公开(公告)号：US06606725B1

公开(公告)日：2003-08-12

申请号：US09558440

申请日：2000-04-25

申请人： Duanyi Wang ,  Hisashi Kobayashi ,  Jay Bao

发明人： Duanyi Wang ,  Hisashi Kobayashi ,  Jay Bao

IPC分类号： H03M1345

CPC分类号： H03M13/3905 ,  H03M13/2957 ,  H03M13/3933 ,  H03M13/616

摘要： A matrix transform method and circuit provides for MAP decoding of turbo codes. The method begins by initializing a forward recursion probability function vector &agr;0, and a backward recursion probability function vector &bgr;N. Then, transition probability matrices &Ggr;(Rk) and &Ggr;i(Rk) are determined according to each received symbol of the sequence R1N. And then, values of &agr;k, corresponding to the received Rk are determined according to &Ggr;(Rk). At the same time of determining &agr;k, a plurality of multiplacation on &Ggr;(Rk) and &Ggr;i(Rk) are accomplished in parallel. By making use of the results of the matrix multiplications, after receiving the complete symbol sequence R1N, values of all of the backward recursion probability vector &bgr;k, where k=1, 2, . . . , N−1, are determined in parallel, and the log likelihood ratio for every decoded bit dk, k=1, 2, . . . , N, are also determined in parallel. The circuit performs successive decoding procedures in parallel using a set of regular matrix operations. These operations substantially accelerate the decoding speed and reduce the computational complexity, and are particularly suited for implementation in special-purpose parallel processing VLSI hardware architectures. Using shift registers, the VLSI implementation effectively reduces memory requirements and simplifies complicated data accesses and transfers.

摘要翻译： 矩阵变换方法和电路提供turbo码的MAP解码。 该方法通过初始化前向递归概率函数向量α0和向后递归概率函数向量βN开始。 然后，根据序列R1N的每个接收符号确定转移概率矩阵GAMMA（Rk）和GAMMAi（Rk）。 然后，根据GAMMA（Rk）确定对应于接收到的R k的alphak值。 在确定alphak的同时，GAMMA（Rk）和GAMMAi（Rk）上的多个乘法并行完成。 通过利用矩阵乘法的结果，在接收到完整符号序列R1N之后，所有向后递归概率向量betak的值，其中k = 1,2。 。 。 ，N-1，并且每个解码比特dk的对数似然比k = 1,2。 。 。 ，N也是并行确定的。 该电路使用一组常规矩阵运算来并行地执行连续解码过程。 这些操作大大加快了解码速度并降低了计算复杂度，特别适用于专用并行处理VLSI硬件体系结构中的实现。 使用移位寄存器，VLSI实现有效地减少了内存需求，简化了复杂的数据访问和传输。