# DRLLA: Deep Reinforcement Learning for Link Adaptation
###### Abstract  
Link adaptation (LA) matches transmission parameters to conditions on the radio link, and therefore plays a major role in telecommunications. Improving LA is within the requirements for next-generation mobile telecommunication systems, and by refining link adaptation, a higher channel efficiency can be achieved (i.e., an increased data rate thanks to lower required bandwidth). Furthermore, by replacing traditional LA algorithms, radio transmission systems can better adapt themselves to a dynamic environment. There are several drawbacks to current state-of-the-art approaches, including predefined and static decision boundaries or relying on a single, low-dimensional metric. Nowadays, a broadly used approach to handle a variety of related input variables is a neural network (NN). NNs are able to make use of multiple inputs, and when combined with reinforcement learning (RL), the so-called deep reinforcement learning (DRL) approach emerges. Using DRL, more complex parameter relationships can be considered in order to recommend the modulation and coding scheme (MCS) used in LA. Hence, this work examines the potential of DRL and includes experiments on different channels. The main contribution of this work lies in using DRL algorithms for LA, optimized for throughput based on a subcarrier observation matrix and a packet success rate feedback system. We apply Natural Actor-Critic (NAC) and Proximal Policy Optimization (PPO) algorithms on simulated channels with a subsequent feasibility study on a prerecorded real-world channel. Empirical results produced by experiments on the examined channels hint that Deep Reinforcement Learning for Link Adaptation (DRLLA) offers good performance indicated by a promising data rate on the additive white Gaussian noise (AWGN) channel, the non-line-of-sight (NLOS) channel, and a prerecorded real-world channel. No matter the channel impairment, the agent is able to respond to changing signal-to-interference-plus-noise-ratio (SINR) levels, as exhibited by expected changes in the effective data rate.  
machine learning; mobile communication; reinforcement learning; link adaptation; channel observation +
Footnote †: journal: _etlecom_  
benefit from dynamic and adaptable link models due to the broad spectrum of changing situations offered by some environments.  
Reinforcement learning (RL), and especially deep reinforcement learning (DRL), which utilizes neural networks for decision-making, is a promising candidate due to its overall optimization strategy and customizable incentives via reward function. Our proposed solution utilizes DRL to build a next-generation LA approach. DRL allows for handling of multiple inputs while finding links in a high-dimensional solution space.  
In this paper, we propose a DRL algorithm called \"Deep Reinforcement Learning for Link Adaptation\" (DRLLA) for a next-generation mobile telecommunication system that can be deployed on individual base stations. DRLLA benefits from a more dynamic approach considering multiple observable states, such as the environment or the physical characteristics of a wireless transmission system.  
A single DRLLA instance was tested on a local machine using a simulation environment to assess the proposed vision of an edge deployment. We implemented DRLLA using OpenAI Gym [5] with an integrated GNU Radio [6] simulation environment. The approach was evaluated on several different channels, including additive white Gaussian noise (AWGN), non-line-of-sight (NLOS), and a prerecorded real-world channel. After analyzing the outcome, we propose the next steps necessary before a deployment.  
In this paper, Section 2 provides an overview of recent efforts made in the area of ML for communications considering related or similar approaches. Section 3 provides background knowledge whereas Section 4 presents the proposed DRLLA approach. Section 5 includes the results of our evaluation, which are then discussed in Section 6. Finally, Section 7 concludes this paper and proposes potential improvements.