Abstract
Accurate channel state information (CSI) is important for the coherent detection of multiple-input-multiple-output (MIMO) system. Especially in a high-speed scenario, fast time-varying CSI gotten by the conventional channel estimation schemes tend to be out of date, thus tracking and predicting CSI are more attractive and indispensable. Motivated by those, a time-varying MIMO channel fading prediction framework is proposed in this paper. The principle behind our scheme is that the cluster-based fading channel has the spatial consistency property, which means the small-scale parameters of channel clusters evolve continuously and smoothly in the time and spatial domains. Thus CSI can be tracked and predicted within several or tens of wavelengths. The proposed scheme is composed of an extended Bayesian Estimation Kalman Filter to track the time-varying CSI evolution, and a Cluster Drifting Based Prediction to obtain the small-scale parameters of channel clusters. The performance of the proposed scheme is simulatively verified by a standard clustered-based channel model.
Highlights
Accurate channel state information (CSI) is important for coherent detection, achieving the high performance and providing quality of service support in the fifth Generation (5G) and beyond 5G (B5G) multiple-input-multipleoutput (MIMO) systems [1]–[4]
Since the channel spatial consistency property (SCP) is considered in this model, it can substantially support the simulation of time-varying MIMO channel fading
With the account of the prediction horizon smaller than one visibility region (VR), it is assumed that no birth and death of the clusters exist in the simulation of tracking and prediction stages
Summary
Accurate channel state information (CSI) is important for coherent detection, achieving the high performance and providing quality of service support in the fifth Generation (5G) and beyond 5G (B5G) multiple-input-multipleoutput (MIMO) systems [1]–[4]. Some research work on channel prediction for massive MIMO and millimeter wave has been published in [24], [25] They mainly focus on the narrow-band system and timeinvariant small-scale parameters, respectively. We present a time-varying MIMO channel fading prediction framework in a high-speed scenario. The Bayesian Estimation Kalman Filter (BEKF) algorithm in [23] is adopted and extended to track the time-varying small-scale parameters with the consideration of the cluster-based channel SCP. It is assumed that the channel fading prediction horizon is smaller than one VR, and the SCP is guaranteed within this horizon With this assumption, a Cluster Drifting Based Prediction (CDBP) algorithm is proposed to predict the MIMO channel fading with the time-varying small-scale parameters.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
