Abstract
Millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) systems with hybrid analog-digital architectures can greatly increase system capacity and communicate with multiple users at the same time. Accurate channel estimation is crucial for multi-user communications, but its accuracy is limited as the number of antennas and users increases. In the matrix high-dimensional operation for multi-user channel estimation, not only is it computationally intensive, but also the estimation accuracy is low. It makes our work turn to channel estimation of the user group within a certain region to improve the accuracy of estimation. In this paper, we propose a tensor dictionary manifold learning method for channel estimation and interference elimination of the multi-user mmWave massive MIMO system. A multi-user digital-analog mixed received signal model is presented. The tensor dictionary manifold learning scheme is proposed to model the received signal as a third-order low-rank tensor to handle the high-dimensional user, antenna, and channel. After segmentation, clustering and manifold learning, multiple tensor dictionary manifold models containing a group of user signals are fitted. Tensor dictionary manifold learning can take advantage of the inherent multi-domain properties of signals in the frequency, time, code and spatial domains to maintain inter-user correlation within a user group while reducing the high-dimensional channels of the user group. Using the convex relaxation property of the tensor alternating direction method, we propose a strategy to eliminate interference from other groups. And with the help of the multi-signal classification method, the channel parameters of user groups are obtained to improve the accuracy of multi-user channel estimation. This method can perform channel estimation for multiple users with only a few pilots, and improve the performance of the system. Numerical results confirm the good performance of this method.
Highlights
Massive multiple-input multiple-output (MIMO) is the key technology of the future 5G wireless communication system [1]
Multiple signal classification (MUSIC) [12] and rotation invariance technology (ESPRIT) [13] are used to process array signals to improve the performance of channel estimation [14]-[16]
We consider a multi-user mmWave massive MIMO system consisting of a base station (BS) and K multiple mobile stations (MSs), i.e., users
Summary
Massive multiple-input multiple-output (MIMO) is the key technology of the future 5G wireless communication system [1]. Multiple signal classification (MUSIC) [12] and rotation invariance technology (ESPRIT) [13] are used to process array signals to improve the performance of channel estimation [14]-[16] These search signal methods are based on the location information of the user source. Tensor modeling [28][30] benefits from multiple diversity This feature is helpful to achieve multi-user signal separation, equalization and channel estimation under more relaxed identifiability conditions than the traditional matrix method. We consider the tensor dictionary manifold learning for channel estimation and interference elimination of the multi-user mmWave massive MIMO system.
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