In the topic dissemination space, various types of messages interact and collectively influence the propagation trends of derivative topics. To address this, a prediction model for group behavior in hotspot topics is proposed based on multitype complex messages. First, considering the complexity and high-dimensional nature of data in the topic feature space, sparse representation is employed to extract key user attributes in the derivative topic space and to calculate sparse vectors. Based on these sparse vectors, a node feature prediction submodel is subsequently constructed using regression algorithms. Second, considering the cooperative and competitive relationships between different message types, evolutionary game theory is applied to transform the dynamic interactions between messages into psychological games among users. By reflecting the optimal benefits of user strategies, it captures the driving forces of derivative messages and establishes a dynamic topic dissemination network. Additionally, a graph convolutional network is employed to extract user structural features, constructing a structure attribute prediction submodel. Finally, to overcome the limitations of individual submodels, the node feature and structure attribute prediction submodels are integrated to form a group behavior prediction model based on multitype message interactions. The input data is discretized using time slicing to enhance the model’s generalization ability. Experiments show that the proposed model accurately captures the complex interactions among multitype messages in the derivative topic space and effectively predicts group behavior and hotspot topic propagation trends.

Compared to traditional media, the open and instantaneous nature of social media makes it easier for rumors to emerge and spread, thereby causing more severe societal impacts. Therefore, identifying potential user connections within rumor-related topics holds critical significance for public opinion monitoring and social stability. Firstly, according to the richness and multi-level nature of user characteristics, the interests and preferences and social attributes of users are comprehensively analyzed, so as to realize the multi-dimensional and in-depth representation of user characteristics. Secondly, to better capture the complexity of rumor-related social interactions, a rumor user interaction node2vec method that leverages user interaction behavior is proposed. The method uses a biased random walk strategy that combines rumor influence and user interaction evolution. It effectively captures key attributes of the rumor interaction space, leading to a more precise representation of the network structure in rumor propagation. Finally, in view of the complexity of the relationship between different nodes, the graph attention network is used to effectively capture the interaction between nodes, and a social network link prediction method for rumor propagation is proposed. Experiments and analyses are conducted on multiple public datasets. The results demonstrate that the proposed link prediction model can accurately predict whether links will form between users, showing significant advantages in the specific and complex scenario of rumor propagation.

The rapid spread of online rumors has significant negative impacts on the online ecosystem and social order, which is closely tied to users’ cognitive traits. To explore the mechanisms of rumor propagation driven by cognition and mitigate its harm, we propose an information diffusion model based on the rumor, antirumor, and cognitive game. First, to address the uncertainty and difficulty in quantifying cognitive biases, and considering the advantages of fuzzy logic theory in handling uncertainty, a fuzzy logic-based algorithm for measuring user cognitive biases is proposed. Moreover, recognizing the nonlinear relationships among various factors, polynomial functions are introduced as the output of fuzzy rules to more accurately describe these complex relationships. Second, regarding the symbiotic and antagonistic relationships among multiple types of rumor information under the influence of cognitive biases, and leveraging the strengths of game theory in analyzing complex systems characterized by coexistence of symbiosis and antagonism, an evolutionary game-based rumor-antirumor user behavior mechanism is developed. This provides a robust theoretical foundation for understanding user state transitions and their evolutionary patterns. Finally, integrating the aforementioned research, and considering that the dynamic evolution of user cognition leads to variations in trust responses and attitudes toward rumor and antirumor information, the concepts of trust states—trust in rumor (TR) and trust in antirumor (TA)—are introduced into the classical susceptible-infectious-recovered (SIR) model. On this basis, a rumor propagation susceptible-trused-infectious-recovered (STIR) model incorporating user cognitive biases and evolutionary game theory is further constructed. Experimental results demonstrate that this model effectively reveals the game dynamics of multiple types of rumor information, providing a more efficient framework for studying rumor propagation in social networks.

In social networks, topics often demonstrate a “fission” trend, where new topics arise from existing ones. Effectively predicting collective behavioral patterns during the dissemination of derivative topics is crucial for public opinion management. Addressing the symbiotic, antagonistic nature of “native-derived” topics, a derivative topic propagation model based on representation learning, topic relevance is proposed herein. First, considering the transition in user interest levels, cognitive accumulation at different evolutionary stages of native-derivative topics, a user content representation method, namely DTR2vec, is introduced, based on topic-related feature associations, for learning user content features. Then, evolutionary game theory is introduced by recognizing the symbiotic, antagonistic nature of “native-derived” topics during their propagation. Moreover, implicit relationships between users are explored, user influence is quantified for learning user structural features. Finally, considering the graph convolutional network’s ability to process non-euclidean structured data, the proposed model integrates user content, structural features to predict user forwarding behavior. Experimental results indicate that the proposed model not only effectively predicts the dissemination trends of derivative topics but also more authentically reflects the association, game relationships between native, derivative topics during their dissemination.

Abstract Only a subset of subcarriers are activated in orthogonal frequency division multiplexing‐index modulation (OFDM‐IM), which achieves higher energy efficiency and resists frequency offset. In the OFDM‐IM, the energy of the received signal is computed and then combined with pre‐processed signal to create the input of detection network. Inspired by image denoising technology, this study enhances the detection performance by denoising the pre‐processed data and improving the energy distribution in the OFDM‐IM system. First, considering that the noise reduction process of the pre‐processed signal can effectively mitigate the distortion by noise which affects the detection accuracy, this study proposes a two‐phase neural network termed as Deep‐Denoising‐IM through the combination of a noise reduction network and a deep learning detection method. Then, to better determine the position of the active carriers, a joint decision method of the denoised data and the received signal is designed as the IQ signal of the denoised data may change the quadrant of original signal distribution. In addition, the pre‐processed data sample has insufficient diversity. Considering that data enhancement can increase the noise of the signal samples, this study proposes a method to strengthen the silent carriers in the model training phase, which improves the generalization ability of the model and further enhances the denoising performance. Simulation results show that Deep‐Denoising‐IM outperforms the existing detectors in terms of mean square error (MSE) and bit error rate (BER) under the Rayleigh fading channel.

Abstract Recently, deep learning (DL) has been successfully applied in computer vision and natural language processing. The communication physical layer based on deep learning has received widespread attention. Introducing domain‐knowledge into neural networks (NNs), autoencoder based end‐to‐end communication system, incorporating radio transformer networks (RTNs) (RTNs‐AE) has achieved desirable performance with a channel model in the middle layer. The advent of RTNs underscores the power of expertise at DL. However, a tap‐length in the design of RTNs network must be assumed, which requires some channel information. To address this issue, a new deep learning wireless transceiver named pilot‐aided autoencoder (PA‐AE) is proposed. It can decode on a multipath fading channel without knowing the channel information and the equalization module design. The proposed scheme introduces a well‐designed auxiliary pilot, which carries the learned channel information into decoding with the transmitted signal. The decoding part recovers the sent information from the collected signal without specially designed modules for parameter estimation and equalization.

The channel estimation for a filter bank multi‐carrier with offset quadrature amplitude modulation has been considered an attractive scheme alternative to the conventional cyclic prefix (CP)‐orthogonal frequency‐division multiplexing. However, all the advantages come at the price of the real‐field orthogonality conditions, which gives rise to the intrinsic imaginary. Especially the channel estimation algorithm with a scattered‐pilot structure, which neutralises the interference by increasing power or reducing spectrum efficiency to get accurate channel estimation coefficients. In this work, dual‐dependent pilot (DDP) channel estimation algorithm is studied. By analysing the interference at the pilot position and pilot power, the authors propose an improved power efficient scattered DDP channel estimation method. The proposed method regards the interference as a part of the pilot rather than cancelling it. Moreover, in order to fully utilise intrinsic interference, phase ambiguity is introduced into predefined pilots to adapt to different intrinsic interference values, combined with the reference pilot and DFT iteration to solve phase ambiguity problems at the receiver. Theoretical analysis shows that the improved method can effectively improve the power efficiency. Furthermore, numerical results show the validity of the theoretical analysis and the reliability of the new method.

Due to the high mobility of communication, channel times can vary rapidly and system performance can be decreased. In this study, pseudo‐random noise is used as the guard interval and the training sequence in the time domain in order to estimate the channel‐based compressive sensing scheme. This scheme reduces the number of pilots in the frequency domain and improves spectrum efficiency. By adequately exploiting the sparse characteristics and temporal correlation of the wireless channel, a low complexity compressive channel estimation scheme is proposed. Firstly, the authors average the successive symbols of the channel impulse response in the coherence time to improve the accuracy of the coarse channel estimation. Secondly, a low complexity partial priori information CoSaMP (PPI‐CoSaMP) algorithm is proposed to accurately estimate the channel state information. Finally, based on the precise time delay, the accurate gains are estimated based on the least‐squares algorithm. The simulation results show that compared with the conventional algorithms, the number of observation points required by the PPI‐CoSaMP algorithm is reduced by at least 25%. Moreover, the proposed scheme is more robust at larger multipath channel delays. The complexity of the proposed scheme is reduced by 51.21% compared with the conventional CoSaMP algorithm.

Capacity region and minimum energy function for a variety of delay-tolerant mobile unicast ad hoc networks are studied by using a cell-partitioned model. First, theorems about analytical expressions of network capacity and upper bound of minimum energy function are proposed and proved. Algorithm aiming at maximizing capacity and minimizing energy cost is presented and analyzed by Lyapunov drift method. Second, these two theorems are applied to several types of ad hoc networks. Expressions of network capacity and minimum energy function are obtained. Third, capacity property of a type of hybrid ad hoc networks is analyzed in detail. Relationship among limitation of capacity, node density, and coverage of base stations are investigated. Numerical analysis and simulation are carried out. Copyright © 2015 John Wiley & Sons, Ltd.