ICACT20260220 Question.1
Questioner: md.t.a.812@ms.saitama-u.ac.jp 2026-02-09 오전 10:44:11 |
ICACT20260220 Answer.1
Answer by Auhor phamhoanglai10a1@gmail.com 2026-02-09 오전 10:44:11
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| In equation (2), what does the term tr(Rₐ) represent, and what type of operation (e.g., convolution, multiplication) is used to compute Rₐ = WₐWₐᴴ? |
It is a matrix multiplication |
ICACT20260220 Question.2
Questioner: md.t.a.812@ms.saitama-u.ac.jp 2026-02-09 오전 10:45:08 |
ICACT20260220 Answer.2
Answer by Auhor phamhoanglai10a1@gmail.com 2026-02-09 오전 10:45:08
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| Which software was used for the simulation? |
We used MATLAB |
ICACT20260220 Question.3
Questioner: md.t.a.812@ms.saitama-u.ac.jp 2026-02-09 오전 10:49:01 |
ICACT20260220 Answer.3
Answer by Auhor phamhoanglai10a1@gmail.com 2026-02-09 오전 10:49:01
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| In Fig. 2, the OMA shows poor results. Why does OMA provide very poor results compared to NOMA? |
NOMA outperforms OMA because it exploits the power domain more efficiently. By serving multiple users on the same resource block with SIC, NOMA achieves higher spectral efficiency and creates a larger SINR gap between legitimate users and eavesdroppers, who cannot perform SIC |
ICACT20260220 Question.4
Questioner: arjunvirvashisht@gmail.com 2026-02-13 오전 2:36:34 |
ICACT20260220 Answer.4
Answer by Auhor phamhoanglai10a1@gmail.com 2026-02-13 오전 2:36:34
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| Every model has its trade-offs. What would you say is the biggest limitation of your current approach when applied to large-scale, high-speed networks? |
The biggest limitation is scalability — as network size and speed increase, the joint optimization becomes computationally heavy and highly CSI-dependent, making real-time implementation challenging. |
ICACT20260220 Question.5
Questioner: hominpark@etri.re.kr 2026-02-13 오전 2:38:49 |
ICACT20260220 Answer.5
Answer by Auhor phamhoanglai10a1@gmail.com 2026-02-13 오전 2:38:49
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| What are the specific advantages of WOA over other metaheuristics in this scenario, and can its convergence speed and efficiency be maintained even with a massive increase in STAR-RIS elements? |
WOA offers a strong exploration–exploitation balance with relatively low parameter tuning complexity and simple update equations, which makes it suitable for high-dimensional continuous optimization such as STAR-RIS phase design.
However, as the number of STAR-RIS elements grows massively, convergence speed naturally degrades due to the curse of dimensionality. To maintain efficiency, we apply structured decomposition, dimensionality reduction, and warm-start initialization, and the algorithm remains scalable with proper architectural support. |
ICACT20260220 Question.6
Questioner: gonghao23@mails.ucas.ac.cn 2026-02-24 오전 10:51:31 |
ICACT20260220 Answer.6
Answer by Auhor phamhoanglai10a1@gmail.com 2026-02-24 오전 10:51:31
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| The paper employs the Whale Optimization Algorithm (WOA) as a representative metaheuristic to solve the non-convex sum secrecy rate maximization problem. However, WOAs convergence behavior can be sensitive to population size and initialization. How would the proposed framework perform if alternative metaheuristics — such as Particle Swarm Optimization (PSO) or Differential Evolution (DE) — were substituted, and what theoretical guarantees, if any, can be established regarding the global optimality of the obtained solutions compared to conventional alternating optimization (AO)-based benchmarks? |
In our framework, WOA is employed as a representative population-based metaheuristic to handle the highly non-convex and coupled nature of the joint beamforming - AN - STAR-RIS optimization problem. The algorithm itself functions as a black-box optimizer, and alternative metaheuristics such as PSO or DE could be integrated into the same framework without structural modification.
We expect comparable qualitative performance among these metaheuristics under the same evaluation budget. PSO may exhibit faster early-stage convergence but can be more prone to premature stagnation in high-dimensional spaces, while DE tends to provide stronger diversity and robustness at the cost of additional function evaluations. In practice, performance is influenced more by constraint handling, initialization, and computational budget than by the specific choice of WOA, PSO, or DE. We are willing to include comparative simulations in a revised version if required.
Regarding theoretical guarantees, due to the non-convex and NP-hard nature of the secrecy-rate maximization problem, no population-based metaheuristic, including WOA, PSO, or DE, can guarantee global optimality in general. These methods aim to obtain high-quality feasible solutions through global exploration but do not provide deterministic optimality guarantees.
Similarly, conventional AO based methods typically guarantee monotonic improvement and convergence to a stationary point under standard assumptions, but they also do not ensure global optimality for the original non-convex formulation. Therefore, the proposed approach does not claim theoretical global optimality; instead, its contribution lies in enhancing global exploration capability and achieving improved empirical secrecy-rate performance compared to AO-based benchmarks. |
ICACT20260220 Question.7
Questioner: gonghao23@mails.ucas.ac.cn 2026-02-24 오전 11:50:34 |
ICACT20260220 Answer.7
Answer by Auhor phamhoanglai10a1@gmail.com 2026-02-24 오전 11:50:34
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| The system model assumes perfect channel state information (CSI) for all links, including BS-to-STAR-RIS, STAR-RIS-to-user, and STAR-RIS-to-eavesdropper channels. In practice, however, the eavesdroppers (sensing targets) are passive and their CSI may only be partially or imperfectly known. How would the proposed joint beamforming and STAR-RIS coefficient optimization framework be extended to account for bounded or statistical CSI uncertainty, and what impact would such robust design have on the achievable secrecy-sensing trade-off demonstrated in Fig. 4(c)? |
We agree that assuming perfect CSI for passive eavesdroppers (sensing targets) is idealized. In practice, their channel information is often imperfect or only partially known.
To address this, the proposed framework can be extended using a robust design approach, where the joint beamforming and STAR-RIS coefficients are optimized to remain secure under bounded or statistical CSI uncertainty. Specifically, the secrecy objective can be reformulated in a worst-case or outage-based manner, ensuring that the system maintains secrecy performance even under unfavorable channel realizations within a predefined uncertainty region.
Such a robust formulation would inevitably lead to a more conservative design. As a result, the achievable secrecy–sensing trade-off demonstrated in Fig. 4(c) would shift downward compared to the perfect-CSI case. In other words, for the same sensing requirement, the achievable secrecy rate would be reduced. However, the resulting performance would be significantly more reliable and practically meaningful, as it reflects guaranteed or high-confidence secrecy under realistic channel uncertainty. |
