INUE Showcases Cutting-Edge Research at IEEE WCNC 2025 in Milan
The INUE proudly contributed three groundbreaking research papers to the IEEE Wireless Communications and Networking Conference (WCNC) 2025, held in the dynamic city of Milan, Italy. As a flagship event of the IEEE Communications Society, WCNC brings together leading experts from academia, industry, and government to explore the latest advances in wireless communications.
The INUE's contributions spanned a diverse range of topics, reflecting its commitment to advancing next-generation wireless technologies:
Bistatic Information Fusion for Positioning and Tracking in Integrated Sensing and Communication
This work addresses the challenges of utilizing bistatic measurements in cellular networks for integrated sensing and communication (ISAC). Unlike traditional geometric approaches, the work proposed a maximum likelihood-based framework that adaptively fuses any subset of bistatic measurements—transmitter angle, receiver angle, and bistatic range—enabling flexible integration even in multistatic setups. The research also introduces novel methods for computing the position error covariance, enhancing tracking accuracy with Bayesian techniques like Kalman filters.
Bounds for Joint Detection and Decoding on the Binary-Input AWGN Channel
Focusing on low-latency communications, this paper tackles the inefficiencies caused by preambles in short-packet transmissions. The INUE researchers analyzed two joint detection and decoding (JDD) strategies: Hybrid Preamble and Energy Detection (HyPED) and Decoder-Aided Detection (DAD). The study presents new achievability and converse bounds for these techniques over the binary-input additive white Gaussian noise (BI-AWGN) channel, along with a general bound on the required blocklength—paving the way for more efficient communication protocols.
Channel Charting-Based Channel Prediction on Real-World Distributed Massive MIMO CSI
With distributed massive MIMO systems poised to revolutionize wireless networks, the INUE team tackled the problem of channel aging in mobile environments. Leveraging channel charting, a self-supervised learning technique, the researchers developed a method to predict future channel state information (CSI) using spatial relationships in a learned latent space. Validated on real-world CSI data, their approach outperformed conventional predictors like the Wiener method, showing improved sum rate performance.
