Due to the ever increasing data rate demand of beyond 5G networks and considering the wide range of Orthogonal Frequency Division Multipllexing (OFDM) technique in cellular systems, it is critical to reduce pilot overhead of OFDM systems in order to increase data rate of such systems. Due to sparsity of multipath channels, sparse recovery methods can be exploited to reduce pilot overhead. OFDM pilots are utilized as random samples for channel impulse response estimation. We propose a three-step sparsity recovery algorithm which is based on sparsity domain smoothing. Time domain residue computation, sparsity domain smoothing, and adaptive thresholding sparsifying are the three-steps of the proposed scheme. To the best of our knowledge, the proposed sparsity domain smoothing based thresholding recovery method known as SDS-IMAT has not been used for OFDM sparse channel estimation in the literature. Pilot locations are also derived based on the minimization of the measurement matrix coherence. Numerical results verify that the performance of the proposed scheme outperforms other existing thresholding and greedy recovery methods and has a near-optimal performance. The effectiveness of the proposed scheme is shown in terms of mean square error and bit error rate.

翻译：随着超5G网络对数据速率需求的不断增长，考虑到正交频分复用（OFDM）技术在蜂窝系统中的广泛应用，为提升系统数据速率，减少OFDM系统的导频开销至关重要。由于多径信道的稀疏性，可利用稀疏恢复方法来降低导频开销。OFDM导频被用作信道冲激响应估计的随机样本。我们提出了一种基于稀疏域平滑的三步稀疏恢复算法。该方案的三个步骤包括：时域残差计算、稀疏域平滑以及自适应阈值稀疏化。据我们所知，文献中尚未将所提出的基于稀疏域平滑的阈值恢复方法（称为SDS-IMAT）用于OFDM稀疏信道估计。同时，基于测量矩阵相干性最小化推导了导频位置。数值结果验证了所提方案性能优于其他现有阈值类及贪婪类恢复方法，并具有接近最优的性能。通过均方误差和误码率指标验证了该方案的有效性。