Lossy Joint Source-Channel Coding for Energy Harvesting Communication Systems
Bibliographic record
Abstract
In this work, we study the problem of lossy joint source-channel coding in an energy harvesting single-user communication system \nwith causal energy arrivals, where the energy storage unit may have leakage. In particular, we investigate the achievable distortion in the transmission of a single source with arbitrary \nalphabets via an energy harvesting transmitter over a point-to-point channel. \n \nWe first establish a lower bound on the achievable distortion. Then, to minimize the distortion we consider an adaptive joint source-channel coding scheme, where the length of channel codewords \nvaries adaptively based on the available battery charge in each communication block. For this scheme, we obtain two coupled equations that determine the mismatch ratio between channel symbols and input symbols as well as the transmission power, both as functions \nof battery charge. \n \nAs examples of continuous and discrete sources, we consider Gaussian and binary sources. In particular, for the Gaussian case, we obtain a closed form expression for the mismatch factor in terms of the $Lambert W$ function, and show that an increasing transmission power results in a decreasing mismatch factor and vice versa. We also numerically show that when the mismatch factor adaptively changes based on the available charge in the battery, the communication system achieves a smaller distortion compared to that of a constant mismatch factor.
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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".