Pdf Deep Reinforcement Learning Based Power Allocation For Minimizing Aoi And Energy

By switzerlandersing On Sep 13, 2025

Deep Reinforcement Learning Based Dynamic Resource Allocation In 5G Ultra-Dense Networks | PDF ...

Deep Reinforcement Learning Based Dynamic Resource Allocation In 5G Ultra-Dense Networks | PDF ... View a pdf of the paper titled deep reinforcement learning based power allocation for minimizing aoi and energy consumption in mimo noma iot systems, by hongbiao zhu and 5 other authors. Optimizing the sample collection commands and power allocation is essential for minimizing both aoi and energy consumption in mimo noma iot systems. in this paper, we propose the optimal.

Table 1 From Deep Reinforcement Learning Based Power Minimization For RIS-assisted MISO-OFDM ...

Table 1 From Deep Reinforcement Learning Based Power Minimization For RIS-assisted MISO-OFDM ... To minimize the time average aoi, we propose an energy aware aoi (ea aoi) algorithm that combines a deep reinforcement learning (drl) based scheduling approach with a low complexity power allocation scheme. The optimal power allocation to minimize the aoi and energy consumption of mimo noma iot system based on deep reinforcement learning (drl) is proposed and extensive simulations are carried out to demonstrate the superiority of the optimal power allocations. In this paper, we investigate a multi agent drl based power control algorithm for minimizing the average age of information (aoi) in the non orthogonal multiple access (noma) enabled istn. Optimizing the sample collection commands and power allocation is essential for minimizing both aoi and energy consumption in mimo noma iot systems. in this paper, we propose the optimal power allocation to achieve it based on deep reinforcement learning (drl).

Deep Reinforcement Learning-based Power Control And Bandwidth Allocation Policy For Weighted ...

Deep Reinforcement Learning-based Power Control And Bandwidth Allocation Policy For Weighted ... In this paper, we investigate a multi agent drl based power control algorithm for minimizing the average age of information (aoi) in the non orthogonal multiple access (noma) enabled istn. Optimizing the sample collection commands and power allocation is essential for minimizing both aoi and energy consumption in mimo noma iot systems. in this paper, we propose the optimal power allocation to achieve it based on deep reinforcement learning (drl). A novel deep reinforcement learning (drl) approach is developed, incorporating an improved african bison optimization (iabo) algorithm to enhance system efficiency. We design the drl framework including the state, action and reward function, then adopt the ddpg algorithm to obtain the optimal power allocation to minimize the aoi and energy consumption of the mimo noma iot system. In this paper, we propose the optimal power allocation to minimize the aoi and energy consumption of mimo noma iot system based on deep reinforcement learning (drl). This paper studies the problem of vehicle data packet scheduling and power allocation for aoi minimization in a manhattan grid vehicle to infrastructure (v2i) network. the challenge of the problem originates from the dynamic wireless environment and different aoi requirements of vehicles.

Deep Reinforcement Learning For Resource Allocation In Blockchain-based Federated Learning | PDF ...

Deep Reinforcement Learning For Resource Allocation In Blockchain-based Federated Learning | PDF ... A novel deep reinforcement learning (drl) approach is developed, incorporating an improved african bison optimization (iabo) algorithm to enhance system efficiency. We design the drl framework including the state, action and reward function, then adopt the ddpg algorithm to obtain the optimal power allocation to minimize the aoi and energy consumption of the mimo noma iot system. In this paper, we propose the optimal power allocation to minimize the aoi and energy consumption of mimo noma iot system based on deep reinforcement learning (drl). This paper studies the problem of vehicle data packet scheduling and power allocation for aoi minimization in a manhattan grid vehicle to infrastructure (v2i) network. the challenge of the problem originates from the dynamic wireless environment and different aoi requirements of vehicles.