Sparse massive MIMO-OFDM channel estimation based on compressed sensing over frequency offset environment

Abstract

In massive MIMO-OFDM systems, channel estimation is a significant module which can be utilized to eliminate multipath interference. However, in realistic communication systems, carrier frequency offset (CFO), which often exists in receive end, will deteriorate the performance of channel estimation. One of the effective solutions is to compensate CFO via the help of pseudo-noise (PN) sequence. At the beginning of this paper, to reduce system complexity and correctly compensate CFO, we propose an improved OFDM frame structure. Subsequently, we theoretically analyze the catastrophic influence of CFO on conventional PN-sequence-based compressed sensing (CS) channel estimation scheme. As our solution, based on the improved OFDM frame structure, a novel massive MIMO-OFDM channel estimation method under CFO environment is proposed. It first estimates CFO by utilizing differential correlation algorithm. Thereby, the interference caused by CFO can be eliminated. Then, relying on the PN sequence, the partial common support (PCS) information of each channel is obtained. Finally, using the PCS information as a priori information, we improve the CS reconstruction scheme to estimate the accurate channel. The simulation result shows that the proposed scheme demonstrates better MSE and BER performance than other mentioned schemes. The major advantage of our scheme is its anti-CFO ability and independence to channel sparsity level. Therefore, the proposed scheme is meaningful for practical use.

Improved autocorrelation method for time synchronization in filtered orthogonal frequency division multiplexing

“…The overall subcarriers in F-OFDM are grouped into several subbands [8], as shown in Figure 1. Thus, the parameters in each subband can be configured independently, such as the number of points of fast Fourier transforms (FFT), the number of subcarriers, the cyclic prefix (CP), and the subcarrier spacing.…”

“…Like OFDM, F-OFDM has some drawbacks, such as sensitivity to symbol timing errors (errors in picking up the starting point of the symbol) and carrier frequency mismatch. One of the applications, such as the internet of vehicles (IoV) [8], requires tight synchronization because high device mobility makes channel communication change rapidly and increases the Doppler frequency shift. Time synchronization performance can also affect the overall OFDM/F-OFDM system performance [9], [10].…”

A novel timing and frequency offset estimation algorithm for filtered OFDM system

“…With the increasing demands of internet of vehicles (IOV) and internet of things (IOT) business, the wireless data traffic explodes [1]. How to further improve transmission efficiency and spectral utilization has become the research focus of 5G.…”

Compressed Sensing Channel Estimation Algorithm Combined with Wavelet Denoising

“…Traditional channel estimation algorithms, such as least squares [1] and minimum mean square error [2] , have obvious drawbacks, and neither algorithm takes into account the sparsity of wireless channels [3][4] . By utilizing this characteristic in the channel, the channel estimation in sparse channels can be transformed into the problem of using greedy algorithms to recover the original signal [5] . Compared to traditional algorithms, they can use fewer pilot resources while ensuring the same performance and completing channel estimation with lower complexity but higher accuracy [6][7] .…”