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arXiv:2408.11726 · IEEE International Conference on Quantum Computing and Engineering (QCE)

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

Srikar Kasi, James Sud, Kyle Jamieson, Gokul Subramanian Ravi

Published Sep 15, 2024• 2 citations
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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.

DOI
10.1109/qce60285.2024.00051

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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.
In mobile communication systems, the increasing densification of radio access networks is creating unprecedented computational stress for baseband processing, threatening the industry's sustainability, and new computing paradigms are urgently needed to improve the efficiency of wireless processors. Quantum computing promises to revolutionize many computing-intensive tasks across diverse fields and therefore may be the key to realizing ultra-dense next-generation mobile systems that remain economically and environmentally viable. This paper investigates the potential of Quantum computing to accelerate Forward Error Correction (FEC), the most compute-heavy component of wireless processors. We first propose Qu4Fec, a novel solution for decoding Low-Density Parity Check (LDPC) codes on Quantum Processing Units (QPUs), which we show to outperform state-of-the-art approaches, by reducing the Block Error Rate (BLER) by nearly an order of magnitude in simulation. We then implement Qu4Fec on a real-world QPU platform to study its practical viability and performance. Our experiments reveal that current cutting-edge QPU architectures curb the capabilities of FEC and expose the underlying factors, including long qubit chains, scaling, and quantization. Based on these insights, we suggest original blueprints for future QPUs that can better support Quantum-based wireless processors. Overall, this paper provides a reliable reality check for the feasibility of wireless processing on Quantum annealers: as QPUs start to be considered part of a possible 6G landscape, our work may open new research paths towards the design of FEC methods for Quantum-powered wireless processors.
Quantum computers could offer substantial energy-saving advantages over conventional compute servers, particularly in the context of 5G signal processing. This paper explores the potential of Quantum Annealing (QA) to implement Forward Error Correction (FEC) and introduces Qu4Fec, an innovative solution for decoding Low-Density Parity Check (LDPC) codes. Our findings demonstrate that Qu4Fec improves state-of-the-art approaches in terms of Block Error Rate (BLER). However, our experiments reveal that the current leading Quantum Processing Units (QPUs) design is suboptimal for the FEC problem formulation. We identify key challenges, including qubit chain length, scaling, and quantization, and propose Quantum architectures to address these limitations.
Decoding Low-Density Parity-Check (LDPC) codes is a fundamental problem in coding theory, and Belief Propagation (BP) is one of the most popular methods for LDPC code decoding. However, BP may encounter convergence issues and suboptimal performance, especially for short-length codes and in high-noise channels. The Quantum Approximate Optimization Algorithm (QAOA) is a type of Variational Quantum Algorithm (VQA) designed to solve combinatorial optimization problems by minimizing a problem-specific cost function. In this paper, we present a QAOA-based decoding framework for LDPC codes by formulating a decoding cost function that incorporates both parity-check constraints and soft channel reliability information. The resulting optimization problem is solved using QAOA to search for low-energy configurations corresponding to valid codewords. We test the proposed method through extensive numerical experiments and compare its performance with BP decoding. The experimental results demonstrate that the QAOA-based decoder achieves a higher probability of correctly recovering the transmitted codeword than BP across multiple experimental settings.
Quantum computers could offer substantial energy-saving advantages over conventional compute servers, particularly in the context of 5G signal processing. This paper explores the potential of Quantum Annealing (QA) to implement Forward Error Correction (FEC) and introduces Qu4Fec, an innovative solution for decoding Low-Density Parity Check (LDPC) codes. Our findings demonstrate that Qu4Fec improves state-of-the-art approaches in terms of Block Error Rate (BLER). However, our experiments reveal that the current leading Quantum Processing Units (QPUs) design is suboptimal for the FEC problem formulation. We identify key challenges, including qubit chain length, scaling, and quantization, and propose Quantum architectures to address these limitations.
The use of quantum computation for wireless network applications is emerging as a promising paradigm to bridge the performance gap between in-practice and optimal wireless algorithms. While today's quantum technology offers limited number of qubits and low fidelity gates, application-based quantum solutions help us to understand and improve the performance of such technology even further. This paper introduces QGateD-Polar, a novel Quantum Gate-based Maximum-Likelihood Decoder design for Polar error correction codes, which are becoming widespread in today's 5G and tomorrow's NextG wireless networks. QGateD-Polar uses quantum gates to dictate the time evolution of Polar code decoding -- from the received wireless soft data to the final decoded solution -- by leveraging quantum phenomena such as superposition, entanglement, and interference, making it amenable to quantum gate-based computers. Our early results show that QGateD-Polar achieves the Maximum Likelihood performance in ideal quantum simulations, demonstrating how performance varies with noise.
The rapid growth of next-generation networks has created a strong demand for communication systems that have delivered high reliability, low latency, and resilience under harsh channel conditions. Although classical error correction codes have improved many wireless links, their performance has proved insufficient as data rates increased and channel dynamics became more unpredictable. This study has explored a quantum-inspired error correction framework that has combined structural principles from quantum stabilizer codes with the efficiency of classical block codes. The aim was to provide an adaptive mechanism that has reduced noise effects and supported ultra-reliable communication targets. The problem has emerged from the gap between existing coding techniques and the reliability requirements of mission-critical services. Classical codes have struggled when the channel has exhibited fast fading or burst noise, and quantum codes, while powerful, have required complex hardware. The proposed approach has addressed this gap by adopting quantum-inspired parity structures that have retained the lightweight processing of classical codes while mimicking the robustness observed in quantum systems. The method has employed a hybrid coding model that has integrated a modified stabilizer-like generator with a classical low-density parity check backbone. The encoder has produced redundant qubit-analogous syndromes that have allowed the decoder to infer error patterns with higher confidence. A sequential belief-propagation algorithm has been used, which has adjusted decoding weights according to channel variation. Simulations have been performed over Rayleigh and Rician channels, and the system has been tested under high mobility. The results of the proposed framework demonstrate substantial improvements over conventional coding methods. The hybrid stabilizer-LDPC structure reduces the bit error rate from 22.3% to 0.5% across an SNR range of 0–20 dB and lowers the frame error rate from 45.7% to 1.1%. Throughput improves from 4.2 Mbps at 0 dB to 13.8 Mbps at 20 dB, while the average decoding iterations decrease from 42 to 5, indicating reduced computational complexity. Under high-speed mobility, BER and FER remain low at 2.3% and 4.6%, respectively, while throughput stays above 10.2 Mbps and convergence requires only 12 iterations. These numerical results confirm that the proposed method provides highly reliable, efficient, and adaptive error correction suitable for next-generation networks.