Frequency-Selective SIMO Channel Estimation Based on One-Bit Measurements

Using one-bit analog-to-digital converters (ADCs) can drastically reduce the cost and energy consumption of a wideband large array system. But it brings about challenges to the signal processing aspect of the system. This letter focuses on the estimation of a frequency-selective single-input multi-output (SIMO) channel from the received pilot signals quantized by one-bit ADCs. We first parameterize the channel by the complex gains, angles-of-arrival (AoAs), and time-delays of the multipath. We then show that a recently-developed modeling tool called quasi neural network (Quasi-NN) can be employed to model the log-likelihood function of the one-bit measurements via artfully designing the network, including its structure and the activation functions. By “training” the network, the maximum likelihood (ML) estimates of the channel parameters are obtained automatically, so is the channel state information (CSI). To expedite the network training, we employ a gradient-based method called the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm, which turns out to be much faster than the alternatives, including the widely used gradient descent algorithm with momentum acceleration.