Assessing the impact of DRS signaling in unlicensed indoor coexistence scenarios

The use of unlicensed bands is one of the most promising features envisaged to increase capacity in 5G. However, this poses multiple challenges associated with the operation when coexisting networks are present, such as WiFi. Previous coexistence analyses have been focused on the user-plane data-related transmissions and mainly based on abstract models. Meanwhile, the effects of the shared channel signaling defined by the standards have been mostly disregarded, particularly for ultra-dense scenarios. This paper assesses how the shared data channel signaling mechanisms influence the performance of the coexisting technologies operating unlicensed bands in indoor environments. Based on this analysis, some DRS signaling modifications are envisaged to additionally enhance the service provision and fairness towards WiFi in these scenarios.

Decentralized spectrum learning for radio collision mitigation in ultra-dense IoT networks: LoRaWAN case study and experiments

This paper describes the theoretical principles and experimental results of reinforcement learning algorithms embedded into IoT devices (Internet of Things), in order to tackle the problem of radio collision mitigation in ISM unlicensed bands. Multi-armed bandit (MAB) learning algorithms are used here to improve both the IoT network capability to support the expected massive number of objects and the energetic autonomy of the IoT devices. We first illustrate the efficiency of the proposed approach in a proof-of-concept, based on USRP software radio platforms operating on real radio signals. It shows how collisions with other RF signals are diminished for IoT devices that use MAB learning. Then we describe the first implementation of such algorithms on LoRa devices operating in a real LoRaWAN network at 868 MHz. We named this solution IoTligent. IoTligent does not add neither processing overhead, so it can be run into the IoT devices, nor network overhead, so that it requires no change to LoRaWAN protocol. Real-life experiments done in a real LoRa network show that IoTligent devices’ battery life can be extended by a factor of 2, in the scenarios we faced during our experiment. Finally we submit IoTligent devices to very constrained conditions that are expected in the future with the growing number of IoT devices, by generating an artificial IoT massive radio traffic in anechoic chamber. We show that IoTligent devices can cope with spectrum scarcity that will occur at that time in unlicensed bands.