Per-survivor processing based iterative equalization method applied to direct-sequence spread-spectrum communication

Abstract

The invention relates to a per-survivor processing based iterative equalization method applied to direct-sequence spread-spectrum communication, belongs to a joint equalization and decoding method adaptable to quick time-varying channels and can effectively track the time-varying channels without insertion of extra training data. Compared with the conventional direct-sequence spread-spectrum communication signal processing method, the method can realize flexible regulation of the spread spectrum ratio and can track varying of the channels adaptively without extra training data overhead. In the method, direct-sequence spread-spectrum symbols are taken as processing units of a receiver, a 'per-survivor channel estimation method' is utilized in an iterative equalization detector, and the channel estimation is updated adaptively according to size of the 'channel estimation matching index'; the detector adopts a soft-input and soft-output detection algorithm, a subsequent decoder adopts a soft-input and soft-output iterative decoding algorithm, and soft information is exchanged between the detector and the decoder, thus, the optional solution is approached by means iteration.

Description

technical field [0001] The invention relates to a joint equalization and decoding method applied to a wireless communication system under fast time-varying channel conditions, and belongs to the technical field of equalization in mobile communication. Background technique [0002] In order to adapt to the future development, the mobile communication system must be able to support high-speed data transmission. As the rate increases, the frequency-selective fading of the channel will significantly affect the performance of the communication system. The usual method is to use equalization technology or spread spectrum technology to counteract the frequency selective fading of the channel. The iterative equalization technology combines equalization and decoding. Using a method similar to Turbo iterative processing can well approach the optimal receiver for joint equalization and decoding, greatly improving the performance of the receiver, that is, maintaining the same frequency...

AI Technical Summary

Problems solved by technology

[0003] However, in the current signal processing method combining direct sequence spread spectrum technology and iterative equalization technology, the detection algorithm of its iterative equalization algorithm, such as the linear MMSE algorithm or MAP algorithm using soft-in soft-out (SISO), needs to be assumed to be The channel is time invariant and the channel response is known

In order to meet the above assumptions, two methods are usually used: one is to continuously update the channel estimate by inserting training pilot data into the data stream, but this will reduce the channel utilization; the other is to use a decision-oriented channel estimation method, that is, using Channel estimation is performed on data judgment results, and the decision-oriented algorithm has unreliable judgment in the direct sequence spread spectrum communication system, which leads to error propagation, which seriously restricts the improvement of system performance

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