🤖 AI Summary
This work addresses the significant yet challenging potential of the FR3 band (7–24 GHz) for 6G, where propagation characteristics and MIMO performance are highly sensitive to frequency and environmental conditions, and spectrum availability is fragmented due to incumbent users. To tackle these issues, we propose a fully digital, frequency-adaptive multi-band MIMO architecture tailored for FR3, which—unlike prior approaches—enables dynamic sharing of ADC/DAC and baseband resources across bands, allowing flexible trade-offs between bandwidth gain and antenna aggregation gain. Leveraging the Sionna RT ray-tracing platform, we model and analyze multi-frequency channel characteristics in representative indoor and outdoor scenarios, integrating full-digital beamforming with adaptive resource allocation. Simulations demonstrate that while bandwidth expansion is preferable under contiguous spectrum conditions, the proposed architecture substantially enhances system performance through adaptive reconfiguration when sub-bands are constrained or channel gains are spatially concentrated.
📝 Abstract
FR3 ($\approx$7-24 GHz), also referred to as the upper mid-band, has recently emerged as promising spectrum for 6G; however, its propagation and MIMO characteristics vary significantly with frequency and environment, and spectrum availability may be intermittent due to incumbents. Using site-specific ray tracing (Sionna RT) in representative indoor and outdoor scenarios, we evaluate 7, 10, 14, 20, and 24 GHz under SISO and MIMO configurations. The results show that FR3 exhibits propagation characteristics intermediate between sub-6 GHz and mmWave bands while supporting meaningful spatial multiplexing, albeit with strong site dependence. Motivated by these findings, we propose a fully digital frequency-adaptive multi-band MIMO architecture that repurposes ADCs/DACs and baseband processing resources across FR3 subbands via switching, enabling dynamic trade-offs between bandwidth (spectrum gain) and antenna consolidation (MIMO gain) under availability and channel constraints. Simulation results demonstrate that exploiting additional spectrum is often optimal, while adaptive resource repurposing becomes beneficial when subbands are unavailable or when multiplexing gains are concentrated at specific frequencies.