Delay-Tolerant Online Convex Optimization for Delayed CSI Constraints
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Solution Overview
Problem
Existing wireless communication systems face challenges in handling time-varying constraints and feedback delays in constrained online convex optimization (OCO), particularly in network virtualization and resource allocation, leading to suboptimal performance and constraint violations.
Innovation Solution
A delay-tolerant constrained online convex optimization algorithm that allows decisions based on delayed feedback, minimizing regret and constraint violations by leveraging past information, applicable to network virtualization and resource allocation, with a focus on coordinated multi-cell MIMO WNV and online precoding schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time optimal decisions are made in OCO, then performance is improved, but feedback delay causes constraint violations and suboptimal decisions
Solution Approach 1:
The patent applies preliminary action by making decisions based on predicted future constraints rather than waiting for real-time feedback. The algorithm predicts constraint violations in advance and adjusts decisions proactively, compensating for the inherent feedback delay in the system.
Solution Approach 2:
The patent implements a feedback mechanism where constraint violations are monitored and used to adjust future decisions. The algorithm incorporates feedback about constraint satisfaction levels to dynamically modify the optimization trajectory, ensuring long-term constraint compliance despite delays.
2Productivity
If aggressive optimization is pursued to minimize regret, then performance gap is reduced, but constraint violations increase
Solution Approach 1:
The patent applies dynamics by making the optimization strategy adaptive rather than static. The algorithm dynamically adjusts its aggressiveness based on the current state of constraint satisfaction and predicted future constraints, balancing regret minimization with constraint compliance in a time-varying manner.
Solution Approach 2:
The patent changes optimization parameters dynamically to balance performance and constraint satisfaction. By adjusting parameters such as step size and regularization terms based on constraint violation levels, the algorithm can shift between aggressive optimization and conservative constraint compliance as needed.
3Stability of the object's composition
If delayed feedback is used for decisions, then system stability is improved, but decision accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary prediction model that bridges the gap between delayed feedback and current system state. This intermediary component estimates the current state based on historical data and feedback, allowing decisions to be made with improved accuracy while maintaining system stability.
Data Source
AI summary
A method and network node configured to perform wireless network virtualization to allocate resources of an infrastructure provider (InP) among a plurality of service providers (SPs) are provided. According to one aspect, a method in a network node includes, in each of a plurality of successive time slots in a time interval, T: receiving precoding feedback information from each SP, allocating a virtual transmit power to each cell served by an SP based at least in part on the received precoding feedback information and determining a precoding matrix for each SP. The method also includes minimizing a loss function based at least in part on the received precoding feedback information received in multiple previous time slots.