AI/ML-Driven Operation of Large-Scale Dynamic Subarrays in Sub-6GHz under 5G NR Standards

초록

To address the trade-offs between performance and hardware complexity in sub-6GHz massive multiple-input multiple-output (MIMO) systems, this paper proposes a deep learning (DL)-based approach for dynamic subarray configuration and mechanical downtilt optimization. The proposed method is tailored for sub-6GHz 5G systems, where constructing compact massive arrays is challenging and power consumption is a critical issue. In the proposed framework, both channel estimation and beamforming rely solely on the precoding matrix indicator (PMI), a 3GPP standardized feedback based on reference signaling. A supervised DL model dynamically configures subarrays and selects desirable array structures from a set of predefined configurations. This approach enables low-complexity and practical transceiver operations, with the advantage of potential applicability to commercial array hardware equipped with many transceiver units (TXRUs). The DL model is trained using diverse virtual environments generated under the 3GPP standard channel model. Performance evaluations in both virtual environments and digital twin-based scenarios show that the proposed method achieves a sum-rate comparable to that of exhaustive tilting and subarray selection, while providing a 13% gain over fixed configurations. Compared to a fully digital array, the method reduces the number of RF chains to one-sixth, incurring only a 7.1% sum-rate loss while achieving an energy efficiency gain of approximately 273%. These results, with the gains validated in digital twin-based scenarios, suggest that the proposed method can be well suited for real-world deployments. © 1967-2012 IEEE.

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