Transmit Power-Efficient Beamforming Design for Integrated Sensing and Backscatter Communication
Bibliographic record
Abstract
Ambient Internet of Things networks use low-cost, low-power backscatter tags in various applications, and sensing is necessary to introduce perceptive intelligence to the network. In this work, an integrated sensing and backscatter communication (ISABC) system is thus introduced, comprising multiple backscatter tags, a user (reader), and a full-duplex base station (BS) with integrated sensing and communication (ISAC). The BS simultaneously detects backscatter tags and communicates with the user using the same time and frequency resources. The tag-reflected signals provide data to the user and allow the BS to sense the tags’ state information. The communication rates for both the user and tags and the BS sensing rate are derived. To minimize the total BS transmit power, the BS communication beamformer, sensing signal, tags’ reflection coefficients, and sensing combiners are jointly optimized using the alternating optimization technique. Closed-form solutions are provided for the sensing combiners, while semi-definite relaxation is applied to the BS communication beamformer and sensing signal, and slack optimization is used for the tags’ reflection coefficients. For instance, with ten BS antennas, ISABC achieves a 75% gain in combined communication and sensing rates compared to traditional backscatter, with only a 3.4% increase in transmit power. Additionally, ISABC with active tags requires just a 0.24% increase in power compared to conventional ISAC systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".