Learning-Based Beam Steering for Long-Range Orbital Angular Momentum Mode Multiplexing
Bibliographic record
Abstract
The utilization of Orbital Angular Momentum (OAM) mode multiplexing offers a promising solution to alleviate the signal processing overhead in line-of-sight communication systems. However, concerns regarding the divergence of OAM beams have raised doubts about its practicality over long transmission distances. In this paper, we propose a novel beam steering strategy aimed at mitigating this issue by ensuring the successful reception of all OAM modes. To accomplish this objective, we present an unsupervised learning-based beam steering approach designed to individually steer each mode, thereby facilitating their reception at the receiver. We refer to this approach as Steered OAM (S-OAM). To mitigate inter-mode interference (IMI) from the steering process, we incorporate an equalizer into both the receiver and the training process. This ensures that our proposed S-OAM system is IMI-aware, improving its performance across steered OAM modes. Using S-OAM provides the flexibility of choosing the number of transmitted modes to accommodate low-capacity scenarios in low signal-to-noise ratio or long transmission distances, where a large number of modes lead to weak streams. Furthermore, the proposed approach leverages codebook-based phase shifters at the transmitter, eliminating the need for real-time calculation of phase shifter values and thereby maintaining a low level of signal processing burden at the transmit side, akin to traditional OAM transmission. Simulation results validate the efficacy of S-OAM, demonstrating its capability to generate multiple equally robust streams at the receiver with a total spectral efficiency reaching up to $0.5 \mathrm{bits} / \mathrm{sec} / \mathrm{Hz}$ from the theoretical upper bound.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".