We describe a new precoder based on optimization of the minimum Euclidean distance d min between signal points at the receiver side and for use in multiple-input multiple-output (MIMO) spatial multiplexing systems. Assuming that channel state information (CSI) can be made available at the transmitter, the three steps noise whitening, channel diagonalization and dimension reduction, currently used in investigations on MIMO systems, are performed. Thanks to this representation, an optimal d min precoder is derived in the case of two different transmitted data streams. For QPSK modulation, a numerical approach shows that the precoder design depends on the channel characteristics. Comparisons with maximum SNR strategy and other precoders based on criteria such as water-filling (WF), minimum mean square error (MMSE) and maximization of the minimum singular value of the global channel matrix are performed to illustrate the significant bit-error-rate (BER) improvement of the proposed precoder.
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.
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