Image Analysis Oriented Integrated Sensing and Communication via Intelligent Reflecting Surface
Bibliographic record
Abstract
Integrated sensing and communication (ISAC) provides a promising paradigm for future beyond 5G (B5G) and 6G networks. As an important application of edge intelligence, image analysis (e.g., recognition) at the edge networks has attracted lots of interests. In this paper, we propose an image analysis oriented ISAC, in which the image captured by a wireless image-sensor is transmitted to an edge server for analysis in parallel with the radar sensing. The key challenge of our considered system lies in that the mutual interference between the transmission of the image data and radar sensing degrades both performances of the image analysis and radar sensing. To address this difficulty, we exploit intelligent reflecting surface (IRS) to mitigate the interference. Specifically, taking IRS into consideration, we characterize the radar estimation information rate as the performance metric of the radar sensing under the impact of the offloading transmission of the image data, and then formulate a joint optimization problem of the IRS phase shift, the image resolution and the transmit-power of image-sensor, with the objective of maximizing a system-wise performance that accounts for both the radar estimation information rate and the image analysis accuracy. To solve this problem, we leverage the block coordinate descent to separate the variables into two subgroups. For the subgroup of the image resolution and the transmit-power of image-sensor, we derive their closed-form solutions. For the subgroup of the IRS phase shift, we take the equivalent transformation and propose a two-tier successive convex optimization (SCA) based algorithm to obtain the solution. Simulation results demonstrate the advantage of leveraging IRS for the image analysis oriented ISAC and the effectiveness of our proposed algorithm.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".