Low Density Parity Check Code Designs For Distributed Joint Source-Channel Coding Over Multiple Access Channels
Bibliographic record
Abstract
The efficient and reliable communication of data from multiple sources to a single receiver plays an important role in emerging applications such as wireless sensor networks. The correlation among observations picked-up by spatially distributed sensors in such a network can be exploited to enhance the efficiency and reliability of communication. In particular, information theory shows that optimal communication of information from correlated sources requires distributed joint source-channel (DJSC) coding. This dissertation develops new approaches to designing DJSC codes based on low density parity check (LDPC) codes. The existence of low complexity code optimization algorithms and decoding algorithms make these codes ideal for joint optimization and decoding of multiple codes operating on correlated sources. The well known EXIT analysis-based LDPC code optimization method for channel coding in single-user point-to-point systems is extended to the optimization of two-user LDPC codes for DJSC coding in multi-access channels (MACs) with correlated users. Considering an orthogonal MAC with two correlated binary sources, an asymptotically optimal DJSC code construction capable of achieving any rate-pair in the theoretically-achievable two-user rate-region is presented. A practical approach to realizing this scheme using irregular LDPC codes is then developed. Experimental results are presented which demonstrate that the proposed codes can approach theoretical bounds when the codeword length is increased. For short codeword length and high inter-source correlation, these DJSC codes are shown to significantly outperform separate source and channel codes. Next, the DJSC code design for the transmission of a pair of correlated binary sources over a Gaussian MAC (GMAC) is investigated. The separate source and channel coding is known to be sub-optimal in this case. For the optimization of a pair of irregular LDPC codes, the EXIT analysis for message passing in a joint factor-graph decoder is analyzed, and an approach to modeling the probability density functions of messages associated with graph nodes which represent the inter-source dependence is proposed. Simulation results show that, for sufficiently large codeword lengths and high inter-source correlation, the proposed DJSC codes for GMAC can achieve rates higher than the theoretical upper bound for separate source and channel coding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".