Dynamic Spectrum Sharing Resource Split Between LTE and NR Cells

The patent implements dynamic spectrum sharing by enabling the same frequency spectrum to be dynamically allocated between LTE and NR cells based on real-time network conditions, traffic demands, and service requirements. The system continuously adjusts resource allocation parameters including time slot distribution, frequency resource blocks, and power allocation to optimize spectrum utilization while managing the complexity through automated control mechanisms.

The system manages resource allocation complexity by dynamically adjusting multiple parameters including time slot configurations, frequency resource block assignments, power allocation levels, and modulation schemes. These parameter changes enable flexible adaptation to varying network conditions while maintaining manageable system complexity through standardized adjustment procedures and automated optimization algorithms.

The patent applies local quality by implementing cell-specific and user-specific resource allocation strategies where different time slots, frequency resources, and power levels are assigned to LTE and NR cells based on their specific requirements and channel conditions. This localized optimization enables high throughput for each cell while minimizing mutual interference through spatial and spectral separation techniques.

The system converts potential interference between LTE and NR cells into a beneficial effect by using coordinated resource allocation where interference patterns are predicted and exploited to schedule transmissions in a way that maximizes overall system throughput. The system transforms the harmful interference issue into an optimization opportunity by adjusting resource allocation to leverage constructive interference patterns while avoiding destructive ones.

The patent implements feedback mechanisms where the system continuously monitors spectrum sharing performance metrics including throughput, interference levels, resource utilization, and quality of service parameters. Based on this feedback, the system dynamically adjusts DSS tuning parameters such as time slot allocations, frequency resource distributions, and power settings to optimize spectrum sharing performance while managing control complexity through automated closed-loop control.

The system employs self-service mechanisms where automated algorithms and artificial intelligence models independently optimize resource allocation and adjust DSS parameters without requiring manual intervention. The system self-adjusts to changing network conditions, performs autonomous parameter tuning, and maintains optimal spectrum sharing performance through self-learning and adaptive control, thereby reducing the operational complexity burden.