Machine Learning and Quantum Approximate Optimization Pipeline for Dynamic License-Tier Reallocation in Mass Gathering RAN Networks

Tahir Hussain Nazir Hussain, Ghulam Muhayy Ud Din Qureshi

Scenarios such as mass religious gathering events face extreme, short-lived surges in radio access network traffic demand that a fixed license or capacity allocation cannot efficiently absorb. We propose a two-stage pipeline that couples a machine-learning demand forecaster with a quantum approximate optimization algorithm (QAOA) to distribute a fixed per-event license budget across six representative massgathering RAN zones. Demand is synthesized over a nine-day Hajj-season window, explicitly grounded in publicly reported real surge statistics (a 42% Arafah Day data-traffic surge and a 44% Eid Al-Adha surge reported by the Kingdom's largest operator, and CST-reported Hajj-season voice/data indicators). A Random Forest forecaster trained on this data achieves R2 = 0.463 on a held-out day, substantially outperforming a naive persistence baseline (R2 = −0.874). The forecasted per-site demand is then treated as a license-tier reallocation problem, formulated as a quadratic unconstrained binary optimization (QUBO) over per-site power/capacity tiers drawn from the real operating range of the Nokia AWHQF AirScale Micro RRH, and solved with a from-scratch exact statevector QAOA simulator. QAOA-derived allocations matched or outperformed a classical greedy heuristic on average across six representative demand scenarios (mean cost reduction of 26.4%), while retaining a mean approximation ratio of 0.499. Our specific contribution – license-tier (not spectrum/resource-block) reallocation under Hajj-scale demand surges, driven by a paired ML-forecast-to- QAOA-allocate pipeline – is the paper's novel element, since QAOA-for-telecom-resource-allocation and QPSO-for-RAN are each independently established directions.