Double-filter smoothing decoding method and device suitable for ovxdm system and ovxdm system

Abstract

This application discloses a double-filter smoothing decoding method and device suitable for the OvXDM system, and the OvXDM system. The importance weight of each particle in the particle set corresponding to the symbol is calculated by forward filtering and backward information filtering, and then combined with the forward Filtered particle importance weights and backward information filtering particle importance weights are screened to output the final decoding sequence. In this process, the mutual information between particles is fully utilized to realize the decoding of the OvXDM system, so that The decoding sequence is closer to the real value. At the same time, as the number of overlaps increases, the decoding complexity is reduced compared with the traditional decoding method, and the decoding efficiency and system performance are improved.

Description

technical field [0001] The present application relates to the field of decoding, and in particular to a double-filter smoothing decoding method and device suitable for an OvXDM system and the OvXDM system. Background technique [0002] For the overlapping multiplexing system OvXDM system, in its traditional decoding, it is necessary to continuously visit the nodes in the trellis graph (Trellis), and set up two memories for each node, one for storing the relative best path to the node , a metric used to store the relative best path to the node. [0003] Let’s take the OvTDM system as an example. Since each node in the trellis diagram needs to be expanded during the decoding process, the number of nodes determines the complexity of decoding. For a system with an overlapping number of K and a modulation dimension of M ( M is an integer greater than or equal to 2), and the number of nodes in the stable state in the corresponding trellis graph is M K-1 , so the decoding complex...

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Problems solved by technology

Therefore, on the one hand, the larger the number of overlapping times K is better for the requirement of improving spectral efficiency; on the other hand, the smaller the number of times of overlapping K is better for the requirement of reducing decoding complexity. For example, when K is greater than 8, the decoding complexity increases sharply, and the existing decoding methods are difficult to meet the requirements of real-time decoding. Spectral efficiency, decoding complexity, and decoding efficiency form a pair of contradictory requirements.

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