Adaptive Scaling and Spectrum-Aware Enhancement of O-LSDC for Robust MIMO Detection under Realistic Channel Conditions
| International Journal of Electrical and Electronics Engineering |
| © 2026 by SSRG - IJEEE Journal |
| Volume 13 Issue 3 |
| Year of Publication : 2026 |
| Authors : Sagar Sutar, Chetan More |
How to Cite?
Sagar Sutar, Chetan More, "Adaptive Scaling and Spectrum-Aware Enhancement of O-LSDC for Robust MIMO Detection under Realistic Channel Conditions," SSRG International Journal of Electrical and Electronics Engineering, vol. 13, no. 3, pp. 260-274, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I3P119
Abstract:
Multiple-Input Multiple-Output (MIMO) systems have significant difficulties observing in strong signals because of inter-antenna interference, channel estimation errors, and signal-propagation errors due to the use of iterative cancellation-based detectors. These problems decrease the accuracy of detection and performance in different SNR and CSI. The paper proposes an ingenious detection system that involves adaptive scaling with spectral-domain analysis, which is known as Enhanced O-LSDC, as a way of eliminating these limitations. The method combines mutually adaptive scaling in which the cancellation mechanism is dynamically changed as a function of real-time SNR, CSI, and network behavior. It is also extended to deal with realistic impairments like imperfect CSI, multi-user interference, and error-propagation estimation, and also performs an analysis in the spectral domain to investigate signal distribution, coding energy, and spectral efficiency. The results of the simulation indicate that the proposed Enhanced O-LSDC outperforms the conventional fixed-scaling techniques in terms of bit-error rates and spectral efficiency, as well as offers more CSI uncertainty tolerance and performance variation with a wide range of SNR and channel conditions. These results suggest that the adaptive-scaling-based O-LSDC is a high-quality and computationally small detection strategy that can be used in next-generation wireless systems to support the demands of emerging 5G/6G MIMO structures.
Keywords:
MIMO, O-LSDC, Adaptive scaling, SNR, CSI, Spectrum analysis, 5G/6G.
References:
[1] Babak Hassibi, and Bertrand Hochwald, “High-Rate Codes that are Linear in Space and Time,” IEEE Transactions on Information Theory, vol. 48, no. 7, pp. 1804-1824, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Xiaodong Wang, V. Krishnamurthy, and Jibing Wang, “Stochastic Gradient Algorithms for Design of Minimum Error-Rate Linear Dispersion Codes in MIMO Wireless Systems,” IEEE Transactions on Signal Processing, vol. 54, no. 4, pp. 1242-1255, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Nan Wu, and Hamid Gharavi, “Asynchronous Cooperative MIMO Systems using a Linear Dispersion Structure,” IEEE Transactions on Vehicular Technology, vol. 59, no. 2, pp. 779-787, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Jinsong Wu, and Steven D. Blostein, “High-Rate Diversity Across Time and Frequency using Linear Dispersion,” IEEE Transactions on Communications, vol. 56, no. 9, pp. 1469-1477, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Robert W. Heath, and Arogyaswami Paulraj, “Linear Dispersion Codes for MIMO Systems based on Frame Theory,” IEEE Transactions on Signal Processing, vol. 50, no. 10, pp. 2429-2441, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Ramy H. Gohary, and Timothy N. Davidson, “Design of Linear Dispersion Codes: Asymptotic Guidelines and their Implementation,” IEEE Transactions on Wireless Communications, vol. 4, no. 6, pp. 2892-2906, 2005.
[CrossRef] [Google Scholar] [Publisher Link]
[7] E. Elakkiyachelvan, and R.J. Kavitha, “Dynamic Channel Estimation in Large-Scale Massive MIMO Systems with Intelligent Reflecting Surfaces using Khatri-Rao Factorization and Bilinear Alternating Least Squares,” Ain Shams Engineering Journal, vol. 15, no. 11, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Spandan Bisoyi et al., “Massive MIMO with Circular Antenna Array: Design, Implementation, and Validation,” IEEE Access, vol. 12, pp. 21071-21083, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Hamidreza Khaleghi, and Stéphane Paquelet, “Adaptive Low-Overhead Channel Estimation Tracking in RIS-Assisted Systems,” IEEE Access, vol. 13, pp. 88589-88599, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Chen Hu et al., “Two-Timescale Channel Estimation for Reconfigurable Intelligent Surface Aided Wireless Communications,” IEEE Transactions on Communications, vol. 69, no. 11, pp. 7736-7747, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Songjie Yang et al., “Reconfigurable Intelligent Surface Aided Full-Duplex mmWave MIMO: Channel Estimation, Passive and Hybrid Beamforming,” IEEE Transactions on Wireless Communications, vol. 23, no. 4, pp. 2575-2590, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Tobias Lindstrøm Jensen, and Elisabeth De Carvalho, “An Optimal Channel Estimation Scheme for Intelligent Reflecting Surfaces based on a Minimum Variance Unbiased Estimator,” ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, pp. 5000-5004, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Rafaela Schroeder et al., “Two-Stage Channel Estimation for Hybrid RIS-Assisted MIMO Systems,” IEEE Transactions on Communications, vol. 70, no. 7, pp. 4793-4806, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Salah Eddine Zegrar, Liza Afeef, and Hüseyin Arslan, “Reconfigurable Intelligent Surface (RIS): Eigenvalue Decomposition-based Separate Channel Estimation,” 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki, Finland, pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Taiyang Ling et al., “Two-Phase Parameter-based Separate Channel Estimation in RIS-Aided MIMO OFDM Systems,” ICC 2023 - IEEE International Conference on Communications, Rome, Italy, pp. 4329-4334, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Hamidreza Khaleghi, and Abdullah Haskou, “Optimized Channel Estimation Strategies for RIS-Aided Communication,” IEEE Communications Letters, vol. 29, no. 3, pp. 453-456, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[17] David Tse, and Pramod Viswanath, Fundamentals of Wireless Communication, Cambridge University Press, 2005.
[Google Scholar] [Publisher Link]
[18] Gerard J. Foschini, “Layered Space-Time Architecture for Wireless Communication in a Fading Environment when using Multi-Element Antennas,” Bell Labs Technical Journal, vol. 1, no. 2, pp. 41-59, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[19] E. Viterbo, and Joseph J. Boutros, “A Universal Lattice Code Decoder for Fading Channels,” IEEE Transactions on Information Theory, vol. 45, no. 5, pp. 1639-1642, 1999.
[CrossRef] [Google Scholar] [Publisher Link]

10.14445/23488379/IJEEE-V13I3P119