A Novel Multiple Access Technique Based on Antenna Radiation Pattern Modulation
Bibliographic record
Abstract
This paper extends Beam Angle Shift Keying (BASK) transmission technique into a multi-user version called Beam Angle Multiple Access (BAMA). BASK is a recently developed antenna beam pattern modulation scheme where at the transmitter, the block of information bits is mapped into a spatial symbol selecting the beam for the unmodulated carrier signal. At the receiver, after specular reflections, the carrier signal arrives at the receiver from one direction over the duration of the spatial symbol and the receiver antenna array is used to detect single-user data through angle-of-arrival (AOA) estimation. In this paper, multiple access on the uplink is achieved by assigning subsets of beams to individual users signaling with BASK. At the base station, over the duration of the spatial symbol the receiver decides on a subset of possible multiple AoAs corresponding to synchronized spatial symbols from different users. Specifically, we propose a maximum-likelihood (ML) detector which can successfully decode incoming data from multiple simultaneous BASK transmissions by deploying a linear array of receive antennas. Unlike in spatial division multiple access where a fixed beam arrangement is used, BAMA detects the beams used by multiple users on a symbol-by-symbol basis. In the proposed scheme, the management and selection of beams are crucial with respect to capacity of the system. It is demonstrated that the detection reliability of the spatial symbols in BAMA is determined by the number of antennas used at the receiver and the actual angles of signal arrival.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".