Soft iterative channel estimation method

Abstract

The invention provides a soft iterative channel estimation method. The method includes the following steps: a receiving terminal data symbol obtains a channel response initial estimated value (shown in the description) of a frequency domain through transform domain estimation at an initial channel estimation section, a frequency domain equalizer in a Turbo equalizer utilizes the value (shown in the description) to perform compensation on receiving data to obtain the frequency domain estimation (shown in the description) of the data symbol, and likelihood ratio soft information (shown in the description) of the data symbol can be obtained through a channel decoder in the Turbo equalizer after processing; and the soft information can be mapped to a specific data symbol (shown in the description), the specific data symbol (shown in the description) can be sent to an iterative estimation section for channel estimation response update and sent to the frequency domain equalizer in the Turboequalizer for self-adaption updating of the receiving terminal data symbol, and iteration can be cycled like this to decide and output a decoding sequence after all output soft information tends to bestable. The method can greatly reduce system error rates under the conditions of low system signal-to-noise ratios.

Description

technical field [0001] The invention relates to the field of wireless communication, in particular to a soft iterative channel estimation method suitable for wireless communication systems, which can be applied to occasions in the field of wireless communication that require high bit error rate performance of a receiving system, especially wireless ad hoc network systems. Background technique [0002] The performance of the wireless communication system is greatly affected by the wireless channel, such as shadow fading and frequency selective fading, etc., which makes the propagation path between the transmitter and receiver very complicated. The wireless channel is not fixed and predictable like the wired channel, but has great randomness, which poses a great challenge to the design of the receiver. In order to accurately restore the transmitted signal at the transmitting end at the receiving end, people use various measures to resist the influence of multipath effects on t...

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Problems solved by technology

[0007] In the channel estimation scheme based on auxiliary information, the Least Square (LS) algorithm is simple to implement and has low computational complexity, but it ignores the interference of noise, and the estimation accuracy is greatly reduced when the signal-to-noise ratio is small; the least mean square Compared with the LS algorithm, the estimation performance of the Minimum Mean Square Error (MMSE) algorithm is greatly improved, but it requires channel prior information and the computational complexity is too high; the Discrete Fourier Transform (DFT) estimation algorithm mainly uses the system The channel impulse response length is smaller than the cyclic prefix (Cyclic Prefix, CP) length, and the time domain information in the channel parameters exceeding the CP length is set to zero, so as to obtain a more accurate channel estimation value, but the algorithm cannot suppress the CP within the CP length. noise

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