In recent years, orthogonal chirp division multiplexing (OCDM) has gained attention as a robust communication waveform due to its strong resistance to both time-domain and frequency-domain interference. However, similar to orthogonal frequency division multiplexing (OFDM), OCDM suffers from a high peak-to-average power ratio (PAPR), resulting in increased hardware costs and reduced energy efficiency of the transmitter’s power amplifiers. In this work, we introduce a novel unitary transform called the generalized discrete Fresnel transform (GDFnT) and propose a new waveform based on this transform, named generalized OCDM (GOCDM). In GOCDM, data symbols from the constellation diagram are independently placed in the generalized Fresnel (GF) domain. We derive the system’s GF-domain channel matrix under a class of time-frequency doubly selective channels. These channels are characterized by multiple lags and multiple Doppler shifts (MLMDSs), making them suitable for application scenarios, such as vehicular mobile communication and narrowband underwater acoustic communication. We leverage the sparsity of the GF-domain channel matrix to design an iterative receiver based on the message-passing algorithm. Simulation results demonstrate that GOCDM achieves better PAPR performance than OCDM without compromising bit error rate (BER) performance.

Nonorthogonal multiple access (NOMA) has been widely recognized as a promising spectral efficiency technique for the next generation of wireless communication networks due to its ability to support multiple users in the same orthogonal resource block. In response to the increasing demands for extensive connectivity and high-volume data transmission, a novel index modulation (IM)-aided NOMA scheme has been conceived to improve downlink transmission by capitalizing on the flexibility provided by the constellation rotation design for superposition coding. In the proposed scheme, known as IM aided NOMA with the constellation rotation (IM-NOMA-CR), users are categorized into cell-edge group (far-user accommodated) and cell-center group (near-user accommodated) based on their channel conditions. The rotated constellation-based IM operation is exclusively applied to users in the near-user group, who receive lower power allocations compared to the far-user group, enabling them to transmit additional information while ensuring system reliability. This approach enhances spectral efficiency by activating all subcarriers (orthogonal resource blocks) to transmit information to scheduled users, in contrast to the conventional IM-NOMA scheme. Moreover, extra information can be transmitted through constellation rotation in the superposition coding process, setting it apart from traditional NOMA schemes. Subsequently, the maximum likelihood detector employing successive interference cancellation (ML-SIC) is utilized at the receiving end to decode the intended symbols for all users. Numerical simulations have been carried out to validate the efficacy of the proposed IM-NOMA-CR design, demonstrating a significant enhancement in spectral efficiency and error performance compared to existing NOMA schemes.

As a new candidate waveform for the next generation of mobile communications, orthogonal chirp division multiplexing (OCDM) has attracted growing attention for its high spectrum efficiency and robustness to narrow-band interference or impulsive noise. Under vehicular communication channels with multiple lags and multiple Doppler shifts (MLMD), the signal suffers doubly selective (DS) fadings in the time and frequency domain, and data symbols modulated on orthogonal chirps interfere with each other. To address the problem of symbol detection of OCDM over MLMD channels, under the assumption that path attenuation factors, delays, and Doppler shifts of the channel are available, we first derive the closed-form channel matrix in the Fresnel domain and then propose a low-complexity method to approximate it as a sparse matrix. Based on the approximated Fresnel-domain channel, we propose a message-passing (MP) based detector to estimate the transmit symbols iteratively. Finally, under two MLMD channels (an underspread channel for terrestrial vehicular communications and an overspread channel for narrow-band underwater acoustic communications), Monte Carlo simulation results and analyses are provided to validate its advantages as a promising detector for OCDM.

Abstract As an emerging technique, index modulation (IM) can improve the system bit error rate (BER) performance due to the robustness of index bits over conventional modulated symbol bits. The existing IM aided orthogonal frequency division multiplexing (OFDM) schemes only employ one index modulator at the transmitter, which transmits a small amount of index bits in each transmission. In this paper, a novel IM technique, called cascade IM (CIM), is proposed to increase the proportion of the index bits in the transmission by combining the conventional IM with the multiple‐mode IM together. Subcarrier‐wise and subblock‐wise CIM schemes are proposed to achieve different spectral efficiency and diversity order for diverse scenarios in the next generation wireless communication networks. The optimal subcarrier‐wise maximum likelihood detector is proposed for OFDM‐CIM. To reduce the demodulation complexity, a novel tree search based and an iterative log‐likelihood ratio based detectors, which can avoid illegal index patterns in the search process, are developed for OFDM‐CIM. Monte Carlo simulations show that the proposed scheme achieves better BER performance than OFDM‐IM and appears as a competitive candidate of multi‐carrier transmission techniques for next generation wireless communication networks.

Community detection is of great help to understand the structures and functions of complex networks. It has become one of popular research topics in the field of complex networks analysis. Due to the simplicity, flexibility, effectiveness and better interpretability, Nonnegative Matrix Factorization (NMF)-based methods have been widely employed for community detection. However, most existing NMF-based community detection methods are linear and their performance is limited when facing networks with diversified structure information. In view of this, we propose a nonlinear NMF-based method named NMFGAAE, which is composed of two main modules: NMF and Graph Attention Auto-Encoder (GAAE). This approach can boost the performance of NMF-based community detection methods by the aid of graph neural networks and deep clustering. More specifically, GAAE introduces an attention mechanism directed by NMF-based community detection to learn the node representations, while NMF can simultaneously factor these representations to uncover the community structure. We design a unified framework to jointly optimize GAAE and NMF modules, which is very beneficial to obtain better community detection results. We conduct extensive experiments on synthetic and real-world networks. The results show that our NMFGAAE not only performs better than state-of-the-art NMF-based community detection methods, but also outperforms some network representation based baselines. More importantly, NMFGAAE indeed can boost the performance of NMF-based community detection methods.

Community detection is one of the popular research topics in the field of complex networks analysis. It aims to identify communities, represented as cohesive subgroups or clusters, where nodes in the same community link to each other more densely than others outside. Due to the interpretability, simplicity, flexibility, and generality, nonnegative matrix factorization (NMF) has become a very ideal model for community detection and lots of related methods have been presented. To facilitate research on NMF-based community detection, in this article, we make a comprehensive review on NMF-based methods for community detection, especially the state-of-the-art methods presented in high prestige journals or conferences. First, we introduce the basic principles of NMF and explain why NMF can detect communities and design a general framework of NMF-based community detection. Second, according to the applicable network types, we propose a taxonomy to divide the existing NMF-based methods for community detection into six categories, namely, topology networks, signed networks, attributed networks, multilayer networks, dynamic networks, and large-scale networks. We deeply analyze representative methods in every category. Finally, we summarize the common problems faced by all methods and potential solutions and propose four promising research directions. We believe that this survey can fully demonstrate the versatility of NMF-based community detection and serve as a useful guideline for researchers in related fields.

This paper proposes a novel multi-level frame interpolation scheme by exploiting the interactions among different levels. The proposed scheme includes three major stages that work at block level, pixel level, and sequence level, respectively. Effective algorithms are designed for each stage, i.e., block-level motion estimation with dropping unreliable motion vectors, pixel-level motion vector-guided partial scale-invariant feature transform flow matching, and sequence-level 3-D total variation regularized completion. Compared to traditional methods that focus mostly at one single level, the proposed scheme manages to recognize and utilize the interactions among the three levels based on their distinct characteristics and intertwined relationships. With a proper exploitation of interactions, unique advantages for each level can be effectively preserved while inherent limitations of a given level can be overcome by utilizing information from other levels. Extensive experiments have confirmed its superior performance over several classical schemes, in both subjective visual quality and objective peak signal-to-noise ratio/structure similarity measurements, and typical artifacts can be significantly reduced.

A new video frame interpolation technique is proposed in this paper.We first use the classical motion compensation interpolation (MCI), but only for motion vectors (MVs) marked as reliable in our classification procedure. To fill in the regions where no such MV is available, we proposed a novel 3-D total variation regularized completion model, which exploits both temporal and spatial smoothness among video frames. Experiments demonstrate its superior performance compared to several classical methods, both in visual quality and PSNR values, while the typical artifacts are significantly reduced.

In this paper, we propose a novel video error concealment method to restore the visual degradation, caused by packet loss in video delivery over unreliable channels. For a video sequence, we exploit its inherent temporal-spatially correlated property, i.e., temporal continuity and spatial smoothness, from a global view point. We then formulate it into a total variation regularized matrix completion model. Compared with the error concealment methods implemented in the H.264 reference software, our algorithm is able to achieve significantly higher PSNR as well as better visual quality.

It has been proven that performance of video coding at low bit rates can be improved by down-sampling a video before compression and then using super-resolution to up-sample it after decompression. Such techniques are especially important for limited bandwidth communications. In this paper we propose an adaptively down-sampling based coding (DBC) method which performs rate distortion (RD) optimization to determine the coding structure between regular coding and down-sampling coding. In order to restore the original resolution of down-sampling coded video signals, a hybrid super-resolution (SR) algorithm which combines motion compensation (MC) based SR and wiener filter based SR is used. Experimental results show that our method has improvement both in rate-distortion performance and perceived visual quality at low bit rate.