Mixed numerologies interference analysis and inter-numerology interference cancellation for windowed OFDM systems

Extremely diverse service requirements are one of the critical challenges for the upcoming fifth-generation (5G) radio access technologies. As a solution, mixed numerologies transmission is proposed as a new radio air interface by assigning different numerologies to different subbands. However, coexistence of multiple numerologies induces the inter-numerology interference (INI), which deteriorates the system performance. In this paper, a theoretical model for INI is established for windowed orthogonal frequency division multiplexing (W-OFDM) systems. The analytical expression of the INI power is derived as a function of the channel frequency response of interfering subcarrier, the spectral distance separating the aggressor and the victim subcarrier, and the overlapping windows generated by the interferer's transmitter windows and the victim's receiver window. Based on the derived INI power expression, a novel INI cancellation scheme is proposed by dividing the INI into a dominant deterministic part and an equivalent noise part. A soft-output ordered successive interference cancellation (OSIC) algorithm is proposed to cancel the dominant interference, and the residual interference power is utilized as effective noise variance for the calculation of log-likelihood ratios (LLRs) for bits. Numerical analysis shows that the INI theoretical model matches the simulated results, and the proposed interference cancellation algorithm effectively mitigates the INI and outperforms the state-of-the-art W-OFDM receiver algorithms

struc2gauss: Structure Preserving Network Embedding via Gaussian Embedding

Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: structure preservation and uncertainty modeling. Almost all previous methods represent a node into a point in space and focus on the local structural information, i.e., neighborhood information. However, neighborhood information does not capture the global structural information and point vector representation fails in modeling the uncertainty of node representations. In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information. struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding. Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated. Experiments conducted on both synthetic and real-world data sets demonstrate that struc2gauss effectively captures the global structural information while state-of-the-art network embedding methods fails to, outperforms other methods on the structure-based clustering task and provides more information on uncertainties of node representations

struc2gauss: Structural role preserving network embedding via Gaussian embedding

Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: role preservation and uncertainty modeling. Almost all previous methods represent a node into a point in space and focus on local structural information, i.e., neighborhood information. However, neighborhood information does not capture global structural information and point vector representation fails in modeling the uncertainty of node representations. In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information. struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding. Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated. Experiments conducted on real-world networks demonstrate that struc2gauss effectively captures global structural information while state-of-the-art network embedding methods fail to, outperforms other methods on the structure-based clustering and classification task and provides more information on uncertainties of node representations

Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration

While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e.g., 4K) images remains an extremely challenging task due to the explosion of computational complexity and memory usage, as well as the deficiency of annotated data. In this paper we present a novel model for ultra high-resolution image restoration, referred to as the Global-Local Stepwise Generative Network (GLSGN), which employs a stepwise restoring strategy involving four restoring pathways: three local pathways and one global pathway. The local pathways focus on conducting image restoration in a fine-grained manner over local but high-resolution image patches, while the global pathway performs image restoration coarsely on the scale-down but intact image to provide cues for the local pathways in a global view including semantics and noise patterns. To smooth the mutual collaboration between these four pathways, our GLSGN is designed to ensure the inter-pathway consistency in four aspects in terms of low-level content, perceptual attention, restoring intensity and high-level semantics, respectively. As another major contribution of this work, we also introduce the first ultra high-resolution dataset to date for both reflection removal and rain streak removal, comprising 4,670 real-world and synthetic images. Extensive experiments across three typical tasks for image background restoration, including image reflection removal, image rain streak removal and image dehazing, show that our GLSGN consistently outperforms state-of-the-art methods.Comment: submmitted to Transactions on Image Processin