… We consider a wireless network containing N correlated channels whose states can be
either vacant (0) or occupied (1). The joint state transition of these channels follows a 2N -states …

… to enhance the capacity and coverage of next-generation wireless networks. A typical
example is that Bluetooth, ZigBee, and wireless local area network (WLAN) [16] all work in the …

… the use of Deep Reinforcement Learning, in particular, Deep Q learning, … deep learning with
Q learning, Deep Q learning or Deep Q … of DQN in the field of dynamic multi-channel access. …

… a dynamic access policy based on deep reinforcement learning algorithm to optimally …
multichannel opportunistic access: Structure, optimality, and performance,” IEEE Trans. Wireless …

… To address this problem, a deep reinforcement learningbased dynamic … Deep reinforcement
learning for dynamic spectrum sensing and aggregation in multi-channel wireless networks…

… His personal research expertise focuses on deep reinforcement learning and wireless
network management. He is a member of the Young Academy of Belgium. He was a recipient of …

… utilization in heterogeneous wireless networks (HetNets), wherein different radio … multi-channel
HetNet where multiple radio networks transmit packets to an AP using multiple wireless …

… to share a common wireless spectrum and each network is unaware of … deep reinforcement
learning (DRL) based MAC protocol for a particular network, and the objective of this network …

… Wireless sensor networks (WSNs) have become an important component in the Internet of
things (IoT) field. In WSNs, multi-channel … developed for the simulation of wireless networks. …

… the deep reinforcement learning (DRL) approach to provide a model-free access method,
where the nodes don’t have a prior knowledge of the wireless networks or … of the multi-channel …