Capacity Analysis and Joint Gaussian Beam Pattern Optimization for Positioning-Assisted Communications
Abstract
Positioning-assisted beamforming is a novel enabling technology for massive multiple-input multiple-output in modern wireless communications, as it provides reliable beam steering without requiring explicit channel state information. However, the channel capacity analysis under positioning error remains mathematically intractable, which limits both performance characterization and beamforming design. In this work, we derive instantaneous and ergodic channel capacity approximations in closed forms for both two-dimensional and three-dimensional positioning-assisted beamforming systems. Based on the new expressions, we provide closed-form optimal joint Gaussian beam pattern that maximizes the asymptotic ergodic capacity. Numerical results verify the theoretical capacity expressions and the optimal beam pattern. The derived expressions enable efficient beam design to maximize channel capacity and provide a theoretical basis for positioning-assisted beamforming design.
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BibTeX
@misc{liu2026capacity,
title = {{Capacity Analysis and Joint Gaussian Beam Pattern Optimization for Positioning-Assisted Communications}},
author = {Yuanbo Liu and Rui Wang and Han Zhang and Shuojin Huang and Zaichen Zhang and Bingcheng Zhu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.28196},
eprint = {2609.28196},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.28196},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Capacity Analysis and Joint Gaussian Beam Pattern Optimization for Positioning-Assisted Communications
AU - Yuanbo Liu
AU - Rui Wang
AU - Han Zhang
AU - Shuojin Huang
AU - Zaichen Zhang
AU - Bingcheng Zhu
PY - 2026
DA - 2026/09/23
PB - arXiv
DO - 10.48550/arxiv.2609.28196
UR - https://arxiv.org/abs/2609.28196
AB - Positioning-assisted beamforming is a novel enabling technology for massive multiple-input multiple-output in modern wireless communications, as it provides reliable beam steering without requiring explicit channel state information. However, the channel capacity analysis under positioning error remains mathematically intractable, which limits both performance characterization and beamforming design. In this work, we derive instantaneous and ergodic channel capacity approximations in closed forms for both two-dimensional and three-dimensional positioning-assisted beamforming systems. Based on the new expressions, we provide closed-form optimal joint Gaussian beam pattern that maximizes the asymptotic ergodic capacity. Numerical results verify the theoretical capacity expressions and the optimal beam pattern. The derived expressions enable efficient beam design to maximize channel capacity and provide a theoretical basis for positioning-assisted beamforming design.
ER -