Design of effective equalizer for modern multiple-input multiple-output orthogonal frequency division multiplexing (MIMO OFDM) wireless system is an important research problem as reconstruction of original information is quite challenging in the presence of different channel impairments. The equalizer design addressed in this paper uses IEEE 802.11 g indoor channel model and quadrature amplitude modulation (QAM) constellation to describe the fading statistics with a limited user mobility. The important research contributions can be visualized looking into two important aspects which includes a split complex extreme learning machine model trained using adaptive Levenberg–Marquardt algorithm and ON/OFF strategy of the equalizer using threshold based on signal to noise ratio (SNR) and inter symbol interference that indirectly helps in designing an energy efficient wireless receiver. Evaluation parameters like mean square error (MSE), eye diagram and symbol error rate (SER) have been considered in this paper to prove the efficiency of the proposed algorithm. The MSE value decreases to the range of \(10^{ - 2}\) with a varying training testing (I/K) ratio of 0.05 to 0.5. In addition, the proposed equalizer model outperforms the other existing methods in terms of SER by revealing a low value of 0.001 in 7 dB SNR in indoor channel conditions.

现代多输入多输出正交频分复用 (MIMO OFDM) 无线系统的有效均衡器设计是一个重要的研究问题，因为在存在不同信道损伤的情况下重建原始信息非常具有挑战性。本文中讨论的均衡器设计使用 IEEE 802.11 g 室内信道模型和正交幅度调制 (QAM) 星座来描述具有有限用户移动性的衰落统计。重要的研究贡献可以从两个重要方面进行可视化，包括使用自适应 Levenberg-Marquardt 算法训练的分裂复杂极限学习机模型和使用基于信噪比 (SNR) 和符号间阈值的阈值均衡器的开/关策略间接帮助设计节能无线接收器的干扰。本文考虑了均方误差（MSE）、眼图和符号错误率（SER）等评估参数，以证明所提出算法的有效性。MSE 值下降到范围\(10^{ - 2}\)具有 0.05 到 0.5 的不同训练测试 (I/K) 比率。此外，所提出的均衡器模型在 SER 方面优于其他现有方法，因为在室内信道条件下 7 dB SNR 中显示出 0.001 的低值。