Model Classification-and-Selection Assisted Robust Receiver for OFDM Systems

Abstract

This paper devises a robust receiver for OFDM systems in the presence of residual timing offsets and unknown channel prior information. The proposed receiver constructs typical receiver models and resorts to the model selection technique to choose the best-matched receiver model to improve the channel estimation and signal detection. The typical receiver models are classified by considering the channel delay spread and the level of timing offset. Based on the receiver model selected by the Bayesian model selection algorithm, the channel length and timing offset parameters in the receiver model can provide the effective channel statistical information to make the channel estimator adapt to the altered circumstances and thus more accurate. Furthermore, the effective interference variance parameters in the selected receiver model are used to enhance the channel estimation and refine the soft symbol detection. The simulation results show that the proposed receiver achieves a significant performance gain compared to the existing methods. It is also shown that the proposed scheme makes the receiver robust to the diverse channel conditions and the timing offset states at a cost of the only a moderate increase in complexity. INDEX TERMSOrthogonal frequency-division multiplexing (OFDM), channel estimation, symbol timing offset, model selection.

LMMSE channel estimation for OFDM systems with channel correlation function selection

“…However, τ max should be as close to the actual maximum delay as possible given consideration to performance. To improve the channel estimation performance, the CCFs based on Equation () with several values of τ max [31] can be used as candidates for the estimator to choose.…”

“…However, 𝜏 max should be as close to the actual maximum delay as possible given consideration to performance. To improve the channel estimation performance, the CCFs based on Equation ( 8) with several values of 𝜏 max [31] can be used as candidates for the estimator to choose. With the parameter set Ω which comprises of CCF vectors based on several assumptions of maximum delay, the estimator can select the one that is closest to the actual value.…”

Enhanced LMMSE Estimation Capable of Selecting Parameters

“…where K is the dimension of h. ĥk Int|θ θ θn is MMSE interpolation based on the parameters in θ θ θ n and specifically expressed as (3) LS is the value of LS estimation and not influenced by the interpolation methods. Thus, the performance index ξ of a good parameter vector tends to be lower than that of a bad one.…”

“…The computational complexity of the proposed estimator mainly depends on the number of correlation candidates N , and computing resource consumption is focused on the calculation of evaluation indexes for the correlation candidates. The computational complexity of the index calculation module is O N K(K − 1) 3 . It is comparable to the complexity of performing KN times (K − 1)-order LMMSE estimation.…”

“…16, long term evolution (LTE), and 5G [1], [2]. Channel estimation is one of the key issues in OFDM systems [3], [4], [5]. The pilot assisted estimation has lower complexity and better performance and is widely used in practical systems.…”