Distributed Soft Coding with a Soft Input Soft Output (SISO) Relay Encoder in Parallel Relay Channels

In this paper, we propose a new distributed coding structure with a soft input soft output (SISO) relay encoder for error-prone parallel relay channels. We refer to it as the distributed soft coding (DISC). In the proposed scheme, each relay first uses the received noisy signals to calculate the soft bit estimate (SBE) of the source symbols. A simple SISO encoder is developed to encode the SBEs of source symbols based on a constituent code generator matrix. The SISO encoder outputs at different relays are then forwarded to the destination and form a distributed codeword. The performance of the proposed scheme is analyzed. It is shown that its performance is determined by the generator sequence weight (GSW) of the relay constituent codes, where the GSW of a constituent code is defined as the number of ones in its generator sequence. A new coding design criterion for optimally assigning the constituent codes to all the relays is proposed based on the analysis. Results show that the proposed DISC can effectively circumvent the error propagation due to the decoding errors in the conventional detect and forward (DF) with relay re-encoding and bring considerable coding gains, compared to the conventional soft information relaying.Comment: to appear on IEEE Transactions on Communication

Bounding of MAP decode and forward relaying

International audienceWe formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations

Bounding of MAP decode and forward relaying

We formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations. We formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations. We formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations. We formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations. We formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations. We formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations. We formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations. We formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations. We formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations. We formulate the maximum a posteriori (MAP) rule for the decode-and-forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations. We formulate the maximum a posteriori (MAP) rule for the decode and forward transmission strategy operating with a noisy relay. From the MAP rule we derive an analytical bound on the error probability, taking into account decoding errors at the relay. We further determine a practical close-to-MAP decoding scheme based on a convenient error model for the decoding operation at the relay. This error model allows for a trellis representation of the code described jointly by the encoding process at the source and the re-encoding process at the relay. Numerical results demonstrate a close agreement between our analytical results and monte carlo simulations