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Due to relatively simple implementation, Uniform Linear Array (ULA) is a popular geometry for array signal processing. Despite this advantage, it does not have a uniform performance in all directions and Angle of Arrival (AOA) estimation performance degrades considerably in the angles close to endfire. In this article, a new configuration is proposed which can solve this problem. Proposed Array (PA) configuration adds two elements to the ULA in top and bottom of the array axis. By extending signal model of the ULA to the new proposed ULAbased array, AOA estimation performance has been compared in terms of angular accuracy and resolution threshold through two well-known AOA estimation algorithms, MUSIC and MVDR. In both algorithms, Root Mean Square Error (RMSE) of the detected angles descends as the input Signal to Noise Ratio (SNR) increases. Simulation results show that the proposed array geometry introduces uniform accurate performance and higher resolution in middle angles as well as border ones. The PA also presents less RMSE than the ULA in endfire directions. Therefore, the proposed array offers better performance for the border angles with almost the same array size and simplicity in both MUSIC and MVDR algorithms with respect to the conventional ULA. In addition, AOA estimation performance of the PA geometry is compared with two well-known 2D-array geometries: L-shape and V-shape, and acceptable results are obtained with equivalent or lower complexity.

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One of the key strategies for jointly increasing throughput and optimum resource allocation in 5G is device-to-device (D2D) communications, which can be obtained by minimizing the outage probability considered as an objective function of optimization problem. To minimize this objective function, we found that outage probability should be modeled by jointly considering the effect of interference, noise, and multipath phenomena. In this paper, the exact formulas for outage probability of in-band D2D communications underlying cellular network are proposed. In the proposed model, additive white Gaussian noise and Rayleigh multipath fading are considered into 2 radio resource reuse scenarios. In the first scenario, each D2D pair is allowed to reuse radio resource block of one cellular user, whereas in the second scenario, 2 resources of 2 cellular users can be reused. The proposed formulas are compared to the approximate (nonexact) ones, which models additive white Gaussian noise by a constant variance. The numerical analysis for the first and second scenarios show that the approximate formulas and respected exact ones are in accordance with simulation results in MATLAB. Moreover, based on nonorthogonal multiple access approach, 2 approximations for the nonexact and the proposed formulas are extracted, which are acceptable for multiple resource reuse scenario. As a remarkable result, simulation results show that when the distance of the D2D pair from the respected cellular user is more than 71 m (2 times greater than average distance between the D2D nodes), multiple-reuse scenarios offer higher throughput compared to 1-reuse scenario in an acceptable outage probability.KEYWORDS device to device communications, multiple access, nonorthogonal, outage probability, underlaying

Designing an efficient data sorting algorithm that requires less time and space complexity is essential for computer science, different engineering disciplines, data mining systems, wireless networks, and the Internet of things. This paper proposes a general low-complex data sorting framework that distinguishes the sorted or similar data, makes independent subarrays approximately in equal length, and sorts the subarrays' data using one of the popular comparison-based sorting algorithms. Two frameworks, one for serial realization and another for parallel realization, are proposed. The time complexity analyses of the proposed framework demonstrate an improvement compared to the conventional Merge and Quick sorting algorithms. Following complexity analysis, the simulation results indicate slight improvements in the elapsed time and the number of swaps of the proposed serial Merge-based and Quick-based frameworks compared to the conventional ones for low/high variance integer/non-integer data sets, in different data sizes and the number of divisions. It is about (1−1.6%) to (3.5−4%) and (0.3−1.8%) to (2−4%) improvements in the elapsed times for 1, 2, 3, and 4 divisions, respectively for small and very large data sets in Mergebased and Quick-based scenarios. Although these improvements in serial realization are minor, making independent low-variance subarrays allows the sorted components to be extracted sequentially and gradually before the end of the sorting process. Also, it proposes a general framework for parallelizing conventional sorting algorithms using non-connected (independent) or connected (dependent) multi-core structures. As the second experiment, the numerical analyses that compare the results of the parallel realization of the proposed framework to the serial one in 1, 2, 3, and 4 divisions, show a speedup factor of (2 − 4) for small to (2 − 16) for very large data sets. The third experiment shows the effectiveness of the proposed parallel framework to the parallel sorting based on the random-access machine model. Finally, we prove that the mean-based pivot is as efficient as the median-based and much better than the random pivot for making subarrays of approximately equal length.

One of the most important research works on the field of adaptive array antennas is to increase the accuracy and resolution of Direction Of Arrival (DOA) estimation in a joint state. In this investigation, two well-known DOA estimation algorithms, MUltiple SIgnal Classification (MUSIC) and Minimum Variance Distortionless Response (MVDR) are modelled and simulated in new proposed array geometry. This paper provides a comparison between Uniform Linear Array (ULA) and Proposed Array (P A) geometries in resolving narrowband signal sources located closely. Proposed array adds two elements to the ULA in top and bottom of the array axis.DOA estimation performance has been compared in terms of accuracy and resolution threshold. Simulation results show that ULA cannot detect the sources located at close angles to the array endfire as well as middle angles and isn't able to resolve closely spaced sources in this area successfully. The proposed array can remove this drawback while having an identical accuracy for middle angles. Consequently, by using the proposed array, a better resolution and performance is achieved for the border angles with almost the same array size and computational complexity in both MUSIC and MVDR algorithms.

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Computer and communication systems and networks deal with many cases that require rearrangement of data either in descending or ascending order. This operation is called sorting, and the purpose of an efficient sorting algorithm is to reduce the computational complexity and time taken to perform the comparison, swapping, and assignment operations. In this paper, we propose an efficient mean-based sorting algorithm that sorts integer/non-integer data by making approximately the same length independent quasi-sorted subarrays. It gradually finds sorted data and checks if the elements are partially sorted or have similar values. The elapsed time, the number of divisions and swaps, and the difference between the locations of the sorted and unsorted data in different samples demonstrate the superiority of the proposed algorithm to the Merge, Quick, Heap, and conventional mean-based sorts for both integer and non-integer large data sets which are random or partially/entirely sorted. Numerical analyses indicate that the mean-based pivot is appropriate for making subarrays with approximately similar lengths. Also, the complexity study shows that the proposed mean-based sorting algorithm offers a memory complexity same as the Quick-sort and a time complexity better than the Merge, Heap, and Quick sorts in the best-case. It is similar to the Merge and Heap sorts in view of the time complexity of the worst-case much better than the Quick-sort while these algorithms experience identical complexity in the average-case. In addition to finding part by part incremental (or decremental) sorted data before reaching the end, it can be implemented by parallel processing the sections running at the same time faster than the other conventional algorithms due to having independent subarrays with similar lengths.