Green modulation and coding schemes in energy-constrained wireless networks
Bibliographic record
Abstract
Introduction The past decade has witnessed many significant advances in the physical layer of wireless communication systems in both theory and implementation. Traditionally, the design of existing cellular networks has focused on increasing the spectral efficiency, throughput, and transmission reliability, while minimizing the latency. The recent research focus has also included studying the energy efficiency in next generation wireless networks; associated with this shift is a new point of view that wireless communications are becoming ubiquitous and that the energy consumption of embedded devices is gradually increasing. Of interest is the next generation of mobile technologies, where the energy resources are scarce and have to be conserved, in particular when the replenishment of the energy resource is not easy. On the other hand, in indoor or short-range communications such as pico-cellular networks and femtocells, or in dense wireless networks when a large number of mobile nodes is deployed over a region, the circuit energy-consumption is comparable to or even dominates the transmission energy due to the short distance between nodes. Thus, minimizing the total energy-consumption in both circuits and signal transmission compared to the current level should be considered primarily as an important requirement in the different layer design of future wireless networks. These requirements and realizations have led to a push towards green wireless communications and have created inter-disciplinary research challenges in hardware and protocols in different layers of the wireless network. Towards green communication radios, the standardization processes for future wireless systems should target power control on circuit components as well as powermanagement algorithms for mobile nodes with sleep-mode processes, where the nodes only transmit a finite number of packets in a duty-cycling fashion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 0.001 |
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".