Load-Aware Cell Shaping for Improved Macrocell and Small Cell Coexistence

This work investigates the joint optimization of coverage, capacity, and cell load by tuning several cell-specific antenna and cell association parameters via data-driven methods. We are particularly focused on the complexities of macrocell and small cell coexistence, and demonstrate an automated learning method whereby macrocells and small cells can strategically adapt their coverage areas. Coupled with adaptive offloading using a tunable small cell bias, we demonstrate significant throughput and coverage improvement in a realistic 5G network simulator developed by AT&T Labs. Concretely, we formulate an optimization problem to maximize network coverage and the application-layer data rate experienced by users, accounting for delays from congestion, cell loading, and packet retransmissions. We propose an algorithm that approaches the optimum via Gaussian process models and the evolutionary search: efficiently navigating the high-dimensional, nonconvex space while managing uncertainty. Our results show that the joint optimization of antenna tuning and load balancing - exemplified by load-aware cell shaping - more than doubles the cell edge throughput and increases the cell edge SINR by 8 dB, compared to bias-only optimization. Furthermore, our algorithm and overall approach appear viable for implementation.