Symbol-Level GRAND for High-Order Modulation Over Block Fading Channels

Guessing random additive noise decoding (GRAND) is a noise-centric method, which suitable for low-latency communications, as it supports error correction codes that generate short codewords. GRAND estimates transmitted codewords by guessing the patterns altered them during transmission. The process requires testing of are arranged in increasing order Hamming weight. This approach fitting binary transmission over white Gaussian channels. letter considers coded and modulated data block fading channels proposes more computationally efficient variant GRAND, leverages information on modulation scheme channel. In core proposed variant, referred to symbol-level an expression approximately computes probability occurrence pattern determines with tested. Analysis simulation results demonstrate produces faster than original at cost small increase memory requirements.