Fixed-rate transmission of correlated analog sources over fading multiple-access channels: performance bounds and code design
Bibliographic record
Abstract
Reliable and efficient communication of analog observations over a fading multiple access channel (MAC) is important in wireless sensor networks. A sensor network can be well modelled by a set of correlated Gaussian sources communicating to a common receiver over a fading Gaussian MAC (GMAC). It is known that traditional separate source-channel (SSC) coding is sub-optimal when channel state information (CSI) is not available to the transmitters. For this case, neither the optimum performance theoretical achievable (OPTA) nor any practical coding schemes that can outperform traditional coding, remain known. This thesis investigates the minimum mean square error (MMSE) of communicating a pair of Gaussian sources over a bandwidth-matched GMAC with block Rayleigh fading (BF-GMAC) in the absence of transmitter CSI. We derive several upper-bounds to the MMSE as a function of transmitter powers, channel signal-to-noise ratio (CSNR), and the correlation coefficient of the two sources. To derive nontrivial upper bounds which improve on those of SSC coding and uncoded transmission, we incorporate ideas from joint source-channel coding and hybrid digital–analog (HDA) coding to construct coding schemes for which the achievable MMSE can be determined. One main contribution is two new MMSE upper bounds, which appear to be the best known characterizations of the OPTA to date. These bounds (JSC-VQ and HDA-JSC-VQ bounds) are derived by considering a transmission scheme where optimally vector quantized Gaussian sources are directly transmitted in analog form over the BF-GMAC. A comparison of these bounds with the MMSE bound for traditional SSC coding shows a gap that grows with source correlation and CSNR. Although there exists a gap even when the sources are uncorrelated, this gap is relatively small. It is shown that, for highly correlated sources and low average CSNR, uncoded transmission can achieve performance approaching the HDA-JSC-VQ bound. The difficulty of designing a practical coding scheme based on JSC-VQ scheme is the requirement of infinite-dimensional vector quantizers (VQ) for each Gaussian source and the joint detection of long codewords at the receiver. We present a practical coding method constructed by replacing the VQs by trellis coded quantizers (TCQ), which can perform close to the JSC-VQ bound.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".