Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach

TL;DR

This paper introduces algorithms for learning optimal antenna tilt control policies in cellular networks using a contextual linear bandit framework, achieving near-optimal sample efficiency in both passive and active learning settings.

Contribution

It formalizes the tilt control problem as a Best Policy Identification task in CL-MAB and develops algorithms that reach fundamental sample complexity limits.

Findings

Algorithms achieve near-optimal sample efficiency

Effective in both passive and active learning scenarios

Applied to real cellular network data with successful results

Abstract

Controlling antenna tilts in cellular networks is imperative to reach an efficient trade-off between network coverage and capacity. In this paper, we devise algorithms learning optimal tilt control policies from existing data (in the so-called passive learning setting) or from data actively generated by the algorithms (the active learning setting). We formalize the design of such algorithms as a Best Policy Identification (BPI) problem in Contextual Linear Multi-Arm Bandits (CL-MAB). An arm represents an antenna tilt update; the context captures current network conditions; the reward corresponds to an improvement of performance, mixing coverage and capacity; and the objective is to identify, with a given level of confidence, an approximately optimal policy (a function mapping the context to an arm with maximal reward). For CL-MAB in both active and passive learning settings, we derive information-theoretical lower bounds on the number of samples required by any algorithm returning an approximately optimal policy with a given level of certainty, and devise algorithms achieving these fundamental limits. We apply our algorithms to the Remote Electrical Tilt (RET) optimization problem in cellular networks, and show that they can produce optimal tilt update policy using much fewer data samples than naive or existing rule-based learning algorithms.