The idea of end-to-end learning of communication systems through neural network (NN)-based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm enables training of communication systems with an unknown channel model or with non-differentiable components. It iterates between training of the receiver using the true gradient, and training of the transmitter using an approximation of the gradient. We show that this approach works as well as model-based training for a variety of channels and tasks. Moreover, we demonstrate the algorithm's practical viability through hardware implementation on software defined radios (SDRs) where it achieves state-of-theart performance over a coaxial cable and wireless channel.

The idea of end-to-end learning of communications systems through neural network (NN)-based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm iterates between supervised training of the receiver and reinforcement learning (RL)-based training of the transmitter. We demonstrate that this approach works as well as fully supervised methods on additive white Gaussian noise (AWGN) and Rayleigh block-fading (RBF) channels. Surprisingly, while our method converges slower on AWGN channels than supervised training, it converges faster on RBF channels. Our results are a first step towards learning of communications systems over any type of channel without prior assumptions.

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A promising solution to achieve autonomous wireless sensor networks is to enable each node to harvest energy in its environment. To address the time-varying behavior of energy sources, each node embeds an energy manager responsible for dynamically adapting the power consumption of the node in order to maximize the quality of service while avoiding power failures. A novel energy management algorithm based on reinforcement learning, named RLMan, is proposed in this work. By continuously exploring the environment, RLMan adapts its energy management policy to time-varying environment, regarding both the harvested energy and the energy consumption of the node. Linear function approximations are used to achieve very low computational and memory footprint, making RLMan suitable for resource-constrained systems such as wireless sensor nodes. Moreover, RLMan only requires the state of charge of the energy storage device to operate, which makes it practical to implement. Exhaustive simulations using real measurements of indoor light and outdoor wind show that RLMan outperforms current state of the art approaches, by enabling almost 70 % gain regarding the average packet rate. Moreover, RLMan is more robust to variability of the node energy consumption.