4 citations · 10 across the 24 of their papers we have counts for
26 papers
Multi-Model Over-the-Air Federated Learning via Transceiver Beamforming and Device Grouping
Chong Zhang, Min Dong, Ben Liang +2
This paper considers uplink communication design for training multiple independent machine learning models via wireless federated learning. To reduce communication delay, we utiliz…
Out-of-Distribution Detection in Wireless Multimodal Foundation Models for 6G ISAC
Mohammad Farzanullah, Akram Bin Sediq, Ali Afana +1
The integration of Foundation Models (FMs), such as the Wireless Multimodal Foundation Model (WMFM), into 6G networks provides a unified framework for Integrated Sensing and Commun…
Chain-of-Thought Reasoning Enhances In-Context Learning for LLM-Based Mobile Traffic Prediction
MohammadMahdi Ghadaksaz, Mohammad Farzanullah, Akram Bin Sediq +2
Accurate short-term mobile traffic prediction is important for proactive resource allocation and low-latency network management in fifth generation (5G) and sixth generation (6G).…
Foundation Model-Aided Hierarchical Deep Reinforcement Learning for Blockage-Aware Link in RIS-Assisted Networks
Mohammad Ghassemi, Han Zhang, Ali Afana +2
Reconfigurable intelligent surface (RIS) technology has the potential to significantly enhance the spectral efficiency (SE) of 6G wireless networks. However, practical deployment r…
Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey
Han Zhang, Mohammad Farzanullah, Mohammad Ghassemi +3
Foundation models (FMs) are recognized as a transformative breakthrough that has started to reshape the future of artificial intelligence (AI) across both academia and industry. Th…
Dual-Transformer Aided Hierarchical Deep Reinforcement Learning for Robust RIS-Assisted Near-Field Communications
Mohammad Ghassemi, Han Zhang, Ali Afana +2
The deployment of extremely large aperture arrays (ELAAs) in sixth-generation (6G) networks is expected to shift communications into the near-field regime, where spherical-wave pro…
Wireless Multimodal Foundation Model (WMFM): Integrating Vision and Communication Modalities for 6G ISAC Systems
Mohammad Farzanullah, Han Zhang, Akram Bin Sediq +2
The emergence of multimodal foundation models has revolutionized learning paradigms by enabling joint understanding across diverse data types. In the context of next-generation wir…
Robust Segmented Analog Broadcast Design to Accelerate Wireless Federated Learning
Chong Zhang, Ben Liang, Min Dong +2
We consider downlink broadcast design for federated learning (FL) in a wireless network with imperfect channel state information (CSI). Aiming to reduce transmission latency, we pr…
Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication
Mohammad Ghassemi, Sara Farrag Mobarak, Han Zhang +3
Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by dynamically controlling signal propagation in the environme…
Conditional Denoising Diffusion for ISAC Enhanced Channel Estimation in Cell-Free 6G
Mohammad Farzanullah, Han Zhang, Akram Bin Sediq +2
Cell-free Integrated Sensing and Communication (ISAC) aims to revolutionize 6th Generation (6G) networks. By combining distributed access points with ISAC capabilities, it boosts s…
Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection
Han Zhang, Akram Bin Sediq, Ali Afana +1
Mobile traffic prediction is an important enabler for optimizing resource allocation and improving energy efficiency in mobile wireless networks. Building on the advanced contextua…
SegOTA: Accelerating Over-the-Air Federated Learning with Segmented Transmission
Chong Zhang, Min Dong, Ben Liang +2
Federated learning (FL) with over-the-air computation efficiently utilizes the communication resources, but it can still experience significant latency when each device transmits a…
Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning
Mohammad Farzanullah, Han Zhang, Akram Bin Sediq +2
Sixth generation (6G) wireless technology is anticipated to introduce Integrated Sensing and Communication (ISAC) as a transformative paradigm. ISAC unifies wireless communication…
Improving Wireless Federated Learning via Joint Downlink-Uplink Beamforming over Analog Transmission
Chong Zhang, Min Dong, Ben Liang +2
Federated learning (FL) over wireless networks using analog transmission can efficiently utilize the communication resource but is susceptible to errors caused by noisy wireless li…
Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication
Mohammad Ghassemi, Han Zhang, Ali Afana +2
In mmWave wireless networks, signal blockages present a significant challenge due to the susceptibility to environmental moving obstructions. Recently, the availability of visual d…
Regularized Top-: A Bayesian Framework for Gradient Sparsification
Ali Bereyhi, Ben Liang, Gary Boudreau +1
Error accumulation is effective for gradient sparsification in distributed settings: initially-unselected gradient entries are eventually selected as their accumulated error exceed…
Multi-Modal Transformer and Reinforcement Learning-based Beam Management
Mohammad Ghassemi, Han Zhang, Ali Afana +2
Beam management is an important technique to improve signal strength and reduce interference in wireless communication systems. Recently, there has been increasing interest in usin…
Novel Gradient Sparsification Algorithm via Bayesian Inference
Ali Bereyhi, Ben Liang, Gary Boudreau +1
Error accumulation is an essential component of the Top- sparsification method in distributed gradient descent. It implicitly scales the learning rate and prevents the slow-down…
Uplink Over-the-Air Aggregation for Multi-Model Wireless Federated Learning
Chong Zhang, Min Dong, Ben Liang +2
We propose an uplink over-the-air aggregation (OAA) method for wireless federated learning (FL) that simultaneously trains multiple models. To maximize the multi-model training con…
Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks
Han Zhang, Akram Bin Sediq, Ali Afana +1
In recent years, machine learning (ML) techniques have created numerous opportunities for intelligent mobile networks and have accelerated the automation of network operations. How…
Generative AI Empowered LiDAR Point Cloud Generation with Multimodal Transformer
Mohammad Farzanullah, Han Zhang, Akram Bin Sediq +2
Integrated sensing and communications is a key enabler for the 6G wireless communication systems. The multiple sensing modalities will allow the base station to have a more accurat…
Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection
Han Zhang, Akram Bin Sediq, Ali Afana +1
Large language models (LLMs), especially generative pre-trained transformers (GPTs), have recently demonstrated outstanding ability in information comprehension and problem-solving…
Fast Group Scheduling for Downlink Large-Scale Multi-Group Multicast Beamforming
Chong Zhang, Min Dong, Ben Liang +2
Next-generation wireless networks need to handle massive user access effectively. This paper addresses the problem of joint group scheduling and multicast beamforming for downlink…
Multi-Model Wireless Federated Learning with Downlink Beamforming
Chong Zhang, Min Dong, Ben Liang +2
This paper studies the design of wireless federated learning (FL) for simultaneously training multiple machine learning models. We consider round robin device-model assignment and…