This work contained two parts of IEEE 802.16e. One was the research in convolution code and implementation on DSP of 802.16e for WirelessMAN-OFDMA. And the other was the reduced-complexity decoding research of the LDPC code.
In the first part work, we first analyzed the Shannon bounds on coding gain and theoretic coding gain based on minimum codeword distance in AWGN. In our implementation, the convolution coding gain in AWGN was less than theoretic value by 1 dB. When we converted the floating-point to fixed-point, the performance was almost the same and we could just use 6 bits to implement the decoder. Finally, the convolutional decoder required multiple DSPs to run in parallel to handle the data rate under a 10 MHz transmission bandwidth.
But encoder could achieve the data rate 10 Mhz.
In the second part work, we first evaluated the performance of LDPC code and compared the results with the numerical results. The coding gain of LDPC code was much better than convolution code. Then we focused on several complexity-reducing decoding algorithm.
Therefore, these simplified reduced-complexity decoding schemes could outperform the BP decoding algorithm. Then we converted the floating-point to fixed-point, and we could use 6 bits to implement the decoder. In the DSP implementation, it could not achieve
the bandwidth 10 MHz both in encoder and decoder. LDPC code is more complex than convolution code in our DSP implementation.
In the future work, we need to revise the coding algorithms to be fixed-point to reduce the complexity for actual DSP implementation. In convolution code, the C64x is equipped with a Viterbi decoder co-processor [30]. Using this co-processor may be helpful in raising the decoding speed. But it use requires study and testing of the “enhanced direct memory access (EDMA)” mechanism of the C64x chips.
In LDPC code, there are two possible methods to enhance our DSP implementation.
First, we may rewrite our code. We discuss in chapter 5, in our code, there are too many loops to execute. These cost too many cycles and must read the memory many times.
Second, we have find some references. If we need further reducing complexity by other decoding algorithms, [34] is one of the references. If we need to remove the effects of cycles in the factor graph to make the BP decoding algorithm optimal or improve the decoding performance, [35] is one of the references.
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