In this paper, the buffer-constrained throughput performance of a multi-user wireless-powered communication (WPC) system is investigated, where energy harvesting follows a non-linear model. The investigation focuses on the buffer overflow performance of sending data in the downlink (DL) from the access point (AP) node to each user equipment (UE) node and that in the uplink from each UE node to the AP node, based on which the throughput performance on both directions when a buffer constraint is enforced is studied. Specifically, the buffer overflow probability at each node is analyzed, based on which the buffer-constrained throughput is studied. In addition, to ensure the throughput performance under the buffer constraint, the DL transmission power allocation policy and the required energy storage capacity at each UE are investigated. Also, the optimal channel time allocation policy is studied with the objective of maximizing the minimum buffer-constrained throughput guaranteed to each UE at the same time. To this aim, an optimization problem is first formulated and then a dichotomy-based time allocation algorithm combined with a one-dimensional search is proposed to solve this problem. The analysis and results, explicitly relating the throughput to the buffer constraint in addition to WPC characteristics, shed new light on the design and performance analysis of WPC systems.

In this paper, a video service enhancement strategy is investigated under an edge-cloud collaboration framework, where video caching and delivery decisions are made at the cloud and edge respectively. We aim to guarantee the user fairness in terms of video coding rate under statistical delay constraint and edge caching capacity constraint. A hybrid human-artificial intelligence approach is developed to improve the user hit rate for video caching. Specifically, individual user interest is first characterized by merging factorization machine (FM) model and multi-layer perceptron (MLP) model, where both low-order and high-order features can be well learned simultaneously. Thereafter, a social aware similarity model is constructed to transfer individual user interest to group interest, based on which, videos can be selected to cache at the network edge. Furthermore, a dual bisection exploration scheme is proposed to optimize wireless resource allocation and video coding rate. The effectiveness of the proposed video caching and delivery scheme is finally validated by extensive experiments with a real-world dataset.

In order to make full use of the network data and guarantee user privacy simultaneously, federated learning (FL) is proposed to enable distributed intelligence for local nodes without sharing data with each other. However, in practice, due to resource limitations, traditional FL suffers from node scheduling and parameter transmission failure, which not only affects the final performance but also further reduces the fairness of the participating nodes. This article addresses the challenge and proposes an FL method to enhance the performance of FL on the basis of guaranteeing the fairness of the local nodes in a resource-constrained Internet of Things (IoT) network. Specifically, an analytical model is first constructed to characterize the performance of FL with joint considerations of node fairness, unreliable parameter transmissions as well as resource limitations. Thereafter, a statistically reweighted aggregation (SRA) scheme is proposed for parameter aggregation and the corresponding model is proved to be unbiased to that based on ideal parameter transmissions. With the knowledge of time dependency of the global model, we further extend SRA and propose a reliable SRA (RSRA) scheme. Additionally, we prove RSRA is able to achieve higher stability performance than SRA in model training. Furthermore, the convergence bound of the proposed RSRA is derived analytically, based on which an adaptive local training scheme is proposed under a given resource budget. Finally, extensive experiments are carried out with a public data set to validate the effectiveness of the proposed scheme with comparisons of other baseline schemes.

Green communication is one of the key goals of the beyond 5G networks. However, as more and more delay sensitive applications emerge, the contradiction between task delay requirement and energy conservation becomes more and more prominent on the device side. This paper focuses on a mobile edge computing system where local computing capability and edge computing capability are both limited, which may lead to task discarding due to delay violation. An energy consumption minimization problem is first formulated under the binary offloading and the partial offloading modes. Then a low-complexity heuristic scheme and a Lagrange dual scheme are proposed to jointly optimize the task scheduling and resource allocation under those two modes respectively. Particularly, a task processing priority model is designed to effectively reduce the number of discarded tasks and improve the service performance of the MEC server. The effectiveness of the proposed schemes is validated by extensive simulations with comparison to other baseline schemes.

Edge caching enables low-delay and high-quality data services for the Industrial Internet. However, traditional popularity-based edge caching ignores the diversity and evolution of user interest, especially among user groups, and therefore has the limited quality of experience guarantees for users. In this regard, a recommendation-aided edge caching approach is proposed to leverage the time-varying user interest. Specifically, a dynamic interest capture model was proposed to mine the individual user interest, based on which, a group interest aggregation algorithm is then studied to determine the content caching strategies for edge nodes. Thereafter, an edge content recommendation is further proposed to optimize the cache hit ratio while ensuring a satisfying recommendation hit ratio based on the personalized user interest and given caching decision. The effectiveness of the proposed approach is finally validated by comparing it with other baseline approaches.

In beamformed wireless cellular systems, such as 5G New Radio (NR) networks, beam management (BM) is a crucial operation. In the second phase of 5G NR standardization, known as 5G-Advanced, which is being vigorously promoted, the key component is the use of artificial intelligence (AI) based on machine learning (ML) techniques. AI/ML for BM is selected as a representative use case. This article provides an overview of AI/ML for BM in 5G-Advanced. The legacy non-AI and prime AIenabled BM frameworks are first introduced and compared. Then, the main scope of AI/ML for BM is presented, including improving accuracy and reducing overhead and latency. Finally, the key challenges and open issues in the standardization of AI/ML for BM are discussed, especially the design of new protocols for AIenabled BM. This article provides a guideline for the study of AI/ML-based BM standardization.