Successive cancellation (SC) decoding of polar codes suffers from high decoding latency due to its sequential nature. Fast-SC decoding alleviates this by identifying special nodes that enable concurrent multi-bit decoding. Recently, a more general special node for high-rate polar codes has been introduced, offering reduced decoding latency but at the cost of substantial memory overhead for storing precomputed flipping sets. Alternative flipping set generation methods eliminate memory overhead but rely on iterative procedures, undermining decoding latency benefits. This paper proposes a reduced-memory fast-SC (RMFSC) decoding method based on the inherent parity constraints of sequence single-parity-check (SSPC) nodes, without the need for flipping set storage. Simulation results and complexity analysis show that the proposed RM-FSC reduces memory requirements by up to $93.9 \%$ compared to the prior arts, while preserving low latency and incurring only negligible performance degradation.
This paper introduces a quantization-aware optimization framework for MIMO detection that jointly optimizes algorithmic parameters and fixed-point quantization across layers in Breadth-First Search Detection (BFSD). Unlike conventional approaches that separate algorithm design from hardware implementation, our framework integrates these perspectives to achieve superior performance-complexity trade-offs. Experimental results on an $8 \times 8$ MIMO system with 16-QAM modulation demonstrate up to $37.7 \%$ reduction in computational complexity while maintaining BER performance comparable to floating-point implementations, with notable advantages at high SNR regions. The proposed framework is algorithm-agnostic, supporting various optimization algorithms, and can be readily extended to other detection problems requiring hardware-efficient implementations.
Automorphism ensemble decoding with the successive cancellation constituent decoder (AED-SC) can improve the decoding performance of the successive cancellation decoder when decoding polar codes with short-to-medium code lengths. To reduce the number of automorphisms used by the AEDSC, simplified repetition handling early-stopping is proposed in this work. Compared to the original repetition handling earlystopping, our simplified version compares the path metric instead of the codeword. Up to a $2 \times$ reduction in the number of automorphisms used is observed when decoding polar codes with different code lengths and dimensions while having little degraded decoding performance. Also, the proposed simplification reduces the number of bit comparisons from a number equal to the code length to the number of bits required to represent the path metric.
This paper proposes an end-to-end radio simultaneous localization and mapping (SLAM) algorithm that directly leverages channel impulse response (CIR) to overcome fundamental limitations in existing approaches. Traditional radio SLAM algorithms assume pre-estimated channel parameters, making performance highly sensitive to estimation accuracy, while recent end-to-end methods jointly perform parameter estimation and SLAM but suffer from high computational complexity and model mismatch vulnerability. The proposed algorithm minimizes information loss by operating directly on raw CIR measurements and utilizes end-to-end learning for enhanced robustness. Simulation results in the 3GPP TR 38.857 indoor factory scenario demonstrate that the proposed algorithm achieves comparable performance to conventional radio SLAM while reducing computational time by less than $2 \%$, confirming its strong potential for practical deployment.
With 5G/6G development, sophisticated scenarios and diverse requirements challenge decoding choices in MultipleInput Multiple-Output (MIMO) system. Traditional methods struggle to rapidly identify optimal algorithms tailored to unique requirements, as parameters vary drastically across scenarios. To tackle those challenges, we propose a Transformer-based intelligent algorithm prediction method, introducing TokenMIMO-a MIMO algorithm classifier for complex scenarios to efficiently predict optimal decoding algorithms across algorithm prediction spaces. It achieves an accuracy of over 96%, outperforms state-of-the-art modules. Its exceptional generalization capability enables it to recommend optimal decoding algorithms to users with a policy distribution exceeding $94 \%$ accuracy within an extremely large algorithm selection space.
