Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression, and Challenges

Operation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm

: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...

Energy efficient radio resource management strategies for green radio

Mobile communication industries are increasingly contributing to the worldwide energy consumption and CO2 emission. This paper addresses a number of key radio resource management strategies across PHY and MAC layers for reducing base station energy consumption, as measured by a “Joules per bit” metric. These strategies including power efficient link adaptation, exploitation of multi-user divers...

Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling

Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years. The incoming jobs require different CPU and memory units, and span different number of time slots. The traditional solution is to design efficient heuristic algorithms with performance guarantee under certain assumptions. In this paper, we improve a recently proposed ...

Deep Reinforcement Learning for Resource Allocation in V2V Communications

In this article, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communication systems based on deep reinforcement learning. Each V2V link is considered as an agent, making its own decisions to find optimal sub-band and power level for transmission. Since the proposed method is decentralized, the global information is not required for each agent to make its...

Architecture and Protocol Support for Radio Resource Management (RRM)