AI/ML for Beam Management in 5G-Advanced

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 the AI/ML for BM in 5G-Advanced. The legacy non-AI and prime AI-enabled BM frameworks are first introduced and compared. Then, the main scope of AI/ML for BM is presented, including improving accuracy, 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 AI-enabled BM. This article provides a guideline for the study of AI/ML-based BM standardization.Comment: 4 figure

Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing

Mobile crowd sensing (MCS) systems usually attract numerous participants with widely varying sensing costs and interest preferences to perform tasks, where accurate task assignment plays an indispensable role and also faces many challenges (e.g., how to simplify the complicated task assignment process and improve matching accuracy between tasks and participants, while guaranteeing submitted data credibility). To overcome these challenges, we propose a service benefit aware multi-task assignment (SBAMA) strategy in this paper. Firstly, service benefits of participants are modeled based on their task difficulty, task history, sensing capacity, and sensing positivity to meet differentiated requirements of various task types. Subsequently, users are then clustered by enhanced fuzzy clustering method. Finally, a gradient descent algorithm is designed to match task types to participants achieving the maximum service benefit. Simulation results verify that the proposed task assignment strategy not only effectively reduces matching complexity but also improves task completion rate

Stochastic Latency Guarantee in Wireless Powered Virtualized Sensor Networks

How to guarantee the data rate and latency requirement for an application with limited energy is an open issue in wireless virtualized sensor networks. In this paper, we integrate the wireless energy transfer technology into the wireless virtualized sensor network and focus on the stochastic performance guarantee. Firstly, a joint task and resource allocation optimization problem are formulated. In order to characterize the stochastic latency of data transmission, effective capacity theory is resorted to study the relationship between network latency violation probability and the transmission capability of each node. The performance under the FDMA mode and that under the TDMA mode are first proved to be identical. We then propose a bisection search approach to ascertain the optimal task allocation with the objective to minimize the application latency violation probability. Furthermore, a one-dimensional searching scheme is proposed to find out the optimal energy harvesting time in each time block. The effectiveness of the proposed scheme is finally validated by extensive numerical simulations. Particularly, the proposed scheme is able to lower the latency violation probability by 11.6 times and 4600 times while comparing with the proportional task allocation scheme and the equal task allocation scheme, respectively

Delay and Delay-Constrained Throughput Performance of a Wireless-Powered Communication System

In this paper, the delay and delay-constrained throughput performance of a point-to-point wireless-powered communication system is investigated. In this system, the wireless-powered node, e.g., a user equipment (UE), receives data at the same time when powered from the other node, e.g., an access point (AP), and uses the harvested wireless energy to send data to the other node. The investigation focuses on the delay performance of sending data in the downlink (DL) from the AP node to the UE node and that in the uplink (UL) from the UE node to the AP node, based on which the throughput performance on both directions when delay constraints are enforced is also studied. To this aim, the cumulative service capacity of the service process is first analyzed for both DL and UL, taking into consideration the delay caused by the nontransmission phase for the AP or UE in each charging cycle. Thereafter, a general upper bound on the delay distribution for stochastic traffic arrivals is obtained for both DL and UL, based on which the delay-constrained throughput performance is further studied. In addition, to ensure the delay performance, the required energy storage capacity and wireless charging rate are investigated. The obtained results are exemplified with two specific traffic types, and the accuracy of the analysis is validated by comparison with extensive simulation results. The analysis and results shed new light on the performance of wireless-powered communication systems

On Buffer-Constrained Throughput of a Wireless-Powered Communication System

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

Robust Hierarchical Federated Learning with Anomaly Detection in Cloud-Edge-End Cooperation Networks

Federated learning (FL) enables devices to collaborate on machine learning (ML) model training with distributed data while preserving privacy. However, the traditional FL is inefficient and costly in cloud–edge–end cooperation networks since the adopted classical client-server communication framework fails to consider the real network structure. Moreover, malicious attackers and malfunctioning clients may be implied in all participators to exert adverse impacts as abnormal behaviours on the FL process. To address the above challenges, we leverage cloud–edge–end cooperation to propose a robust hierarchical federated learning (R-HFL) framework to enhance inherent system resistance to abnormal behaviours while improving communication efficiency in practical networks and keeping the advantages of the traditional FL. Specifically, we introduce a hierarchical cloud–edge–end collaboration-based FL framework to reduce communication costs. For the framework, we design a detection mechanism as partial cosine similarity (PCS) to filter adverse clients to improve performance, where the proposed lightweight technique has high computation parallelization. Besides, we theoretically discuss the influence of the proposed PCS on the convergence and stabilization of FL. Finally, the experimental results show that the proposed R-HFL always outperforms baselines in general cases under malicious attacks, which further shows the effectiveness of our scheme

Delay and delay-constrained throughput analysis of a wireless powered communication system

In this paper, we investigate the delay and delay-constrained throughput performance of a point-to-point wireless-powered communication system, where one node, e.g. a user equipment (UE), is powered by the wireless energy transferred from the other node, e.g. an access point (AP), and uses the harvested wireless energy to send data to the other node. Our focus is on the delay performance of sending data over the uplink from the UE node to the AP node, and on its throughput performance when a delay constraint is enforced. Two representative time allocation schemes in using the link for the AP node to transfer energy (maybe together with data) and for the UE node to send data are considered. In particular, a lower bound on the cumulative capacity of the uplink is derived. In addition, an upper bound on the delay distribution is obtained for stochastic traffic arrivals, based on which, the delay-constrained throughput performance is further analyzed. Moreover, the accuracy of the analysis is validated by comparison with extensive simulation results. The analysis and results shed new light on the performance of such a wireless-powered communication system