh Vien ◽  
Nhien-An Le-Khac ◽  
Minh-Nghia Nguyen
Keyword(s):  
Reinforcement Learning ◽  
Power Allocation ◽  
Energy Efficient ◽  
D2d Communication ◽  
Learning Approach ◽  
Multi Agent ◽  
Efficient Power ◽  
Cooperative Energy

scholarly journals Power Allocation and Energy Cooperation for UAV-Enabled MmWave Networks: A Multi-Agent Deep Reinforcement Learning Approach

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 270
Author(s):  
Mari Carmen Domingo
Keyword(s):  
Renewable Energy ◽  
Reinforcement Learning ◽  
Energy Harvesting ◽  
Power Allocation ◽  
Cellular Networks ◽  
High Energy ◽  
Propagation Loss ◽  
Throughput Maximization ◽  
Energy Cooperation ◽  
Multi Agent

Unmanned Aerial Vehicle (UAV)-assisted cellular networks over the millimeter-wave (mmWave) frequency band can meet the requirements of a high data rate and flexible coverage in next-generation communication networks. However, higher propagation loss and the use of a large number of antennas in mmWave networks give rise to high energy consumption and UAVs are constrained by their low-capacity onboard battery. Energy harvesting (EH) is a viable solution to reduce the energy cost of UAV-enabled mmWave networks. However, the random nature of renewable energy makes it challenging to maintain robust connectivity in UAV-assisted terrestrial cellular networks. Energy cooperation allows UAVs to send their excessive energy to other UAVs with reduced energy. In this paper, we propose a power allocation algorithm based on energy harvesting and energy cooperation to maximize the throughput of a UAV-assisted mmWave cellular network. Since there is channel-state uncertainty and the amount of harvested energy can be treated as a stochastic process, we propose an optimal multi-agent deep reinforcement learning algorithm (DRL) named Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to solve the renewable energy resource allocation problem for throughput maximization. The simulation results show that the proposed algorithm outperforms the Random Power (RP), Maximal Power (MP) and value-based Deep Q-Learning (DQL) algorithms in terms of network throughput.

scholarly journals Reinforcement Learning-Based Joint User Pairing and Power Allocation in MIMO-NOMA Systems

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7094
Author(s):  
Jaehee Lee ◽  
Jaewoo So
Keyword(s):  
Reinforcement Learning ◽  
Computational Complexity ◽  
Power Allocation ◽  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Spectrum Efficiency ◽  
Q Learning ◽  
User Pairing ◽  
Input Multiple Output ◽  
Radio Resources

In this paper, we consider a multiple-input multiple-output (MIMO)—non-orthogonal multiple access (NOMA) system with reinforcement learning (RL). NOMA, which is a technique for increasing the spectrum efficiency, has been extensively studied in fifth-generation (5G) wireless communication systems. The application of MIMO to NOMA can result in an even higher spectral efficiency. Moreover, user pairing and power allocation problem are important techniques in NOMA. However, NOMA has a fundamental limitation of the high computational complexity due to rapidly changing radio channels. This limitation makes it difficult to utilize the characteristics of the channel and allocate radio resources efficiently. To reduce the computational complexity, we propose an RL-based joint user pairing and power allocation scheme. By applying Q-learning, we are able to perform user pairing and power allocation simultaneously, which reduces the computational complexity. The simulation results show that the proposed scheme achieves a sum rate similar to that achieved with the exhaustive search (ES).

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