… In Mobile-PON proposal, the authors propose to dynamically or statically map the Long-Term
Evolution (LTE) radio resource blocks (RBs) into PON fronthaul transport blocks (TBs) to …

… a machine learning paradigm called reinforcement learning (RL… agent reinforcement learning
(MARL) based on Q-learning was … Cognitive radio operation: The SDR maps the baseband …

… 2) DRL-Based Adaptive Beam Training Algorithm: To process a large and continuous state
space, ie, the vectors ct and dt, we use a DNN to approximate the mapping from each state …

… transfer reinforcement learning (DTRL) scheme for joint radio … Q-valuebased deep transfer
reinforcement learning (QDTRL) … Consequently, we define two different mapping functions to …

… with severe pilot contamination by learning the mapping between users’ location pattern and
… A combination of RL and radio service maps is used in [274] to switches off BSs effectively …

Fog radio access networks (F-RANs) are seen as potential … of artificial intelligence, a deep
reinforcement learning (DRL)-… resource managed includes both radio resource and computing …

… Abstract—In this paper, we study radio and energy resource … framework based on deep
reinforcement learning. The proposed … Under the power constraints, a distributed radio resource …

… approaches for emerging issues including edge caching and computing, multiple radio …
We denote π as a “policy” which is a mapping from a state to an action. The goal of an MDP …

… maps as the output. The second Conv layer consists of F2 filters each of size N2 ×N2, stride
n2, and F2 feature maps … with reinforcement learning in cooperative cognitive radio networks …

… To address this, we propose a deep Q-learning approach for enabling reliable UWB
communication, maximizing packet reception rate (PRR) and minimizing energy consumption. Deep …