Adaptive Multi-Mode Blind Equalization Scheme for OFDM-NOMA Systems

YANG Long; YU Kaixin; LI Jin; JIA Ziyi

doi:10.11999/JEIT250153

Volume 47 Issue 8

Aug. 2025

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YANG Long, YU Kaixin, LI Jin, JIA Ziyi. Adaptive Multi-Mode Blind Equalization Scheme for OFDM-NOMA Systems[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2509-2520. doi: 10.11999/JEIT250153

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YANG Long, YU Kaixin, LI Jin, JIA Ziyi. Adaptive Multi-Mode Blind Equalization Scheme for OFDM-NOMA Systems[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2509-2520. doi: 10.11999/JEIT250153

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Adaptive Multi-Mode Blind Equalization Scheme for OFDM-NOMA Systems

doi: 10.11999/JEIT250153 cstr: 32379.14.JEIT250153

School of Communication Engineering, Xidian University, Xi’an 710000, China

Funds: The National Natural Science Foundation of China (62271368, 62371367), The Key Research and Development Program of Shaanxi (2023-ZDLGY-50), The Fundamental Research Funds for the Central Universities(QTZX23066), The Youth Science and Technology Star Program of Shaanxi (2024ZC-KJXX-080)

Received Date: 2025-03-12
Rev Recd Date: 2025-07-25

Available Online: 2025-07-30

Publish Date: 2025-08-27

Abstract

Abstract

Objective Orthogonal Frequency Division Multiplexing (OFDM) combined with Non-Orthogonal Multiple Access (NOMA) is widely applied in next-generation wireless communication systems for its high spectral efficiency and support for concurrent multi-user transmission. However, in downlink transmission, the superposition of signals from multiple users on the same subcarrier yields non-standard Quadrature Amplitude Modulation (QAM) constellations, rendering conventional equalization techniques ineffective. In addition, channel variability and impulsive noise introduce severe distortion, further degrading system performance. To overcome these limitations, this paper proposes an unsupervised adaptive multi-mode blind equalization scheme designed for OFDM-NOMA systems. Methods The proposed equalization scheme combines the Multi-Mode Algorithm (MMA) with a Soft-Decision Directed (SDD) strategy to construct an adaptive cost function. This function incorporates the power allocation factors of NOMA users to compensate for amplitude and phase distortions introduced by the wireless channel. To minimize the cost function efficiently, an optimized Newton method is employed, which avoids direct matrix inversion to reduce computational complexity. An iterative update rule is derived to enable fast convergence with low processing overhead. The algorithm is implemented on a real-time Software-Defined Radio (SDR) system using the GNURadio platform for practical validation. Results and Discussions Simulation results show that the proposed equalization algorithm substantially outperforms conventional methods in both convergence speed and accuracy. Compared with the traditional Minimum Mean Square Error (MMSE) algorithm, it reduces convergence time by 90% while achieving comparable performance without the use of pilot signals (Fig. 8). Constellation diagrams before and after equalization confirm that the algorithm effectively restores non-standard QAM constellations distorted by NOMA signal superposition (Fig. 9). The method also demonstrates strong robustness to impulsive noise and dynamic channel variations. Complexity analysis indicates that the proposed algorithm incurs lower computational overhead than conventional Newton-based equalization approaches (Table 1). Experimental validation on the GNURadio platform confirms its ability to separate user signals and support accurate decoding in real-world OFDM-NOMA downlink conditions (Fig. 12). Conclusions This study presents a blind equalization scheme for OFDM-NOMA systems based on an MMA-SDD adaptive cost function and an optimized Newton method. The proposed algorithm compensates for amplitude and phase distortions, enabling reliable signal recovery without pilot information. Theoretical analysis, simulation results, and experimental validation confirm its fast convergence, robustness to noise, and low computational complexity. These characteristics support its potential for practical deployment in future NOMA-based wireless communication networks.
- Orthogonal frequency division multiplexing,
- Non-orthogonal multiple access,
- Blind equalization,
- Multi-mode algorithm,
- Soft-decision directed

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References(16)

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