Recursive QAOA for Interference-Aware Resource Allocation in Wireless Networks

TL;DR

The paper tackles the NP-hard problem of discrete radio resource management in dense wireless networks by encoding channel assignment as a QUBO/Ising objective and solving it with a Recursive QAOA (RQAOA) pipeline. By interleaving shallow QAOA layers with correlator-guided variable elimination, the method fixes high-confidence relations to shrink the problem and concentrates quantum effort on a core that is solved exactly, while maintaining feasibility via penalties or constraint-preserving mixers. Empirical results on small wireless topologies show feasible allocations and, in at least one case, a global optimum, and large-scale hotspot benchmarks demonstrate near-term scalability with a fixed-size quantum core and classical extension. Overall, recursion mitigates parameter growth and feasibility issues in plain QAOA, offering a practical quantum-classical pathway for interference-aware wireless resource allocation.

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.

Figures (5)

• Figure 1: Classical pre-solving and graph reductions for RQAOA. Spatial user clusters (left) are aggregated into a reduced interference graph (center), which is then processed by RQAOA to generate channel assignments (right).
• Figure 2: Interference topology and RQAOA-derived channel assignment for a $U=4, C=4$ instance.
• Figure 3: Scaled approximation ratio (mean $\pm$ std, five seeds) versus problem size for RQAOA with a ring-XY mixer ($C=3$, $p=2$, $n_{\min}=8$), normalized between each instance’s best and worst feasible energies. RQAOA remains near-optimal at small sizes and around $0.7\text{--}0.8$ at larger sizes, with feasibility preserved.
• Figure 4: Normalized absolute deviation between R-QAOA and greedy baseline, as a function of the number of users $U$ (mean $\pm$ std over random hotspot topologies).
• Figure 5: End-to-end pipeline runtime versus number of users $U$ (log scale) for the greedy heuristic, QAOA pipeline, and R-QAOA pipeline (mean $\pm$ std over random hotspot topologies).