相关论文: Wireless Federated Learning (WFL) for 6G Networks …

Devices participating in federated learning (FL) typically have heterogeneous communication, computation, and memory resources. However, in synchronous FL, all devices need to finish training by the same deadline dictated by the server. Our…

Massive numbers of nodes will be connected in future wireless networks. This brings great difficulty to collect a large amount of data. Instead of collecting the data individually, computation over multi-access channel (CoMAC) provides an…

With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a promising learning framework for beyond 5G wireless networks. It is anticipated that future wireless networks will jointly serve both FL…

By combining the concepts of superposition coding at the transmitter(s) and successive interference cancellation (SIC) at the receiver(s), non-orthogonal multiple access (NOMA) has recently emerged as a promising multiple access technique…

Though the concept of non-orthogonal multiple access (NOMA) was proposed several years ago, the performance of uplink NOMA has only been verified in theory, but not in practice. This paper presents an over-the-air implementation of a uplink…

The proliferating number of devices with short payloads as well as low power budget has already driven researchers away from classical grant-based access schemes that are notorious for their large signalling overhead as well as…

Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive traffics and has recently received…

Non-orthogonal multiple access (NOMA) is a powerful transmission technique that enhances the spectral efficiency of communication links, and is being investigated for 5G standards and beyond. A major drawback of NOMA is the need to apply…

Non-orthogonal multiple access (NOMA) is an interesting technology that enables massive connectivity as required in future 5G and 6G networks. While purely linear processing already achieves good performance in NOMA systems, in certain…

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users' local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless environment; however, training models in a…

A non-orthogonal multiple access (NOMA)-aided joint communication, sensing, and multi-tier computing (JCSMC) framework is proposed. In this framework, a multi-functional base station (BS) carries out target sensing, while providing edge…

Optical wireless communication (OWC) is envisioned as a key enabler for immersive indoor data transmission in future wireless communication networks. However, multi-user interference management arises as a challenge in dense indoor OWC…

Benefited from the widely deployed infrastructure, the LTE network has recently been considered as a promising candidate to support the vehicle-to-everything (V2X) services. However, with a massive number of devices accessing the V2X…

The roll-out of various emerging wireless services has triggered the need for the sixth-generation (6G) wireless networks to provide functions of target sensing, intelligent computing and information communication over the same radio…

In this paper, the problem of energy efficient transmission and computation resource allocation for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local…

OWC has been considered as a key enabling technology to unlock unprecedented speeds of communication, supporting high demands of data traffic. In this paper, infrared lasers are used as optical transmitters operating in an indoor…

As 5G networks rolling out in many different countries nowadays, the time has come to investigate how to upgrade and expand them towards 6G, where the latter is expected to realize the interconnection of everything as well as the…

In order to meet the extremely heterogeneous requirements of the next generation wireless communication networks, research community is increasingly dependent on using machine learning solutions for real-time decision-making and radio…

AI-native 6G networks are envisioned to tightly embed artificial intelligence (AI) into the wireless ecosystem, enabling real-time, personalized, and privacy-preserving intelligence at the edge. A foundational pillar of this vision is…

Federated learning (FL) has emerged as a key technique for distributed machine learning (ML). Most literature on FL has focused on ML model training for (i) a single task/model, with (ii) a synchronous scheme for updating model parameters,…