Deep HyperNetwork-Based MIMO Detection

Optimal symbol detection for multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Conventional heuristic algorithms are either too complex to be practical or suffer from poor performance. Recently, several approaches tried to address those challenges by implementing the detector as a deep neural network. However, they either still achieve unsatisfying performance on practical spatially correlated channels, or are computationally demanding since they require retraining for each channel realization. In this work, we address both issues by training an additional neural network (NN), referred to as the hypernetwork, which takes as input the channel matrix and generates the weights of the neural NN-based detector. Results show that the proposed approach achieves near state-of-the-art performance without the need for re-training

GRAPMAN: Gradual Power Manager for Consistent Throughput of Energy Harvesting Wireless Sensor Nodes

International audienceIn this work, Wireless Sensor Network (WSN) applications that require long-term sustainability are considered. Energy harvesting forms a promising technology to address this challenge, by allowing each node to be entirely powered by energy harvested from its environment. To be sustainable, each node must dynamically adapt its Quality of Service (QoS), regarding the harvested energy using a power management strategy. This strategy is implemented on each node by the Power Manager (PM). In this paper, GRAPMAN (GRAdual Power MANager) is proposed, a novel PM for Energy-Harvesting WSN (EH-WSN) powered by pseudo-periodic energy sources. Unlike most state of the art PMs, GRAPMAN aims to achieve high average throughput while maintaining consistent QoS, i.e. with low fluctuations with respect to time, by looking for the highest throughput that can be supplied by the node over a finite time horizon while remaining sustainable. We show through extensive trace-driven network simulations that GRAPMAN outperforms state of the art PMs in both average throughput and throughput consistency

End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints

Orthogonal frequency-division multiplexing (OFDM) is widely used in modern wireless networks thanks to its efficient handling of multipath environment. However, it suffers from a poor peak-to-average power ratio (PAPR) which requires a large power backoff, degrading the power amplifier (PA) efficiency. In this work, we propose to use a neural network (NN) at the transmitter to learn a high-dimensional modulation scheme allowing to control the PAPR and adjacent channel leakage ratio (ACLR). On the receiver side, a NN-based receiver is implemented to carry out demapping of the transmitted bits. The two NNs operate on top of OFDM, and are jointly optimized in and end-to-end manner using a training algorithm that enforces constraints on the PAPR and ACLR. Simulation results show that the learned waveforms enable higher information rates than a tone reservation baseline, while satisfying predefined PAPR and ACLR targets

Protocoles de communication faibles latences et faibles consommations à base de wake-up radio

International audienceLa durée de vie est une caractéristique importante des réseaux de capteurs. La communication etant généralement la tâche la plus gourmande en energie, de nombreux protocoles de communication ont eté proposés afin de réduire les communications, ils reposent majoritairement sur le concept du réveil périodique des noeuds. Toutefois, ces dernières années, de nouveaux types de récepteurs Ultra Low Power (ULP), appelés wake-up radios, sont apparus. Ces dispositifs permettent l'écoute en continu du canal de communication tout en ayant une consommation d'énergie au moins un ordre de grandeur inférieur à celle des emetteurs-récepteurs typiques. La wake-up radio ne peut réveiller le reste du système qu'en cas de besoin, minimisant ainsi l'écoute inutile. Dans cette étude, nous présentons une expérimentation et une étude analytique qui permettent le dimensionnement des protocoles de communication utilisant les wake-up radios

Incremental checkpointing of program state to NVRAM for transiently-powered systems

International audienceAs technology improves, it becomes possible to design autonomous, energy-harvesting networked embedded systems, a key building block for the Internet of Things. However, running from harvested energy means frequent and unpredictable power failures. Programming such Transiently Powered Computers will remain an arduous task for the software developer, unless some OS support abstracts energy management away from application design. Various approaches were proposed to address this problem. We focus on checkpointing, i.e. saving and restoring program state to and from non-volatile memory. In this paper, we propose an incremental checkpointing scheme which aims at minimizing the amount of data written to non-volatile memory, while keeping the execution overhead as low as possible