Network function virtualization (NFV) enables the provision of different quality of service (QoS) levels through service function chains (SFCs), where NFV outsources big data tasks of end users to nearby edge servers. In multi-access edge computing (MEC), its dynamic and uncertainty nature poses great challenges to the SFC placement problem, which requires optimizing multiple potentially-conflicting objectives, such as network latency and load balancing. Moreover, user preferences may vary along with time, adding another layer of complexity to the problem. To address the problem above, we propose a novel distributed deep reinforcement learning (DRL) architecture based on a spatio-temporal encoder (STE), denoted as DDRL-STE. DDRL-STE is featured with equal-weight pre-training and transformer-based STE. Experimental results show that DDRL-STE outperforms three state-of-the-art DRL algorithms regarding latency and load balancing under three well-known network topologies, exhibiting its excellent potential in exploration and generalization.

This paper proposes a task-oriented semantic communication system for electrocardiogram (ECG) signal classification, called ECG-SC-DARTS. Based on deep learning, this system adopts the differentiable neural architecture search (DARTS) to automatically design the neural architecture of the semantic encoder under various channels. This paper improves the performance of the original DARTS by introducing a new recurrent neural network (RNN) cell with residual structure and a noise adding scheme for skip-connections. The RNN cell enhances the temporal semantic information extraction ability while the added noise reduces the risk of performance collapse caused by skip-connections. Experimental results demonstrate that ECGSC-DARTS generates appropriate neural architectures for the semantic encoder under AWGN, Rayleigh and Rician channels and these architectures outperform a number of baseline models, such as the original DARTS, fully convolutional network, multi-layer perception, and ResNet, regarding F1-score. Moreover, ECGSC-DARTS is more reliable than the traditional communication system in harsh channel environment.

We present a many-to-one distributed semantic communication system for multivariate time series classification. The system adopts a federated learning-based architecture to achieve low-redundancy collaborative inference, where an unsupervised auxiliary task is designed to coordinate the feature vectors at different dimensions between semantic encoders and the classifier. For each transmitter, we design a scale-adaptive semantic encoder by applying weighted sum to a number of predefined convolutional layers. The scale-adaptive semantic encoder can extract multi-scale features from time series following various distributions. A dynamic channel encoder is developed to adapt to the scale-adaptive semantic encoder, converting semantic features to complex symbols appropriate for wireless transmission. For the receiver, we apply the same scale-adaptive structure to the semantic decoder to extract multi-scale semantic features from all transmitters for accurate classification. Simulation results show that the proposed distributed semantic communication system outperforms two baseline systems under AWGN, Rician, and Rayleigh channels and achieves excellent Top-1 accuracy performance on three UEA2018 datasets, especially when the signal-to-noise ratio is low.

This paper studies the trajectory control and task offloading (TCTO) problem in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flies along a planned trajectory to collect computation tasks from smart devices (SDs). We consider a scenario that SDs are not directly connected by the base station (BS) and the UAV has two roles to play: MEC server or wireless relay. The UAV makes task offloading decisions online, in which the collected tasks can be executed locally on the UAV or offloaded to the BS for remote processing. The TCTO problem involves multi-objective optimization as its objectives are to minimize the task delay and the UAV's energy consumption, and maximize the number of tasks collected by the UAV, simultaneously. This problem is challenging because the three objectives conflict with each other. The existing reinforcement learning (RL) algorithms, either single-objective RLs or single-policy multi-objective RLs, cannot well address the problem since they cannot output multiple policies for various preferences (i.e. weights) across objectives in a single run. An evolutionary multi-objective RL (EMORL) algorithm is applied to address the TCTO problem. We improve the multi-task multi-objective proximal policy optimization of the original EMORL by retaining all new learning tasks in the offspring population, which can preserve promissing learning tasks. The simulation results demonstrate that the proposed algorithm can obtain more excellent non-dominated policies by striking a balance between the three objectives regarding policy quality, compared with two evolutionary algorithms, two multi-policy RL algorithms, and the original EMORL.

Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution. This article studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where the data from an incoming task is partitioned into two parts, with one part executed locally and the other offloaded to the edge infrastructure for execution. These two parts are independent of each other, but both need to be processed by the same SFC. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. This problem consists of two closely related decision-making steps, namely task partition and VNF placement, which is highly complex and quite challenging. To address this, we propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where two agents interact with each other. Simulation results show that the proposed algorithm outperforms three combinations of deep reinforcement learning algorithms with respect to cumulative reward and it overweighs a number of baseline algorithms in terms of execution delay, energy consumption, and usage charge.