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A shortage of frequencies exists due to the demand of extensive connectivity and limited availability of spectrum. Thus a prominent solution of sharing spectrum between radar and communication systems has been proposed. Integrated sensing and communication (ISAC) aims to achieve complete integration and reciprocal advantages between communication and sensing processes. In this work, we consider Simultaneous Transmission and Reflection (STAR) Reconfigurable Intelligent Surface (S-RIS) and passive RIS (P-RIS) assisted dual-function radar communication system where S-RIS and P-RIS assist communication and sensing functionalities, respectively. We formulate an optimization problem that jointly optimizes the beamforming vector at the multi-antenna ISAC transmitter and phase shift vector to maximize the weighted sum-rate (WSR) at the communication users while taking care of the maximum power limit the ISAC transmitter and ensuring the performance of sensing model to detect targets and limitations of phase and amplitude of S-RIS elements. To address the non-convexity, we propose a low-complexity alternating optimization (AO) algorithm. Furthermore, we provide simulation results to verify the viability of the proposed framework with its a) only P-RIS assisted scheme, and b) proposed model with random phase shift, counterparts. The proposed algorithm is also shown to be effective in delivering nearly optimal design outcomes in scenario where the channel state information is imperfect (ICSI). Simulation results demonstrate the impact of RIS elements, number of antennas at the ISAC transmitter and the transmit power on WSR. Accordingly, we illustrate the impact of S-RIS and reflection users (RU) locations and the effect of sensing threshold and the number of targets which highlights the trade-off between sensing and communication. The S-RIS framework offer approximately performance gain of 12.5% and 38.8% as compared to conventional and random cases, respectively.

Abstract This paper studies the beamforming design for an active reconfigurable intelligent surface (RIS) aided non‐orthogonal multiple access (NOMA) network. It aims to minimize the transmission power while satisfying the requirement of the quality of service (QoS). To solve this problem, an iterative algorithm based on approximate transformations and penalty functions is proposed. Finally, simulation results illustrate that an active RIS‐aided NOMA system can achieve significant performance gains over the passive RIS‐aided NOMA network.

Abstract This paper focuses on a reconfigurable intelligent surface (RIS)‐aided full‐duplex multi‐user massive multiple‐input multiple‐output system with transceiver hardware impairments (THWIs). Different from the existing works, the two‐timescale design scheme is considered. The phase shift at the RIS is designed only based on the statistical channel state information. Firstly, the closed‐form expression of the uplink and downlink achievable rate is derived. Then, the power scaling laws are revealed. Finally, the impact of THWIs on the system performance is analysed and genetic algorithm to maximize the achievable rate is used.

An intelligent reflecting surface (IRS) is proposed to enhance the physical layer security in the Rician fading channel wherein the angular direction of the eavesdropper (ED) is aligned with a legitimate user. A two-phase communication system under active attacks and passive eavesdropping is considered in this scenario. The base station avoids direct transmission to the attacked user in the first phase, whereas other users cooperate in forwarding signals to the attacked user in the second phase, with the help of IRS and energy harvesting technology. Under the occurrence of active attacks, an outage-constrained beamforming design problem is investigated under the statistical cascaded channel error model, which is solved by using the Bernstein-type inequality. An average secrecy rate maximization problem for the passive eavesdropping is formulated, which is then addressed by a low-complexity algorithm. The numerical results of this study reveal that the negative effect of the ED’s channel error is larger than that of the legitimate user.

Reconfigurable intelligent surface (RIS)-aided wireless communications is a hot research topic in academic and industry communities since it can enhance both the spectrum and energy efficiency of wireless systems by artificially reconfiguring the wireless propagation environment. An RIS can configure tiny antenna elements or scatterers, which can be judiciously tuned to enhance the signal power for desired users, such as primary users in cognitive radio networks, or suppress the signal power for undesired users, such as eavesdroppers for physical layer security. RISs also find promising applications in dense urban areas or indoor scenarios, where the electromagnetic waves are prone to be blocked by obstacles such as buildings and walls. There are numerous advantages associated with RISs. For instance, since an RIS needs no analog-to-digital converters or radiofrequency chains, it reduces the power consumption, hence improving sustainability and reducing cost. RISs can be fabricated at a limited complexity and cost, and they can be easily deployed on buildings facades, walls, ceilings, and streetlamps. Furthermore, RISs can be readily integrated into current wireless networks (both cellular networks and WiFi). Due to these appealing advantages, RIS is envisioned to be one of the key technologies for the sixth-generation (6G) wireless networks.

Fault diagnosis of industrial equipments is extremely important for the safety requirements of modern production processes. Lately, deep learning (DL) has been the mainstream fault diagnosis tool due to its powerful representational ability in learning and flexibility. However, most of the existing DL-based methods may suffer from two drawbacks: Firstly, only one metric is used to construct networks, thus multiple kinds of potential relationships between nodes are not explored. Secondly, there are few studies on how to obtain better node embedding by aggregating the features of different neighbors. To compensate for these deficiencies, an advantageous intelligent diagnosis scheme termed AE-MSGCN is proposed, which employs graph convolutional networks (GCNs) on multi-layer networks in an innovative manner. In detail, AE is carried out to extract deep representation features in process measurement and then combined with different metrics (i.e., K-nearest neighbors, cosine similarity, path graph) to construct the multi-layer networks for better multiple interaction characterization among nodes. After that, intra-layer convolutional and inter-layer convolutional methods are adopted for aggregating extensive neighbouring information to enrich the representation of nodes and diagnosis performance. Finally, a benchmark platform and a real-world case both verify that the proposed AE-MSGCN is more effective and practical than the existing state-of-the-art methods.

Aiming at the typical faults in the coal mills operation process, the kernel extreme learning machine diagnosis model based on variational model feature extraction and kernel principal component analysis is offered. Firstly, the collected signals of vibration and loading force, corresponding to typical faults of coal mill, are decomposed by variational model decomposition, and the intrinsic model functions at different scales are obtained. Then, the eigenvectors consisting of feature energy and sample entropy in these functions are respectively calculated, and the kernel principal component analysis is used for noise removal and dimensionality reduction. Finally, the kernel extreme learning machine model is trained and tested with the dimension reduced feature vector as input and the corresponding coal mill state as output. The results show that the variational model decomposition extraction can improve the input features of the model compared with the single eigenvector model, and the kernel principal component analysis method can significantly reduce the information redundancy and the correlation of eigenvectors, which can effectively save time and cost, and improve the prediction performance of the model to some extent. The establishment of this model provides a new idea for the study of coal mill fault diagnosis.

This paper focuses on the coverage probability and ergodic capacity for millimeter wave (mmWave) user-centric dense networks, where multiple access points (APs) consist of a virtual cell for each user equipment and transmit data with mmWave antennas cooperatively. All APs are distributed according to a homogeneous Poisson point process. Different from the low-frequency band (below 3 GHz), blockages have a non-negligible effect on mmWave band. To illustrate the effect, we utilize a line-of-sight probability function, which is dependent on the link-length. Then, via stochastic geometry, the expressions for coverage probability and ergodic capacity are derived, which accounts for: blockages, different small-scale fading distributions (Nakagami, Rayleigh, and no fading), and AP cooperation. In addition, we deduce the approximate expressions for coverage probability and ergodic capacity by using the noise-limited approximation. The numerical results validate our analytical expressions and show that the AP cooperation can provide high coverage performance and distinct capacity gain in a lower-AP-density region.

In this paper, we study the power control and resource allocation problem in downlink orthogonal frequency division multiplexing networks, where mutual interference exists among cells. This mutual relation is characterized by the load coupling model. Both cell load and transmit power, where cell load measures the average proportion of resource usage in the cell, interact via the coupling model. We consider three kinds of problems, sum power minimization, sum rate maximization, and sum energy efficiency maximization. For each problem, we develop a correspondingly distributed power control and resource allocation algorithm with low complexity. Numerical results verify the effectiveness of our proposed algorithms compared with the existing algorithm.

In cognitive radio networks, cooperative spectrum sensing (CSS) is generally adopted for improving spectrum sensing accuracy to increase spectrum utilization and avoid interference with the primary users. However, some malicious secondary users (SUs) may affect the CSS performance by inducing false observation bits for fusion. The message authentication code (MAC) is a promising technique to avoid the damage from the spectrum sensing data falsification (SSDF) attacks. In this paper, as both the more spectrum sensing nodes and the MAC reporting bits result in extra energy consumption, we propose an energy efficiency model to capture the effects of the length of MAC and the number of cooperative SUs under independent and collaborative SSDF attacks, respectively, and analyze the existence of the optimal length of MAC and the optimal number of cooperative SUs that can achieve the maximum value of energy efficiency, respectively. Simulation results are provided to show that the CSS scheme based on MAC can resist SSDF attacks and the accuracy of the theoretical analysis is also validated.