Limiting Doppler Shift Effect on Cell-Free Massive MIMO Systems: A Stochastic Geometry Approach
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Bibliographic record
Abstract
Cell-free (CF) massive multiple-input multiple-output (MIMO) system is currently considered as a promising network architecture to satisfy the anticipated rate requirements of beyond-5G networks. However, in practical scenarios with the presence of high-velocity users, the network experiences an inevitable performance degradation due to the Doppler shift effect. This paper analyzes the potential of frame length optimization in limiting the Doppler shift effect on the performance of time-division duplexing CF massive MIMO under different mobility conditions. In doing so, we derive novel expressions for tight lower bound of the average downlink (DL) and uplink (UL) rates. Capitalizing on the derived analytical results, we provide an analytical framework to determine the optimal frame length that limits the Doppler shift effect on DL and UL rates according to some criterion. Our results show perfect match of both analytical and simulated results under different system settings. Also, we reveal that the optimal frame lengths for maximizing the DL and UL rates are different and depend mainly on the transmission criterion and the users' velocities. Besides, our results demonstrate the high potential of adapting the frame length according to the velocity conditions in limiting the Doppler shift effect compared to applying a fixed frame length.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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