A Spectrally Efficient ML Estimation Method for OFDM Systems over Sparse Multipath Channels

Rekha Gupta, A. Trivedi
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Abstract

For getting satisfactory error rate performance, channel estimation is required at receiver. Maximum likelihood (ML) estimation method is preferred due to its low complexity. The conventional ML estimation for sparse multipath channels requires a large number of pilot symbols and thus increases the spectral inefficiency. This paper investigates an efficient way to apply pilot aided ML estimation technique in orthogonal frequency division multiplexing (OFDM) systems to estimate the channel impulse response (CIR) of sparse multipath channels. An algorithm which exploits channel sparsity is proposed to carry out the ML estimation using lesser number of pilot symbols as compared to the conventional method. The symbol error rate performance (SER) of OFDM system assisted by the proposed scheme is analyzed. Simulation results show that significant reduction in spectral efficiency loss can be achieved with small increase in required SNR.
稀疏多径OFDM系统的频谱高效ML估计方法
为了获得满意的误码率性能,需要在接收端进行信道估计。最大似然(ML)估计方法由于其较低的复杂度而成为首选方法。传统的稀疏多径信道的机器学习估计需要大量的导频符号,从而增加了频谱的低效率。研究了在正交频分复用(OFDM)系统中利用导频辅助ML估计技术估计稀疏多径信道脉冲响应(CIR)的有效方法。提出了一种利用信道稀疏性的算法,与传统方法相比,使用较少的导频符号进行机器学习估计。分析了该方案辅助OFDM系统的码元误码率性能。仿真结果表明,在提高所需信噪比很小的情况下,可以显著降低频谱效率损失。
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