Abstract-In this paper, we are interested in fundamentally understanding the spatial predictability of wireless channels. We propose a probabilistic channel prediction framework for predicting the spatial variations of a wireless channel, based on a small number of measurements. By using this framework, we then develop a mathematical foundation for understanding the spatial predictability of wireless channels. More specifically, we characterize the impact of different environments, in terms of their underlying parameters, on wireless channel predictability. We furthermore show how sampling positions can be optimized to improve the prediction quality. Finally, we show the performance of the proposed framework in predicting (and justifying the predictability of) the spatial variations of real channels, using several measurements in our building.
In this paper, we consider a cooperative network that is trying to reach consensus on the occurrence of an event, by communicating over time-varying network topologies with fast fading channels. We mathematically characterize both the asymptotic and transient behaviors of the network. We show that the network converges to a memoryless state asymptotically, which is undesirable. However, the network can still be in consensus for a long period of time. In order to characterize the transient behavior, we then derive a tight approximation for the second largest eigenvalue of the underlying average probability transition matrix in fading environments. We show the impact of channel unreliability and network topology on consensus performance and shed light on the underlying tradeoffs in terms of speed of convergence and memoryless asymptotic behavior.
Level-crossing (LC) analog-to-digital (A/D) converters can efficiently sample certain classes of signals. An LC A/D converter is a real-time asynchronous system, which encodes the information of an analog signal into a sequence of nonuniformly spaced time instants. In particular, this class of A/D converters uses an asynchronous data conversion approach, which is a power efficient technique. In this study, the authors propose adaptive and multi-level adaptive LC sampling models as alternatives to conventional LC schemes and apply an iterative algorithm to improve the reconstruction quality of LC A/D converters. This simulation results show that multi-level adaptive LC outperforms conventional A/D converters such as sigma-delta A/D converters in terms of performance and computational complexity.
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