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

Abstract

Abstract-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 reencoding and bring considerable coding gains, compared to the conventional soft information relaying.

A novel SISO trellis strategy for relaying distorted signals in wireless networks

“…In this part, we revisit the Viterbi algorithm, which aims to maximum likelihood, instead of using BCJR decoding algorithm [8] for our soft convolutional code. In our scenario, the inputs for the encoder are no longer digital bits, but the soft information.…”

“…1) Reference system 1 (legend "AF"): no decoding and no encoding are performed at the relay node, and the relay node transmits what it receives with its transmitting power constraint. The decoder at the destination performs a conventional BCJR algorithm; 2) Reference system 2 (legend "digital"): the relay forces a binary hard decision (+1 or −1) to the received signal and encodes them via a conventional 1+D, 1+D 2 +D 3 digital encoder, and the destination performs the conventional Viterbi decoding; 3) Reference system 3 (legend "tanh"): a soft-encoding distributed code recently proposed in [8], in which the soft-encoder at the relay takes in the hyperbolic tangent function of the received signals, and the decoder at the destination performs a modified BCJR algorithm specifically designed for the individual convolutional code; 4) The proposed new scheme(legend "analog convolutional code"): the SISO encoder at the relay takes in judiciously-truncated LLRs of the received signals, and the decoder performs a modified Viterbi algorithm matched to the individual convolutional code. In Fig.…”

“…For example, the input signal to the relay node, U , may be converted to appropriate forms such as probabilistic values and log-likelihood ratios (LLR) [3]- [6], and may further be protected using a soft-input encoding scheme [7], [8], before being forwarded to the destination. As shown in [7], it is actually possible and beneficial to leverage the rich results developed for analog coding (e.g.…”

“…As shown in [7], it is actually possible and beneficial to leverage the rich results developed for analog coding (e.g. [8]- [11]), and to directly apply the existing analog codes to signal U . Nonetheless, the gains achieved for relaying U turns out to be considerably smaller than what the same analog code does to a conventional analog source (e.g.…”

Distributed Irregular Codes Relying on Decode-and-Forward Relays as Code Components

“…47 Distributed coding [5], which involves joint coding design 48 between the source node (SN) and relay nodes (RNs), is one of 49 the promising coding techniques conceived for approaching the 50 achievable capacity of the relay channel with the aid of itera-51 tive detection at the destination node (DN). More specifically, 52 distributed turbo codes [6]- [9], distributed low-density parity-53 check codes [10]- [12], distributed turbo trellis coded modula-54 tion [13], distributed space-time codes [14]- [17], distributed 55 self-concatenated convolutional codes [18], distributed rateless 56 codes [19], and distributed soft coding [20] have been proposed 57 for cooperative communications. Furthermore, selecting benefi- 58 cial RNs that exhibit high-quality source-to-relay and relay-to-59 destination links is capable of significantly reducing the overall 60 transmission power of the relay network [21], [22].…”