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Optimizing Reconfigurable Antenna MIMO Systems with Coherent Ising Machines

Ioannis Krikidis, Abhishek Kumar Singh, Kyle Jamieson

TL;DR

This work tackles antenna configuration selection in reconfigurable antenna MIMO systems for 6G by casting the problem as a binary Ising optimization solvable with a Coherent Ising Machine (CIM). A rigorous framework converts the constrained, NP-hard selection problem into an unconstrained quadratic form compatible with CIM hardware, using a penalty parameter to balance objective fidelity and feasibility. Through MATLAB-based CIM emulation, the method achieves near-optimal SNR performance with polynomial preprocessing complexity and shows robustness to parameter choices when $ ext{lambda}$ is tuned, outperforming conventional benchmarks in many settings. The approach is general to other Ising machines and can be extended to joint power allocation or alternative objectives, offering a scalable path for practical CIM-assisted wireless optimization.

Abstract

Reconfigurable antenna multiple-input multiple-output (MIMO) is a promising technology for upcoming 6G communication systems. In this paper, we deal with the problem of configuration selection for reconfigurable antenna MIMO by leveraging Coherent Ising Machines (CIMs). By adopting the CIM as a heuristic solver for the Ising problem, the optimal antenna configuration that maximizes the received signal-to-noise ratio is investigated. A mathematical framework that converts the selection problem into a CIM-compatible unconstrained quadratic formulation is presented. Numerical studies show that the proposed CIM-based design outperforms classical counterparts and achieves near-optimal performance (similar to exponentially complex exhaustive searching) while ensuring polynomial complexity.

Optimizing Reconfigurable Antenna MIMO Systems with Coherent Ising Machines

TL;DR

This work tackles antenna configuration selection in reconfigurable antenna MIMO systems for 6G by casting the problem as a binary Ising optimization solvable with a Coherent Ising Machine (CIM). A rigorous framework converts the constrained, NP-hard selection problem into an unconstrained quadratic form compatible with CIM hardware, using a penalty parameter to balance objective fidelity and feasibility. Through MATLAB-based CIM emulation, the method achieves near-optimal SNR performance with polynomial preprocessing complexity and shows robustness to parameter choices when is tuned, outperforming conventional benchmarks in many settings. The approach is general to other Ising machines and can be extended to joint power allocation or alternative objectives, offering a scalable path for practical CIM-assisted wireless optimization.

Abstract

Reconfigurable antenna multiple-input multiple-output (MIMO) is a promising technology for upcoming 6G communication systems. In this paper, we deal with the problem of configuration selection for reconfigurable antenna MIMO by leveraging Coherent Ising Machines (CIMs). By adopting the CIM as a heuristic solver for the Ising problem, the optimal antenna configuration that maximizes the received signal-to-noise ratio is investigated. A mathematical framework that converts the selection problem into a CIM-compatible unconstrained quadratic formulation is presented. Numerical studies show that the proposed CIM-based design outperforms classical counterparts and achieves near-optimal performance (similar to exponentially complex exhaustive searching) while ensuring polynomial complexity.
Paper Structure (13 sections, 21 equations, 4 figures)

This paper contains 13 sections, 21 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System model
  3. Conventional solutions and benchmarks
  4. Exhaustive searching (ES)
  5. Norm-based selection algorithm (NSA) SMI
  6. Random selection (RS)
  7. CIM for MIMO configuration selection
  8. CIM background
  9. CIM formulation: Design
  10. Evaluation
  11. Varying the penalty parameter $\lambda$
  12. Time-evolution of the CIM solutions
  13. Conclusion

Figures (4)

  • Figure 1: Point-to-point MIMO with $N_T$ and $N_R$ antennas at the transmitter and the receiver, respectively; $N$ configurations in each antenna. The symbol ($\times$) represents an antenna configuration, while the bold symbol ($\boldsymbol{\times}$) corresponds to the selected configuration.
  • Figure 2: Performance of CIM-based antenna selection for different values of penalty parameter $\lambda$.
  • Figure 3: Performance of CIM-based antenna selection as CIM dynamics evolves with time.
  • Figure 4: (Top) Probability of constraint satisfaction by CIM-based antenna selection solutions for different values of penalty parameter $\lambda$. (Bottom) Probability of constraint satisfaction by CIM-based antenna selection solutions as CIM dynamics evolve with time.