A Quantum Approximate Optimization Algorithm-based Decoder Architecture for NextG Wireless Channel Codes

TL;DR

This paper introduces FDeQ, a quantum approximate optimization algorithm-based decoder architecture for NextG wireless channel codes, demonstrating potential advantages in decoding performance and resource efficiency over classical methods.

Contribution

The paper proposes a novel QAOA-based FEC decoder architecture, FDeQ, optimized for NextG wireless codes, integrating temporal similarity to improve decoding efficiency and feasibility.

Findings

FDeQ achieves error performance comparable to classical decoders at low code lengths.

Resource estimation indicates future quantum devices could outperform classical decoders in certain scenarios.

FDeQ exploits fixed code structures to accelerate quantum decoding processes.

Abstract

Forward Error Correction (FEC) provides reliable data flow in wireless networks despite the presence of noise and interference. However, its processing demands significant fraction of a wireless network's resources, due to its computationally-expensive decoding process. This forces network designers to compromise between performance and implementation complexity. In this paper, we investigate a novel processing architecture for FEC decoding, one based on the quantum approximate optimization algorithm (QAOA), to evaluate the potential of this emerging quantum compute approach in resolving the decoding performance-complexity tradeoff. We present FDeQ, a QAOA-based FEC Decoder design targeting the popular NextG wireless Low Density Parity Check (LDPC) and Polar codes. To accelerate QAOA-based decoding towards practical utility, FDeQ exploits temporal similarity among the FEC decoding tasks. This similarity is enabled by the fixed structure of a particular FEC code, which is independent of any time-varying wireless channel noise, ambient interference, and even the payload data. We evaluate FDeQ at a variety of system parameter settings in both ideal (noiseless) and noisy QAOA simulations, and show that FDeQ achieves successful decoding with error performance at par with state-of-the-art classical decoders at low FEC code block lengths. Furthermore, we present a holistic resource estimation analysis, projecting quantitative targets for future quantum devices in terms of the required qubit count and gate duration, for the application of FDeQ in practical wireless networks, highlighting scenarios where FDeQ may outperform state-of-the-art classical FEC decoders.