Mobile Base Station Resource Allocation via Quantum Annealing
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Solution Overview
Problem
Existing technologies face challenges in efficiently allocating resources to mobile base stations, leading to interference, lower throughput, and high traffic loads, especially in scenarios with multiple unmanned aerial vehicles (UAVs) and increasing user demand.
Innovation Solution
The development of a Quadratic Unconstrained Binary Optimization (QUBO) model capable of quantum computing, which is used to determine subchannels and power levels that maximize throughput for mobile base stations, enabling rapid optimization through quantum annealing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mobile base stations share licensed spectrum bands to ensure high quality communication performance, then communication quality is improved, but interference occurs between base stations
Solution Approach 1:
The patent segments the licensed spectrum band into multiple subchannels and assigns different subchannels to different mobile base stations. This segmentation allows multiple base stations to operate simultaneously on the same licensed band without causing harmful interference, as each base station operates on a dedicated subset of the spectrum resources.
Solution Approach 2:
The patent applies local quality by assigning different power levels to different mobile base stations based on their specific service conditions, user distribution, and interference environment. Each base station operates with an optimized local power level that maximizes its communication quality while minimizing interference to others, rather than using a uniform power level across all base stations.
2Measurement precision
If conventional optimization methods are used to allocate resources to mobile base stations, then allocation accuracy is improved, but calculation time increases and real-time optimization becomes difficult
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing optimal resource allocation schemes for various service scenarios and conditions. When a mobile base station needs resource allocation, the system quickly retrieves the pre-computed optimal scheme that matches the current conditions, avoiding time-consuming real-time calculations while maintaining high allocation accuracy.
Solution Approach 2:
The patent uses copying by creating and storing multiple pre-computed resource allocation schemes for different service scenarios. Instead of performing complex calculations from scratch for each allocation decision, the system copies and applies the most appropriate pre-computed scheme to the current situation, significantly reducing calculation time while preserving allocation precision.
3Area of stationary object
If multiple mobile base stations are deployed to expand coverage and support more users, then service coverage is improved, but resource allocation complexity and interference management become more difficult
Solution Approach 1:
The patent segments both the spectrum resources and the base station network into manageable units. Spectrum is divided into subchannels, and base stations are organized into clusters or groups. This segmentation reduces resource allocation complexity by allowing independent optimization of smaller subsets rather than managing all resources globally, while still achieving coordinated interference management across the entire network.
Solution Approach 2:
The patent applies local quality by optimizing resource allocation and power levels for each individual base station or small cluster based on local conditions such as user distribution, traffic load, and interference environment. This localized optimization approach reduces overall system complexity by avoiding the need for centralized global optimization, while still achieving effective interference management and expanded coverage.
Data Source
AI summary
A method and an apparatus for optimizing communication of mobile base stations are provided. The method responds to an optimization problem of maximizing a sum rate of transmission and generates a QUBO model capable of quantum computing to rapidly enable optimal resource allocation through quantum annealing.