Ant Colony Optimization for Full Duplex Resource Allocation
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Solution Overview
Problem
In multi-cell wireless communication systems, the challenge is to effectively allocate wireless resources to maximize data rate and spectrum efficiency, particularly in full-duplex communication systems where self-interference is a significant obstacle.
Innovation Solution
The implementation of an Ant Colony Optimization (ACO) algorithm for resource allocation, where virtual nodes and edges are used to model user pairs and resource blocks, with pheromone trails guiding the allocation process to optimize data rates across multiple cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource allocation methods are used in full duplex communication systems, then implementation is simpler, but data rate and spectrum efficiency are limited due to self-interference constraints
Solution Approach 1:
The patent introduces virtual nodes and virtual edges as intermediary concepts to model the complex full duplex communication system. Virtual nodes represent user pairs (downlink and uplink users sharing a resource block), while virtual edges represent interference relationships between them. This intermediary modeling layer transforms the complex self-interference management problem into a graph optimization problem that can be solved using ant colony optimization, thereby achieving high data rates without excessive implementation complexity
Solution Approach 2:
The patent transforms the resource allocation problem by changing the parameter representation from traditional time-frequency resource blocks to virtual graph parameters (nodes, edges, and pheromone values). By representing interference levels and data rates as virtual edge weights and node properties, the system can dynamically adjust allocation based on real-time conditions while maintaining computational tractability through the ACO algorithm's pheromone-based parameter updates
2Productivity
If resource blocks are allocated to maximize data rate in multi-cell systems, then spectrum efficiency improves, but interference management becomes more difficult
Solution Approach 1:
The patent converts the harmful self-interference effect into a useful optimization criterion. Instead of treating self-interference as an obstacle to be avoided, the invention models interference relationships as virtual edges in a graph structure, where the strength of interference becomes a parameter that guides the ant colony optimization. The ACO algorithm naturally explores allocation patterns that exploit interference conditions to achieve high spectrum efficiency, transforming the harmful interference into a factor that can be leveraged for optimal resource allocation
Solution Approach 2:
The patent adds a virtual dimension to the resource allocation problem by creating a graph-based representation layer above the physical resource blocks. This virtual dimension allows the system to manage multi-cell interference by representing spatial and interference relationships in a structured graph format, enabling the ACO algorithm to find optimal allocations across multiple cells while accounting for self-interference constraints that would be difficult to manage in the traditional time-frequency domain
Data Source
AI summary
The present disclosure provides a resource allocation method. The resource allocation method includes the following steps: selecting multiple first selected virtual nodes according to multiple virtual pheromonal trails on multiple virtual edges, in which the first selected virtual nodes forms at least one virtual tour, and the virtual tour includes multiple first virtual edges; updating the virtual pheromonal trails on the virtual edges according to virtual distances corresponding to the first virtual edges of the virtual tour; selecting multiple second selected virtual nodes according to the updated virtual pheromonal trails on the virtual edges, in which the second selected virtual nodes form at least one resulting virtual tour; allocating the resource blocks to selected user pairs according to the resulting virtual tour.