activity
20242026
most citedRSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing

8 citations · 11 across the 16 of their papers we have counts for

collaborators
Showing eess.SPShow all

6 papers · 1 filter

eess.SP2026

Characterization of Beam-Squint and Beam-Split Effects in RIS-assisted Multi-Frequency Networks

Mohammad Amin Saeidi, Hina Tabassum

Multi-frequency operation in beyond-5G and 6G systems renders the beam directions of large-aperture arrays frequency-dependent, giving rise to beam misalignment effects critical fo…

eess.SP2026

MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation

Ahmed Y. Radwan, Hina Tabassum

Deep learning has been widely adopted for WiFi CSI-based human activity recognition (HAR) due to its ability to learn spatio-temporal features in a privacy-preserving and cost-effe…

eess.SP2026

AMAR: Lightweight Attention-Based Multi-User Activity Recognition from Wi-Fi CSI

Amirhossein Mohammadi, Hina Tabassum

Wi-Fi-based human activity recognition (HAR) has emerged as a promising approach for contactless sensing, leveraging channel state information (CSI) collected from wireless transce…

eess.SP2025

Resource Allocation in Cooperative Mid-band/THz Networks in the Presence of Mobility

Mohammad Amin Saeidi, Hina Tabassum

This paper develops a comprehensive framework to investigate and optimize the downlink performance of cooperative multi-band networks (MBNs) operating on upper mid-band (UMB) and t…

eess.SP2025

Placement, Orientation, and Resource Allocation Optimization for Cell-Free OIRS-aided OWC Network

Jalal Jalali, Hina Tabassum, Jeroen Famaey +2

The emergence of optical intelligent reflecting surface (OIRS) technologies marks a milestone in optical wireless communication (OWC) systems, enabling enhanced control over light…

eess.SP2024

Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing

B. Barahimi, H. Tabassum, M. Omer +1

WiFi sensing is an emerging technology that utilizes wireless signals for various sensing applications. However, the reliance on supervised learning, the scarcity of labelled data,…