in terms of energy consumption, communication coverage, coverage fairness, and network ... training rate, energy efficiency, geographic fairness, and data ...

Since it is highly inefficient or even impractical to replace or recharge batteries in many source nodes, energy harvesting so- lutions have been considered to ...

Mar 1, 2020 · Yin, “Software-defined networks with mobile edge computing and caching for smart cities: A big data deep reinforcement learning approach,” IEEE ...

A deep reinforcement learning (DRL) algorithm is proposed that can learn the age-optimal policy in a computationally-efficient manner and characterize the ...

Abstract—We consider an IoT sensing network with multiple users, multiple energy harvesting sensors, and a wireless edge node acting as a gateway between ...

The next-generation network demands mobile devices (e.g., smartphones and Internet-of-Things devices) to generate zil- lions of bytes of data and accomplish ...

0 < η < 1 is the learning rate. The policy is chosen as the one that ... multi-user cellular networks: Deep reinforcement learning approaches,”. arXiv ...

Simulation results show that the proposed algorithm can converge 25% to 50% faster than a policy gradient baseline algorithm that optimizes each device's ...

Deep Reinforcement Learning for IoT Networks: Age of Information and Energy Cost Tradeoff. 442 0 0.0 ( 0 ). تحميل البحث استخدام كمرجع. نشر من قبل Xiongwei Wu.

Age of information, energy harvesting, hybrid automatic repeat request (HARQ), Markov decision process, reinforcement learning, policy gradient, deep Q-network ...