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 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 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".