A novel joint polarization demultiplexing and IQ imbalance compensation scheme for coherent optical communications that uses global update is proposed and analyzed through numerical simulations. We describe the system model and derive its related equations. Next, we formulate our blind M-QAM arbitrary approach based on EASI algorithm and using the second order statistics of the observed signals. A comparison of the proposed joint method with the traditional CMA for polarization demultiplexing followed by BASS for IQ imbalance compensation is also reported. Evaluated metrics (EVM, MSE, BER) demonstrate its effectiveness compared with CMA cascaded with BASS algorithm.

Energy harvesting technologies are constantly evolving to help power sensor network nodes. Ranging from miniature power solar panels to micro wind turbines, nodes still express a deep need to harvest energies in order to keep both good performance level and energy autonomy. Recently, the simultaneous use of multiple sources has been proposed to tackle the time-varying characteristics of certain sources that can induce energy scarcity period and thus alter the node performance. In this context, this paper presents a methodology aimed at classifying the energy sources to choose the most efficient energy manager. As sensor nodes are embedded devices, it is necessary to ensure a balance between computational effort and classification accuracy. Feature extraction and selection phases can be processed and analyzed offline before deployment, and only a subset of features will be needed by the nodes to achieve efficient energy management. Simulations on real energy traces show that the proposed approach achieves classification accuracy higher than 95% through the computation of 4 features only.

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