Underwater acoustic (UWA) communications have attracted a lot of interest in recent years motivated by a wide range of applications including offshore oil field exploration and monitoring, oceanographic data collection, environmental monitoring, disaster prevention, and port security. Different signaling solutions have been developed to date including non-coherent communications, phase coherent systems, multi-input and multi-output solutions, time-reversal-based communication systems, and multi-carrier transmission approaches. This paper deviates from the traditional approaches to UWA communications and develops a scheme that exploits biomimetic signals. In the proposed scheme, a transmitter maps the information bits to the parameters of a biomimetic signal, which is transmitted over the channel. The receiver estimates the parameters of the received signal and demaps them back to bits to estimate the message. As exemplary biomimetic signals, analytical signal models with nonlinear instantaneous frequency are developed that match mammal sound signatures in the time-frequency plane are developed. Suitable receiver structures as well as performance analysis are provided for the proposed transmission scheme, and some results using data recorded during the Kauai Acomms MURI 2011 UWA communications experiment are presented.
We propose upper and lower bounds on the capacity of multiple-input multiple-output (MIMO) systems with amplitude-limited inputs. The results are derived by considering an equivalent channel via singular value decomposition, and by enlarging and reducing the corresponding feasible region of the channel input vector, for the upper and lower bounds, respectively. We analytically characterize the asymptotic behavior of the derived bounds for high and low noise levels, and study the gap between them. We also consider parallel Gaussian channels with peak and average power-constrained inputs. For such channels, the capacity-achieving distribution has been reported in the literature to be discrete, which can be computed using numerical optimization techniques. However, there is no closedform expression and finding the capacity-achieving distribution is computationally tedious. With this motivation, we derive approximate expressions for the capacity at low and high noise variance levels. We illustrate our findings on both MIMO channels and parallel Gaussian channels via several numerical examples.
In this paper, we provide a review of the benefits of employing multiple-input multiple-output (MIMO) signal processing techniques in vehicular ad hoc networks (VANETs). These benefits include increasing the range of communication via beamforming, improving the reliability of communication via spatial diversity, increasing the throughput of the network via spatial multiplexing, and managing multiuser interference due to the presence of multiple transmitting terminals. We also present a number of key research challenges facing MIMO VANETs. The first one is deriving statistical MIMO vehicular channel models that take into account the spatial correlation between the transmit and receive antennas and validating them via extensive channel measurement campaigns. Deriving channel estimation and tracking algorithms for MIMO intervehicular channels is another challenging problem due to their non-stationary behavior and high Doppler spread. Further research is also needed to fully reap the benefits of multiple antennas in VANETs via space-time and spacefrequency processing. In addition, cross layer optimization spanning the medium access control (MAC) and networking layers besides the physical layer is essential to satisfy the emerging applications of VANETS ranging from safety, convenience to infotainment.
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