MATLAB Implementation of Device-to-Device Underlying Communication Resource Allocation Based on Matching Theory

In wireless communication networks, resource allocation for underlying device-to-device communication presents a critical challenge. Solutions based on matching theory provide an efficient and fair approach to resource distribution. This theory draws from stable matching concepts in economics, effectively addressing many-to-many resource allocation problems.

Implementing this theory in MATLAB enables simulation of resource competition and allocation processes among devices. The core approach treats communication devices as one party and available resources as another, constructing a bilateral matching model. The algorithm employs iterative optimization to find the most suitable communication resource blocks for each device while ensuring optimal overall system performance. Key functions would include preference matrix initialization, deferred acceptance algorithms, and stability verification routines.

The implementation must consider critical factors such as channel quality, interference levels, and device priority. Matching algorithms typically involve three phases: initial matching, preference list construction, and stable matching. The final allocation scheme must satisfy stability conditions, meaning no device-resource pair can form a superior matching combination. Implementation would require channel state information matrices, utility functions for preference ranking, and convergence checks for stability validation.

This method proves particularly effective in device-dense scenarios, significantly improving spectrum utilization and reducing communication latency. It serves as an ideal solution for 5G and IoT underlying communication systems, where code implementation would involve resource block assignment optimization and Pareto efficiency verification through numerical simulations.