Two-Time Scale Interference Management in LTE HetNets
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Solution Overview
Problem
Existing wireless networks face challenges in managing interference in heterogeneous wireless networks due to high backhaul latency, where existing methods either focus on partial muting of macro transmission points or user association alone, and often relax discrete variables, leading to performance degradation.
Innovation Solution
A computer-implemented method that varies user association with multiple transmission points, involving partial muting of macro transmission points and load balancing, using a two-time scale approach to optimize user association and resource allocation, where coarse time-scale decisions are made based on averaged metrics and fine time-scale decisions are made independently by each active transmission point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If partial muting of macro TP and user association are jointly optimized, then interference management performance is improved, but computational complexity increases
Solution Approach 1:
The patent segments the optimization problem into two distinct parts: (1) discrete variables including muting fraction selection and user association decisions, and (2) continuous variables including resource block allocation and power control. This segmentation allows the discrete part to be solved using dynamic programming with manageable state spaces, while the continuous part is optimized given the discrete decisions, thereby reducing overall computational complexity while maintaining performance.
Solution Approach 2:
The patent extracts and separately handles the discrete decision variables (muting fraction and user association) from the continuous optimization variables. By taking out the discrete part and solving it independently using dynamic programming, the patent avoids the exponential complexity that would result from jointly optimizing all variables, thus resolving the contradiction between performance and complexity.
2Device complexity
If discrete variables are relaxed in optimization, then computational complexity is reduced, but performance degrades
Solution Approach 1:
The patent applies dynamic programming to solve the discrete optimization problem, which allows the system to adaptively select optimal muting fractions and user associations based on current network conditions. This dynamic approach ensures that discrete variables are not simply relaxed but are optimally determined through systematic exploration of the decision space, maintaining performance while managing complexity through efficient state transitions.
3Manufacturing precision
If fine slot-level coordinated resource management is implemented, then resource allocation precision is improved, but coordination overhead increases due to backhaul latency
Solution Approach 1:
The patent segments resource management into coordinated decisions (muting fraction, user association) made at frame boundaries and independent slot-level scheduling decisions made locally at each TP. This segmentation allows precise resource allocation within slots without requiring real-time coordination, as the coordinated parameters remain fixed throughout the frame, thus reducing backhaul overhead while maintaining allocation precision.
Solution Approach 2:
The patent performs preliminary coordination decisions for muting fraction and user association at the beginning of each frame before slot-level transmissions occur. This preliminary action establishes the interference management framework in advance, allowing subsequent slot-level operations to proceed independently without requiring continuous coordination, thereby reducing backhaul latency impact while maintaining precise resource management.
Data Source
AI summary
We show that for any given muting fraction, a more constrained version of the problem of interest can be optimally solved in an efficient manner. In addition, the obtained solution is also a near-optimal solution for the original problem (for the given muting ratio). This allows us provide an algorithm that offers a good solution to the original problem with a tractable complexity. We also derive a lower complexity greedy that offers good performance and a certain worst-case performance guarantee. Simulations over an example LTE HetNet topology reveal the superior performance of the proposed algorithms and underscore the benefits of jointly exploiting partial muting of the macro and load balancing.