Yasamin Mostofi

ICI mitigation for pilot-aided OFDM mobile systems

Orthogonal frequency-division multiplexing (OFDM) is robust against frequency selective fading due to the increase of the symbol duration. However, for mobile applications channel time-variations in one OFDM symbol introduce intercarrier-interference (ICI) which degrades the performance. This becomes more severe as mobile speed, carrier frequency or OFDM symbol duration increases. As delay spread increases, symbol duration should also increase in order to maintain a near-constant channel in every frequency subband. Also, due to the high demand for bandwidth, there is a trend toward higher carrier frequencies. Therefore, to have an acceptable reception quality for the applications that experience high delay and Doppler spread, there is a need for ICI mitigation within one OFDM symbol. We introduce two new methods to mitigate ICI in an OFDM system with coherent channel estimation. Both methods use a piece-wise linear model to approximate channel time-variations. The first method extracts channel time-variations information from the cyclic prefix. The second method estimates these variations using the next symbol. We find a closed-form expression for the improvement in average signal-to-interference ratio (SIR) when our mitigation methods are applied for a narrowband time-variant channel. Finally, our simulation results show how these methods would improve the performance in a highly time-variant environment with high delay spread.

On the Spatial Predictability of Communication Channels

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: 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.

Robotic Router Formation in Realistic Communication Environments

In this paper, we consider the problem of robotic router formation, where two nodes need to maintain their connectivity over a large area by using a number of mobile routers. We are interested in the robust operation of such networks in realistic communication environments that naturally experience path loss, shadowing, and multipath fading. We propose a probabilistic router formation and motion-planning approach by integrating our previously proposed stochastic channel learning framework with robotic router optimization. We furthermore consider power constraints of the network, including both communication and motion costs, and characterize the underlying tradeoffs. Instead of taking the common approach of formation optimization through maximization of the Fiedler eigenvalue, we take a different approach and use the end-to-end bit error rate (BER) as our performance metric. We show that the proposed framework results in a different robotic configuration, with a considerably better performance, as compared with only considering disk models for communication and/or maximizing the Fielder eigenvalue. Finally, we show the performance with a simple preliminary experiment, with an emphasis on the impact of localization errors. Along this line, we show interesting interplays between the localization quality and the channel correlation/learning quality.

Estimation of communication signal strength in robotic networks

In this paper we consider estimating the spatial variations of a wireless channel based on a small number of measurements in a robotic network. We use a multi-scale probabilistic model in order to characterize the channel and develop an estimator based on this model. We show that our model-based approach can estimate the channel well for several scenarios, with only a small number of gathered measurements. We furthermore consider a sparsity-based channel estimation approach, in which we utilize the compressibility of the channel in the frequency domain. Our results show that this approach can also be effective in several scenarios. We then discuss the underlying tradeoffs between the two approaches. For the model-based approach, we show the impact of the error in the underlying model as well as the error in the estimation of the parameters of the model on the overall performance. For the sparsity-based approach, we show the impact of channel compressibility on the performance. Overall, the proposed framework can be utilized for communication-aware motion planning in robotic networks, where a prediction of the link qualities is needed.

Communication-Aware Motion Planning in Mobile Networks

In this technical note, we propose a communication-aware motion planning framework to increase the probability that a robot maintains its connectivity to a fixed station, while accomplishing a sensing task, in realistic communication environments. We use a probabilistic multi-scale model for channel characterization. Using this model, we propose a probabilistic framework for assessing the spatial variations of a wireless channel, based on a small number of measurements. We then show how our channel learning framework can be utilized for devising communication-aware motion planning strategies. We first present communication-aware objective functions that can plan the trajectory of the robot in order to improve its online channel assessment in an environment. We then propose a communication-aware target tracking approach for the case where a fixed station utilizes a robot (or a number of them) to keep track of the position of a moving target. In this approach, probabilistic channel assessment metrics are combined with sensing goals, when controlling the motion, in order to increase the amount of information that the fixed station receives about the target. Finally, we show the performance of our framework, using both real and simulated channel measurements. Overall, our results indicate that networked robotic operations can benefit considerably from our probabilistic channel assessment and its integration with sensing/motion planning.

Characterization and modeling of wireless channels for networked robotic and control systems - a comprehensive overview

The goal of this paper is to serve as a reference for researchers in robotics and control that are interested in realistic modeling, theoretical analysis and simulation of wireless links. To realize the full potentials of networked robotic systems, an integration of communication issues with motion planning/control is necessary. While considerable progress has been made in the area of networked robotic systems, communication channels are typically considered ideal or ideal within a certain radius of the transmitter, both considerable oversimplifications of wireless channels. It is the goal of this paper to provide a comprehensive overview of the key characteristics of wireless channels, as relevant to networked robotic operations. In particular, we provide a probabilistic framework for characterization of the underlying multi-scale dynamics of a wireless link: small-scale fading, large-scale fading and path loss. We furthermore confirm these mathematical models with channel measurements made in our building. We also discuss channel characterization based on the knowledge available on the geometry and dielectric properties of the environment.

Occupancy Estimation Using Only WiFi Power Measurements

In this paper, we are interested in counting the total number of people walking in an area based on only WiFi received signal strength indicator (RSSI) measurements between a pair of stationary transmitter/receiver antennas. We propose a framework based on understanding two important ways that people leave their signature on the transmitted signal: blocking the line of sight (LOS) and scattering effects. By developing a simple motion model, we first mathematically characterize the impact of the crowd on blocking the LOS. We next probabilistically characterize the impact of the total number of people on the scattering effects and the resulting multipath fading component. By putting the two components together, we then develop a mathematical expression for the probability distribution of the received signal amplitude as a function of the total number of occupants, which will be the base for our estimation using Kullback-Leibler divergence. To confirm our framework, we run extensive indoor and outdoor experiments with up to and including nine people and show that the proposed framework can estimate the total number of people with a good accuracy with only a pair of WiFi cards and the corresponding RSSI measurements.

Compressive Cooperative Sensing and Mapping in Mobile Networks

In this paper we consider a mobile cooperative network that is tasked with building a map of the spatial variations of a parameter of interest, such as an obstacle map or an aerial map. We propose a new framework that allows the nodes to build a map of the parameter of interest with a small number of measurements. By using the recent results in the area of compressive sensing, we show how the nodes can exploit the sparse representation of the parameter of interest in the transform domain in order to build a map with minimal sensing. The proposed work allows the nodes to efficiently map the areas that are not sensed directly. To illustrate the performance of the proposed framework, we show how the nodes can build an aerial map or a map of obstacles with sparse sensing. We furthermore show how our proposed framework enables a novel non-invasive approach to mapping obstacles by using wireless channel measurements.

Cooperative Wireless-Based Obstacle/Object Mapping and See-Through Capabilities in Robotic Networks

In this paper, we develop a theoretical and experimental framework for the mapping of obstacles (including occluded ones), in a robotic cooperative network, based on a small number of wireless channel measurements. This would allow the robots to map an area before entering it. We consider three approaches based on coordinated space, random space, and frequency sampling, and show how the robots can exploit the sparse representation of the map in space, wavelet or spatial variations, in order to build it with minimal sensing. We then show the underlying tradeoffs of all the possible sampling, sparsity and reconstruction techniques. Our simulation and experimental results show the feasibility and performance of the proposed framework. More specifically, using our experimental robotic platform, we show preliminary results in successfully mapping a number of real obstacles and having see-through capabilities with real structures, despite the practical challenges presented by multipath fading.