Problem
Research questions and friction points this paper is trying to address.
Sparse Arrays
Rotatable Antennas
Leakage Suppression
User Distribution
MIMO Systems
Sparse Rotatable Arrays (SRA): Unifying Array Aperture and Antenna Directivity for Wireless Communications
🤖 AI Summary
This work addresses the challenges of sidelobe and grating lobe leakage in sparse arrays for extremely large-scale MIMO systems, which, while cost-effective and aperture-preserving, suffer from interference, whereas rotatable antennas that mitigate such leakage incur high control overhead. To overcome this trade-off, the paper proposes a user-group-oriented sparse rotatable antenna architecture that partitions users into service groups, each served by a dedicated sparse subarray. It jointly optimizes aperture allocation, antenna orientation, and beamforming to maximize the minimum SINR. Leveraging group-level geometric information, the design enables low-complexity configuration and reveals an approximately decoupled beam structure—inter-group leakage suppression and intra-group orthogonalization—that guides antenna placement and allocation. A two-layer structured algorithm, integrating analysis-guided initialization, multi-start search, and closed-form orientation rules, achieves near-ideal performance with substantially reduced hardware cost, significantly outperforming both compact and omnidirectional sparse baselines.
📝 Abstract
Sparse arrays reduce the hardware cost of extremely large-scale multiple-input multiple-output systems while preserving a large effective aperture, but may suffer from severe sidelobe and grating-lobe leakage. Rotatable antennas (RAs) provide directional leakage suppression, whereas frequent boresight adaptation to instantaneous user locations incurs considerable control overhead. In practice, long-term user distributions are often non-uniform, with compact hotspots coexisting with scattered users, which enables low-complexity RA configuration based on group-level geometry. Motivated by this observation, we propose a group-aware sparse RA architecture in which users are organized into service groups and the activated RAs are partitioned into group-specific sparse subarrays. We maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all users by jointly optimizing the sparse-aperture allocation, RA orientations, and transmit beamforming. We show that RA-induced inter-group leakage suppression and sparse-aperture-induced intra-group orthogonalization jointly yield an approximately decoupled group-wise beamforming structure, providing physical guidance for group-wise antenna-number allocation and non-periodic sparse-position initialization.
Guided by these insights, we develop a structured low-complexity two-layer algorithm that embeds a closed-form projected group-center RA orientation rule into the sparse-aperture allocation and beamforming design. The algorithm combines analysis-guided initialization, sampled multi-start antenna-allocation search, and bisection-based second-order cone programming for beamforming. Numerical results show that the proposed design closely approaches the fully shared and orientation-optimized benchmarks while substantially outperforming the compact and omnidirectional sparse-array schemes.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Sparse Rotatable Arrays
Group-aware Beamforming
SINR Maximization
Inter-group Interference Suppression
Structured Low-complexity Algorithm
🔎 Similar Papers
No similar papers found.
