Research on an improved parallel algorithm based on user’s requirement in cognitive radar network

Abstract

In cognitive radar network, a parallel algorithm can allocate idle spectrum to cognitive radars, but it often ignores the user's requirement. According to the graph theory, the topology of the network is divided into two different groups. In the process of distributing spectrum, each group summarizes the distributed information after completing a distribution. When a user's demand is satisfied, every group is informed to not allocate the spectrum for the user anymore and deletes it from the topology at the same time. As a result, by deleting the node which the user's requirement has already been met, the other nodes which are interfered with the node on the same channel can participate into spectrum allocation. The improved algorithm can ensure high spectrum utilization taking a little time that is spent on the spectrum allocation. The simulation result shows that the spectrum utilization of the improved algorithm is higher than that of the traditional parallel algorithm, and the user's satisfaction increases greatly. Therefore, this paper aims to attempt to explore an improved parallel algorithm to resolve the problem by considering user's requirement.

Nonuniform antenna array design by parallelizing three-parent crossover genetic algorithm

“…Min et al considered pulse compression and sidelobe suppression in [20]. Yan et al studied parallel algorithm in cognitive radar network [21]. Dhaliwal and Pattnaik compared several evolutionary algorithms on the design of fractal antenna [22].…”

A non-revisiting artificial bee colony algorithm for phased array synthesis

“…These kinds of algorithms are mainly heuristic-based methods since traditional optimization methods or analytical methods are not suitable. It turns out that evolutionary computing methods present good performance in handling phased array design problems [7,8].…”