Setting up the future Internet of Things (IoT) networks will require to support more and more communicating devices. We prove that intelligent devices in unlicensed bands can use Multi-Armed Bandit (MAB) learning algorithms to improve resource exploitation. We evaluate the performance of two classical MAB learning algorithms, UCB1 and Thomson Sampling, to handle the decentralized decision-making of Spectrum Access, applied to IoT networks; as well as learning performance with a growing number of intelligent end-devices. We show that using learning algorithms does help to fit more devices in such networks, even when all end-devices are intelligent and are dynamically changing channel. In the studied scenario, stochastic MAB learning provides a up to 16% gain in term of successful transmission probabilities, and has near optimal performance even in non-stationary and non-i.i.d. settings with a majority of intelligent devices.

In this paper, we study the performance of QPSK modulation in the context of multiuser downlink NOMA with a successive interference canceller (SIC) at the receiver side. The first objective is to evaluate the benefit of such a technique in terms of error probability, regardless of the number of involved users. Analytical derivations on its closed-form have been verified by both simulation and experimental validation. The article uses numerical simulations not only to corroborate the tightness of our theoretical expressions, but also to analyze the problem of power allocation in the two and three users cases. Finally, this paper provides an interplay between NOMA and software radio by building an experimental validation testbed. INDEX TERMS Non-orthogonal multiple access (NOMA), power allocation, experimental validation.

International audienceMoving from the current power grid to the Smart Grid (SG) requires decentralizing management. This should be done by distributing intelligence over the entire grid, thereby, the intermittent production of renewable energy, customer consumption and electricity storage in electrical vehicles (EVs) could be managed in real time. In this paper, the Hierarchical and Distributed Cognitive Radio Architecture Management (HDCRAM), initially proposed to manage Cognitive Radio systems, is proposed for the management of the SG. This architecture can both be applied to the whole SG and to any sub-part (distribution network, production network, microgrid). In this paper we focus on the distribution network and the hierarchical position of each element is identified. As an example, HDCRAM is used for smart home management and multi-agent based modeling shows benefits of such an architecture. In the simulated scenario, without any management the peak power consumption is 5500 W and the hierarchical and distributed management allows to reduce it to 900 W. This diminution allows to reduce the pressure on the grid and can decrease the risk of failure

Low power wide area networks (LPWANs) have been recently deployed for long-range machine-to-machine (M2M) communications. These networks have been proposed for many applications and in particular for the communications of the advanced metering infrastructure (AMI) backhaul of the smart grid. However, they rely on simple access schemes that may suffer from important latency, which is one of the main performance indicators in smart grid communications. In this article, we apply reinforcement learning (RL) algorithms to reduce the latency of AMI communications in LPWANs. For that purpose, we first study the collision probability in an unslotted ALOHA-based LPWAN AMI backhaul which uses the LoRaWAN acknowledgement procedure. Then, we analyse the effect of collisions on the latency for different frequency access schemes. We finally show that RL algorithms can be used for the purpose of frequency selection in these networks and reduce the latency of the AMI backhaul in LPWANs. Numerical results show that non-coordinated algorithms featuring a very low complexity reduce the collision probability by 14% and the mean latency by 40%.

Power control (PC) and discontinuous transmission (DTx) can reduce the power consumption of a base station (BS). When both are used, there is, for each user, a trade-off between service time and transmit power in order to minimize the energy consumption of the base station. In this paper, we analyse this trade-off and we propose a new efficient algorithm for the computation of the optimal service time and transmit power of all users. We show that in most cases, closed-form expressions can be used. For others, we prove that the search for the optimum can be changed into a root-finding problem which can be solved efficiently with the Newton's method. Numerical results show that, compared to the use of DTx only, the proposed strategy allows to save up to 4% (7W) of the total energy consumption.