Cognitive Co-Existence of Unlicensed Wireless Networks through Beamforming
Bibliographic record
Abstract
Wireless fidelity (Wi-Fi) has become a key access technology, which offloads significant percentage of mobile Internet traffic. Nevertheless, QoS provisioning over dense WiFi faces many challenges due to the nature of its contention-based protocol and poor inter-network coordination. In light of this, long-term evolution (LTE) technology, which benefits from centralized coordination, has been proposed to exploit unlicensed spectrum through LTE-U for data offloading. However, the prosperity of deploying LTE in very crowded unlicensed band relies on the effective coexistence among LTE-U and increasingly dense Wi-Fi networks. In this paper, we propose to use the space dimension and beamforming to facilitate the effective coexistence and inter-network coordination. Two distinct approaches to evaluate the proposed coexistence mechanism, namely, Wi-Fi received power minimization and LTE user signal to noise ratio (SNR) maximization, have been investigated. The two proposed algorithms are simulated to show the potential and effective feasibility of coexistence between Wi-Fi and LTE in unlicensed spectrum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".