Abstract
As only a few parts of wireless resources can be utilized for pilot transmission, channel estimation, especially the interpolation process, has often been recognized as a challenging ill-posed reconstruction problem. To deal with this task, we formulate it as a typical image super resolution problem, and propose a recurrent residual learning framework named LSRN. Our proposed scheme jointly utilizes the advantages of recurrent and residual structure in the machine learning area to approximate the non-linear interpolation relations between the reference signal and surrounding resource elements. In addition, we propose a low complexity implementation scheme called LSRN-L to address the stringent processing delay requirement in the channel estimation tasks. Through numerical examples as well as prototype verification, the proposed LSRN/LSRN-L can easily outperform the convolutional GI plus DFT based interpolation scheme by 10dB in terms of normalized mean square error. Meanwhile, the low complexity LSRN-L can maintain the processing delay within one millisecond.
Highlights
Evolved mobile broadband transmission has been identified as one of the most important scenarios in the fifth generation (5G) communication systems [1]
As a few part of resources can be utilized for pilot transmission in modern wireless communications, channel estimation has been recognized as a challenging ill-posed reconstruction problem with only a small amount of observations [4]
Based on some numerical simulations and prototype verification, we show that the proposed LSRN-L can provide 10 dB to 11 dB normalized mean square error (NMSE) improvement if compared with the conventional guassian interpolation (GI) plus discrete fourier transform interpolation (DFTI) based interpolation schemes and preserve a processing delay within one millisecond
Summary
Evolved mobile broadband (eMBB) transmission has been identified as one of the most important scenarios in the fifth generation (5G) communication systems [1]. In our previous work [9], to address the above challenges, we have proposed a novel channel estimation scheme using the super resolution image recovery concept, which achieves significant performance improvement based on the numerical evaluations. We propose to use the recurrent architecture together with the traditional residual learning task to balance the achieved NMSE performance and the processing delay, which is a novel concept based on our literature survey. We apply the residual network model to approximate the non-linear interpolation relations of real-time CSI between the reference signals (RSs) and the neighboring resource elements (REs) to improve the estimation accuracy, and utilize the recurrent structure to learning the slow-varying time domain correlation among consecutive OFDM symbols.
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