Subspace-Confined QAOA with Generalized Dicke States for Multi-Channel Allocation in 5G CBRS Networks

TL;DR

This paper introduces a subspace-confined QAOA approach for multi-channel allocation in 5G CBRS networks, using Generalized Dicke states to efficiently encode constraints and improve solution quality.

Contribution

It develops a novel QAOA ansatz that confines dynamics to feasible states, reducing search space and enhancing performance over standard penalty-based methods.

Findings

Reduces search space from 2^24 to 2916 configurations.

Achieves near-optimal conflict levels in simulations.

Maintains high feasibility under noise in NISQ regimes.

Abstract

Efficient spectrum sharing in the Citizens Broadband Radio Service (CBRS) band is essential for maximizing 5G network capacity, particularly when high-traffic base stations require simultaneous access to multiple channels. Standard formulations of the Quantum Approximate Optimization Algorithm (QAOA) impose such multi-channel constraints using penalty terms, so most of the explored Hilbert space corresponds to invalid assignments. We propose a subspace-confined QAOA tailored to CBRS multi-channel allocation, in which each node-wise channel register is initialized in a Generalized Dicke state and evolved under an intra-register XY mixer. This ansatz confines the dynamics to a tensor product of Johnson graphs that exactly encode per-node Hamming-weight constraints. For an 8-node CBRS interference graph with 24 qubits, the effective search space is reduced from the full Hilbert space of size $2^{24}$ to 2916 feasible configurations. Within this subspace, the algorithm converges rapidly to low-conflict assignments without large penalty coefficients. Simulations on instances with up to eight nodes show that the proposed ansatz achieves near-optimal conflict levels and consistently outperforms standard penalty-based QAOA and a greedy classical heuristic in terms of feasibility. Noise simulations with depolarizing channels further indicate that the constraint-preserving structure maintains a high feasibility ratio in NISQ-relevant error regimes.