Prediction method, prediction system and application based on multi-order channel

Abstract

The invention belongs to the technical field of channel prediction, and discloses a prediction method, a prediction system and an application based on a multi-order channel. The method comprises the steps of determining a channel model, popularizing a classical Prony method into a vector form, and propose an algorithm 1; and predicting a future channel vector through the algorithm 1 and by using an acquired channel vector sample. Aiming at the actual challenge-mobility problem of Massive MIMO, the invention provides the prediction method based on the multi-order channel, and the error-free channel prediction can be approximated when the known channel sample is accurate enough. If the known channel sample is inaccurate, the sample precision is improved based on a sub-space structure and thelong-term statistical information of the channel observation, so that the channel prediction precision can be improved. The method, the system and the application of the present invention can be applied to a 5G base station, or a future communication base station, and other wireless transmitting or receiving devices.

Description

technical field [0001] The invention belongs to the technical field of channel prediction of wireless communication, and in particular relates to a multi-stage channel prediction method, prediction system and application. Background technique [0002] Currently, the closest existing technology: massive multiple-input multiple-output (massive multiple-input multiple-output, or massive MIMO), is one of the key enablers of 5G cellular systems. Compared with traditional MIMO with fewer antennas, massive MIMO can provide, at least in theory, greater spectral efficiency and energy efficiency. One of the basic concepts is based on the fact that as the number of base station antennas increases, the orthogonality between the target UE's vector channel and the interfering UE's vector channel gradually increases, allowing the base station to eliminate interference through low-complexity precoding. But its premise is that the base station has known the channel state information (Channe...

AI Technical Summary

Problems solved by technology

K.T.Truong and R.W.Heath studied the influence of channel expiration under the simple autoregressive model of channel time variation, and proposed a linear finite impulse response (FIR) Wiener predictor, but the computational complexity is high and the performance gain is not obvious ( See details Figure 4-6 performance curve)

[0004] To sum up, the problem with the existing technology is: in the initial stage of industrial testing, a serious challenge that may destroy the actual deployment effect of massive MIMO: channel Doppler caused by user mobility

[0007] Mobility imposes a huge performance penalty on practical 5G deployments

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