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Ultra-wideband technology has become increasingly popular for indoorlocalization and location-based services. This has led recent advances to befocused on reducing the ranging errors, whilst research focusing on enablingmore reliable and energy efficient communication has been largely unexplored.The IEEE 802.15.4 UWB physical layer allows for several settings to be selectedthat influence the energy consumption, range, and reliability. Combined withthe available link state diagnostics reported by UWB devices, there is anopportunity to dynamically select PHY settings based on the environment. Toaddress this, we propose a deep Q-learning approach for enabling reliable UWBcommunication, maximizing packet reception rate (PRR) and minimizing energyconsumption. Deep Q-learning is a good fit for this problem, as it is aninherently adaptive algorithm that responds to the environment. Validation in arealistic office environment showed that the algorithm outperforms traditionalQ-learning, linear search and using a fixed PHY layer. We found that deepQ-learning achieves a higher average PRR and reduces the ranging error whileusing only 14% of the energy compared to a fixed PHY setting in a dynamicoffice environment.

Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings