Utility-Based SDMA Uplink Scheduling for QoS and Complexity
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Solution Overview
Problem
Existing SDMA scheduling algorithms for wireless communications networks are computationally intensive and fail to consider quality of service (QoS) constraints, fairness, and other factors beyond maximizing throughput, making them insufficient for advanced multimedia traffic in 3G and 4G networks.
Innovation Solution
A utility-based scheduling method that assigns utility functions to users, iteratively selects users to maximize the combined utility value, and allocates transmission resources, reducing computational complexity and considering constraints like delay, throughput, and fairness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search algorithms are used for SDMA user scheduling, then user selection optimality is improved, but computational complexity increases exponentially
Solution Approach 1:
The patent segments the user selection process into iterative steps where users are selected one at a time based on utility function evaluation. Instead of evaluating all possible user combinations simultaneously (exhaustive search), the algorithm builds the selected user set incrementally by adding one user per iteration, reducing computational complexity from exponential to linear while maintaining near-optimal performance.
Solution Approach 2:
The patent changes the selection criterion from pure throughput maximization to utility function-based selection that incorporates multiple QoS parameters (delay, throughput, fairness). This parameter transformation allows the system to optimize a composite utility metric that reflects overall system performance rather than single-dimensional throughput, resolving the contradiction between optimization quality and computational burden.
2Productivity
If throughput maximization is the sole scheduling criterion, then data rate is improved, but quality of service constraints and fairness deteriorate
Solution Approach 1:
The patent transforms the scheduling criterion from a single parameter (throughput) to a multi-parameter utility function that incorporates throughput, delay, and fairness metrics. The utility function U(r) = log(1 + α·r) transforms raw throughput into a utility value that accounts for diminishing returns, while separate fairness indices and delay constraints ensure QoS requirements are met. This parameter transformation resolves the contradiction by making the scheduler consider multiple objectives simultaneously.
Solution Approach 2:
The patent implements feedback mechanisms where the scheduler evaluates QoS constraints and fairness metrics after each user selection, and adjusts subsequent selections accordingly. The iterative process continuously monitors whether selected user sets satisfy delay constraints and fairness requirements, providing feedback that guides the selection of additional users. This feedback loop ensures that throughput maximization does not compromise QoS or fairness.
3Reliability
If iterative user selection is performed to maximize utility functions, then QoS constraints and fairness are improved, but selection process complexity increases
Solution Approach 1:
The patent segments the complex multi-objective optimization into simple iterative steps. Each iteration evaluates only one additional user against the current selected set, checking QoS constraints and utility function improvements sequentially. This segmentation breaks down the complex selection process into manageable discrete decisions, reducing overall computational complexity while maintaining QoS and fairness guarantees.
Solution Approach 2:
The patent performs partial optimization by selecting a limited number of users (up to a maximum K) rather than optimizing the complete user set. The iterative process stops when either K users are selected or no additional users can be added without violating QoS constraints. This partial action approach achieves sufficient QoS and fairness improvement without the excessive computational burden of complete optimization.
Data Source
AI summary
System and method for utility-based scheduling for SDMA transmissions on an uplink of a wireless communications network. A method comprises assigning a utility function to each user in a set of users, selecting a set of selected users from the set of users, assigning uplink transmission resources to each user in the set of selected users, and transmitting a control message to each user in the set of selected users. The selecting comprises iteratively selecting up to K users one user at a time from the set of users, wherein a combination of the selected users maximally increases a value of the assigned utility functions, wherein K is a maximum number of users in the set of selected users.