This work studied two parts of IEEE802.16e: one was the implementation and optimization of 802.16e FEC scheme on DSP platform for WirelessMAN-OFDM and the other investigated the reduced-complexity decoding of the LDPC codes for WirelessMAN-OFDMA.
In the first part’s work, the programs will require multiple DSPs to run in parallel to handle the data rate under a 10 MHz transmission bandwidth. Acknowledgeably, further optimization of the programs may be possible. In addition, the C64x is equipped with a Viterbi decoder co-processor [29]. Using this co-processor may be helpful in raising the decoding speed. But its use requires study and testing of the “enhanced direct memory access (EDMA)” mechanism of the C64x chips, we skipped this study in my thesis.
In the second part’s work, first we analyzed the girth and threshold values in AWGN channel. Then, we evaluated the performance of LDPC codes and compared the results with the numerical results. Then we proposed a modified version BP algorithm based on algorithmic transformation to balance the computation load. Another topic is about the complexity reducing. We focused on two directions to reduce the complexity. One was to reduce the iteration numbers by using early termination technique. Another was to evaluate the performance by three kinds of approximate algorithms. The approximation
approaches used can lead to performance degradation but, interestingly, there appeared to be a dependence on the properties of the LDPC codes selected, such as the choice of modulation types. Our LDPC codes in IEEE 802.16e have a girth 6 at most, this is not a large enough number to make the conventional BP decoding approach optimal ([34] gives examples to derive a rate 12 code with girth 14, which is large enough). Therefore, these simplified reduced-complexity decoding schemes sometimes can outperform the BP decoding algorithm and offer significant advantages for hardware implementation.
In the future work, we need to revise the coding algorithms to be fixed-point to reduce the complexity for actual DSP implementation. But the two improved decoding algorithms may not have as good performance as our simulation results. Besides, we need more realistic simulations in multipath channel to show how the LDPC codes are performed. In our before analysis, the performance of code rate type 23A was better than 23B, and code rate type 34B was better than 34A. But why did these two code rate types both exist? We guess that if in multipath channel simulation, not in AWGN channel, the performance of code rate type 23B is better than that of 23A, and the performance of code rate type 34A is better than that of
3
4B. But the exact answer should be done by more research.
About subsequent algorithm modifications, we had find some references. If we need further reducing complexity by other decoding algorithms, [35] 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, [36] is one of the references.
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