Mobile edge computing (MEC) has been regarded as a promising paradigm to support the compute-intensive and delay-sensitive industrial Internet of things (IIoT) applications. However, the nature of broadcasting in wireless communications may cause that the task offloading security is easy to be threatened from eavesdroppers. Aiming at improving the task offloading security, this article studies the benefit of deploying the emerging reconfigurable intelligent surface (RIS) in MEC-enabled IIoT networks with eavesdroppers, and forms the RIS-aided secure MEC system with time-division multiple access. In addition, we formulate a joint RIS phase shift, power control, local computation rate, and time-slot allocation optimization problem to maximize the weighted sum secrecy computation efficiency (WSSCE) among IIoT devices. To address this intractable problem, we propose a deep reinforcement learning (DRL)-based algorithm, where a deep deterministic policy gradient (DDPG) agent is adopted. Numerical results demonstrate that 1) deploying the RIS can improve the WSSCE performance; 2) the proposed DDPG-based algorithm can obtain higher WSSCE than other baseline methods.

In this paper, we apply reconfigurable intelligent surface (RIS) technique to aid the computation offloading of mobile edge computing (MEC) network and investigate how it can be exploited to reduce computation offloading delay. In order to minimize the long-term computation offloading delay, we formulate an optimization problem, which jointly optimizes the power control, computation offloading volume, the edge computing resource assigned to each user equipment (UE), as well as the RIS phase shift. To tackle this problem, we first convert it into a Markov decision process (MDP), then propose an efficient algorithm based on deep reinforcement learning (DRL), namely deep deterministic policy gradient (DDPG). Numerical results demonstrate that 1) compared to the MEC network without RIS, the RIS aid MEC network can achieve lower delay; 2) the proposed DDPG-based scheme can learn from the RIS aided environment to effectively reduce the computation offloading delay.

Integrating unmanned aerial vehicles (UAVs) into emergency communications is a promising way to accomplish efficient network recovery with the advantages of UAV flexibility. To ensure information forwarding from the disaster area, this article considers an emergency communication scenario where a UAV provides uplink relaying services based on nonorthogonal multiple access (NOMA) for a set of disconnected ground wireless access points (APs) under the urban environment. To maximize the system uplink throughput, the UAV altitude, power control, as well as the bandwidth allocation between the access and backhaul links are jointly optimized. Especially, the constraint for the uplink rate fairness is also considered. Our formulated problem is nonconvex due to the complex uplink co-channel interference under the Line-of-Sight (LoS) probability-based Air-to-Ground (AtG) channel. To tackle this issue, we change our formulated problem into an equivalent form by coping with the information-causality and fairness constraints. Then, a joint altitude and resource allocation (JARA) algorithm is developed, which iteratively solves the altitude optimization subproblem and resource optimization subproblem until convergence. For each subproblem, we further introduce auxiliary variables so that it can be solved by using the successive convex approximation (SCA) method. Finally, two benchmarks are used for the throughput comparison, and simulation results verify that the system uplink throughput of our proposed algorithm is improved through the AtG LoS propagation advantage, uplink power control, as well as the bandwidth allocation between the access and backhaul links.

Multi-access mobile edge computing (MEC) is envisioned as a key enabling technology to support compute-intensive and delay-sensitive applications in railway Internet of Things (RIoT) networks. However, the time-varying channel variations in RIoT scenarios make it challenging to achieve efficient resource allocation. The emerging deep reinforcement learning (DRL) is able to respond to the above-mentioned challenge. In this paper, with the aim of reducing the total computational cost (weighted sum of consumed energy and delay), we investigate the dynamic resource management issue of joint subcarrier assignment, offloading ratio, power allocation and computation resource allocation in multi-access MEC assisted RIoT networks. To address this intractable mixed integer nonlinear programming issue, we put forward a hybrid DRL (HDRL) scheme, which is an integration of deep double Q-learning (DDQN) and deep deterministic policy gradient (DDPG). The HDRL algorithm is capable of learning the advisable strategies for actions including discrete-continuous hybrid variables. In HDRL algorithm, DDQN plays the role of making subcarrier assignment decision, and DDPG plays the role of making offloading ratio, power allocation as well as computation resource allocation decisions. Numerical results demonstrate that HDRL scheme can yield much less computational cost than the existing baselines for multi-access MEC assisted RIoT networks. In addition, the HDRL scheme is close to the near-optimal performance with comparatively low execution time.

Device-to-device (D2D) communication can significantly improve network coverage and spectral efficiency. Meanwhile, non-orthogonal multiple access (NOMA) has recently been integrated with D2D communication to further improve connection density and satisfy explosive data rate requirements of end users. Considering quality of experience (QoE) has become an important indicator from the user perspective, in this paper, we study the QoE-driven resource allocation problem in a device-to-device (D2D) underlaying NOMA cellular network coexisting with D2D pairs and NOMA-based cellular users (CUs). Our target is to maximize the sum mean opinion scores (MOSs) of all users while guaranteeing the minimum QoE requirement of each CU and D2D pair, by jointly optimizing subchannel assignment and power allocation at CUs and D2D pairs. Since this problem is mixed-integer and non-convex, we first transform it into an equivalent yet more tractable form. Then, a two-stage iterative algorithm based on the alternating optimization framework and constrained concave-convex procedure technique is proposed to optimize subchannel assignment and power allocation alternately. Simulation results show that the proposed scheme outperforms the orthogonal multiple access solution and three NOMA based benchmark schemes in terms of QoE performance.

Achieving high data transmission rate in the highspeed railway (HSR) communications has always been the significant yet challenging goal. Unfortunately, through an enormous number of measurements, current spectrum efficiency in HSR scenarios is still far from satisfactory to meet the data rate requirements for HSR passengers. To tackle this problem, we investigate the power allocation in the extremely time-varying millimeter-wave (mmWave) HSR systems with hybrid beamforming in the paper. With the purpose of maximizing the achievable sum rate, we first formulate a joint hybrid beamforming and power allocation problem in the mmWave HSR channel model. Then we obtain the solution of beamforming design. Finally, for the sake of achieving dynamic power control, a low-complexity iterative optimization approach is proposed. The numerical results indicate that the proposed iterative algorithm is capable of achieving a significantly higher spectral efficiency compared with the existing schemes.