In this paper, we propose an algorithm for timing synchronization that attains fundamental bounds derived by Weiss and Weinstein. These bounds state that, in addition to improving with time-bandwidth product and signal-to-noise ratio (SNR), timing accuracy also improves as the carrier frequency gets larger, if the SNR is above a threshold. Our algorithm essentially follows the logic of the Weiss-Weinstein bound, and has the following stages: coarse estimation using time domain samples, fine-grained estimation using a Newton algorithm in the frequency domain, and final refinement to within a small fraction of a carrier cycle. While the results here are of fundamental interest, we are motivated to push the limits of synchronization to enable the tight coordination required for emulating virtual antenna arrays using a collection of cooperating nodes.

In this paper, we develop algorithms for distributed computation of averages of the node data over networks with arbitrary but fixed connectivity. The algorithms we develop are linear dynamical systems that generate sequences of improving approximations to the desired computation at each node, via iterative processing and broadcasting. The algorithms are locally constructed at each node by exploiting only locally available and macroscopic information about the network topology. We present methods for optimizing the convergence rates of these algorithms to the desired computation, and evaluate their performance characteristics in the context of a problem of signal estimation from multinode noisy observations. By conducting simulations based on simple power-loss propagation models, we perform a preliminary comparison of the algorithms we develop against other types of distributed algorithms for computing averages, and identify transmit-power optimized algorithmic implementations as a function of the size and density of the sensor network.

In this paper we develop algorithms for distributed computation of a broad range of estimation and detection tasks over networks with arbitrary but fixed connectivity. The distributed algorithms we develop are linear dynamical systems that generate sequences of approximations to the desired computation. The algorithms are locally constructed at each node by exploiting only locally available and macro-scopic information about the network topology. We present methods for designing these distributed algorithms so as to optimize the convergence rates to the desired computation and demonstrate their performance characteristics in the context of a problem of signal estimation from multi-node signal observations in Gaussian noise.