Two-Time-Scale Resource Management in Heterogeneous Wireless Networks
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Solution Overview
Problem
Current resource management systems in heterogeneous wireless networks with high backhaul latency struggle to optimize activation fractions of transmission points effectively, leading to inefficient resource allocation and spectral efficiency, especially in complex topologies with multiple high power macro and low power pico cells.
Innovation Solution
A two-time-scale resource management method is implemented, using a Greedy Stage and Local Search Stage within a GLS procedure to determine user association and activation fractions, considering average single-user rates and fairness factors, which allows for coarse and fine time-scale optimizations, improving system utility and spectral efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If coordinated resource management is performed at fine slot-level granularity, then resource allocation efficiency is improved, but backhaul latency causes coordination failures
Solution Approach 1:
The patent segments resource management into two distinct time scales: coarse time-scale management for coordination decisions and fine time-scale management for immediate resource allocation. This segmentation allows each level to operate at its optimal granularity without being constrained by backhaul latency, resolving the contradiction between fine-grained efficiency and coordination reliability.
Solution Approach 2:
The patent performs preliminary coordination decisions at the coarse time scale before actual resource allocation occurs at the fine time scale. By making activation decisions in advance and caching them, the system prepares coordination results beforehand, allowing fine-grained resource allocation to proceed without real-time backhaul coordination, thus maintaining both efficiency and reliability.
2Productivity
If activation fractions of all TPs are optimized, then system utility is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the optimization process into two stages: first determining user association, then determining activation fractions. This segmentation breaks the complex joint optimization problem into simpler sequential sub-problems, reducing computational complexity while maintaining system utility improvement.
Solution Approach 2:
The patent implements dynamic optimization where activation fractions are adjusted based on current system conditions and user associations. By making the optimization adaptive and condition-dependent rather than static, the system achieves better utility with reduced computational burden by only optimizing parameters that actually impact performance.
3Device complexity
If cell dormancy is implemented for entire frame duration, then resource management is simplified, but resource utilization efficiency decreases
Solution Approach 1:
The patent implements periodic activation where TPs are activated in cycles rather than continuously or completely dormant. By using periodic activation patterns at the coarse time scale, the system achieves simpler management compared to continuous optimization while improving resource utilization compared to complete dormancy, as TPs can be activated intermittently based on demand.
Solution Approach 2:
The patent uses partial activation fractions where TPs are activated for only a portion of the frame duration rather than being fully active or fully dormant. This partial action allows the system to achieve better resource utilization than complete dormancy while maintaining simpler management than continuous optimization, by activating TPs just enough to meet demand without over-provisioning.
Data Source
AI summary
A system and method for resource management in a heterogeneous wireless network wherein the resources of the mobile communications system are managed on a coarse time-scale and a fine time-scale. The coarse time-scale management comprises a first stage of determining the user association for each of the plurality of TPs followed by a second stage of determining activation fractions for all TPs. The determining of the user association is performed by utilizing a GLS procedure having a Greedy Stage and a Local Search Stage. In the Greedy Stage, new user, TP pairs are analyzed and the pair with the greatest improvement in system utility is selected. In the Local Search Stage, potential swaps are analyzed and a pair offering the greatest improvement that exceeds a threshold is selected. The determining of activation fractions for all TPs is performed by utilizing an auxiliary function method.