Recursive QAOA for Interference-Aware Resource Allocation in Wireless Networks

TL;DR

This paper introduces a recursive quantum-classical algorithm for interference-aware resource allocation in dense wireless networks, demonstrating its effectiveness on simulated instances and its potential for near-term quantum heuristics.

Contribution

It develops a recursive QAOA-based method that improves scalability and feasibility in solving large QUBO problems for wireless resource management.

Findings

Consistently finds feasible solutions in simulated instances.

Attains the global optimum in a four-user, four-channel example.

Recursion reduces parameter growth and enhances stability.

Abstract

Discrete radio resource management problems in dense wireless networks are naturally cast as quadratic unconstrained binary optimization (QUBO) programs but are difficult to solve at scale. We investigate a quantum-classical approach based on the Recursive Quantum Approximate Optimization Algorithm (RQAOA), which interleaves shallow QAOA layers with variable elimination guided by measured single- and two-qubit correlators. For interference-aware channel assignment, we give a compact QUBO/Ising formulation in which pairwise interference induces same-channel couplings and one-hot constraints are enforced via quadratic penalties (or, optionally, constraint-preserving mixers). Within RQAOA, fixing high-confidence variables or relations reduces the problem dimension, stabilizes training, and concentrates measurement effort on a shrinking instance that is solved exactly once below a cutoff. On simulated instances of modest size, including a four-user, four-channel example, the method consistently returns feasible assignments and, for the demonstrated case, attains the global optimum. These results indicate that recursion can mitigate parameter growth and feasibility issues that affect plain QAOA, and suggest a viable pathway for near-term quantum heuristics in wireless resource allocation.