Direct Target Localization With Low-Bit Quantization in Wireless Sensor Networks
Bibliographic record
Abstract
Wireless sensor networks (WSNs) have been recognized to have great potential in the field of target localization. To address the challenges of limited power consumption, channel throughput, and non-ideal transmission channel conditions in WSNs, this paper proposes a channel-aware direct localization method with low-bit quantization data. At its core, the received baseband signal is quantized into a small number of bits at each local sensor before being transmitted to the fusion center (FC). A channel-aware target localization method is designed at the FC to account for potential data distortion in the wireless channels due to the effects of fading and noise. The Cramér-Rao lower bound (CRLB) of the proposed method is derived to assess its localization performance. To achieve optimal localization performance, an optimization problem for quantizer design is formulated to guide each local sensor in quantizing the raw data. Since the formulated optimization problem is high-dimensional and non-convex, a low-dimensional optimization method based on position decoupling is proposed, which decouples the quantizer design problem into multiple low-dimensional sub-optimization problems. Specifically, for one-bit quantization, we prove that the optimal quantizer can be obtained analytically. For multi-bit quantization, we employ an efficient particle swarm optimization (PSO) based method for designing the quantizer. Our numerical results validate the theoretical analysis, demonstrating that the proposed method effectively addresses the low-bit quantization target localization problem especially in the presence of channel distortion.
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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 | 0.001 |
| 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".