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On the Capacity of Pixel Antenna based MIMO Communication

Shenrui Lin, Shuowen Zhang

TL;DR

This work addresses the capacity of pixel-antenna based MIMO by formulating a non-convex MINLP that jointly optimizes the transmit covariance $m{Q}$ and binary antenna coders at both ends. It contributes an optimal exhaustive-search solution and two scalable suboptimal methods—branch-and-bound and element-wise alternating optimization—each balancing rate performance and computational complexity. Numerical results show that well-designed antenna coding can significantly boost achievable rates over conventional MIMO, even with a modest number of pixel ports, and provide practical guidance on when to use each algorithm. The results underscore the practical potential of pixel antennas to unlock new degrees of freedom in radiative patterns for next-generation high-rate wireless systems.

Abstract

Pixel antenna is a promising technology to enhance the wireless communication data rate by adaptively reconfiguring each antenna's radiation pattern via a so-called antenna coding technique which controls the states of switches connected to multiple pixel ports. This paper studies a multiple-input multiple-output (MIMO) system where both the transmitter and the receiver are equipped with multiple pixel antennas. We aim to characterize the fundamental capacity limit of this MIMO system by jointly optimizing the transmit covariance matrix and the antenna coders at both the transmitter and the receiver. This problem is a mixed-integer non-linear program (MINLP) which is non-convex and particularly challenging to solve due to the binary-valued optimization variables corresponding to the antenna coders. We first propose an exhaustive search based method to obtain the optimal solution to this problem, which corresponds to the fundamental capacity limit. Then, we propose a branch-and-bound based iterative algorithm aiming to find a high-quality suboptimal solution with lower complexity than exhaustive search as the number of pixel ports becomes large. Finally, we devise an alternating optimization (AO) based algorithm with polynomial complexity. Numerical results show that our proposed algorithms achieve a flexible trade-off between performance and complexity. Moreover, equipping the transceivers with pixel antennas can enhance the achievable rate of MIMO communications.

On the Capacity of Pixel Antenna based MIMO Communication

TL;DR

This work addresses the capacity of pixel-antenna based MIMO by formulating a non-convex MINLP that jointly optimizes the transmit covariance and binary antenna coders at both ends. It contributes an optimal exhaustive-search solution and two scalable suboptimal methods—branch-and-bound and element-wise alternating optimization—each balancing rate performance and computational complexity. Numerical results show that well-designed antenna coding can significantly boost achievable rates over conventional MIMO, even with a modest number of pixel ports, and provide practical guidance on when to use each algorithm. The results underscore the practical potential of pixel antennas to unlock new degrees of freedom in radiative patterns for next-generation high-rate wireless systems.

Abstract

Pixel antenna is a promising technology to enhance the wireless communication data rate by adaptively reconfiguring each antenna's radiation pattern via a so-called antenna coding technique which controls the states of switches connected to multiple pixel ports. This paper studies a multiple-input multiple-output (MIMO) system where both the transmitter and the receiver are equipped with multiple pixel antennas. We aim to characterize the fundamental capacity limit of this MIMO system by jointly optimizing the transmit covariance matrix and the antenna coders at both the transmitter and the receiver. This problem is a mixed-integer non-linear program (MINLP) which is non-convex and particularly challenging to solve due to the binary-valued optimization variables corresponding to the antenna coders. We first propose an exhaustive search based method to obtain the optimal solution to this problem, which corresponds to the fundamental capacity limit. Then, we propose a branch-and-bound based iterative algorithm aiming to find a high-quality suboptimal solution with lower complexity than exhaustive search as the number of pixel ports becomes large. Finally, we devise an alternating optimization (AO) based algorithm with polynomial complexity. Numerical results show that our proposed algorithms achieve a flexible trade-off between performance and complexity. Moreover, equipping the transceivers with pixel antennas can enhance the achievable rate of MIMO communications.

Paper Structure

This paper contains 10 sections, 13 equations, 5 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Modeling of Each Pixel Antenna
  4. Modeling of Pixel Antenna based MIMO Communication
  5. Problem Formulation
  6. Optimal Solution to (P1)
  7. Branch-and-Bound based Solution to (P1)
  8. Element-wise AO based Solution to (P1)
  9. Numerical Results
  10. Conclusions

Figures (5)

  • Figure 1: Illustration and modeling of pixel antenna.
  • Figure 2: Illustration of a pixel antenna model with $S=3$ in CST Studio Suite.
  • Figure 3: Achievable rate versus receive SNR.
  • Figure 4: Achievable rate versus the number of pixel ports, $S$, at $\mathrm{SNR}=0$ dB.
  • Figure 5: Computation time versus the number of pixel ports, $S$, at $\mathrm{SNR}=0$ dB.