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Due to emerging mobile applications and an Internet of Things, data traffic carried by wireless networks has increased dramatically. As a result, maximizing the utilization of the limited network resource is a top priority. To date, there exists a large body of work on the stochastic network utility maximization (NUM) problem. On the one hand, the stochastic NUM problem and its associated solutions have been mainly investigated under the light-tailed (LT) data condition. On the other hand, empirical results show that data delivered by wireless networks is not only large in volume but also highly variable, which can be characterized by heavy-tailed (HT) distributions. Such HT traffic exhibits high burstiness and strong temporal correlations, which are fundamentally different from the behavior of LT traffic. In this paper, first it is shown that the classic stochastic gradient algorithms (SGAs), as effective solutions for stochastic NUM problems, fail to prevent HT traffic flows from aggressively competing with LT flows for limited resources. This leads to unbounded queueing delay for LT flows, which results in network instability. To counter such a challenge, the time-average stochastic gradient routing algorithm is proposed, which prevents competition among HT and LT traffic flows so that utility optimality and network stability can be achieved simultaneously. Moreover, because HT data have unbounded moments, such as mean and variance, it is not feasible to apply the conventional convergence analysis, which assumes the moment boundedness of traffic arrivals. To address this problem, the ordinary differential equation method is adopted to prove that the proposed algorithm still converges even with highly variable HT traffic. Comprehensive simulation results are shown to verify the derived theoretical results. [ABSTRACT FROM AUTHOR]

We propose a technique that enhances the performance of Torrieri and Bakhru's maximin algorithm in the presence of frequency offsets and modulated interferences, a common scenario in multiple-access environments, where we observed that the Maximin algorithm suffers performance degradation in the aforesaid scenario. To combat modulated interferences and fading effects, we propose to update the step length during every smart antenna weight vector update. We found that our scheme improves the robustness of the maximin algorithm to a greater extent. Simulation results for a frequency-hopping system confirm the validity of the approach and demonstrate improvements in the performance of the maximin algorithm in the presence of frequency offsets and data modulated interferences in a fading channel environment. [ABSTRACT FROM AUTHOR]

Abstract: Recently, Wu et al. proposed a scheme for two-dimensional direction of arrival angle estimation for azimuth and elevation angles, using the propagator method. An advantage of this method over the classical subspace based algorithms, such as ESPRIT and MUSIC, is that it does not apply any eigenvalue decomposition (EVD) to the cross spectral matrix or singular value decomposition (SVD) to the received data. This significantly reduces the computational complexity, compared to the EVD and SVD. However, Wu''s method has some drawbacks, such as pair matching between the azimuth and elevation angle estimations for multiple different sources. Furthermore, Wu''s method has an estimation failure problem in the range of practical mobile elevation angles. The objectives of this paper are two-fold: (1) to overcome these two problems with less arithmetic operation counts than Wu used; and (2) to improve the performance significantly. To achieve these objectives, we propose an antenna array configuration which avoids these problems. Simulation results verify that the proposed scheme can remove these problems and give much better performance. [Copyright &y& Elsevier]

In this paper, we present an algorithm to estimate the direction of the arrival angles (DOAs) from noncoherent one-dimensional (1-D) signal sources such as binary phase shift keying (BPSK) and M-ary amplitude shift keying (MASK) The proposed algorithm can provide a more precise DOA estimation and can detect more signals than we D-known classical subspace.methods MUSIC and ESPRIT for the 1-D signals. The complexity is the same as that of ESPRIT since the proposed algorithm uses the same array geometry and subarray processing that ESPRIT does. The main differences between the proposed algorithm and the ESPRIT algorithm are as follows: 1) the number of overlapping array elements between two subarrays is equal to M in the proposed algorithm, while in ESPRIT the maximum number of overlapping elements is M - 1, where M denotes the total number of array elements, and 2) the proposed algorithm employs the conjugate of rotation matrix (CRM) V while ESPRIT uses 1 with no conjugate for the second subarray geometry. [ABSTRACT FROM AUTHOR]