Performance analysis of (TDD) massive MIMO with Kalman channel prediction

Abstract

In massive MIMO systems, which rely on uplink pilots to estimate the channel, the time interval between pilot transmissions constrains the length of the downlink. Since switching between up-and downlink takes time, longer downlink blocks increase the effective spectral efficiency. We investigate the use of low-complexity channel models and Kalman filters for channel prediction, to allow for longer intervals between the pilots. Specifically, we quantify how often uplink pilots have to be sent when the downlink rate is allowed to degrade by a certain percentage. To this end, we consider a time-correlated channel aging model, whose spectrum is rectangular, and use autoregressive moving average (ARMA) processes to approximate the time-variations of such channels. We show that ARMA-based predictors can increase the interval between pilots and the spectral efficiency in channels with high Doppler spreads. We also show that Kalman prediction is robust to mismatches in the channel statistics.

Analysis of Nonorthogonal Training in Massive MIMO Under Channel Aging With SIC Receivers

“…It has recently been shown that in addition to conventional impairments such as estimation errors and pilot contamination [4], channel aging [5]- [8], caused due to the dynamic nature of the wireless channel, is a major source of inaccuracy in the Channel State Information (CSI) available in massive MIMO systems. Channel aging manifests as a mismatch between the acquired CSI and the channel state at the time of data transmission [9], [10], and a consequent reduction in the achievable data rates [8]. Past work has not considered the effect of aging on different Multiple Access (MA) techniques such as Non-Orthogonal MA (NOMA), which is the focus of this paper.…”

Machine Learning-Based Channel Prediction in Massive MIMO With Channel Aging

“…Therefore, a kernel with larger size may not improve the decision accuracy because of increased "interference". Then, we verify the accuracy of the CSI prediction for the proposed ML-based architecture, and choose the AR estimator and nonlinear (NL) Kalman predictor [37,39] as the benchmarks to illustrate the performance improvement. It is worth noting that the parameters in NL Kalman predictor require real-time training, and the results for NL Kalman in Fig.…”

“…According to (42), it is required to estimate the LSF at each coherence interval, which, unfortunately, is not possible for a single subcarrier system since small scale fading also remains constant during a coherence interval. However, in the widely used OFDM systems, the expectation over small scale fading is still reasonable since the LSF across all subcarriers is the same[30,37].…”

A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments

“…When the channel time variations are high, the time distance between two consecutive pilot blocks reduces which leads to low spectral efficiency. In [84], the authors investigated the possibility of increasing the time distance between pilot blocks with the aid of channel prediction. Therefore, a channel prediction technique based on Kalman filter and autoregressive moving average (ARMA) channel time variation model was proposed.…”