Cooperative relaying is a promising technology that can improve the spectral and energy efficiency of cellular networks. However, the deployed relays consume a lot of energy and system resources. To improve the energy...Cooperative relaying is a promising technology that can improve the spectral and energy efficiency of cellular networks. However, the deployed relays consume a lot of energy and system resources. To improve the energy efficiency of the relay-assisted cellular networks, this paper considers the use of energy harvesting(EH) on relay nodes. A random sleeping strategy is also introduced in macro base stations(MBS) as a possible method to reduce energy consumption. In this paper, an analytical model is proposed to investigate the energy efficiency of cellular networks with EH relays and sleep mode strategy. Numerical results confirm a significant energy efficiency gain of the proposed networks comparing to the cellular networks with non-EH relays and MBSs without sleep mode strategy. The effects of the density and transmit power of MBSs on energy efficiency are also given through simulations.展开更多
In this paper,an efficient resource allocation scheme based on deep reinforcement learning( DRL) for nonorthogonal multiple access( NOMA)-based vehicle platooning cellular vehicle-to-everything( C-V2X) networks was pr...In this paper,an efficient resource allocation scheme based on deep reinforcement learning( DRL) for nonorthogonal multiple access( NOMA)-based vehicle platooning cellular vehicle-to-everything( C-V2X) networks was proposed. Based on the DRL algorithm,a multi-objective optimization problem is formulated to minimize the age of information( AoI) and improve energy efficiency( EE) and transmission efficiency of cooperative awareness message( CAM). Deep Q-network( DQN) and the twin delayed deep deterministic policy gradient( TD3)algorithms are constructed to achieve the optimization. To enhance training efficiency and model generalization,the traditional sampling method of the experience replay buffer is abandoned,and a dual three-layer network based on a neural network experience replay mechanism is proposed. Based on the proposed DRL algorithm,reward function,AoI,EE,and transmission efficiency of CAM are investigated. The results demonstrate that the proposed DRL algorithm outperforms the existing algorithms.展开更多
基金supported by National Basic Research Program of China ( No.2012CB316002 )China’s 863 Project (No.2014AA01A703)+2 种基金National Major Project (No.2014ZX03003002-002)Program for New Century Excellent Talents in University (NCET-13-0321)Tsinghua University Initiative Scientific Research Program (No.2011THZ02-2.)
文摘Cooperative relaying is a promising technology that can improve the spectral and energy efficiency of cellular networks. However, the deployed relays consume a lot of energy and system resources. To improve the energy efficiency of the relay-assisted cellular networks, this paper considers the use of energy harvesting(EH) on relay nodes. A random sleeping strategy is also introduced in macro base stations(MBS) as a possible method to reduce energy consumption. In this paper, an analytical model is proposed to investigate the energy efficiency of cellular networks with EH relays and sleep mode strategy. Numerical results confirm a significant energy efficiency gain of the proposed networks comparing to the cellular networks with non-EH relays and MBSs without sleep mode strategy. The effects of the density and transmit power of MBSs on energy efficiency are also given through simulations.
基金supported by the National Natural Science Foundation of China (62001166)the Provincial Science and Technology Program of China (2023KFKT002)the Natural Science Foundation of Hebei Province of China(F2024201053)。
文摘In this paper,an efficient resource allocation scheme based on deep reinforcement learning( DRL) for nonorthogonal multiple access( NOMA)-based vehicle platooning cellular vehicle-to-everything( C-V2X) networks was proposed. Based on the DRL algorithm,a multi-objective optimization problem is formulated to minimize the age of information( AoI) and improve energy efficiency( EE) and transmission efficiency of cooperative awareness message( CAM). Deep Q-network( DQN) and the twin delayed deep deterministic policy gradient( TD3)algorithms are constructed to achieve the optimization. To enhance training efficiency and model generalization,the traditional sampling method of the experience replay buffer is abandoned,and a dual three-layer network based on a neural network experience replay mechanism is proposed. Based on the proposed DRL algorithm,reward function,AoI,EE,and transmission efficiency of CAM are investigated. The results demonstrate that the proposed DRL algorithm outperforms the existing algorithms.
