A Maximum Likelihood Decoding Algorithm for Tail-biting Codes

A technology of maximum likelihood decoding algorithm, applied in the field of maximum likelihood decoding algorithm of tail-biting code, which can solve the problems of high computational complexity, large memory consumption, and algorithm non-convergence, so as to achieve low complexity and simple decoding. effect achieved

Inactive Publication Date: 2017-06-27
SHANGHAI RES CENT FOR WIRELESS COMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a maximum likelihood decoding algorithm for tail-biting codes, which is used to solve the problem of high computational complexity and large memory consumption of the tail-biting code decoding algorithm in the prior art. , and the problem that the algorithm does not converge

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  • A Maximum Likelihood Decoding Algorithm for Tail-biting Codes
  • A Maximum Likelihood Decoding Algorithm for Tail-biting Codes
  • A Maximum Likelihood Decoding Algorithm for Tail-biting Codes

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Embodiment 1

[0064] Applying the maximum likelihood decoding algorithm of the tail-biting code to figure 1 The (8, 4) tail-biting convolutional code for the generator polynomial {7, 5} shown, with figure 1 Take the tail-biting convolutional code represented by the tail-biting grid as an example. In this embodiment, the information sequence is {0, 1, 0, 1, 1, 1, 0, 0}, and the encoded code word sequence is v={(00), (11), (10), (00), (01), (10), (01), (11)}. After the signal-to-noise ratio E b / N 0 After the AWGN channel of =0dB, the receiving sequence r={(1.144,0.458), (-0.986,-1.234), (0.291,1.364), (0.472,0.350), (1.578,-1.594), (0.050, -0.399), (2.260, 0.359), (-1.501, 0.234)}. For clarity, the figure 1 The tail-biting trellis shown in is split from position l=0 as Figure 4 -(a); and the state {00,01,10,11} is expressed as S in octal form 0 = {0, 1, 2, 3}. The decoding process is as Figure 4 As shown, the decoding steps include:

[0065] S1, initialize the surviving state set, ...

Embodiment 2

[0086] The maximum likelihood decoding algorithm of the tail-biting code is applied to the (120, 40) tail-biting convolutional code in LTE, and the generator polynomial of the tail-biting convolutional code is expressed as {133, 171, 165} in octal. The information bit length is 40, which corresponds to the shortest information sequence length of the broadcast channel in LTE. Figure 5 Shows the BFS-ML decoder (Bounded Forward Searching-MLDecoder, BFS-ML decoder, constrained forward search maximum likelihood decoding), TP-ML decoder and BEAST decoder in the tail-biting convolution Code (120, 40) is the storage unit required for the decoding process. It can be seen that the memory required by the maximum likelihood decoding algorithm of the tail-biting code provided by the present invention remains unchanged during the decoding process, and is only 1 / 2-2 of that of the TP-ML decoder and the BEAST decoder. 1 / 3. Image 6 Shows the average number of states visited by the BFS-ML d...

Embodiment 3

[0088] Apply the maximum likelihood decoding algorithm of the tail-biting codes to (24,12) Golay codes, the 64-state time-invariant tail-biting trellis of the Golay codes can be obtained by generating polynomial {103,166}[Stahl99 ] mentioned decoders. [Stahl99] The decoder refers to the paper published by P.Stahl in IEEE Transactions on Information Theory in 1999: P.Stahl, J.B.Anderson, R.Johannesson. "Optimal and near-optimalencoders for short and moderate-length tail-biting trellises”, IEEE Transactions on Information Theory, 1999. Figure 7 Shown are the storage units required by the BFS-ML decoder, TP-ML decoder and BEAST decoder in the process of decoding (24,12) Golay codes. Figure 8 Shown as the average number of states visited by the BFS-ML decoder, TP-ML decoder and BEAST decoder in the process of decoding (24,12) Golay codes. Therefore, the performance of BFS-ML decoder is better than that of TP-ML decoder and BEAST-ML decoder.

[0089] The maximum likelihood dec...

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Abstract

The present invention provides a maximum likelihood decoding algorithm for tail-biting codes. The maximum likelihood decoding algorithm includes: first initializing the surviving state set, starting from the cumulative metric value of any surviving state in the surviving state set, and ending The lower bound value of the tail-biting path metric value of each surviving state and the optimal tail-biting path metric value; then perform i iterations to obtain the surviving state set of the i+1th iteration, and prepare for the i+1th iteration ; Finally, stop decoding and output the codewords associated with the optimal maximum likelihood tail-biting path. The maximum likelihood decoding algorithm of the tail-biting code described in the present invention is based on the Viterbi algorithm, which requires the least storage unit in the execution process of all known decoding algorithms, and the complexity of the decoding algorithm is low , which is simple to implement and enables the decoder to quickly converge to the global optimal result.

Description

technical field [0001] The invention relates to the channel decoding field of wireless communication, and relates to a decoding algorithm, in particular to a maximum likelihood decoding algorithm of a tail-biting code. Background technique [0002] Convolutional codes can be divided into traditional convolutional codes and tail-biting convolutional codes (Tail-Biting Convolutional Codes, TBCC) according to the different initialization methods of their encoders. Some block codes can be represented by tail-biting trellis diagrams, so such block codes and tail-biting convolutional codes are called tail-biting codes. The encoder of the traditional convolutional code is initialized with known bits (usually all 0 bits) and ends in a known state at the end of encoding; the encoder of TBCC is initialized with the last v' bit of the information sequence , where v'≤v, v is the length of the register in the encoder. According to the relationship between v' and v, TBCC can be divided ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03M13/23
Inventor 王晓涛钱骅徐景杨旸
Owner SHANGHAI RES CENT FOR WIRELESS COMM
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