Recursive QAOA for Interference-Aware Resource Allocation in Wireless Networks
Kuan-Cheng Chen, Hiromichi Matsuyama, Wei-hao Huang, Yu Yamashiro

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.…
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Taxonomy
TopicsCognitive Radio Networks and Spectrum Sensing · Advanced MIMO Systems Optimization · Advanced Bandit Algorithms Research
