The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex relationship between computing and radio dynamics make vRAN resource control particularly daunting. We present vrAIn, a resource orchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map contexts into resource control decisions. We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over a production RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30% over CPU-agnostic methods; (ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning, vrAIn improves throughput by 25% over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-free solution that does not need to assume any particular platform or context.

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This paper introduces a smart assistant for professional volleyball training based on machine-learning techniques (SAETA). SAETA addresses two main aspects of elite sports coaching: 1) technical-tactical effort control, which aims at controlling exercise effort and fatigue levels and 2) exercise quality training, which complements the former by analyzing the execution of player movements. SAETA relies on a sensing infrastructure that monitors both players and their environment, and produces real-time data that is analyzed by different modules of a decision engine. Technical-tactical effort control is based on a dynamic programming model, which selects the best activity and rest durations in interval training, with the goal of maximizing effort while preventing fatigue. Exercise quality control consists of two stages. In the first stage, movements are detected by means of a k-nearest neighbors classifier and in the second stage, movement intensity is classified according to recent statistical data from the player being analyzed. These analyses are reported to coaches and players in real-time. SAETA has been developed in close collaboration with the Universidad Católica San Antonio de Murcia volleyball team, which competes in the Spanish women's premier league. Data gathered during training sessions has provided a knowledge base for the algorithms developed, and has been used for the validation of results.