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Multi-beam Beamforming in RIS-aided MIMO Subject to Reradiation Mask Constraints -- Optimization and Machine Learning Design

Created by
  • Haebom

Author

Shumin Wang, Hajar El Hassani, Marco Di Renzo, Marios Poulakis

Outline

This paper studies the joint design of transmit precoding matrix and RIS phase shift vector in a multi-user multiple-input multiple-output (MIMO) communication system using reconfigurable intelligent surfaces (RIS). The max-min optimization problem is posed to maximize the minimum achievability ratio, considering transmit power and reradiation mask constraints. The Arimoto-Blahut algorithm is used to simplify the achievable rate, and the alternate optimization technique is used to decompose the problem into quadratic programming (QPQC) subproblems with quadratic constraints. To improve the efficiency, a model-based neural network optimization is developed that utilizes one-hot encoding for the incident and reflected angles. A greedy search algorithm is used to solve the optimization problem for discrete phase shifting, solving the real RIS constraints. Simulation results show that the proposed method effectively forms multi-beam radiation patterns in the desired direction while satisfying the reradiation mask constraints. The neural network design reduces the execution time, and the discrete phase shifting scheme achieves excellent performance with only four phase shift levels, while slightly reducing the beamforming gain.

Takeaways, Limitations

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Takeaways:
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Presenting an efficient joint design method for transmit precoding and RIS phase control in RIS-based MIMO systems
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Neural network-based optimization reduces computation time and suggests real-time implementation possibilities
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Presentation of practical design and performance evaluation considering discrete phase shifting
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Confirmation of the possibility of effective signal transmission in the desired direction through multi-beam forming
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Limitations:
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Further verification of the generalization performance of the proposed neural network model is needed.
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Need for robust evaluation of more complex channel environments or various system parameters
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Need to improve computational complexity of greedy search algorithm
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Need to analyze the trade-off between performance improvement and computational complexity as the number of discrete phase shift levels increases
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