In order to increase the transceiver performance in frequency selective fading channel environment, orthogonal frequency division multiplexing (OFDM) system is used to combat inter-symbol-interference. In this work, a channel estimation scheme for an OFDM system in the presence of sparse multipath channel is studied using the artificial neural networks (ANN). By means of ANN’s learning capability, it is shown that how to model and obtain a channel estimate and how it allows the proposed technique to give a better system throughput. The performance of proposed method is compared with the Matching Pursuit (MP) and Orthogonal MP (OMP) algorithms that are commonly used in compressed sensing literature in order to estimate delay locations and tap coefficients of a sparse multipath channel. In this work, we propose a performance- efficient ANN based sparse channel estimator with lower computational cost than that of MP and OMP based channel estimators. Even though there is a slight performance lost in a few simulation scenarios in which we have lower computational complexity advantage, in most scenarios, our computer simulations corroborate that our low complexity ANN based channel estimator has better mean squared error and the corresponding symbol error rate performances comparing with MP and OMP algorithms.

为了提高选频衰落信道环境中的收发器性能，使用正交频分复用（OFDM）系统来应对符号间干扰。在这项工作中，使用人工神经网络（ANN）研究了存在稀疏多径信道的OFDM系统的信道估计方案。通过人工神经网络的学习能力，表明如何建模和获得信道估计，以及如何允许所提出的技术提供更好的系统吞吐量。将所提出的方法的性能与压缩感知文献中常用的匹配追踪（MP）和正交MP（OMP）算法进行比较，以估计稀疏多径通道的延迟位置和抽头系数。在这项工作中 我们提出了一种基于性能的基于ANN的稀疏信道估计器，其计算成本低于基于MP和OMP的信道估计器。即使在某些模拟场景中，我们在较低的计算复杂度优势上损失了少许性能，在大多数情况下，我们的计算机模拟也证实了我们基于低复杂度ANN的信道估计器具有更好的均方误差和相应的符号错误率性能与MP和OMP算法进行比较。