Environment-dependent applications, such as high-definition maps, that rely on environmental information as input data deserve further research to reduce the amount of transmission data. The cooperation of crowndsensing and local preprocessing is a potential solution to sense and preprocess the environmental data in real-time. However, the sensing and computing capabilities of different participants are heterogeneous, which may lead to significant differences in the total amount of transmission data (TATD). Therefore, a novel communication-efficient participant selection strategy is proposed, incorporating heterogeneous sensing, communication, and computing resources. The matching process between target sensing subregions and participants, along with preprocessing task allocation, is jointly optimized to minimize the TATD. A heuristic algorithm with low complexity is developed to solve the optimization problem. Performance evaluations show that the proposed mechanism can reduce the TATD by up to 62.8% compared with benchmark mechanisms.

Collaborative mobile edge computing enables resource-constrained edge facilities to work cooperatively for computation-intensive tasks. However, as the number of tasks demanded by various applications increases, resource competition is inevitable in edge facilities. Existing works tackle the resource competition problem with a first come first served (FCFS) scheme, which is blind to different delay requirements among tasks. This may result in tasks with higher delay requirements waiting a long time for service, thereby reducing overall service quality. This paper proposes a prioritized queuing scheme with task dependency (PQTD), which allows high-prioritized sub-tasks with higher delay requirements to jump into the queue ahead of low-prioritized sub-tasks with lower delay requirements. To describe the complicated delay change caused by queue-jumping, a joint DAG-queue delay (JDQD) model is proposed, which analyzes the chain reaction of delay changes caused by the processing queue on the server and the task dependency. With JDQD, a multi-task assignment optimization problem is formulated to maximize the average satisfaction degree (AvgSatD), which is defined according to the priorities of the tasks and their delay requirements. Then, a tree-based algorithm is proposed to solve the NP-hard optimization problem, i.e., Monte Carlo Tree Search (MCTS). Simulation results demonstrate the effectiveness of the PQTD queuing scheme and tree search mechanism of MCTS. Overall, PQTD + MCTS can increase AvgSatD by at least 45.8% with an acceptable complexity.

Federated Learning (FL) empowers wireless intelligent applications, by leveraging distributed data of edge clients for training without compromising privacy. Client selection is inevitable in FL, since clients have diversified learning capabilities arising from heterogeneous computing and communication resources. Existing methods like fair-selection and dropping-straggler are either inefficient or unfair (resulting in a less effective trained model). Therefore, we propose FedAoI, an Age-of-Information (AoI) based client selection policy. FedAoI ensures fairness by allowing all clients, including stragglers, to submit their model updates while maintaining high training efficiency by keeping round completion times short. This trade-off is achieved by minimizing Peak-AoI (PAoI), the interval between a client's consecutive participations. An optimization problem is formulated by minimizing the Expected-Weighted-Sum-of-PAoI. This NP-hard problem is addressed with a two-step sub-optimal algorithm, PriorS. It first calculates client priority in a round using Lyapunov optimization and then selects the highest-priority clients through G-FPFC (Greedy minimization of the round weighted-sum-of-PAoI with First-Priority-First-Considered). Simulation results demonstrate that, compared to fair-selection, FedAoI improves average efficiency by 83.8% and achieves an average model accuracy of 97.3% (or at the cost of averaging 2.7% degradation in model accuracy). Compared to dropping-straggler, FedAoI reduces the average model accuracy degradation from 9.5% to 2.7%.

Due to the high mobility of vehicles, service migration is inevitable in vehicular edge computing (VEC) networks. Frequent service migrations incur prohibitive migration cost including the computing cost (e.g., increased computing delay) and communication cost (e.g., occupied backhaul bandwidth). Yet existing service migration schemes are usually designed without considering the impact of the computing cost. This paper considers the impact of computing and communication cost jointly, and proposes a computing and communication cost-aware service migration scheme for VEC networks (i.e., CA-migration). Taking the service delay as a QoS metric for VEC networks, this paper formulates a migration optimization problem aiming to maximize the services' satisfaction degree of delay (i.e., the probability that the service delay is smaller than the service delay requirement), where both the communication cost and computing cost affect the services' satisfaction degree. Since the optimization problem is a constrained non-linear integer programming problem, it is difficult to solve. Moreover, the VEC networks are highly dynamic. Thus, a fast transfer reinforcement learning (fast-TRL) method combining transfer learning and reinforcement learning is proposed to provide an adaptive service migration scheme in dynamic VEC networks. Simulation results show that compared with existing schemes, the proposed CA-migration scheme can increase the satisfaction degree by up to 30%, and needs 25% less training time to obtain the optimal service migration policy.

In this paper, we study a secure communication scheme assisted by multiple distributed friendly users over fading channels. To defeat a malicious user, friendly users transmit artificial noise continuously regardless of whether the transmitter is sending signals or not. The idle nodes in wireless networks can be selected as friendly users to assist in achieving secure communications. Due to the different positions of friendly users relative to the malicious user and receiver, the assisting abilities of friendly users in secure communications are different. To explore the potential of friendly users, a metric factor is proposed in this paper to evaluate the assisting abilities of friendly users. Based on the evaluating results, an optimum user selection and power allocation scheme is designed to maximize the secure throughput under the power limit and secure constraint. Compared with the benchmark scheme, the proposed secure communication scheme is easy to implement. Moreover, according to the numerical results, the proposed scheme can improve the secure throughput by more than 16% compared with the benchmark scheme when the secure constraint is larger than 0.5.

Federated learning (FL) over mobile networks has attracted intensive attention recently. User mobility is a fundamental feature of mobile networks, which leads to dynamic network topology and wireless connectivity losses. As such, user mobility is usually considered a “trouble maker” and a great challenge to FL over mobile networks. Interestingly, we found that small user mobility can positively contribute to improving FL performance. This is because the total dataset size and the data diversity that the FL can utilize are increased by user mobility. Based on this observation, we aim to tame and exploit mobility instead of treating it as a hostile “trouble maker”. To this end, we first investigate how the FL performance changes with user mobility theoretically by jointly taking into account the positive and negative aspects of mobility. Specifically, a closed-form expression to quantify the impact of mobility on the FL loss is derived, which explains when negative or positive aspects of mobility dominate the FL performance. Next, a joint FL and communication optimization problem is formulated based on theoretical analyses to minimize the FL loss function by optimizing wireless resource allocation. Finally, we propose a two-step optimization algorithm to solve the formulated problem. The simulation results verify the theoretical analyses. It is also shown that the proposed method can significantly enhance learning performance considering users with high mobility. When the average velocity is larger than 150 km/h, the proposed method achieves more than 80% accuracy in the MNIST dataset, while the existing methods may fail during training.

Recently, automatic modulation recognition (AMR), i.e., identifying the modulation modes of signals using deep learning (DL) has received much attention. This paper proposes an AMR method based on DL (i.e., SG-NET), including a novel DL architecture (i.e., GuResNet) based on the deep residual network (ResNet) and a SNR-aware mechanism, which can effectively extract signal characteristics to achieve a better recognition accuracy. Specifically, we design a deep residual network model that mainly consists of six novel Residual Units to abstract effective signal features and prevent over-fitting. Then, to further improve the recognition performance in low SNR scenarios (i.e., the SNR is lower than 0dB), we train different parameters in the GuResNets based on the aware SNR. That is, we firstly use original signals with high and low SNRs to train a general GusResNet. When SNR is larger than 0dB, we directly adopt the general GuResNet for AMR. While, in low SNR scenarios, we exploit the Legendre method to extract signal features and then re-train the parameters in the GuResNets under different SNR conditions to improve the recognition accuracy further. The simulations demonstrate that our proposed SG-NET can obtain nearly 30% accuracy gain when the SNR is lower than 0dB, and 10% improvement when SNR is larger than 0dB compared with existing schemes.

High-definition map (HD Map) for autonomous driving brings huge pressure on networks due to its bandwidth-greedy, computing-intensive, and latency-sensitive characteristics. Data collection, transmission, and processing for HD Map update should cooperate to meet these requirements. In this paper, crowd-sensing which exploits the sensing ability of autonomous vehicles is adopted for real-time data collection. Vehicular distributed computing is adopted to improve the computing capability and reduce the transmission of massive raw environmental data. And a crowd-sensing assisted vehicular distributed computing (CS-VDC) mechanism is proposed based on the convergence of sensing, communication, and computation. In addition, considering the differences in sensing range and computing capability of different vehicles, the selection of crowd-sensing nodes and task allocation are jointly optimized to further minimize the communication load. A heuristic algorithm is developed to solve the optimization problem. The performance of the proposed mechanism is evaluated and CS-VDC can always achieve the minimum missing update ratio and amount of equivalent transmission data regardless of the parameter configuration. Especially, the amount of equivalent transmission data under the proposed CS-VDC can be reduced by 37% compared with the nearest node selection mechanism.

With the explosive growth of computation-intensive and latency-sensitive vehicular applications, limited on-board computing resources can hardly satisfy these heterogeneous requirements and task offloading becomes a potential solution. However, task offloading in vehicular networks may face the dilemma of unaffordable uploading time caused by the huge amount of uploading traffic. Therefore, considering the applications which use the environmental data as their input, the sensing abilities of serving nodes (SNs) are exploited and a traffic-aware task offloading (TATO) mechanism based on convergence of communication and sensing is proposed. In the TATO mechanism, a task vehicle can adaptively upload the input data to some SNs and transmit the computation instructions to others which use the environmental data sensed by themselves as input. The objective is to minimize the overall response time (ORT) by jointly optimizing the task and wireless bandwidth ratios. Next, a binary search and feasibility check (BSFC) algorithm is designed to solve the optimization problem. Simulation results demonstrate the effectiveness of the proposed BSFC algorithm and show that the TATO mechanism always outperforms the benchmark mechanisms (i.e., communication-based offloading and sensing-based offloading) in terms of the ORT. Specifically, when the task offloading traffic is huge and the wireless transmission capability becomes a bottleneck, TATO can reduce the ORT by 42.8% compared with that of the communication-based offloading.

As an important component of resource management, admission control is vital to prevent the wireless network from congestion and ensure the quality of service (QoS). Mobile edge computing (MEC), which provides computing resources at the edge of radio access networks (RAN), is able to better support new mobile services. Therefore, enhanced admission control policies should be designed for mobile cellular networks based on MEC. In this paper, a novel MEC-assisted admission control mechanism is proposed from the perspective of convergence of communication and computation. In this mechanism, MEC computing resources are leveraged to pre-process the transmission content. The purpose is to reduce the consumption of wireless bandwidth and increase the number of accepted services. Next, the admission control process is modeled as a Markov decision process (MDP) with objective to maximize the long-term expected average effective throughput. In consideration of the large state space, a simulation-based optimization algorithm of MDP is adopted to obtain the optimal policy. Simulation results show that our proposed admission control mechanism achieves higher effective throughput than that without MEC computing resources. And the probability of accepted services can also be improved significantly. Furthermore, the optimal amount of MEC computing resources can be acquired according to the system traffic statistics.