Activity Factor Estimation for Wireless QoS Management
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Solution Overview
Problem
In wireless communication systems, particularly in HSDPA networks, accurately estimating the activity factor (AF) of users is challenging, especially for non-real-time (NRT) applications, as user behavior varies significantly and depends on network load, making it difficult for the RNC to determine the required power for admission control and QoS management.
Innovation Solution
A method to estimate the activity factor based on provided bit rate and guaranteed bit rate for each priority class, using these estimates to calculate network-related parameters such as power usage, enhancing QoS-aware admission control and load management by averaging AF over all HS-DSCH connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the activity factor is estimated by measuring actual data received and transmitted in the RNC, then the AF value can be obtained, but the value varies considerably depending on the averaging period used
Solution Approach 1:
The patent introduces an intermediary approach by using Node B measurements (provided bit rate and guaranteed bit rate) as a mediator to estimate the activity factor, rather than directly measuring at the RNC. This intermediary measurement approach provides a more stable and less complex estimation method that avoids the variability introduced by different averaging periods.
Solution Approach 2:
The patent replaces the mechanical measurement system at the RNC (direct data measurement) with a computational estimation system using Node B measurements. By substituting direct measurement with a calculation-based approach using provided bit rate and guaranteed bit rate, the system achieves more consistent activity factor estimation without the complexity of multiple averaging period configurations.
2Reliability
If the required power is defined assuming 100% user activity, then the minimum necessary power for guaranteed bit rate is ensured, but the actual power consumption cannot be accurately determined for NRT applications
Solution Approach 1:
The patent applies parameter changes by using the estimated activity factor (derived from provided bit rate and guaranteed bit rate measurements) to adjust the required power calculation. Instead of using a fixed 100% activity assumption, the system dynamically changes the activity parameter based on actual network conditions and user behavior patterns, enabling accurate power consumption determination for NRT applications while maintaining GBR assurance.
3Reliability
If admission control is performed for NRT services to guarantee minimum bit rate, then QoS requirements are met, but the user behavior variation and network load dependence make accurate AF estimation difficult
Solution Approach 1:
The patent implements feedback by continuously measuring the provided bit rate and guaranteed bit rate at the Node B and using these measurements to update the activity factor estimation. This feedback loop allows the system to adapt to changing user behavior and network load conditions, maintaining accurate AF detection while ensuring QoS guarantees for NRT services.
Data Source
AI summary
In one non-limiting, exemplary embodiment, a method includes: estimating an activity factor for a priority class based at least in part on a provided bit rate for the priority class and a guaranteed bit rate of the priority class; and using the estimated activity factor to estimate at least one network-related parameter. In another non-limiting, exemplary embodiment, a method includes: obtaining a measurement for a priority class; and estimating an activity factor for the priority class based at least in part on the measurement and a quality of service attribute of the priority class. As a non-limiting example, exemplary embodiments of this invention employ a framework providing an estimation of effective activity factor per service priority indicator (SPI) class to provide, for example, enhanced quality of service awareness in radio resource management functionality, such as for estimation of the amount of power used per SPI class/group.