We believe that interdisciplinary and cross-layer research, ranging from information-theoretic principles to VLSI implementation, is the most promising way to make future communications systems even faster, more reliable, and more energy efficient. For this reason, our work lies on the intersection between communications, hardware design, and machine learning. The specific research activities of our lab include algorithm and architecture co-design of VLSI circuits for communications, error-correction coding theory and practice, non-linear signal processing, massive MIMO, as well applications of approximate computing and machine learning to signal processing for communications.
Meet some of our Researchers
Our most recent peer reviewed publications
A Maximum-Likelihood-based Two-User Receiver for LoRa Chirp Spread-Spectrum ModulationIEEE Internet of Things Journal (2022)
Multi-Factor Pruning for Recursive Projection-Aggregation Decoding of RM Codes(2022)
Fast Sequence Repetition Node-Based Successive Cancellation List Decoding for Polar Codes(2022)
Device-free Movement Tracking using the UWB Channel Impulse Response with Machine Learning(2022)
A Two-User Successive Interference Cancellation LoRa Receiver with Soft-Decoding(2022)