Reinforcement Learning for Real-Time Optimization in NB-IoT Networks

• Published in: IEEE journal on selected areas in communications 2019-06, Vol.37 (6), p.1424-1440
• Main Authors: Jiang, Nan, Deng, Yansha, Nallanathan, Arumugam, Chambers, Jonathon A.
• Format: Article
• Language: English
• Subjects: Analytical models; Artificial neural networks; Cellular communication; Configurations; cooperative learning; Data communication; Data transmission; Devices; Interference; Internet of Things; Long Term Evolution; Machine learning; Multiagent systems; Narrowband; Narrowband Internet of Things; Numerical models; Optimization; Parameters; Random access; Real time; real-time optimization; reinforcement learning; resource configuration; Signal to noise ratio; Training; Uplink
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2019.2904366

NarrowBand Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resource allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, there exists an important challenge in "how to determine the configuration that maximizes the long-term average number of served IoT devices at each transmission time interval (TTI) in an online fashion." Given the complexity of searching for optimal configuration, we first develop real-time configuration selection based on the tabular Q-learning (tabular-Q), the linear approximation-based Q-learning (LA-Q), and the deep neural network-based Q-learning (DQN) in the single-parameter single-group scenario. Our results show that the proposed reinforcement learning-based approaches considerably outperform the conventional heuristic approaches based on load estimation (LE-URC) in terms of the number of served IoT devices. This result also indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve almost the same performance with much less training time. We further advance LA-Q and DQN via actions aggregation (AA-LA-Q and AA-DQN) and via cooperative multi-agent learning (CMA-DQN) for the multi-parameter multi-group scenario, thereby solve the problem that Q-learning agents do not converge in high-dimensional configurations. In this scenario, the superiority of the proposed Q-learning approaches over the conventional LE-URC approach significantly improves with the increase of configuration dimensions, and the CMA-DQN approach outperforms the other approaches in both throughput and training efficiency.