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Movable-Antenna-Array-Enabled Communications with CoMP Reception

Guojie Hu, Qingqing Wu, Jian Ouyang, Kui Xu, Yunlong Cai, Naofal Al-Dhahir

TL;DR

This work investigates communications from a transmitter equipped with a movable antenna (MA) array to multiple CoMP destinations. By showing that the effective SNR reduces to maximizing the principal eigenvalue of a Hermitian matrix $\mathbf{B}(\mathbf{x},\{\theta_m\})$, the authors derive a closed-form beamforming solution ${\bf W}^* = P_S \mathbf{v}_{\max}(\mathbf{B}) \mathbf{v}_{\max}^H(\mathbf{B}) / \sigma^2$ for any antenna placement $\mathbf{x}$. They then solve the non-convex MA-position optimization via a minorization-maximization (MM) algorithm, constructing a convex surrogate ${f_3}$ in each iteration and solving a convex subproblem with CVX; a theoretical upper bound $MN$ on the maximum principal eigenvalue is provided. The results show significant gains of the MA-CoMP design over fixed-position antenna and APS benchmarks, demonstrating the practical value of moving antennas to boost capacity or rate.

Abstract

We consider the movable-antenna (MA) arrayenabled wireless communication with coordinate multi-point (CoMP) reception, where multiple destinations adopt the maximal ratio combination technique to jointly decode the common message sent from the transmitter equipped with the MA array. Our goal is to maximize the effective received signal-to-noise ratio, by jointly optimizing the transmit beamforming and the positions of the MA array. Although the formulated problem is highly non-convex, we reveal that it is fundamental to maximize the principal eigenvalue of a hermite channel matrix which is a function of the positions of the MA array. The corresponding sub-problem is still non-convex, for which we develop a computationally efficient algorithm. Afterwards, the optimal transmit beamforming is determined with a closed-form solution. In addition, the theoretical performance upper bound is analyzed. Since the MA array brings an additional spatial degree of freedom by flexibly adjusting all antennas' positions, it achieves significant performance gain compared to competitive benchmarks.

Movable-Antenna-Array-Enabled Communications with CoMP Reception

TL;DR

This work investigates communications from a transmitter equipped with a movable antenna (MA) array to multiple CoMP destinations. By showing that the effective SNR reduces to maximizing the principal eigenvalue of a Hermitian matrix , the authors derive a closed-form beamforming solution for any antenna placement . They then solve the non-convex MA-position optimization via a minorization-maximization (MM) algorithm, constructing a convex surrogate in each iteration and solving a convex subproblem with CVX; a theoretical upper bound on the maximum principal eigenvalue is provided. The results show significant gains of the MA-CoMP design over fixed-position antenna and APS benchmarks, demonstrating the practical value of moving antennas to boost capacity or rate.

Abstract

We consider the movable-antenna (MA) arrayenabled wireless communication with coordinate multi-point (CoMP) reception, where multiple destinations adopt the maximal ratio combination technique to jointly decode the common message sent from the transmitter equipped with the MA array. Our goal is to maximize the effective received signal-to-noise ratio, by jointly optimizing the transmit beamforming and the positions of the MA array. Although the formulated problem is highly non-convex, we reveal that it is fundamental to maximize the principal eigenvalue of a hermite channel matrix which is a function of the positions of the MA array. The corresponding sub-problem is still non-convex, for which we develop a computationally efficient algorithm. Afterwards, the optimal transmit beamforming is determined with a closed-form solution. In addition, the theoretical performance upper bound is analyzed. Since the MA array brings an additional spatial degree of freedom by flexibly adjusting all antennas' positions, it achieves significant performance gain compared to competitive benchmarks.
Paper Structure (6 sections, 23 equations, 2 figures, 1 algorithm)

This paper contains 6 sections, 23 equations, 2 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Algorithm Design
  4. Simulation Results
  5. Conclusion
  6. The expressions of $\nabla {f_2}\left( {{\bf{B}}\left( {{{\bf{x}}^{(k)}},\left\{ {{\theta _m}} \right\}_{m = 1}^M} \right)} \right)$ and $\delta$

Figures (2)

  • Figure 1: A typical scenario of the considered system model.
  • Figure 2: (a) Convergence behavior of the proposed algorithm and the AO algorithm; (b) Beam gain of the MA array and the FPA array; (c) Achievable rate w.r.t. the number of transmit antennas at S ($N$).