Channel equalization method and communication system thereof based on precoding

Abstract

The invention provides a channel equalization method based on precoding. The method comprises that geometric mean decomposition is carried out on the extended channel matrix H of an original channel matrix H, R matrix, P matrix and Q matrix are obtained and then equalization is carried out, at a sending end, T-H precoding processing is carried out on modulated data according to the R matrix and the data after the T-H precoding is sent and filtered according to the P matrix and at a receiving end, the data after frequency domain signal processing is received and filtered according to the Q matrix. The invention further provides a communication system based on the precoding, which comprises a geometric mean decomposition module based on H, a receiving filtering module and a sending filtering module added to the sending end. The invention can eliminate error floor under high SNR and improve the system performance under low SNR and is compatible with a single or multiple carrier waves communication system. The invention also provides flexible gang scheme so that the system can obtain excellent performance under low complexity.

Description

technical field [0001] The invention belongs to the technical field of wireless broadband access, and specifically proposes a precoding-based channel equalization method and a precoding-based communication system. Background technique [0002] As an effective anti-multipath fading technology for high-speed wireless communication, channel equalization technology is adopted by the 3G long-term evolution system LTE. When the data transmission rate is high, because the maximum multipath delay greatly exceeds the duration of a single transmission symbol, the data received at a certain moment is the superposition of tens or even hundreds of symbols before, so the recovery at the receiving end Raw data flow is incredibly difficult. Mathematically speaking, the channel equalizer first estimates the impulse response matrix of the channel through the channel estimation module, and then performs the inverse operation of the channel matrix on the received signal to restore the data. T...

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Problems solved by technology

The reason is that under high-order modulation, the deterioration of system performance due to residual ISI will become very significant, thus seriously curbing the bit error performance

It can be seen from the figure that the slope of the time-frequency equalization scheme based on the QR decomposition of the original channel matrix is ​​not very steep at high SNR, and there are different degrees of error flat. This is because the QR decomposition technology is used in these schemes, so It will cause the signal-to-noise ratio of some sub-data streams at the end of the data block to be significantly lower than the data at the head, that is, the receiving signal-to-noise ratio is uneven, so that the tail data with low signal-to-noise ratio will drag down the system performance at high signal-to-noise ratio

like Figure 4 as shown, Figure 4 It is a schematic diagram of the error flat bottom of the time-frequency equalization scheme based on the QR decomposition of the original channel matrix under high SNR. In this case, no matter how the SNR is improved, the effective SNR of some data cannot meet the requirements; at the same time , it can also be seen from the figure that although the existing technology can completely eliminate ISI, the noise is amplified at the same time, so that the system performance is still low in the area of ​​medium and low signal-to-noise ratio

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