The noncoherent single-input-multiple-output (SIMO) system experiences severe cross-product mutual interference with a limited number of receive antennas, severely limiting the number of simultaneous users that can be served in the uplink. In Rician channel, a multiuser energy difference detection (EDD) scheme is introduced for dual-frequency complementary amplitude signals. It eliminates cross-product interference, significantly reducing the number of antennas required by the base station (BS), while extending the constellation space for energy detection (ED). Based on this extended constellation space and channel gain information, two optimized multiuser constellations are obtained by maximizing the minimum joint constellation distance and directly equalizing the receive constellation space. Additionally, the impacts of user mobility are analyzed, and a simple strategy is designed to maintain performance by updating the moving user’s path gain. Simulation results show that the proposed scheme can handle more simultaneous users with the same scale antennas, and it also exhibits strong robustness to user mobility at the same time.

Under the condition of limited number of antennas in current noncoherent single-input-multiple-output (SIMO), the key problems of serious mutual interference and small number of supported users are still difficult to overcome. In this paper, we proposed a multi-user massive SIMO based on energy difference detection (EDD) in Rician channels. The proposed method only needs to simply estimate the line-of-sight (LOS) amplitude gain of users. The cross-product interference caused by energy detection is eliminated by designing complementary amplitude constellations and performing energy difference detection at the base station (BS). It reduces the number of antennas required by the BS to distinguish data streams from different users. In addition, we also analyze the impact on the system performance when users move, and adopt the low-complexity approach of re-estimating the moving user’s coefficient to maintain a good performance. Simulation results show that the proposed method can use the LOS component of the Rician channels to support more concurrent users, and obtain a better performance than existing methods.

Anomaly detection is a critical technique that ensures the reliability of WSNs. However, most existing anomaly detection methods only consider the case of single modal data flow anomaly detection for each node or multiple modal time series data flow anomaly detection for a single node and do not consider the case of multiple nodes and multiple time series data flow simultaneously,and it limited the ability of anomaly detection. In this paper, a novel anomaly detection model is proposed for multimodal WSN data flows. First, the temporal features and modal correlation features extracted from each sensor node are fused into one vector representation, then it is further aggregated with the spatial features represented the spatial position relationship of the nodes; finally,the current time-series data of WSN nodes are predicted, and abnormal states are identified according to the fusion features. The simulation results obtained on a public dataset show that the proposed approach can significantly improve upon existing methods interms of robustness, and its F1 score reaches 0.90, which is 14.2% higher than that of the graph convolution network (GCN) with longshort-term memory (LSTM).

The original minimum exposure path (MEP) problem in WSNs (wireless sensor networks) requires the starting point and ending point of a moving target to be fixed, thus limiting the evaluation of coverage quality relative to the locations of these points. To resolve this issue, a minimum exposure path problem with various boundaries (VB-MEP) is proposed in this paper. Because the corresponding graph model cannot be established, the original classical methods (grid method and Voronoi diagram method) used to solve the minimum exposure path problem are no longer effective for the VB-MEP problem. This paper first transforms the problem into a hybrid optimization problem with constraint conditions and then considers the characteristics of the transformed mathematical optimization model with high dimensionality, unfixed dimensionality and high nonlinearity so that the deterministic optimization methods are no longer applicable. A hybrid artificial colony solution algorithm that incorporates the actual characteristics of the problem is designed, and a convergence analysis and a proof of the designed algorithm are given. Through simulation experiments based on large-scale node distribution scenarios, it is found that the designed hybrid optimization model with constraints and hybrid artificial bee colony solution algorithm can effectively solve the proposed VB-MEP problem.

In recent years, interval‐valued Pythagorean fuzzy number is playing a more and more important role in decision management. It is a more effective and powerful tool to handle fuzzy information in decision problems. The multicriteria decision‐making theory has been widely used in solving practical problems, such as the risk assessment of financial investment, engineering and construction, medical and health care, and information security. The main purpose of this paper is to apply a new interval‐valued Pythagorean fuzzy decision‐making method to practice and to analyze and solve the problem of wireless communication infrastructure. In this paper, a new interval‐valued Pythagorean fuzzy ranking method, extending scope of application of the VIKOR method to interval‐valued Pythagorean fuzzy set, is proposed. In order to adapt to actual needs, subjective and objective weights are combined to solve decision‐making problems to enhance its practicality, validity, and effectiveness. An example of wireless communication infrastructure problem is provided to illustrate the rationality of this method and verify its advantages.

Recommender systems, inferring users' preferences from their historical activities and personal profiles, have been an enormous success in the last several years. Most of the existing works are based on the similarities of users, objects or both that derived from their purchases records in the online shopping platforms. Such approaches, however, are facing bottlenecks when the known information is limited. The extreme case is how to recommend products to new users, namely the so-called cold-start problem. The rise of the online social networks gives us a chance to break the glass ceiling. Birds of a feather flock together. Close friends may have similar hidden pattern of selecting products and the advices from friends are more trustworthy. In this paper, we integrate the individual's social relationships into recommender systems and propose a new method, called Social Mass Diffusion (SMD), based on a mass diffusion process in the combined network of users' social network and user-item bipartite network. The results show that the SMD algorithm can achieve higher recommendation accuracy than the Mass Diffusion (MD) purely on the bipartite network. Especially, the improvement is striking for small degree users. Moreover, SMD provides a good solution to the cold-start problem. The recommendation accuracy for new users significantly higher than that of the conventional popularity-based algorithm. These results may shed some light on the new designs of better personalized recommender systems and information services.