Mobile edge computing can effectively reduce service latency and improve service quality by offloading computation-intensive tasks to the edges of wireless networks. Due to the characteristic of flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed as assisted edge clouds (ECs) for large-scale sparely-distributed user equipment. Considering the limited computation and energy capacities of UAVs, a collaborative mobile edge computing system with multiple UAVs and multiple ECs is investigated in this paper. The task offloading issue is addressed to minimize the sum of execution delays and energy consumptions by jointly designing the trajectories, computation task allocation, and communication resource management of UAVs. Moreover, to solve the above non-convex optimization problem, a Markov decision process is formulated for the multi-UAV assisted mobile edge computing system. To obtain the joint strategy of trajectory design, task allocation, and power management, a cooperative multi-agent deep reinforcement learning framework is investigated. Considering the high-dimensional continuous action space, the twin delayed deep deterministic policy gradient algorithm is exploited. The evaluation results demonstrate that our multi-UAV multi-EC task offloading method can achieve better performance compared with the other optimization approaches.

Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment costs, which have been considered to be a promising technique in the next-generation wireless network. Due to the non-convex and combinatorial characteristics, it is challenging to obtain an optimal strategy for the joint user association and resource allocation issue. In this paper, a reinforcement learning (RL) approach is proposed to achieve the maximum long-term overall network utility while guaranteeing the quality of service requirements of user equipments (UEs) in the downlink of heterogeneous cellular networks. A distributed optimization method based on multi-agent RL is developed. Moreover, to solve the computationally expensive problem with the large action space, multi-agent deep RL method is proposed. Specifically, the state, action and reward function are defined for UEs, and dueling double deep Q-network (D3QN) strategy is introduced to obtain the nearly optimal policy. Through message passing, the distributed UEs can obtain the global state space with a small communication overhead. With the double-Q strategy and dueling architecture, D3QN can rapidly converge to a subgame perfect Nash equilibrium. Simulation results demonstrate that D3QN achieves the better performance than other RL approaches in solving large-scale learning problems.

With the purpose of further mastering and grasping the course of speech signal processing, a novel Android-based, mobile-assisted educational platform (AEPS) is proposed in this paper. The goal of this work was to design AEPS as an educational signal-processing auxiliary system by simulating signal analysis methods commonly used in speech signal processing and bridging the gap for transition from undergraduate study to industry practice or academic research. The educational platform is presented in a highly intuitive, easy-to-interpret and strongly maneuverable graphical user interface. It also has the characteristics of high portability, strong affordability, and easy adoptability for application extension and popularization. Through adequate intuitive user interface, rich visual information, and extensive hands-on experiences, it greatly facilitates students in authentic, interactive, and creative learning. This paper details a subjective evaluation of AEPS’s effectiveness as an educational tool. The result of the experiences shows that the proposed platform not only promotes the students’ learning interest and practical ability but also consolidates their understanding and impression of theoretical concepts.

Cooperative communication is a promising technique to mitigate channel impairment and improve spectral efficiency. Due to the mobility of relay nodes (RNs) and the unfavourable effects of wireless channels, channel conditions may change as time passes. Therefore, how to design proper long-term incentive mechanisms for RNs in such dynamic communication environments is an essential issue. This paper investigates the contract incentive method under asymmetric information scenario, where the relay channel condition and cost are the RNs' private information. To capture the dynamic characteristic of the RNs' cooperative information during the long-term cooperation, two dynamic contract mechanisms are introduced into long-term relay incentive with different RNs' relay information structures. The optimal contract is derived to maximize the source's expected utility for both independent asymmetric information and correlated asymmetric information. Numerical results are presented to evaluate the effectiveness of the proposed dynamic contract-based incentive mechanism.

Cellular traffic prediction plays an important role in network management and resource utilization. However, due to the high nonlinearity and dynamic spatial-temporal correlation, it is challenging to obtain the traffic prediction accurately. In this letter, a spatial-temporal attention-convolution network is proposed to predict the citywide cellular traffic. Considering the temporal correlation of the cellular traffic, the traffic data is modeled by the hourly, daily and weekly traffic components independently. In each traffic component, the spatial-temporal attention module is designed to capture the dynamic spatial-temporal correlation of cellular traffic; the spatial-temporal convolution module utilizes the graph convolution and standard convolution to obtain the spatial and temporal features simultaneously. By considering the external factors, the prediction of final mobile traffic is obtained. Experiment results indicate that the proposed approach outperforms the existing prediction methods on the real-world cellular traffic datasets.

Unmanned aerial vehicle (UAV) is regarded as an effective technology in future wireless networks. However, due to the non-convexity feature of joint trajectory design and power allocation (JTDPA) issue, it is challenging to attain the optimal joint policy in multi-UAV networks. In this paper, a multi-agent deep reinforcement learning-based approach is presented to achieve the maximum long-term network utility while satisfying the user equipments’ quality of service requirements. Moreover, considering that the utility of each UAV is determined based on the network environment and other UAVs’ actions, the JTDPA problem is modeled as a stochastic game. Due to the high computational complexity caused by the continuous action space and large state space, a multi-agent deep deterministic policy gradient method is proposed to obtain the optimal policy for the JTDPA issue. Numerical results indicate that our method can obtain the higher network utility and system capacity than other optimization methods in multi-UAV networks with lower computational complexity.