Distributed Detection over Fading MACs with Multiple Antennas at the Fusion Center

A distributed detection problem over fading Gaussian multiple-access channels is considered. Sensors observe a phenomenon and transmit their observations to a fusion center using the amplify and forward scheme. The fusion center has multiple antennas with different channel models considered between the sensors and the fusion center, and different cases of channel state information are assumed at the sensors. The performance is evaluated in terms of the error exponent for each of these cases, where the effect of multiple antennas at the fusion center is studied. It is shown that for zero-mean channels between the sensors and the fusion center when there is no channel information at the sensors, arbitrarily large gains in the error exponent can be obtained with sufficient increase in the number of antennas at the fusion center. In stark contrast, when there is channel information at the sensors, the gain in error exponent due to having multiple antennas at the fusion center is shown to be no more than a factor of (8/pi) for Rayleigh fading channels between the sensors and the fusion center, independent of the number of antennas at the fusion center, or correlation among noise samples across sensors. Scaling laws for such gains are also provided when both sensors and antennas are increased simultaneously. Simple practical schemes and a numerical method using semidefinite relaxation techniques are presented that utilize the limited possible gains available. Simulations are used to establish the accuracy of the results.Comment: 21 pages, 9 figures, submitted to the IEEE Transactions on Signal Processin

An Overview of Recent Advances on Distributed and Agile Sensing Algorithms and Implementation

We provide an overview of recent work on distributed and agile sensing algorithms and their implementation. Modern sensor systems with embedded processing can allow for distributed sensing to continuously infer intelligent information as well as for agile sensing to configure systems in order to maintain a desirable performance level. We examine distributed inference techniques for detection and estimation at the fusion center and wireless networks for the sensor systems for real time scenarios. We also study waveform-agile sensing, which includes methods for adapting the sensor transmit waveform to match the environment and to optimize the selected performance metric. We specifically concentrate on radar and underwater acoustic signal transmission environments. As we consider systems with potentially large number of sensors, we discuss the use of resource-agile implementation approaches based on multiple-core processors in order to efficiently implement the computationally intensive processing in configuring the sensors. These resource-agile approaches can be extended to also optimize sensing in distributed sensor networks