Applying Distributed Constraint Optimization Approach to the User Association Problem in Heterogeneous Networks.

Area of Science:

• Computer Science
• Telecommunications Engineering

Background:

• User association in heterogeneous networks (HetNets) presents a distributed resource allocation challenge.
• Existing methods like combinatorial optimization and game theory risk local optima and lack parameter-relation analysis for practical deployment.

Purpose of the Study:

• To address limitations in HetNet user association, this research models the problem as a distributed constraint optimization problem (DCOP) within a multi-agent system framework.
• Develop and evaluate novel DCOP models (ECAV, EBUAV) and a message-passing based solver for efficient distributed resource allocation.

Main Methods:

• Proposed two distinct modeling approaches: Each Connection as Variable (ECAV) and Each Base Station and User as Variable (EBUAV).
• Developed a DCOP solver utilizing a complete DCOP algorithm with distributed message-passing for efficient solution finding.
• Conducted theoretical analysis and simulations to validate the DCOP approach.

Main Results:

• The DCOP solver achieved a 6% throughput improvement compared to existing methods.
• Demonstrated up to an 18% increase in the capacity of base stations (BSs) to serve more users, especially when user numbers exceed 200 with limited resource blocks (RBs).
• Showcased improved distribution of RBs allocated by BSs to users, adapting to varying RB volumes at the macro BS.

Conclusions:

• The proposed DCOP approach effectively models and solves the distributed user association problem in HetNets.
• The DCOP solver offers significant performance gains in throughput and user service capacity, outperforming traditional optimization techniques.
• This method provides a practical and efficient solution for resource allocation in complex, real-world HetNet environments.