Distributed-Optimization With Centralized-Refining for Efficient Resource Allocation in Future Wireless Networks

Future wireless networks are expected to support diverse Internet of Things (IoT) applications under dynamic network conditions through effective resource allocation. However, the growing complexity of underlying optimization problems for resource allocation has brought many challenges to traditional centralized network operations due to inherent computational constraints. To overcome these challenges, this paper proposes a Distributed-Optimization with Centralized-Refining (DO-CR) mechanism to achieve more efficient resource allocation by engaging both access point and all devices. Specifically, the new DO-CR mechanism first utilizes the distributed processing capacity of all devices, allowing them to optimize their own resource allocation schemes through a new resource reservation and reporting technique. Then a centralized optimizer generates a graph of resource trading topology based on individual optimization results and achieves the Pareto optimal solution by the graph-based algorithm. This Pareto optimal solution simplifies the overall optimization problem and enables the central optimizer to solve it with smaller feasible regions. The analysis presents that the DO-CR mechanism's performance is bounded by Pareto optimality as lower limit and global optimality as upper limit. Simulation results demonstrate that the proposed DO-CR mechanism significantly reduces processing time on the centralized optimizer while maintaining near-optimal utility performance compared to conventional optimization methods.