Dynamic Base Station Scheduling for UE Throughput and Latency

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Solution Overview

Problem

Advanced cellular networks face challenges in optimizing wireless communications due to complex transmission and reception behaviors of technologically advanced User Equipment (UE) devices, which can lead to suboptimal settings at base stations, resulting in degraded throughput and latency, particularly under varying network loads.

Innovation Solution

Implementing schedulers that dynamically adjust scheduling strategies based on Key Performance Indicators (KPIs) and UE parameters to optimize frequency band and scheduling time period, allowing for adaptive resource allocation and improved throughput and latency management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If base stations employ a wide range of technologies to handle complex UE transmission and reception behavior, then the system can support advanced UE devices, but identifying and adjusting optimal settings becomes difficult and time-consuming

Engineering Contradiction:

Improvesupport for advanced UE devicesVSAvoiddifficulty in identifying and adjusting settings

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system implements feedback mechanisms where scheduling strategy selectors receive information about actual transmission and reception performance from base stations, analyze the effectiveness of current settings, and automatically adjust scheduling strategies accordingly. This closed-loop feedback system eliminates the need for manual identification and adjustment of optimal settings while maintaining support for advanced UE devices.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The scheduling strategy selector autonomously performs the function of identifying and adjusting optimal base station settings without external intervention. The system self-configures scheduling parameters based on observed performance metrics and UE characteristics, replacing manual optimization processes with automated self-adjustment capabilities.

Inventive Principle:

Principle #25Self-service

2Productivity

If fixed scheduling strategies are used at base stations, then system operation is simple, but throughput and latency performance degrades under varying network loads

Engineering Contradiction:

Improvethroughput and latency performanceVSAvoidscheduling strategy adjustment complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system transitions from fixed scheduling strategies to dynamic scheduling strategies that automatically adapt to varying network conditions. The scheduling strategy selector continuously monitors network load, UE performance metrics, and transmission conditions, then adjusts scheduling parameters in real-time to optimize throughput and latency while managing the complexity through automated decision-making algorithms.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes scheduling parameters such as time period, frequency band allocation, and resource block assignment based on observed performance and network conditions. By dynamically modifying these parameters rather than using fixed values, the system achieves improved productivity under varying loads while the scheduling strategy selector manages the complexity of parameter adjustment.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If scheduling time period and frequency band are dynamically adjusted, then throughput and latency are optimized, but the scheduling system complexity increases

Engineering Contradiction:

Improvethroughput and latency optimizationVSAvoidscheduling system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The scheduling strategy selector uses feedback from performance metrics and network conditions to automatically determine optimal scheduling parameters. This feedback-driven approach allows dynamic adjustment of time period and frequency band while managing complexity through data-driven decision-making rather than complex manual configuration.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The scheduling strategy selector acts as an intermediary component that simplifies the complexity of dynamic scheduling parameter adjustment. It receives input from multiple sources (performance metrics, network conditions, UE characteristics) and translates them into appropriate scheduling parameter adjustments, shielding the rest of the system from the complexity of real-time optimization decisions.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250220665A1Systems and methods for improving throughput and latency at base stations and user equipment devices

Publication Date: 2025.07.03 VERIZON PATENT & LICENSING INC

AI summary

A device may include a processor. The processor may be configured to: receive, from one or more network components, key performance indicators (KPIs) and parameters that are associated with a User Equipment device; select a scheduling strategy, for data communications over a wireless link between the device and the UE, based on the KPIs and the parameters; apply the selected scheduling strategy to schedule data for transmission to the UE; and transmit the data to the UE based on the scheduling.