The goal of this paper is to gain deep understanding of how location-dependent propagation latency affects medium access control (MAC) by using ALOHA as a case study. MAC protocols in underwater acoustic networks suffer from latency that is five orders-of-magnitude larger than that in radio networks. Existing work on analyzing MAC throughput in RF networks, where the propagation latency is negligible, generally makes assumptions that render propagation latency irrelevant. As a result, only transmit time is considered as being uncertain in contention-based protocols. We introduce the spatial dimension of uncertainty that is inherent to varying locations of transmitters, resulting in unequal propagation latency to a receiver, where collision occurs. We show through simulation that the benefit of synchronization in slotted ALOHA is lost due to such latency. We propose a modification that adds guard bands to transmission slots to handle spatial uncertainty. We then perform simulation and first order analysis on this modified MAC to find its optimal operating parameters.

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A new method of phase error estimation that utilizes the weighted least-squares (WLS) algorithm is presented for synthetic aperture radar (SAR)/inverse SAR (ISAR) autofocus applications. The method does not require that the signal in each range bin be of a certain distribution model, and thus it is robust for many kinds of scene content. The most attractive attribute of the new method is that it can be used to estimate all kinds of phase errors, no matter whether they are of low order, high order, or random. Compared with other methods, the WLS estimation is optimal in the sense that it has the minimum variance of the estimation error. Excellent results have been obtained in autofocusing and imaging experiments on real SAR and ISAR data.

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Abstract:The maneuvering target echo of high-resolution radar can be expressed as a multicomponent polynomial phase signal (mc-PPS). However, with improvements in radar resolution and increases in the synthetic period, classical time frequency analysis methods cannot satisfy the requirements of maneuvering target radar echo processing. In this paper, a novel frequency domain extraction-based adaptive joint time frequency (FDE-AJTF) decomposition method was proposed with three improvements. First, the maximum frequency spectrum of the phase compensation signal was taken as the fitness function, while the fitness comparison, component extraction, and residual updating were operated in the frequency domain; second, the time window was adopted on the basis function to fit the uncertain signal component time; and third, constant false alarm ratio (CFAR) detection was applied in the component extraction to reduce the ineffective components. Through these means, the stability and speed of phase parameters estimation increased with one domination ignored in the phase parameter estimation, and the accuracy and effectiveness of the signal component extraction performed better with less influence from the estimation errors, clutters, and noises. Finally, these advantages of the FDE-AJTF decomposition method were verified through a comparison with the classical method in simulation and experimental tests.