Feasibility of Simultaneous Information and Energy Transfer in LTE-A Small Cell Networks

Simultaneous information and energy transfer is attracting much attention as an effective method to provide green energy supply for mobiles. However the very low power level of the harvested energy from RF spectrum limits the application of such technique. Thanks to the improvement of sensitivity and efficiency of RF energy harvesting circuit as well as the dense deployment of small cells base stations, the SIET becomes more practical. In this paper, we propose a unified receiver model for SIET in LTE-A small cell base staion networks, formulate the feasibility problem with Poisson point process model and analysis the feasibility for a special and practical senario. The results shows that it is feasible for mobiles to charge the secondary battery wih harvested energy from BSs, but it is still infeasible to directly charge the primary battery or operate without any battery at all

Engineering Semantic Communication: A Survey

As the global demand for data has continued to rise exponentially, some have begun turning to the idea of semantic communication as a means of efficiently meeting this demand. Pushing beyond the boundaries of conventional communication systems, semantic communication focuses on the accurate recovery of the meaning conveyed from source to receiver, as opposed to the accurate recovery of transmitted symbols. In this survey, we aim to provide a comprehensive view of the history and current state of semantic communication and the techniques for engineering this higher level of communication. A survey of the current literature reveals four broad approaches to engineering semantic communication. We term the earliest of these approaches classical semantic information, which seeks to extend information-theoretic results to include semantic information. A second approach makes use of knowledge graphs to achieve semantic communication, and a third utilizes the power of modern deep learning techniques to facilitate this communication. The fourth approach focuses on the significance of information, rather than its meaning, to achieve efficient, goal-oriented communication. We discuss each of these four approaches and their corresponding studies in detail, and provide some challenges and opportunities that pertain to each approach. Finally, we introduce a novel approach to semantic communication, which we term context-based semantic communication. Inspired by the way in which humans naturally communicate with one another, this context-based approach provides a general, optimization-based design framework for semantic communication systems. Together, this survey provides a useful guide for the design and implementation of semantic communication systems.Comment: 30 pages, 14 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Energy-aware distributed tracking in wireless sensor networks

We consider a wireless sensor network engaged in the task of distributed tracking. Here, multiple remote sensor nodes estimate a physical process (for example, a moving object) and transmit quantized estimates to a fusion center for processing. At the fusion node a BLUE (Best Linear Unbiased Estimation) approach is used to combine the sensor estimates and create a final estimate of the state. In this framework, the uncertainty of the overall estimate is derived and shown to depend on the individual sensor transmit energy and quantization levels. Since power and bandwidth are critically constrained resources in battery operated sensor nodes, we attempt to quantify the trade-off between the lifetime of the network and the estimation quality over time. A unique feature of this work is that instead of merely allowing a greedy minimization of uncertainty in each time instance, the lifetime of the wireless sensor network is improved by incorporating a heuristic scaling on the operating capability of each node. This heuristic in turn depends on the remaining energy, equivalent to the past history of power and quantization decisions. Simulation results demonstrate the quality of the state estimate as well as the extended lifetime of the network when power and quantization levels are dynamically updated

A General Framework for Uncertainty Quantification via Neural SDE-RNN

Uncertainty quantification is a critical yet unsolved challenge for deep learning, especially for the time series imputation with irregularly sampled measurements. To tackle this problem, we propose a novel framework based on the principles of recurrent neural networks and neural stochastic differential equations for reconciling irregularly sampled measurements. We impute measurements at any arbitrary timescale and quantify the uncertainty in the imputations in a principled manner. Specifically, we derive analytical expressions for quantifying and propagating the epistemic and aleatoric uncertainty across time instants. Our experiments on the IEEE 37 bus test distribution system reveal that our framework can outperform state-of-the-art uncertainty quantification approaches for time-series data imputations.Comment: 7 pages, 3 figure