Many wireless communication systems such as IS- 54, enhanced data rates for the GSM evolution (EDGE), worldwide interoperability for microwave access (WiMAX) and long term evolution (LTE) have adopted low-density parity-check (LDPC), tail-biting convolutional, and turbo codes as the forward error correcting codes (FEC) scheme for data and overhead channels. Therefore, many efficient algorithms have been proposed for decoding these codes. However, the different decoding approaches for these two families of codes usually lead to different hardware architectures.
Since these codes work side by side in these new wireless systems, it is a good idea to introduce a universal decoder to handle these two families of codes. The present work exploits the parity-check matrix (H) representation of tail biting convolutional and turbo codes, thus enabling decoding via a unified belief propagation (BP) algorithm. Indeed, the BP algorithm provides a highly effective general methodology for devising low-complexity iterative decoding algorithms for all convolutional code classes as well as turbo codes. While a small performance loss is observed when decoding turbo codes with BP instead of MAP, this is offset by the lower complexity of the BP algorithm and the inherent advantage of a unified decoding architecture.
For analysis purposes the packet-loss process resulting from the single-multiplexer model was assumed to be independent and, consequently, the simulation results provided show that this simplified analysis considerably overestimates the performance of FEC. Evaluation of FEC performance in multiple session was more complex in existing applications. Surprisingly, all numerical results given indicates that the resulting residual packet-loss rates with coding are always greater than without coding, i.e., FEC is ineffective in this application. The increase in the redundant packets added to the data will increase the performance, but it will also make the data large and it will also lead to increase in data loss.
In this work we have evaluated the performance of FEC coding more accurately than previous works.
We have reduced the complexity in multiple session and introduced a simple way for its implementation.
We show that the unified approach provides an integrated framework for exploring the tradeoffs between the key coding parameters: specifically, Interleaving depths, channel coding rates and block lengths.
Thus by choosing the coding parameter appropriately we have achieved high performance of FEC, reduced the time delay for Encoding and Decoding with Interleaving.
- FEC Encoder
- Inter Leaver
- Implementation of the Queue
- FEC Decoder
- Performance Evaluation