Crowd Counting Through Walls Using WiFi

Counting the number of people inside a building, from outside and without entering the building, is crucial for many applications. In this paper, we are interested in counting the total number of people walking inside a building (or in general behind walls), using readily-deployable WiFi transceivers that are installed outside the building, and only based on WiFi RSSI measurements. The key observation of the paper is that the inter-event times, corresponding to the dip events of the received signal, are fairly robust to the attenuation through walls (for instance as compared to the exact dip values). We then propose a methodology that can extract the total number of people from the inter-event times. More specifically, we first show how to characterize the wireless received power measurements as a superposition of renewal-type processes. By borrowing theories from the renewal-process literature, we then show how the probability mass function of the inter-event times carries vital information on the number of people. We validate our framework with 44 experiments in five different areas on our campus (3 classrooms, a conference room, and a hallway), using only one WiFi transmitter and receiver installed outside of the building, and for up to and including 20 people. Our experiments further include areas with different wall materials, such as concrete, plaster, and wood, to validate the robustness of the proposed approach. Overall, our results show that our approach can estimate the total number of people behind the walls with a high accuracy while minimizing the need for prior calibrations.Comment: 10 pages, 14 figure

Timing synchronization in high mobility OFDM systems

Abstract — OFDM systems are sensitive to timing synchronization errors. Utilizing pilot-aided channel estimators in OFDM systems can further increase this sensitivity. This is shown in [2] where authors have analyzed the effect of such errors on a pilotaided channel estimator in a fixed wireless environment. They proposed an algorithm that exploits this sensitivity to improve timing synchronization without additional training overhead. The effect of these errors and design of suitable synchronization algorithms for high mobility applications, however, have not been studied before. In this paper we extend the analysis in [2] to high mobility environments. Timing synchronization becomes more challenging for mobile applications since powerdelay profile of the channel may change rapidly due to the sporadic birth and death of the channel paths. We find analytical expressions for channel estimation error in the presence of timing synchronization errors and mobility. We show that the sensitivity of the channel estimator can still be exploited to improve timing synchronization in high mobility environments. Then we extend the algorithm proposed in [2] to high mobility applications. Finally simulation results show the performance of the algorithm in high delay and Doppler spread environments. I

On the spatial predictability of communication channels

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

To Drop or Not to Drop: Receiver Design Principles for Estimation over Wireless Links

In this paper we consider estimation of a multiple-input multiple-output dynamical system over a wireless fading communication channel using a Kalman filter. We are interested in finding the optimum receiver design in terms of handing noisy samples. We reformulate the estimation problem to include the impact of stochastic communication noise in the noisy packets. We will show how the eigenvalues of the state transition matrix A affect the optimum receiver design. We prove that, in the absence of a cross-layer information path, packet drop should be designed to balance information loss and communication noise in order to optimize the performance. In the presence of a cross-layer path, we show that keeping all the packets will minimize the average estimation error covariance. We also derive the stability condition in the presence of noisy packets and prove that it is independent of the shape of the communication noise variance or availability of a cross-layer information path

To Drop or Not to Drop: Design Principles for Kalman Filtering Over Wireless Fading Channels

It is the general assumption that in estimation and control over wireless links, the receiver should drop any erroneous packets. While this approach is appropriate for non real-time data-network applications, it can result in instability and loss of performance in networked control systems. In this technical note we consider estimation of a multiple-input multiple-output dynamical system over a mobile fading communication channel using a Kalman filter. We show that the communication protocols suitable for other already-existing applications like data networks may not be entirely applicable for estimation and control of a rapidly changing dynamical system. We then develop new design paradigms in terms of handling noisy packets for such delay-sensitive applications. We reformulate the estimation problem to include the impact of stochastic communication noise in the erroneous packets. We prove that, in the absence of a permanent cross-layer information path, packet drop should be designed to balance information loss and communication noise in order to optimize the performance