Self-Energized Bidirectional Sensor Networks over Hoyt Fading Channels under Hardware Impairments
Bibliographic record
Abstract
With the rapid emergence of the Internet of Things (IoT) paradigm, the evolution of wireless senor networks (WSNs) is expected to witness a major blow. However, the accelerated upsurge of sensors in the future IoT networks will face significant challenges due to their limited battery life capacity. Thus, it is essential to devise efficient schemes to prolong the battery life of the connected sensors in order to derive their full potential in the future interconnected IoT networks. Towards this end, different energy harvesting (EH) techniques relying on wide array of sources namely solar, wind, thermal, coupled magnetic resonances and radio frequency have been proposed in the literature. Working in the similar direction, in this work, an EH system based on time-switching has been proposed for half-duplex bidirectional WSN with intermediate relay over a Hoyt fading channel. For its extensive performance analysis, exact closed-form expressions have been derived with respect to outage probability (OP) and achievable throughput of the system under the hardware impairment condition. Additionally, asymptotic analysis of high signal-to-noise-ratio (SNR) regime for these performance measures has also been provided. Further, an approach for the symbol-error-rate (SER) analysis is also presented in context of the observed system. In a nutshell, the work provides a detailed analysis of the effects of various parameters on the performances of energy harvesting applied in wireless sensor networks over a Hoyt fading channel.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".