Mobile edge computing (MEC) emerges as a paradigm to free mobile devices (MDs) from increasingly dense computing workloads in 6G networks. The quality of computing experience can be greatly improved by offloading computing tasks from MDs to MEC servers. Renewable energy harvested by energy harvesting equipments (EHQs) is considered as a promising power supply for users to process and offload tasks. In this paper, we apply the uniform mobility model of MDs to derive a more realistic wireless channel model in a multi-user MEC system with batteries as EHQs to harvest and storage energy. We investigate an optimization problem of the weighted sum of delay cost and energy cost of MDs in the MEC system. We propose an effective joint partial computation offloading and resource allocation (CORA) algorithm which is based on deep reinforcement learning (DRL) to obtain the optimal scheduling without prior knowledge of task arrival, renewable energy arrival as well as channel condition. The simulation results verify the efficiency of the proposed algorithm, which undoubtedly minimizes the cost of MDs compared with other benchmarks.

The satellite-terrestrial cooperative network is considered an emerging network architecture, which can adapt to various services and applications in the future communication network. In recent years, the combination of satellite communication and Mobile Edge Computing (MEC) has become an emerging research hotspot. Satellite edge computing can provide users with full coverage on-orbit computing services by deploying MEC servers on satellites. This paper studies the task offloading of multi-user and multi-edge computing satellites and proposes a novel algorithm that joint task offloading and communication computing resource optimization (JTO-CCRO). The JTO-CCRO is decoupled into task offloading and resource allocation sub-problems. After the mutual iteration of the two sub-problems, the system utility function can be further reduced. For the task offloading sub-problem, it is first confirmed that the offloading problem is a game problem. The offloading strategy can be obtained from the Nash equilibrium solution. We confirm resource optimization sub-problem is a convex optimization problem that can be solved by the Lagrange multiplier method. Simulation shows that the JTO-CCRO algorithm can converge quickly and effectively reduce the system utility function.

To relieve the backhaul link stress and reduce the content acquisition delay, mobile edge caching has become one of the promising approaches. In this paper, a novel federated reinforcement learning (FRL) method with adaptive training times is proposed for edge caching. Through a new federated learning process with the asynchronous model training process and synchronous global aggregation process, the proposed FRL-based edge caching algorithm mitigates the performance degradation brought by the non-identically and independently distributed (non-i.i.d.) characteristics of content popularity among edge nodes. The theoretical bound of the loss function difference is analyzed in the paper, based on which the training times adaption mechanism is proposed to deal with the tradeoff between local training and global aggregation for each edge node in the federation. Numerical simulations have verified that the proposed FRL-based edge caching method outperforms other baseline methods in terms of the caching benefit, the cache hit ratio and the convergence speed.

As a promising edge learning framework in future 6G networks, federated learning (FL) faces a number of technical challenges due to the heterogeneous network environment and diversified user behaviors. Data imbalance is one of these challenges that can significantly degrade the learning efficiency. To deal with data imbalance issue, this work proposes a new learning framework, called clustered federated learning with weighted model aggregation (weighted CFL). Compared with traditional FL, our weighted CFL adaptively clusters the participating edge devices based on the cosine similarity of their local gradients at each training iteration, and then performs weighted per-cluster model aggregation. Therein, the similarity threshold for clustering is adaptive over iterations in response to the time-varying divergence of local gradients. Moreover, the weights for per-cluster model aggregation are adjusted according to the data balance feature so as to speed up the convergence rate. Experimental results show that the proposed weighted CFL achieves a faster model convergence rate and greater learning accuracy than benchmark methods under the imbalanced data scenario.

Satellite communication has been seen as a vital part of the sixth generation communication, which greatly extends network coverage.In satellite communication, resource management is a key problem attracting many research interests. However, previous study mainly focuses on throughput improvement via power allocation and spectrum assignment and the proposed approaches are mostly model-based and dedicated to specific problem structures. Fortunately, with the trend of edge intelligence, complex resource management problems can be efficiently resolved in a model-free manner. In this paper, a joint beam activation, user-beam association and time resource allocation approach is proposed. The core idea is using stochastic learning at the ground station to identify active user-link beams to meet user rate demand. In addition, the convergence, optimality and complexity of our proposal are rigorously discussed. By simulation, it is shown that the rate goal of most of the users can be met and meanwhile satellite energy is saved owing to much less active beams.

Deep learning (DL) has been applied to the physical layer of wireless communication systems, which directly extracts environment knowledge from data and outperforms conventional methods either in accuracy or computation complexity. However, most related research works employ centralized training that inevitably involves collecting training data from edge devices. The data uploading process usually results in excessive communication overhead and privacy disclosure. Alternatively, a distributed learning approach named federated edge learning (FEEL) is introduced to physical layer designs. In FEEL, all devices collaborate to train a global model only by exchanging parameters with a nearby access point. Because all datasets are kept local, data privacy is better protected and data transmission overhead can be reduced. This paper reviews the studies on applying FEEL to the wireless physical layer including channel state information acquisition, transmitter, and receiver design, which represent a paradigm shift of the DL-based physical layer design. In the meantime they also reveal several limitations inherent in FEEL, particularly when applied to the wireless physical layer, thus motivating further research efforts in the field.

Edge intelligence is anticipated to underlay the pathway to connected intelligence for 6G networks, but the organic confluence of edge computing and artificial intelligence still needs to be carefully treated. To this end, this article discusses the concepts of edge intelligence from the semantic cognitive perspective. Two instructive theoretical models for edge semantic cognitive intelligence (ESCI) are first established. Afterwards, the ESCI framework orchestrating deep learning with semantic communication is discussed. Two representative applications are present to shed light on the prospect of ESCI in 6G networks. Some open problems are finally listed to elicit the future research directions of ESCI.