Abstract
A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies.
Highlights
Future cellular communication systems should be capable of adaptively changing their radio access functions in response to dynamic changes in the environment [1,2]
Our paper is complementary with respect to [36], since we present additional U-radio access networks (RANs) applications and discuss potential challenges by highlighting the possible solutions based on supervised learning (SL) and reinforcement learning (RL) methods
Survey [37] provides a detailed description of all relevant research, wherein machine learning (ML) techniques have been employed in UAV-based RANs (U-RANs) to improve different design and functional aspects, such as channel modeling, resource management, positioning, and security
Summary
Future cellular communication systems should be capable of adaptively changing their radio access functions in response to dynamic changes in the environment [1,2]. Radio access networks (RANs) have to face with the high variability of data traffic patterns, by deploying additional stations, acting either as base (BSs) or relay stations (RSs), whenever required and in case of unexpected events, such as, e.g., peaks of multimedia data traffic and occurrence of disasters. UAVs can operate (see Figure 1) as flying stations (BSs or RSs) to increase the coverage area, balance traffic load, and enhance network capacity [4] Along with their inherent features, such as, e.g., mobility, flexibility, and variable altitude, UAV-based stations can be deployed faster, offer higher flexibility for reconfiguration, and provide line-of-sight (LoS) connectivity towards users.
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