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Pareto-Optimal Multi-Objective STARS Beamforming

Determine Pareto-optimal simultaneous transmission and reflection beamforming configurations for simultaneously transmitting and reflecting surfaces (STARS) that jointly optimize multiple objectives—including capacity, energy efficiency, coverage, and latency—by formulating and solving the multi-objective STARS beamforming design problem and characterizing the trade-offs among these metrics.

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Background

The paper reviews full-space STARS beamforming design and highlights that most existing works optimize a single metric (e.g., transmit power or sum rate). Given next-generation (NG) wireless networks must balance conflicting metrics such as capacity, energy efficiency (EE), coverage, and latency, multi-objective optimization becomes necessary.

The authors explicitly point out the need to obtain Pareto-optimal solutions for multi-objective STARS beamforming problems and note the high-dimensional search spaces involved, suggesting machine learning as a potential tool compared to conventional convex optimization methods.

References

With the stringent requirements and diverse applications in NG wireless networks, there are still many open problems and future research directions for STARS beamforming. Some of them are highlighted as follows. How to obtain the Pareto-optimal solution for such a set of multiple-objective STARS beamforming design problems constitutes an interesting and challenging research topic.

— Simultaneously Transmitting and Reflecting Surfaces for Ubiquitous Next Generation Multiple Access in 6G and Beyond (2406.11082 - Mu et al., 16 Jun 2024) in Section IV.D, Discussions and Outlook