Joint Error Correction and Fading Channel Estimation Enhancement Leveraging GRAND

TL;DR

This paper introduces two GRAND-based methods to improve error correction in fading channels with estimation errors, achieving significant BLER gains without increasing pilot length.

Contribution

It proposes novel GRAND decoder techniques that enhance error correction by accounting for channel estimation errors, avoiding traditional pilot-based refinement.

Findings

Achieved several dB BLER improvements over ignoring CEE.

Validated methods on 5G NR CA-Polar and CRC codes.

Demonstrated effectiveness in realistic fading channel scenarios.

Abstract

We present a novel method for error correction in the presence of fading channel estimation errors (CEE). When such errors are significant, considerable performance losses can be observed if the wireless transceiver is not adapted. Instead of refining the estimate by increasing the pilot sequence length or improving the estimation algorithm, we propose two new approaches based on Guessing Random Additive Noise Decoding (GRAND) decoders. The first method involves testing multiple candidates for the channel estimate located in the complex neighborhood around the original pilot-based estimate. All these candidates are employed in parallel to compute log-likelihood ratios (LLR). These LLRs are used as soft input to Ordered Reliability Bits GRAND (ORBGRAND). Posterior likelihood formulas associated with ORBGRAND are then computed to determine which channel candidate leads to the most probable codeword. The second method is a refined version of the first approach accounting for the presence of residual CEE in the LLR computation. The performance of these two techniques is evaluated for [128,112] 5G NR CA-Polar and CRC codes. For the considered settings, block error rate (BLER) gains of several dBs are observed compared to cases where CEE is ignored.