Individual cellular networks have been very robust to random cell tower failures due to redundant cell tower deployments. However, a large-scale clustered failure with multiple cell towers can lead to the loss of services of a cellular network. Recently, off-the-shelf smartphones can support multiple network standards, so cellular network infrastructure sharing is a promising direction to improve the service robustness under potential large-scale clustered tower failures. The existing work on cellular network robustness is usually limited to large-scale studies of individual networks or small-scale studies of multiple networks. In this work, we conduct the first investigation, to our knowledge, on cross-network infrastructure sharing benefits for enhancing robustness with afull cellular penetration rate. Our work is based on all cellular networks in Shenzhen, China, covering over 10 million cellular users. Specifically, we design a new metric to quantify cellular network robustness with or without cross-network sharing under both random and clustered cell tower failures. We further study the impact of different factors on robustness, including the number of networks, spatiotemporal dynamics, contextual factors, and a case study at two key transportation hubs. We provide a set of lessons learned based on our study, along with discussions of the results.

Abstract Frequency‐time selective fading degrades the performance of communication systems, but it also provides an opportunity to collect multipath diversity and Doppler diversity. In this paper, an oversampled grouped‐linear‐constellation‐precoding (GLCP) scheme is proposed for orthogonal frequency division multiplexing (OFDM) systems to extract the diversity inherent in doubly selective fading channels. In the proposed scheme, GLCP is employed to precode the information symbols of OFDM at the transmitter, and the precoded OFDM symbols are oversampled in the time domain at the receiver. A full‐groups maximum likelihood (ML) decoder and a successive group decoder to decode the oversampled OFDM symbols with diversity gains are developed. The successive group decoder combines the group ML decoding algorithm with a block‐decision‐feedback‐equalization (BDFE) algorithm. The proposed scheme can take advantage of both the GLCP scheme and the oversampling scheme to achieve multipath diversity gains and Doppler diversity gains in doubly selective fading channels. Simulation results show that the proposed scheme can significantly improve the performance of OFDM systems in the frequency selective fading channel and the doubly selective fading channel in terms of bit error ratio, indicating that the diversity gains can be greatly enhanced by the proposed scheme in doubly selective fading channels.

In this paper, we present a pose based approach for locating and recognizing human actions in videos. In our method, human poses are detected and represented based on deformable part model. To our knowledge, this is the first work on exploring the effectiveness of deformable part models in combining human detection and pose estimation into action recognition. Comparing with previous methods, ours have three main advantages. First, our method does not rely on any assumption on video preprocessing quality, such as satisfactory foreground segmentation or reliable tracking; Second, we propose a novel compact representation for human pose which works together with human detection and can well represent the spatial and temporal structures inside an action; Third, with human detection taken into consideration in our framework, our method has the ability to locate and recognize multiple actions in the same scene. Experiments on benchmark datasets and recorded cluttered videos verified the efficacy of our method.