Reconfigurable intelligent surface (RIS) assisted symbiotic radio communication (SRC) networks are considered a key solution to address the challenges of massive Internet of Things connectivity. There, the resource optimization problems are always NP-hard and formulated with time varying parameters. Learning via deep neural network has proven to be an effective way to encapsulate the optimized solution as a function of the optimization problem. However, the learning framework is challenged by meeting complex communication constraints involved. To handle this issue and accommodate the time varying SRC environment, this work proposes a novel deep reinforcement learning framework to solve the classical passive beamforming optimization problem. System sum-rate is maximized while ensuring strict communication requirements. The formulated constrained Markov decision process is transformed into a Lagrange dual problem. The deep deterministic policy gradient framework is adopted introducing a monitor module to approximate Lagrange multipliers. The actor and monitor networks are trained using the derived Lagrangian-related gradients, ensuring constraint satisfaction during the training. Simulation results demonstrate that the proposed method achieves the performance upper bound, significantly improving RIS transmission rate and system sum-rate compared to the counterparts. It also demonstrates adaptability to environmental dynamics, computational efficiency, reduced transmission resource usage, and lower probability of violating communication requirements.

Anomaly detection in multivariate time series is critical for applications such as industrial maintenance, early warning systems, and environmental monitoring. However, existing reconstruction-based models often exhibit poor generalization and high false-negative rates, limiting their effectiveness. To address these challenges, this paper proposes a time–frequency contrastive learning (TFCL) anomaly detection model that integrates a contrastive learning module with a gated feature fusion module. The contrastive learning module employs a time-weighted and temperature-controlled loss function to enhance feature representation from both time and frequency perspectives. Meanwhile, the feature fusion module utilizes a gating mechanism to dynamically adjust the integration of time and frequency features, generating robust joint representations. Additionally, a latent-feature-driven and component-weighted anomaly discrimination strategy is proposed, leveraging reconstruction residuals and feature importance to precisely identify anomalies. The model’s performance was evaluated on publicly available datasets (SWaT and WADI) and real-world air quality monitoring data (AQMD). Experimental results demonstrate that TFCL achieves an average F1 score of 92.47

Localized optical resonances in silicon nanostructures have been increasingly used in color printing. By changing the geometric parameters of the silicon nanostructures to obtain different structural colors, it is possible to get a larger coverage range than the sRGB color gamut in the CIE color space, and achieve ultra-high-resolution color printing. However, the design of specific colors involves iterative optimization of geometric parameters, which is computationally expensive. Thus, it is very challenging to obtain millions of different colors in the color space. In this paper, we trained a feature-crossed neural network with attention mechanism to predict the structural color produced by random silicon nano truncated cones with high accuracy. On the problem of inverse design, we improve the loss function of the tandem network, which solves the non-uniqueness problem in the inverse design process and avoids the tandem net from falling into the wrong solution space. Our model can accurately predict millions of different color points in the CIE color gamut. In addition, the proposed methods can be easily extended to solve the optimization design problems in the field of nanophotonics.

Reconfigurable intelligent surface (RIS) assisted symbiotic radio communication (SRC) emerges as a novel technology that alleviates spectrum and energy demand of Internet-of-Things (IoT) applications. RIS concurrently backscatters IoT messages and strengthens radio links of primary user (PU) with intelligent passive beamforming. However, in the scenario of multiple RISs co-located, backscatter links may interfere with each other. Moreover, RIS consumes extra power for phase shifting compared to conventional backscatter device (BD), which may degrade energy efficiency (EE). To handle these issues, this paper proposes to maximize EE by jointly optimizing power allocation for PUs and passive beamforming for RISs. Alternating optimization (AO) algorithm is used to decompose the non-convex original problem into two subproblems which are respectively solved with semi-definite relaxation (SDR) and Dinkelbach’s algorithm. To comprehensively study the pros and cons of RIS passive beamforming, joint optimization problem of power allocation and backscatter coefficients design is modeled under the conventional SRC scenario with BDs. Besides AO and SDR, successive convex approximation is used to handle the non-convexity caused by inter BDs interference. Simulation results show that RIS effectively reduces outages and improves EE compared to BD under certain conditions.

User terminals are subjected to frequent handovers in the multi-beam low-orbit satellite networks. In order to achieve a better balance between the user's communication quality and system performance, we construct a multi-beam low-orbit satellite coverage model and propose a multi-attribute decision scheme to select the handover target beam. This paper considers not only beam handover but also inter-satellite handover. The weight of the attribute is based on user preference and realtime system measurements, which include both subjective and objective metric. Simulation results show that the proposed handover scheme outperforms the existing traditional ones in terms of inter-satellite handover times, the average signal strength, and the average channel utilization variance.

In large-scale Internet of Thing (IoT), ambient backscatter communication has become a new green technology of concern. At present, the research scenario on ambient backscatter communication mainly focuses on single-transmitter and single-receiver, and a few studies have multiple receivers. And backscatter system is always only allowing one transmitter-receiver pair active. However, the condition multiple transmitters communicate with multiple receivers is essential to achieve giant connection in Internet of Everything. Besides, the system with active multipair can transmit more byte and use energy more effectively. So there is an urgent need of research on multi-transmitter multi-receiver system. We propose an ambient back scatter communication system which allows multi-transmitter and multi-receiver active. This paper is devoted to studying the user association problem in such system. When studying the ambient backscatter communication system, we pay attention to the power limitation. Because the energy collected by the device is relatively small and limit communication performance. In the case of limited link budget, this paper maximized system communication capacity. A priority-based access strategy is proposed in this paper. It arranges priority to the receiver according to the power threshold and link budgets. Then, the strategy handles association problem according to receiver's priority from high to low. Simulation results show that the proposed access strategy has better convergence than the random access strategy. It achieves maximum communication capacity and suboptimal bit rate. What's more, it has low complexity.

Network slicing is a key technology for addressing the issue of differentiated performance requirements of diversified services in mobile networks. We focus on the radio resource allocation for RAN slicing to ensure the isolation between slices, and improve radio resource utilization. This paper proposes a radio resource allocation algorithm for Service Level Agreement (SLA) contract rate maximization. Firstly, the business parameters in SLA are mapped to the measurable network performance metrics. Then, radio resources are allocated to network slices on the basis of the collected SLA requirements. Meanwhile, Radio resources of slices that do not meet the requirements are dynamically updated without affecting the performance of slices which has met the SLA requirements, to maximize the SLA contract rate of all slices. The simulation results show that the algorithm can achieve a better SLA contract rate on the premise of ensuring isolation between slices, additionally increase the number of service users.

Proactive resource allocation (PRA) is an essential technology boosting intelligent communication, as it can make full use of prediction and significantly improve network performance. However, most promising gains base on perfect prediction which is unrealistic. How to make PRA robust against prediction uncertainty and maximize benefits brought by prediction becomes an important issue. In this paper, we tackle this problem and propose a mobility-aware robust PRA approach (MRPRA) in heterogeneous networks. MRPRA pre-allocates resources in both time and frequency domains among mobile users with users' trajectories predicted by hidden Markov model. The objective is to minimize service delay under constraints of different levels of quality-of-service (QoS) requirement and mobility intensity. MRPRA is robust against prediction uncertainty by exploiting probabilistic constraint programming to model QoS requirements in a probabilistic sense. To this end, the probabilistic distribution of achievable rate is derived. To flexibly coordinate resource allocation among multiple mobile users over time horizon, a deep reinforcement learning based multi-actor deep deterministic policy gradient algorithm is designed. It learns robust PRA policies by distributed acting and centralized criticizing. Extensive numerical simulations are performed to analyze performances of the proposed approach.

To satisfy tight latency constraints, ultra-reliable low latency communications (URLLC) traffic is scheduled by overlapping the on-going enhanced mobile broad band (eMBB) transmissions (i.e., puncturing approach), which causes eMBB users unprecedented rate loss and hence degraded quality-of-service (QoS). To tackle this issue, this letter proposes to achieve QoS tradeoff between eMBB and URLLC in 5G networks. We jointly optimize bandwidth allocation and overlapping positions of URLLC users' traffic with deep deterministic policy gradient algorithm observing channel variations and URLLC traffic arrivals. Simulation results show that the proposed system-wide tradeoff method achieves the best tradeoff performance.

User and network behavior prediction by big data makes the traditional heterogeneous networks (HetNets) a learning and knowledgeable network. However, how much and under what conditions that prediction can benefit the upcoming 5G HetNets have not been comprehensively studied. Furthermore, how to use the quantified conditions to guide network operation is still under investigation. In term of resource allocation, this paper proposes a mobility-based proactive resource scheduling (MPRS) strategy and explores the above questions. Taking advantage of predicted information of user mobility, network residual frequency bandwidth and channel gains, MPRS aims at a) minimizing service delay and enhancing successful scheduling probability, and b) adapting to users' mobility intensity together with quality of service requirements, in long term. Comparing with the reactive strategy, fair scheduling (FS), simulation results show that with accurate prediction, MPRS achieves about 20% performance gain. And when the proportion of average residual frequency bandwidth is less than 50%, FS can be performed instead for it achieves similar performance with MPRS and its computational simplicity. With imperfect prediction, the tolerable upper bound of prediction error becomes tighter as the residual frequency bandwidth decreases.

Motivated by the need for loosely coupled and asynchronous dissemination of information, message queues are widely used in large-scale application areas. With the advent of virtualization technology, cloud-based message queueing services (CMQSs) with distributed computing and storage are widely adopted to improve availability, scalability, and reliability; however, a critical issue is its performance and the quality of service (QoS). While numerous approaches evaluating system performance are available, there is no modeling approach for estimating and analyzing the performance of CMQSs. In this paper, we employ both the analytical and simulation modeling to address the performance of CMQSs with reliability guarantee. We present a visibility-based modeling approach (VMA) for simulation model using colored Petri nets (CPN). Our model incorporates the important features of message queueing services in the cloud such as replication, message consistency, resource virtualization, and especially the mechanism named visibility timeout which is adopted in the services to guarantee system reliability. Finally, we evaluate our model through different experiments under varied scenarios to obtain important performance metrics such as total message delivery time, waiting number, and components utilization. Our results reveal considerable insights into resource scheduling and system configuration for service providers to estimate and gain performance optimization.