Channel Prediction in Time-Varying Massive MIMO Environments | Zendy

Wei Peng | Zendy; Meng Zou | Zendy; Tao Jiang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Channel Prediction in Time-Varying Massive MIMO Environments

Author(s) -

Wei Peng,

Meng Zou,

Tao Jiang

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2766091

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

The massive MIMO channel is characterized by non-stationarity and fast variation, thereby the channel state information obtained by traditional methods will be outdated and the system performance will be degraded. In this paper, we propose a channel prediction algorithm in massive MIMO environments. First, considering the channel characteristics, we propose a first-order Taylor expansion-based predictive channel modeling method. Then, a channel prediction algorithm consisting of the estimation stage and prediction stage is proposed and the interval of effective prediction (IEP) is derived. The performance of the proposed algorithm is testified by numerical simulations. It is shown that, within the IEP, a reliable channel prediction can be obtained with low computational complexity.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research