Millimeter-wave Channel Estimation Research Articles

Channel estimation based on the PSS-MUSIC for millimeter-wave MIMO systems equipped with co-prime arrays

The millimeter-wave channel estimation problem can be solved by estimating the path directions and the path gains. The previous schemes are nearly all based on the uniform linear arrays (ULAs). However, compared with the ULAs, the co-prime array can use fewer array elements to realize larger array aperture, which is beneficial to improve the performance of arrival direction estimation contributing to the estimation performance of angle of arrival (AOA) and angle of departures (AOD). Encouraged by the property of co-prime arrays, a novel channel estimation scheme is proposed for two-dimensional (2-d) co-prime arrays. For each path direction, multiple peaks are generated in the spatial spectrum of each subarray and by selecting over any limited sector to search for an arbitrary peak, which can recover the rest peaks. Then by comparing the peaks of the two subarrays, the common peaks are the correct path direction. Compared with the total-spectrum search (TSS) method, this method effectively reduces the complexity. Simulation results show that the proposed algorithm can reduce the computational complexity and maintain accurate channel estimation.

Mono-bit millimeter-wave channel estimation: Bayesian and adaptive quantization frameworks

To achieve a more reasonable cost, employment of a mono-bit quantization structure is essential in wide bandwidth millimeter-wave communications (mmW). However, in mmW channel estimation, one of the critical challenges of mono-bit quantization in each branch of the antenna is the loss of data amplitude. The latest researches offer several approaches to estimate the channel in mono-bit single and multiple antenna structures. Such methods not consider the recovery of channel amplitude and consequently are limited to finding direction. Moreover, they do not provide sparsity into account. Thus, two new schemes are introduced based on a non-adaptive Bayesian algorithm and adaptive sigma–delta modulation (SDM) to tackle these issues. In both methods, we exploit the sparsity of mmWC channel in the angle domain. To extract the sparse-based Laplacian density in case that the sparsity level is unknown, we propose a Bayesian algorithm which employs a hierarchical chain composed of Gaussian and exponential prior. Furthermore, in case that the sparsity level of an application is known or can be estimated, the adaptive mono-bit quantization schemes is proposed which retrieves the amplitude of the sparse channel by the aid of SDM and CS tools. Simulation results show that the adaptive mono-bit estimator outperforms the non-adaptive Bayesian approach comparing the mean square error as an index. Also, the proposed adaptive method, especially when a sufficient number of one-bit measurements are available, can asymptotically converge into optimum full-bit observations with lower computational complexity.

Rank-defective millimeter-wave channel estimation based on subspace-compressive sensing

Millimeter-wave communication (mmWC) is considered as one of the pioneer candidates for 5G indoor and outdoor systems in E-band. To subdue the channel propagation characteristics in this band, high dimensional antenna arrays need to be deployed at both the base station (BS) and mobile sets (MS). Unlike the conventional MIMO systems, Millimeter-wave (mmW) systems lay away to employ the power predatory equipment such as ADC or RF chain in each branch of MIMO system because of hardware constraints. Such systems leverage to the hybrid precoding (combining) architecture for downlink deployment. Because there is a large array at the transceiver, it is impossible to estimate the channel by conventional methods. This paper develops a new algorithm to estimate the mmW channel by exploiting the sparse nature of the channel. The main contribution is the representation of a sparse channel model and the exploitation of a modified approach based on Multiple Measurement Vector (MMV) greedy sparse framework and subspace method of Multiple Signal Classification (MUSIC) which work together to recover the indices of non-zero elements of an unknown channel matrix when the rank of the channel matrix is defected. In practical rank-defective channels, MUSIC fails, and we need to propose new extended MUSIC approaches based on subspace enhancement to compensate the limitation of MUSIC. Simulation results indicate that our proposed extended MUSIC algorithms will have proper performances and moderate computational speeds, and that they are even able to work in channels with an unknown sparsity level.