Research On Joint Equalization And Decoding

The condition of wireless channels is always very poor. Besides the additive noise, it isusually accompanied with the inter-symbol interference which is caused by multipathpropagation. To solve the problem of inter-symbol interference and high bit error rates under thecondition of low signal to noise ratio, the common methods are equalization and channel codingtechniques. In order to improve the system performance, the thesis research is mainly on the jointequalization and decoding technology, which is the combination of the two techniques. The mainwork in the paper is divided into the following three aspects:Firstly, the paper introduces two kinds of iterative channel coding-Turbo code and the TPCcode-and the SISO decoding algorithms. In this part, we compare and summarize the softdecoding methods, as well as the theory of iteration. Two improvements are made to the TPCdecoding. An adaptive threshold decoding algorithm and a simple iterative stopping method areproposed. The two improvements reduce the decoding complexity under the premise of ensuringthe bit error rates performance of TPC code.Secondly, according to the trellis characteristics for convolutional codes and discretemultipath channels, we regard the equalization and decoding as a whole process. Combining theViterbi equalizer algorithm and convolution decoding, the joint algorithm is maximum likelihoodestimation of the signal. Joint equalization and decoding technique of TCM system, which isbased on Viterbi algorithm, is introduced. The extended trellis for Viterbi equalization anddecoding is analyzed in detail. Learning from the theory of extended trellis, we establish a newsignal model which is applied to the particle filter algorithm for joint blind equalization andconvolutional codes. An adaptive noise power algorithm of particle filter for joint blindequalization and convolutional codes is also proposed.Finally, the MAP algorithm in Turbo equalization techniques and MMSE TurboEqualization are introduced. This aspect mainly focuses on the Turbo blind equalization, inwhich the applications of PSP and particle filter blind equalization are further studied. Bymodifying the particle filter algorithm for blind equalization we obtain a kind of SISO particlefilter blind equalization algorithm. Combined with the structure framework of the Turboequalization, soft information of the channel decoder functions as the extrinsic information tochange the importance sampling function of the particle filter algorithm. At last, Turbo blindequalization algorithm based on particle filter is proposed. The algorithm greatly improves theframe error rates performance of the system by exploring the gain of convolutional codes.