Songyi Liu

Zeping Yi, Yongjun Wang, Baoshan Wang et al.

In this paper, we present an improved numerical algorithm for computing the intersection area of multiple circles and a complex polygon efficiently. This geometric problem is fundamental to applications such as wireless sensor networks and base station deployment. The key idea is a curvature-multiplicity-guided adaptive sampling strategy that dynamically concentrates sampling points in geometrically complex boundary regions. The algorithm integrates three components: (i) adaptive quadtree partitioning, (ii) analytical integration via Green's theorem for cells intersecting a single circle, and (iii) curvature-multiplicity-guided Monte Carlo subsampling for cells intersecting multiple circles, where a minimum sample count and a constant factor are introduced into the sampling size. Theoretical analysis shows that the algorithm achieves O(1/ε3/2) computational complexity while maintaining an O(ε) error bound, improving upon the O(1/ε2) complexity of classical Monte Carlo and uniform grid methods for the same error tolerance ε. Numerical experiments on complex polygons, including synthetic data and real-world scenarios, demonstrate that our algorithm outperforms five classical methods in terms of relative error. Furthermore, parameter sensitivity analysis confirms that the algorithm is robust and could make it suited for practical applications such as wireless sensor network coverage estimation.

Yangyang Li, Yuhua Xu, Wen Li et al.

This paper addresses the challenge of anti-jamming in moving reactive jamming scenarios. The moving reactive jammer initiates high-power tracking jamming upon detecting any transmission activity, and when unable to detect a signal, resorts to indiscriminate jamming. This presents dual imperatives: maintaining hiding to avoid the jammer's detection and simultaneously evading indiscriminate jamming. Spread spectrum techniques effectively reduce transmitting power to elude detection but fall short in countering indiscriminate jamming. Conversely, changing communication frequencies can help evade indiscriminate jamming but makes the transmission vulnerable to tracking jamming without spread spectrum techniques to remain hidden. Current methodologies struggle with the complexity of simultaneously optimizing these two requirements due to the expansive joint action spaces and the dynamics of moving reactive jammers. To address these challenges, we propose a parallelized deep reinforcement learning (DRL) strategy. The approach includes a parallelized network architecture designed to decompose the action space. A parallel exploration-exploitation selection mechanism replaces the $\varepsilon $-greedy mechanism, accelerating convergence. Simulations demonstrate a nearly 90\% increase in normalized throughput.