Nov 2, 2023 · In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the ...

Nov 6, 2023 · In [5], authors suggested a dynamic reinforcement learning scheme for power allocation in order to jointly maximize the sum rate and the spectral efficiency in ...

Apr 17, 2024 · This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping ...

Oct 18, 2023 · To overcome these limitations, we propose a multi-agent deep reinforcement learning (MADRL) based power control scheme for the HetNet, where each access point ...

Jun 24, 2024 · We introduce the unsupervised DNN-based method for JSPA to handle the simplified problem. The presented scheme yields improved SE and outage performance.

Feb 7, 2024 · Abstract—In this article, we propose a secrecy-aware energy- efficient scheme for a two-tier heterogeneous network (HetNet),.

Feb 15, 2024 · The main purpose of the RL is to find an optimal policy 𝜋 that has a maximum cumulative reward. The policy function is mapping each state to the bast actions ...

May 15, 2024 · Deep reinforcement learning (DRL) methods have emerged as a feasible solution for addressing the power resource allocation problem in ultra-dense small-cell ...

May 6, 2024 · We establish an optimization problem for joint air platform (AP) flight path selection, ground power facility (GPF) association, and power control.

May 29, 2024 · In this paper, we propose a distributed task offloading and wireless resource management framework that optimizes task offloading, local computation frequency ...