Accuracy Comparison of Hard, Soft, and UnQuantized Decisions across Diverse MIMO Channels and M-QAM Demodulation

Qusay Jalil Kadhim (), Abdullah Al-Husseini () and Mohanad Al-Ibadi ()

Asian Journal of Computing and Engineering Technology, 2025, vol. 6, issue 1, 1 - 16

Abstract: Purpose: In this paper, hard, soft, and non-quantized Viterbi decision algorithms are examined and compared over multiple-input multiple-output (MIMO), Quadratic amplitude modulation, QAM schemes, and M-ary channels. Methodology: The proposed system supports the design methodology of a decoding scheme of the LLR algorithm between MIMO and M-ary channels. The proposed system is designed and tested using MATLAB. Findings: The results provide valuable insights into the balance between computational efficiency and decoding accuracy, which helps in choosing appropriate decision algorithms for specific MIMO and M-QAM communication systems. The results demonstrated different scenarios, including MIMO (2x2), (4x4), and M-ary (4,16, and 32 QAM) technologies. Unique Contribution to Theory, Practice, and Policy: Experimental results show that although non-quantized decisions achieve the highest accuracy, they require significantly more computational resources. Hard decisions provide simpler implementation with lower computational costs but lower accuracy. The soft decision-making method balances performance and complexity and outperforms hard choices regarding bit error rate (BER) in all tested scenarios. It also enhances our understanding of Viterbi decoding in LLR estimation across different connectivity conditions. The scenarios showed that the soft solution is better than the hard solution, as previously known, depending on the conditions. However, from the results obtained using Matlab simulations, the silky solution appears to be the best in terms of the balance between complexity and better performance as the data show.

Keywords: Bit Error Rates (BER); Convolutional Encoding; Soft-decision; Hard-decision; Log Likelihood Ratio (LLR); Multi Input Multi Output (MIMO); M-QAM (search for similar items in EconPapers)
Date: 2025
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