This paper studies the joint computing offloading and resource allocation problem in an MEC network. We formulate the problem as an optimization problem with the objective to maximize the network throughput while satisfying the delay requirements of as many service requests as possible. Meanwhile, we propose a deep-Q-network (DQN) based joint computing offloading and resource allocation (D-CORAL) algorithm to solve the formulated problem. The proposed D-CORAL algorithm attempts to jointly optimize edge node selection, and spectrum resource and computing resource allocation for each service request by learning online to better adapt to a dynamic environment. Simulation results show the proposed algorithm can achieve larger network throughput than two benchmark algorithms.

This paper considers the spectrum resource and computing resource allocation in an mobile edge computing (MEC) network. The problem is formulated as an optimization problem with an objective to maximize the average successful processing ratio of users’ service requests while satisfying users’ service delay requirements. To solve the formulated problem, a federated learning (FL) and deep-Q-network (DQN) based cooperative resource allocation (CoRA) algorithm is proposed to adaptively select optimal offloading nodes and allocate spectrum and computing resources for users’ service requests by training DQNs in a master node and slave nodes in the form of federated learning. Simulation results show that the proposed CoRA algorithm outperforms two benchmark algorithms in terms of the average successful processing ratio.

This paper studies the initial contention window (CW) size adjustment problem in an NR-U and WiFi coexistence network with an NR-U system and a WiFi system. The problem is formulated as an initial CW size optimization problem with an objective to adaptively find an optimal initial CW size such that the throughput of the NR-U system is maximized while the throughput of the WiFi system is guaranteed. To solve the problem, a Deep Recurrent Q-Network (DRQN) based initial CW size optimization algorithm is proposed to adaptively find an optimal CW size by training the main DQN in a DRQN and observing the current status of the coexistence network, including the current CW size, the throughput of the WiFi system, the throughput of the NR-U system, and the number of NR-U users that have data to transmit. Simulation results show that the proposed DRQN-based CW optimization algorithm outperforms a fixed CW mechanism and an adaptive CW mechanism in terms of the throughput of the NR-U system and the fairness of the coexistence network.

This paper studies the power allocation problem in device-to-device (D2D) communications underlaying cellular networks. A Q-learning based distributed power allocation (Q-PA) algorithm is first proposed, which attempts to obtain optimal power allocation through iterative updating of Q-value tables. Based on the Q-PA algorithm, a neural network (NN) based power allocation (N-PA) algorithm is further proposed, in which training data obtained using the Q-PA algorithm are used to train an NN model, and the trained NN model is used to perform power allocation for D2D users. Simulation results show that both the Q-PA algorithm and the N-PA algorithm can improve the system throughput as compared with two traditional algorithms. The N-PA algorithm can significantly reduce the time cost for power allocation at a small expense of the system throughput as compared to the Q-PA algorithm.

Brain-computer interface (BCI) systems can translate the human mind into control commands, which makes it feasible to improve the life quality of physically challenged people. However, in real-life situations, it is still difficult for users to utilize robots to provide basic services with BCI systems. We aimed to propose a BCI-based system with a visual servo module to operate a service robot. We recorded single-channel steady-state visual evoked potentials (SSVEP) as input signals for the BCI system of this study. The visual stimuli for inducing SSVEP were modulated at seven different frequencies with the sampled sinusoidal method. Correspondingly, this SSVEP-based BCI system can generate seven control commands for the operation of the service robot, which can provide three fundamental services: mobility, manipulation, and delivery. The visual servo module was established to reduce the burden of users and accelerate service procedures. To evaluate the performance of this system, subjects were recruited to participate in the experiments. All the participants succeed in operating the robot to provide the basic services. According to the experimental results, this SSVEP-based BCI system that incorporates the visual servo module can be effectively used to operate service robots with reduced number of channels and increased ability to perform multiple tasks.