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Robust Divergence Angle for Inter-satellite Laser Communications under Target Deviation Uncertainty
Signal Processing · Submitted 13 May 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Simulation results show the approach leads to significantly more robust performance than using the divergence angle as if there is no deviation, or other ad-hoc schemes.
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The abstract, sentence by sentence
Performance degradation due to target deviation by, for example, drift or jitter, presents a significant issue to inter-satellite laser communications. In particular, with periodic acquisition for positioning the satellite receiver, deviation may arise in the time period between two consecutive acquisition operations. One solution to mitigate the issue is to use a divergence angle at the transmitter being wider than that if the receiver position is perfectly known. However, as how the deviation would vary over time is generally very hard to predict or model, there is no clear clue for setting the divergence angle. We propose a robust optimization approach to the problem, with the advantage that no distribution of the deviation need to be modelled. Instead, a so-called uncertainty set (often defined in form of a convex set such as a polytope) is used, where each element represents a possible scenario, i.e., a sequence of deviation values over time. Robust optimization seeks the solution that maximizes the performance (e.g., sum rate) that can be guaranteed, no matter which scenario in the uncertainty set materializes. To solve the robust optimization problem, we deploy a process of alternately solving a decision maker's problem and an adversarial problem. The former optimizes the divergence angle for a subset of the uncertainty set, whereas the latter is used to explore if the subset needs to be augmented. Simulation results show the approach leads to significantly more robust performance than using the divergence angle as if there is no deviation, or other ad-hoc schemes.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Performance degradation due to target deviation by, for example, drift or jitter, presents a significant issue to inter-satellite laser communications. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s1 | In particular, with periodic acquisition for positioning the satellite receiver, deviation may arise in the time period between two consecutive acquisition operations. | background 1.00 | 0.09 | 0.02 | hedged possibility 0.99 |
| s2 | One solution to mitigate the issue is to use a divergence angle at the transmitter being wider than that if the receiver position is perfectly known. | background 0.92 | 0.06 | 0.06 | asserted 0.60 |
| s3 | However, as how the deviation would vary over time is generally very hard to predict or model, there is no clear clue for setting the divergence angle. | gap 0.95 | 0.09 | 0.02 | asserted 0.94 |
| s4 | We propose a robust optimization approach to the problem, with the advantage that no distribution of the deviation need to be modelled. | contribution 0.99 | 0.90 | 0.26 | asserted 1.00 |
| s5 | Instead, a so-called uncertainty set (often defined in form of a convex set such as a polytope) is used, where each element represents a possible scenario, i.e., a sequence of deviation values over time. | method 0.96 | 0.31 | 0.04 | asserted 0.97 |
| s6 | Robust optimization seeks the solution that maximizes the performance (e.g., sum rate) that can be guaranteed, no matter which scenario in the uncertainty set materializes. | method 0.67 | 0.10 | 0.12 | asserted 1.00 |
| s7 | To solve the robust optimization problem, we deploy a process of alternately solving a decision maker's problem and an adversarial problem. | method 1.00 | 0.90 | 0.04 | asserted 1.00 |
| s8 | The former optimizes the divergence angle for a subset of the uncertainty set, whereas the latter is used to explore if the subset needs to be augmented. | method 1.00 | 0.76 | 0.04 | asserted 1.00 |
| s9 | Simulation results show the approach leads to significantly more robust performance than using the divergence angle as if there is no deviation, or other ad-hoc schemes. | result 1.00 | 0.96 | 0.83 | asserted 1.00 |
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The paper
Original abstract
Performance degradation due to target deviation by, for example, drift or jitter, presents a significant issue to inter-satellite laser communications. In particular, with periodic acquisition for positioning the satellite receiver, deviation may arise in the time period between two consecutive acquisition operations. One solution to mitigate the issue is to use a divergence angle at the transmitter being wider than that if the receiver position is perfectly known. However, as how the deviation would vary over time is generally very hard to predict or model, there is no clear clue for setting the divergence angle. We propose a robust optimization approach to the problem, with the advantage that no distribution of the deviation need to be modelled. Instead, a so-called uncertainty set (often defined in form of a convex set such as a polytope) is used, where each element represents a possible scenario, i.e., a sequence of deviation values over time. Robust optimization seeks the solution that maximizes the performance (e.g., sum rate) that can be guaranteed, no matter which scenario in the uncertainty set materializes. To solve the robust optimization problem, we deploy a process of alternately solving a decision maker's problem and an adversarial problem. The former optimizes the divergence angle for a subset of the uncertainty set, whereas the latter is used to explore if the subset needs to be augmented. Simulation results show the approach leads to significantly more robust performance than using the divergence angle as if there is no deviation, or other ad-hoc schemes.
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