Related papers: Model-based Deep Learning for Wireless Resource Al…
Enabled by hybrid grant-based (GB) and grant-free (GF) transmission techniques, GF users of internet of things (IoT) devices and massive machine-type communications (mMTC) meet opportunities to share wireless resources with GB users. In…
Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the clients may result in a high delay and energy consumption…
Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today's edge learning arena. However, its performance is often limited by slow convergence and corresponding…
We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks. To address the non-convex nature of the problem, the proposed method consists…
Maneuvering target tracking will be an important service of future wireless networks to assist innovative applications such as intelligent transportation. However, tracking maneuvering targets by cellular networks faces many challenges. For…
Rate Splitting Multiple Access (RSMA) has recently emerged as a promising technique to enhance the transmission rate for multiple access networks. Unlike conventional multiple access schemes, RSMA requires splitting and transmitting…
Modern multi-access 5G+ networks provide mobile terminals with additional capacity, improving network stability and performance. However, in highly mobile environments such as vehicular networks, supporting multi-access connectivity remains…
In this paper, we aim at maximizing the weighted sum-rate (WSR) of rate splitting multiple access (RSMA) in multi-user multi-antenna transmission networks through the joint optimization of rate allocation and beamforming. Unlike…
Weighted Minimum Mean Square Error (WMMSE) precoding is widely recognized for its near-optimal weighted sum rate performance. However, its practical deployment in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal…
This letter focuses on integrating rate-splitting multiple-access (RSMA) with time-division-duplex Cell-free Massive MIMO (multiple-input multiple-output) for massive machine-type communications. Due to the large number of devices, their…
This paper investigates the problem of resource allocation for joint communication and radar sensing system on rate-splitting multiple access (RSMA) based unmanned aerial vehicle (UAV) system. UAV simultaneously communicates with multiple…
Dual-Functional Radar-Communication (DFRC) system is an essential and promising technique for beyond 5G. In this work, we propose a powerful and unified multi-antenna DFRC transmission framework, where an additional radar sequence is…
We consider multi-antenna base stations using orthogonal frequency-division multiple access (OFDMA) and space division multiple access (SDMA) techniques to serve single antenna users, where some of those users have minimum rate requirements…
Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance. One way to alleviate this problem is to manage the radio resources in order to maximize either the average or the worst-case…
The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated learning (SFL) has emerged as a promising solution by of…
The data heterogeneity across devices and the limited communication resources, e.g., bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL). To tackle these challenges, we first devise a novel FL…
Joint optimization for common rate allocation and beamforming design have been widely studied in rate splitting multiple access (RSMA) empowered multiuser multi-antenna transmission networks. Due to the highly coupled optimization variables…
Deploying foundation models (FMs) on uncrewed aerial vehicles (UAVs) promises broad ``low-altitude economy'' applications. Split federated learning (SFL)-based fine-tuning leverages distributed data while keeping raw data local and reduces…
With the rapid proliferation of smart devices in wireless networks, more powerful technologies are expected to fulfill the network requirements of high throughput, massive connectivity, and diversify quality of service. To this end, rate…
In order to efficiently cope with the high throughput, reliability, heterogeneity of Quality-of-Service (QoS), and massive connectivity requirements of future 6G multi-antenna wireless networks, multiple access and multiuser communication…