DeepAir has been published
I am pleased to announce that our paper "DeepAir: A Multi-Agent Deep Reinforcement Learning-Based Scheme for an Unknown User Location Problem" has been published in IEEE Access. In this paper, we focused on the unknown user locations in an infrastructure-less environment in which users cannot connect to any communication device or computation-providing server, which is essential to task offloading in order to achieve the required quality of service (QoS). We investigated this problem thoroughly and proposed a novel deep reinforcement learning (DRL) based scheme, DeepAir. DeepAir uses four main phases including sensing, localization, resource allocation, and multi-access edge computing (MEC) to provide the corresponding QoS requirements for the offloaded tasks without violating the maximum tolerable delay. For more info:
