Cell-free Integrated Sensing and Communication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cell-free (CF) integrated sensing and communication (ISAC) merges the CF architecture with ISAC functionalities. CF-ISAC leverages distributed access points, removes cell boundaries, and enhances coverage, spectral efficiency, and reliability. It also improves energy efficiency, enabling robust multi-user communication, distributed multi-static sensing, and seamless resource optimization. A comprehensive survey on CF-ISAC has been lacking. This monograph addresses that gap by covering the foundational principles, cooperative transmission, radar cross-section, target parameter estimation, ISAC integration levels, sensing metrics, and key applications. It also explores the advantages of multi-static sensing. Performance analysis, resource allocation, security, and user/ target-centric designs are discussed. Finally, synchronization, multi-target detection, interference management, and fron-thaul limitations are discussed. Advanced antenna technologies, network-assisted systems, near-field CF-ISAC, cross-technology integration, and machine learning approaches are presented. Diluka Galappaththige and Chintha Tellambura (2025), “Cell-free Integrated Sensing and Communication”, Foundations and Trends® in Networking: Vol. 16, No. 1-2, pp 1-170. DOI: 10.1561/1300000079. ©2025 D. Galappaththige and C. Tellambura
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it