For vehicular ad hoc networks (VANETs), edge caching has attracted considerable research attention to maximize the efficiency and reliability of infotainment applications. In this article, we propose a two-layer distributed content caching scheme for VANETs by jointly exploiting the cache at both vehicles and roadside units (RSUs). Specifically, we formulate the content caching problem to minimize the overall transmission delay and cost as a nonlinear integer programming (NLIP) problem and propose an alternate dynamic programming search (ADPS)-based algorithm to solve it. In ADPS, we divide the original problem into three subproblems and then we use the dynamic programming (DP) method to solve each subproblem separately. To reduce the complexity, we further propose a cooperation-based greedy (CBG) algorithm to solve the large-scale original problem. Both numerical simulation results and experiments in the testbed show that the proposed caching scheme outperforms existed caching schemes, and the transmission delay and cost can be reduced by 10% and 24%, respectively, while the hit ratio can be increased by 30% in a practical environment, as compared to the popularity-based caching scheme.

Non-orthogonal multiple access (NOMA) technology offers higher communication throughput than its orthogonal multiple access counterpart. However, it also poses new challenges for spectrum sensing technology. Accurate spectrum sensing of a channel occupied by multiple users is challenging, especially in low signal-to-noise ratio environments. To improve the spectrum sensing performance, a spectrum sensing algorithm based on deep denoising and clustering is developed for power domain NOMA. First, a novel auto-encoder for deep denoising that can filter out the noise of signals is proposed. Then the auto-encoder is transplanted to a variational auto-encoder for extracting features with high separability. Finally, a ring K-means++ algorithm is proposed to classify features. In the experiments, simulations of algorithms are carried out in various scenarios with different numbers of primary users. The results show that the proposed algorithm outperforms other algorithms.

Received signal strength (RSS)-based wireless localization is easy to implement at low cost. In practice, exact positions of anchors may not be available. This paper focuses on determining the location of a source in the presence of inaccurate positions of anchors based on RSS directly. We first use Taylor expansion and a min-max approach to get a maximum likelihood estimator of the coordinates of the source. Then we propose a relaxed semi-definite programming model to circumvent the non-convexity. We also propose a rounding algorithm considering both inaccurate source locations and inaccurate anchor locations. Simulation results together with analysis are presented to validate the proposed method.

In recent years, the ever-growing number of mobile users and demands for mobile services exert great pressure on wireless content transmission. It is still a challenge to provide high-quality service in mobile scenario. To improve the quality of mobile service, proactive caching is proposed to push popular and requested service content to the nearest base stations or access points from mobile users through mobility prediction and content prefteching and caching. Despite a lot of proactive caching algorithms have been proposed, there is few research has been done to design and establish a network architecture which is capable of providing smart and native support for proactive caching. Therefore, in this paper, a Smart Proactive Caching Network (SPCN) is proposed to fill the gap. The architecture and working procedure of SPCN is presented and network model is established. The performance is also evaluated.

Received signal strength (RSS)-based wireless localization is easy to implement at low cost. In practice,exact positions of anchors may not be available. This paper focuses on determining the location of a source in the presence of inaccurate positions of anchors based on RSS directly. We first use Taylor expansion and a min-max approach to get a maximum likelihood estimator of the coordinates of the source. Then we propose a relaxed semi-definite programming model to circumvent the non-convexity. We also propose a rounding algorithm considering both inaccurate source location and inaccurate anchor locations.Simulation results together with analysis are presented to validate the proposed method.

The intelligent Processing technique is more and more attractive to researchers due to its ability to deal with key problems in Vehicular Ad hoc networks. However, several problems in applying intelligent processing technologies in VANETs remain open. The existing applications are comprehensively reviewed and discussed, and classified into different categories in this paper. Their strategies, advantages/disadvantages, and performances are elaborated. By generalizing different tactics in various applications related to different scenarios of VANETs and evaluating their performances, several promising directions for future research have been suggested.