Abstract-Compared with MPI, OpenMP provides us an easy way to parallelize the multilevel fast multipole algorithm (MLFMA) on shared-memory systems. However, the implementation of OpenMP parallelization has many pitfalls because different parts of MLFMA have distinct numerical characteristics due to its complicated algorithm structure. These pitfalls often cause very low efficiency, especially when many threads are employed. Through an in-depth investigation on these pitfalls with analysis and numerical experiments, we propose an efficient OpenMP parallel MLFMA. Two strategies are proposed in the parallelization, including: 1) loop reorganization for far-field interaction in the MLFMA; 2) determination of a transition level. Numerical experiments on large scale targets show the proposed OpenMP parallel scheme can perform as efficiently as the MPI counterpart, and much more efficiently than the straightforward OpenMP parallel one.
This paper studies interference in a data collection scenario in which multiple unmanned aerial vehicles (UAVs) are dispatched to wirelessly collect data from a set of distributed sensors. To improve the communication throughput and minimize the completion time, we design a joint resource allocation and trajectory optimization framework that not only is compatible with the traditional time-division scheme and interference coordination scheme but also combines their advantages. First, we analyse a basic quasi-stationary scenario with two UAVs and four devices, in which the two UAVs hover at optimal displacements to execute the data collection mission, and it is proven that the proposed optimal resource allocation and trajectory solution is adaptively adjustable according to the severity of the interference and that the common throughput of the network is non-decreasing. Second, for the general mobile case, we design an efficient algorithm to jointly address resource allocation and trajectory optimization, in which we first apply the block coordinate descent method to decompose the original non-convex problem into three non-convex sub-problems and then employ a dedicated genetic algorithm, a penalty function and the sequential convex approximation (SCA) technique to efficiently solve the individual sub-problems and obtain a satisfactory locally optimal solution with an adaptive initialization scheme. Subsequently, numerical experiments are presented to demonstrate that the completion time of the data collection task with our proposed method is at least 25% shorter than those with several baseline dynamic orthogonal schemes when 4 UAVs are deployed. Finally, we provide a practical application principle concerning the maximum suitable number of UAVs to avoid the inherent deficiencies of the proposed algorithm.
In this letter, we focus on designing constantmodulus waveform with discrete phases for the multi-input multi-output (MIMO) radar, where the signal-to-interferenceplus-noise ratio (SINR) is maximized in the presence of both the signal-dependent clutter and the noise. Given the NP-hardness of the formulated problem, we propose to relax the original optimization as a sequence of continuous quadratic programming (QP) subproblems by use of the convex hull of the discrete feasible region, which yields approximated solutions with much lower computational costs. Finally, we assess the effectiveness of the proposed waveform design approach by numerical simulations.
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