<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giannopoulos, Anastasios</style></author><author><style face="normal" font="default" size="100%">Spantideas, Sotirios</style></author><author><style face="normal" font="default" size="100%">Kapsalis, Nikolaos</style></author><author><style face="normal" font="default" size="100%">Panagiotis Karkazis</style></author><author><style face="normal" font="default" size="100%">Trakadas, Panagiotis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep reinforcement learning for energy-efficient multi-channel transmissions in 5G cognitive hetnets: Centralized, decentralized and transfer learning based solutions</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">129358–129374</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>