Implementation Issues cấu trúc hệ thống của mã RS cho phép rút ngắn mã, tức là, loại bỏ các đầy 239 - K không byte trước khi truyền. Sau đó, mỗi gói tin được mã hóa chiều dài của K + 16 byte sẽ được nối tiếp bit chuyển đổi. | 162 Implementation Issues coding the systematic structure of the RS code allows one to shorten the code . remove the filled 239 K zero bytes before transmission. Then each coded packet of length K 16 bytes will be serial bit converted. At the end of the each packet tailbits . 6 bits for memory 6 can be inserted for inner code trellis termination purposes. A block consisting of K 16 X 8 6 bits is encoded by the inner convolutional mother binary code of rate 1 2. After convolutional coding the puncturing operation is applied following the used inner code rate R for the given packet. This results in a total of K 16 X 8 6 R bits. Finally the punctured bits are serial-to-parallel converted and submitted to the symbol mapper. If the BER before RS decoding is guaranteed to be about 2 10 4 then with sufficient interleaving . 8 RS code words for the same SNR values given in Table 4-3 a quasi error-free . BER 10 12 transmission after RS decoding is guaranteed. However if no interleaving is employed depending on the inner coding rate a loss of about dB has to be considered to achieve a quasi error-free transmission 20 . Turbo Coding Recently interest has focused on iterative decoding of parallel or serial concatenated codes using soft-in soft-out SISO decoders with simple code components in an interleaved scheme 4 28 29 30 66 . These codes after several iterations provide nearShannon performance 29 30 . We will consider here two classes of codes with iterative decoding convolutional and block Turbo codes. These codes are already adopted in several standards. Convolutional Turbo Coding By applying systematic recursive convolutional codes in an iterative scheme and by introducing an interleaver between the two parallel encoders impressive results can be obtained with so-called convolutional Turbo codes 4 . Convolutional Turbo codes are currently of great interest because of their good performance at low SNRs. Figure 4-38 shows the block diagram
