Showing papers by "Milica Stojanovic published in 2017"

Low Complexity Residual Doppler Shift Estimation for Underwater Acoustic Multicarrier Communication

Abstract: We propose two computationally efficient residual Doppler shift estimation methods for underwater acoustic multicarrier communication. The first method is based on computing the phase of the root of a low order polynomial. The second method is a closed-form least squares estimate given the unwrapped phases of the minimal eigenvector of a small data matrix. The complexities of both estimates are significantly lower compared to the methods commonly used in underwater acoustic multicarrier communication, which result in nonlinear least squares estimators and thus require a fine grid search in the frequency domain. Numerical simulations show that the mean square errors of the proposed methods have similar performance as the common estimation techniques, achieve the Cramer–Rao lower bounds at low noise levels, and agree with their theoretically derived variances. Pool experiments and sea trial results further demonstrate that the suggested estimates yield similar results as the common nonlinear least squares estimates but at a lower complexity.

Joint Power and Rate Control for Packet Coding Over Fading Channels

Abstract: We consider random linear packet coding for fading channels with long propagation delays, such as underwater acoustic channels. We propose a scheme in which the number of coded packets to transmit is determined to achieve a prespecified outage/reliability criterion and investigate joint power and rate control with constrained resources. Using the channel state information that is obtained via feedback from the receiver, the transmitter adjusts its power and the number of coded packets so that the average energy per successfully transmitted bit of information is minimized. Two optimization constraints are imposed: 1) the transmit power should not exceed a maximum level; and 2) the number of coded packets should not exceed a maximum value dictated by the desired throughput and delay. We further extend the results to take into account the effect of inevitable channel estimation errors, and consider the case in which the transmitter has only an estimate of the channel gain. We design adaptation policies for such a case based on minimum mean square error (MMSE) channel estimation, taking into account the effect of channel estimation errors in an optimal manner to satisfy the required outage/reliability criterion. Finally, we compare the proposed technique to standard automatic repeat request (ARQ) protocols for underwater communications in terms of the throughput efficiency. Analytical results show that substantial energy savings and improvements in throughput efficiency are available from adaptive power/rate control. We also present experimental results obtained using channel gains measured during the Surface Process Acoustic Communication Experiment (SPACE-08), an at-sea underwater experiment conducted off the coast of Martha's Vineyard in fall 2008.

Delay-Constrained Energy Optimization in High-Latency Sensor Networks

Abstract: Sensor networks deployed in high-latency environments, such as underwater acoustic and satellite channels, find critical applications in disaster prevention and tactical surveillance. The sensors in these networks have limited energy reserves. In order to extend the lifetime of these sensors, energy must be conserved in all layers of the protocol stack. In addition to long propagation delays, these channels are characterized by limited bandwidth and a lack of well-established closed-form analytical models. This fact makes finding cross-layer energy-optimal solutions a difficult problem to solve. Our goal is to compute near-optimal routes, schedules and transmit power levels for delay-constrained applications of high-latency sensor networks. We present a mixed-integer programming relaxation of the optimization problem. We further propose a decentralized algorithm to iteratively solve the relaxed optimization problem. Comparative simulation analysis shows that our decentralized approach is approximately 3~6 dB more energy-efficient and 2~5 dB more throughput-efficient than the heuristic, time-sensitive greedy forwarding, and least-cost routing algorithms.

Path-based channel estimation for acoustic OFDM systems: Real data analysis

Abstract: We address detection of acoustic OFDM signals using a channel estimation method based on a physical model of multipath propagation rather than an equivalent sample-spaced model. The path identification (PI) algorithm focuses on explicit estimation of delays and complex amplitudes of the channel paths. We apply this algorithm, along with the conventional least squares (LS) and orthogonal matching pursuit (OMP) to a set of signals recorded over a mobile acoustic channel. We demonstrate excellent performance of the PI algorithm and show that its complexity is considerably lower than that of the OMP algorithm. The PI algorithm consistently outperforms the conventional LS and compares favorably with the OMP algorithm in terms of the mean-squared data detection error observed for a varying number of OFDM carriers and receiver array configurations.

Object Tracking Using Modified Lossy Extended Kalman Filter

Abstract: We address the problem of object tracking in an underwater acoustic sensor network in which distributed nodes measure the strength of field generated by moving objects, encode the measurements into digital data packets, and transmit the packets to a fusion center in a random access manner. We allow for imperfect communication links, where information packets may be lost due to noise and collisions. The packets that are received correctly are used to estimate the objects' trajectories by employing an extended Kalman Filter, where provisions are made to accommodate a randomly changing number of obseravtions in each iteration. An adaptive rate control scheme is additionally applied to instruct the sensor nodes on how to adjust their transmission rate so as to improve the location estimation accuracy and the energy efficiency of the system. By focusing explicitly on the objects' locations, rather than working with a pre-specified grid of potential locations, we resolve the spatial quantization issues associated with sparse identification methods. Finally, we extend the method to address the possibility of objects entering and departing the observation area, thus improving the scalability of the system and relaxing the requirement for accurate knowledge of the objects' initial locations. Performance is analyzed in terms of the mean-squared localization error and the trade-offs imposed by the limited communication bandwidth.

Eigenvalue decomposition based estimators of carrier frequency offset in multicarrier underwater acoustic communication

Abstract: We propose computationally efficient carrier frequency offset estimators for multicarrier underwater acoustic communication using identical pilot tones equi-spaced in the frequency domain. The first estimator uses the phase of the maximal eigenvector of a channel-dependent correlation matrix. Next, the phase of the minimal eigenvector of a channel-independent correlation matrix is combined with the first estimation using a weighted linear least squares principle. The third estimator solves a generalized eigenvalue decomposition problem by jointly considering the two correlation matrices, and then performs a similar second step as the previous estimator. Simulations and pool trials show that the proposed estimators achieve similar performance as common estimation techniques while surpassing them in severe environments.