Research
My research connects fundamental information theory with practical code and decoder design. Current themes include high-rate channel coding, quantum error correction, coding for quantum key distribution, and learning-assisted decoding.
Polar and PAC Codes
I study the construction, rate-profile design, and low-complexity decoding of polar-like and PAC codes. My work includes sequential decoding metrics, tree pruning for SCL decoding, Monte-Carlo construction, and search-constrained optimization.
A central result establishes bounded sequential-decoding complexity when PAC rate profiles follow polarized cutoff-rate constraints. Polarization increases the average cutoff rate while preserving channel capacity, permitting reliable communication closer to capacity without exponential decoding complexity.
Fast Decoder Design
I develop metric-polarization and pruning methods for fast SC- and SCL-based decoders. These methods reduce sorting operations and latency while maintaining error-correction performance. I have also studied bit flipping, stack decoding, guessing-based decoding, and iterative decoding for PAC codes.
Quantum Error Correction
My quantum research includes sequential belief-propagation decoding of QLDPC codes, reinforcement-learning approaches for quantum LDPC decoding, quantum convolutional codes, and coding for quantum key distribution. The aim is to connect structured quantum-code construction to implementable, scalable decoders.
Wireless Communications and Machine Learning
I have worked with fading and multipath channel models, QAM and OFDM modulation, TDMA/OFDMA/NOMA, MIMO systems, and spectrum sharing. My work also covers learning-based beamforming, channel estimation, and reinforcement-learning-enhanced belief-propagation decoding.