… Abstract—Heterogeneous cellular networks can offload the … technique in the next-generation
wireless network. Due to the non… In this paper, a reinforcement learning (RL) approach is pro…

… -enabled Heterogeneous Cellular Networks (HCNs) have been a promising technology for
satisfying the growing demands of smart mobile devices in fifth-generation mobile networks. …

… We consider time-slotted heterogeneous wireless networks in which different radio nodes
transmit packets to an access point (AP) via a shared wireless channel, as illustrated in Fig. 2. …

… of the macrocells in a heterogeneous cellular network (HetNet). … Utilizing a single agent
reinforcement learning (RL) … , which employs a deep neural network to learn users locations. This …

… In this regard, here we present a novel deep reinforcement learning algorithm, first for …
priority network. The algorithm is then further enhanced to accommodate heterogeneous MTCDs …

… deep reinforcement learning for RRAM in wireless networks, we included the following
terms during the search stage along with ”AND/OR” combinations of them; ”deep reinforcement …

… Asheralieva [31] proposed a novel Bayesian RL framework to address distributed resource
sharing problem in heterogeneous cellular networks. The literature [32] proposed a deep …

… cause radio access network (RAN) congestions. Heterogeneous cellular networks (HetNets) …
In particular, we exploit a deep reinforcement learning(DRL)-based method to train the …

… network traces to model more stochastic, time-variant dynamics inherent to wireless networks.
… In this paper, we analyze weeks of historical network data across heterogeneous cells to …

… tool to manage the traffic in heterogeneous cellular network deployments, since it allows a …
the use of a deep reinforcement learning solution based on a Deep Q-Network (DQN) in order …