DeepAI AI Chat
Log In Sign Up

Centralized Distributed Deep Reinforcement Learning Methods for Downlink Sum-Rate Optimization

by   Ahmad Ali Khan, et al.

For a multi-cell, multi-user, cellular network downlink sum-rate maximization through power allocation is a nonconvex and NP-hard optimization problem. In this paper, we present an effective approach to solving this problem through single- and multi-agent actor-critic deep reinforcement learning (DRL). Specifically, we use finite-horizon trust region optimization. Through extensive simulations, we show that we can simultaneously achieve higher spectral efficiency than state-of-the-art optimization algorithms like weighted minimum mean-squared error (WMMSE) and fractional programming (FP), while offering execution times more than two orders of magnitude faster than these approaches. Additionally, the proposed trust region methods demonstrate superior performance and convergence properties than the Advantage Actor-Critic (A2C) DRL algorithm. In contrast to prior approaches, the proposed decentralized DRL approaches allow for distributed optimization with limited CSI and controllable information exchange between BSs while offering competitive performance and reduced training times.


page 27

page 28

page 30


One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control in Active Distribution Networks

A one-step two-critic deep reinforcement learning (OSTC-DRL) approach fo...

Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches

The model-based power allocation algorithm has been investigated for dec...

Joint Power Allocation and Beamformer for mmW-NOMA Downlink Systems by Deep Reinforcement Learning

The high demand for data rate in the next generation of wireless communi...

Multi-Agent Deep Reinforcement Learning in Vehicular OCC

Optical camera communications (OCC) has emerged as a key enabling techno...

Multi-Agent Deep Reinforcement Learning based Spectrum Allocation for D2D Underlay Communications

Device-to-device (D2D) communication underlay cellular networks is a pro...

A Multi-Agent Deep Reinforcement Learning based Spectrum Allocation Framework for D2D Communications

Device-to-device (D2D) communication has been recognized as a promising ...

Join Query Optimization with Deep Reinforcement Learning Algorithms

Join query optimization is a complex task and is central to the performa...