Coexistent Evaluation on the Effects of Radar PRF and 5G Symbol Based Scheduling
Bibliographic record
Abstract
The increasing demand for spectrum necessitates the shared use of frequency bands between commercial wireless systems and incumbent radar operations. The 3.5 GHz Citizens Broadband Radio Service (CBRS) band in the United States is a prime example where 5G New Radio (NR) deployments must coexist with federal radar systems. This paper explores the relationship between pulsed sensor interference, particularly its Pulse Repetition Frequency (PRF), and the resulting effects on 5G downlink throughput. We present a comprehensive research approach combining a simulation framework utilizing open-source 5G software stacks and custom radar modeling in MATLAB, validated by over-the-air (OTA) experiments with a commercial UE and Software Defined Radios (SDRs). Our findings demonstrate that 5G and radar coexistence is achievable under certain conditions, and critically, not all radar interference scenarios result in complete blockage of 5G communications. The simulations reveal that for low radar PRFs (below 3 kHz) and under 15% duty cycle, strategic adjustment of 5G symbol-based scheduling can lead to notable improvements in downlink throughput. Furthermore, the results from our real-world OTA testing show good alignment with the simulation outcomes, confirming the viability of our modeling approach. This work identifies specific sensor PRF ranges that are more conducive to cellular coexistence and highlights the potential for preserving 5G performance under high duty cycle conditions by adapting TTI boundaries and symbol allocations.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".