Abstract: Motivated by hyper-reliable low-latency communication in 6G, we consider error-control coding for short block lengths in SIMO flat-fading channels. In general, the channel fading coefficients are unknown to both the transmitter and the receiver, and this setting is referred to as non-coherent communication. Conventionally, pilot symbols are transmitted to facilitate channel estimation, thereby incurring power and bandwidth overhead. In this thesis we consider sparse regression codes (SPARCs) for non-coherent flat-fading channels without using pilots. We develop a novel greedy decoder for SPARCs using maximum-likelihood (ML) principles, referred to as maximum likelihood matching pursuit (MLMP). Unlike conventional greedy algorithms based on successive cancellation, MLMP is based on a successive combining principle. We also obtain a noiseless perfect recovery condition for the proposed successive combining algorithm. To mitigate error propagation in greedy decoding and improve block error rate (BLER) performance, we further introduce an enhanced version termed parallel-MLMP (P-MLMP). In addition, we develop an approximate message passing (AMP) decoder for SPARCs under the non-coherent SIMO flat-fading model. Through simulation studies, we show that the MLMP decoder for SPARCs outperforms AMP and other greedy decoders. We also show that SPARCs with the P-MLMP decoder outperform polar codes employing pilot-based channel estimation and polar codes with non-coherent decoders. Finally, we introduce power allocation schemes for SPARC encoding, which significantly improve the BLER performance of SPARCs at high code rates.

Event Details
Title: Sparse regression codes for Non-coherent SIMO channels(PhD viva voce)
Date: July 02, 2026 at 03:00 PM
Venue: ESB 244 / Google Meet (https://meet.google.com/nfb-joob-ygs)
Speaker: Mr. Sai Dinesh (EE20D401)
Guide: Dr. Arun Pachai Kannu
Type: PHD seminar

Updated: