Novel SαS PDF approximations and their applications in wireless signal detection

Three new approximations to the probability density function (PDF) of the symmetric alpha stable (SαS) distribution are proposed. The first two approximations use rational functions while the third approximation uses power functions. Using these approximations, new detectors for signals in symmetric alpha stable noise are also derived. Numerical results show that all these new approximations have good accuracies. Numerical results also show that the new detectors based on these approximations outperform the existing detectors, especially when the characteristic exponent of the symmetric alpha stable distribution is small

A Scalable Hybrid MAC Protocol for Massive M2M Networks

In Machine to Machine (M2M) networks, a robust Medium Access Control (MAC) protocol is crucial to enable numerous machine-type devices to concurrently access the channel. Most literatures focus on developing simplex (reservation or contention based)MAC protocols which cannot provide a scalable solution for M2M networks with large number of devices. In this paper, a frame-based Hybrid MAC scheme, which consists of a contention period and a transmission period, is proposed for M2M networks. In the proposed scheme, the devices firstly contend the transmission opportunities during the contention period, only the successful devices will be assigned a time slot for transmission during the transmission period. To balance the tradeoff between the contention and transmission period in each frame, an optimization problem is formulated to maximize the system throughput by finding the optimal contending probability during contention period and optimal number of devices that can transmit during transmission period. A practical hybrid MAC protocol is designed to implement the proposed scheme. The analytical and simulation results demonstrate the effectiveness of the proposed Hybrid MAC protocol

Game among Interdependent Networks: The Impact of Rationality on System Robustness

Many real-world systems are composed of interdependent networks that rely on one another. Such networks are typically designed and operated by different entities, who aim at maximizing their own payoffs. There exists a game among these entities when designing their own networks. In this paper, we study the game investigating how the rational behaviors of entities impact the system robustness. We first introduce a mathematical model to quantify the interacting payoffs among varying entities. Then we study the Nash equilibrium of the game and compare it with the optimal social welfare. We reveal that the cooperation among different entities can be reached to maximize the social welfare in continuous game only when the average degree of each network is constant. Therefore, the huge gap between Nash equilibrium and optimal social welfare generally exists. The rationality of entities makes the system inherently deficient and even renders it extremely vulnerable in some cases. We analyze our model for two concrete systems with continuous strategy space and discrete strategy space, respectively. Furthermore, we uncover some factors (such as weakening coupled strength of interdependent networks, designing suitable topology dependency of the system) that help reduce the gap and the system vulnerability

Full Wave Modeling of Ultrasonic Scattering Using Nystrom Method for NDE Applications

Approximate methods for ultrasonic scattering like the Kirchhoff approximation and the geometrical theory of diffraction (GTD) can deliver fast solutions with relatively small computational resources compared to accurate numerical methods. However, these models are prone to inaccuracies in predicting scattered fields from defects that are not very large compared to wavelength. Furthermore, they do not take into account physical phenomena like multiple scattering and surface wave generation on defects. Numerical methods like the finite element method (FEM) and the boundary element method (BEM) can overcome these limitations of approximate models. Commercial softwares such as Abaqus FEA and PZFlex use FEM, while CIVA has a 2D FEM solver [1-3]. In this work, we study the performance of the Nyström method (NM) [4,5], an alternative boundary integral equation solver to the BEM, in modeling ultrasonic scattering from defects. To handle larger problems, the Nyström method is accelerated by the multilevel fast multipole algorithm (MLFMA). We apply the NM to benchmark problems and compare its predictions with those of exact and approximate analytical models as well as with experimental results from the World Federation of NDE Centers (WFNDEC). Several examples will be presented to demonstrate the prediction of creeping waves by the NM while also illustrating its improved accuracy over the Kirchhoff approximation. We will conclude with a discussion on the validity and limitations of the NM in modelling practical NDE problems

Intention-aware Denoising Diffusion Model for Trajectory Prediction

Trajectory prediction is an essential component in autonomous driving, particularly for collision avoidance systems. Considering the inherent uncertainty of the task, numerous studies have utilized generative models to produce multiple plausible future trajectories for each agent. However, most of them suffer from restricted representation ability or unstable training issues. To overcome these limitations, we propose utilizing the diffusion model to generate the distribution of future trajectories. Two cruxes are to be settled to realize such an idea. First, the diversity of intention is intertwined with the uncertain surroundings, making the true distribution hard to parameterize. Second, the diffusion process is time-consuming during the inference phase, rendering it unrealistic to implement in a real-time driving system. We propose an Intention-aware denoising Diffusion Model (IDM), which tackles the above two problems. We decouple the original uncertainty into intention uncertainty and action uncertainty and model them with two dependent diffusion processes. To decrease the inference time, we reduce the variable dimensions in the intention-aware diffusion process and restrict the initial distribution of the action-aware diffusion process, which leads to fewer diffusion steps. To validate our approach, we conduct experiments on the Stanford Drone Dataset (SDD) and ETH/UCY dataset. Our methods achieve state-of-the-art results, with an FDE of 13.83 pixels on the SDD dataset and 0.36 meters on the ETH/UCY dataset. Compared with the original diffusion model, IDM reduces inference time by two-thirds. Interestingly, our experiments further reveal that introducing intention information is beneficial in modeling the diffusion process of fewer steps.Comment: 14 pages, 9 figure