Planning 5G Networks for Rural Fixed Wireless Access
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Bibliographic record
Abstract
We study the planning of a rural 5G multi-user massive MIMO fixed wireless access system to offer fixed broadband service to homes. Specifically, we aim to determine the user limit, i.e., the maximum number of homes that can simultaneously receive target minimum bit rates (MBRs) on the downlink (DL) and on the uplink (UL) given a set of network resources and a cell radius. To compute that limit, we must understand how resources should be shared between the DL and UL and how user and stream selection, precoding and combining, and power distribution should be performed. We use block diagonalization and propose a static grouping strategy that organizes homes into fixed groups (of possibly different sizes) in the DL and UL; then we develop a simple approach to compute the user limit that we validate numerically. We study the impact of group size and show that smaller groups yield larger user limits in a 3.5 GHz band. We show how the user limit at different cell radii is impacted by the system bandwidth, the number of antennas at the base station and homes, the transmit power, and the MBRs. Lastly, we offer insights into how the network could be operated.
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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 | 0.000 |
| 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