Utilizing Attitude Information for Efficient Multi-User Millimeter-Wave Communications
Bibliographic record
Abstract
Sensing has played an important role in the 5G wireless network. It is well recognized that the energy efficiency of mobile devices can be further improved with the assistance of sensing. As an important sensing resource, attitude information is highly related to the angle of arrivals (AoAs) information when user equipments (UEs) rotate. It provides great potential to improve transceiver designs for multi-user millimeter-wave (mmWave) green communications. In this paper, we propose an attitude information aided block diagonalization receiver design algorithm. We first adopt angle of departure aided block diagonalization (AoD-BD) to eliminate inter-user interference (IUI). Then we compensate the rotation for each UE by only updating the receiver with attitude information from motion sensors. Compared to the joint transceiver design performance, we have theoretically proved that the sum spectral efficiency performance penalty of keeping transmitter unchanged is generally negligible. Furthermore, we consider the effect of imperfect attitude information, and derive a robust receiver by modeling the measurement error as Gaussian. Finally, the simulation results are presented to verify the effectiveness of the proposed design. It is noted that no feedback is required between the base station (BS) and UE during the rotation, which substantially reduces the system overhead.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".