MU-MIMO OFDMA Resource Allocation via Weighted Sum Optimization
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Solution Overview
Problem
Existing resource allocation strategies in MU-MIMO OFDMA wireless systems face challenges in achieving optimal trade-offs between network capacity and wireless device fairness, particularly due to transmit power sharing and inter-wireless device interference.
Innovation Solution
A dynamic resource allocation method that selects user sets of wireless devices, estimates expected data rates, computes a weighted sum of these rates, and assigns radio resources to the user set with the highest weighted sum, thereby optimizing the trade-off between capacity and fairness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple wireless devices are co-scheduled on the same PRB via MU-MIMO, then network capacity is improved, but inter-wireless device interference increases
Solution Approach 1:
The patent converts the harmful inter-wireless device interference into a beneficial selection criterion. By calculating interference metrics between co-scheduled devices and using these metrics to guide PRB allocation, the system transforms the previously harmful interference effect into a useful signal for making optimal scheduling decisions, thereby enabling capacity improvement while controlling interference.
Solution Approach 2:
The patent implements a feedback mechanism where interference metrics are calculated based on the channel states and allocation decisions of previously scheduled devices. This feedback information is then used to adjust subsequent PRB allocations, creating a closed-loop system that continuously optimizes the trade-off between capacity and interference.
2Productivity
If transmit power is shared among co-scheduled wireless devices, then system throughput is improved, but channel quality deteriorates
Solution Approach 1:
The patent applies dynamic power allocation where the transmit power for each co-scheduled device is adjusted based on real-time channel conditions and interference metrics. Rather than using fixed power sharing, the system dynamically optimizes power distribution to maintain channel quality while maximizing system throughput, adapting to changing conditions across different PRBs and scheduling instances.
3Productivity
If resource allocation prioritizes network capacity, then system throughput increases, but wireless device fairness deteriorates
Solution Approach 1:
The patent applies different allocation strategies to different wireless devices and PRBs based on their local conditions. By evaluating interference metrics and channel qualities individually for each device-PRB combination, the system can make localized optimization decisions that balance overall capacity with individual device fairness, rather than applying a uniform allocation policy.
Data Source
AI summary
Embodiments of a method in a network node for resource allocation are disclosed. The method includes selecting a user set of wireless devices from available predefined user sets; estimating expected data rates for the wireless devices in the selected user set for a selected radio resource; computing a weighted sum of the expected rates of the wireless devices in the user set; and assigning a given the selected radio resource to the user set with the highest weighted sum. A criterion for dynamic resource allocation that can opportunistically exploit the channel variations towards achieving the optimal trade-off between the network capacity and wireless device-fairness is also disclosed.