This paper considers a high energy efficiency dynamic connected (HEDC) structure, which promotes the practicability and reduces the power consumption of hybrid precoding system by low-resolution phase shifters (PSs). Based on the proposed structure, a new hybrid precoding algorithm is presented to optimize the energy efficiency, namely, HP-HEDC algorithm. Firstly, via a new defined effective optimal precoding matrix, the problem of optimizing the analog switch precoding matrix is formulated as a sparse representation problem. Thus, the optimal analog switch precoding matrix can be readily obtained by the branch-and-bound method. Then, the digital precoding matrix optimization problem is modeled as a dictionary update problem and solved by the method of optimal direction (MOD). Finally, the diagonal entries of the analog PS precoding matrix are optimized by exhaustive search independently since PS and antenna is one-to-one. Simulation results show that the HEDC structure enjoys low power consumption and satisfactory spectral efficiency. The proposed algorithm presents at least 50\% energy efficiency improvement compared with other algorithms when the PS resolution is set as 3-bit.

This paper investigates the effective capacity of a point-to-point ultra-reliable low latency communication (URLLC) transmission over multiple parallel sub-channels at finite blocklength (FBL) with imperfect channel state information (CSI). Based on reasonable assumptions and approximations, we derive the effective capacity as a function of the pilot length, decoding error probability, transmit power and the sub-channel number. Then we reveal significant impact of the above parameters on the effective capacity. A closed-form lower bound of the effective capacity is derived and an alternating optimization based algorithm is proposed to find the optimal pilot length and decoding error probability. Simulation results validate our theoretical analysis and show that the closed-form lower bound is very tight. In addition, through the simulations of the optimized effective capacity, insights for pilot length and decoding error probability optimization are provided to evaluate the optimal parameters in realistic systems.

One of the challenges of Information-centric Networking (ICN) is finding the optimal location for caching content and processing users' requests. In this paper, we address this challenge by leveraging Software-defined Networking (SDN) for efficient ICN management. To achieve this, we formulate the problem as a mixed-integer nonlinear programming (MINLP) model, incorporating caching, routing, and load balancing decisions. We explore two distinct scenarios to tackle the problem. Firstly, we solve the problem in an offline mode using the GAMS environment, assuming a stable network state to demonstrate the superior performance of the cache-enabled network compared to non-cache networks. Subsequently, we investigate the problem in an online mode where the network state dynamically changes over time. Given the computational complexity associated with MINLP, we propose the software-defined caching, routing, and load balancing (SDCRL) algorithm as an efficient and scalable solution. Our evaluation demonstrates that the SDCRL algorithm significantly reduces computational time while maintaining results that closely resemble those achieved by GAMS.

To meet the communication services with diverse requirements, dynamic resource allocation has shown increasing importance. In this paper, we consider the multi-slot and multi-user resource allocation (MSMU-RA) in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness. We first model the MSMU-RA problem as a dual-sequence decision-making process, and then solve it by a novel Transformer-based deep reinforcement learning (TDRL) approach. Specifically, the proposed TDRL approach can be achieved based on two aspects: 1) To adapt to the dynamic wireless environment, the proximal policy optimization (PPO) algorithm is used to optimize the multi-slot RA strategy. 2) To avoid co-channel interference, the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence. Experimental results show that: i) the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness, ii) the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

In this paper, a simple adaptive power dividing function for the design of a dual-input Doherty power amplifier (DPA) is presented. In the presented approaches, the signal separation function (SSF) at different frequency points can be characterized by a polynomial. And in the practical test, the coefficients of SSF can be determined by measuring a small number of data points of input power. Same as other dual-input DPAs, the proposed approach can also achieve high output power and back-off efficiency in a broadband operation band by adjusting the power distribution ratio flexibly. Finally, a 1.5-2.5 GHz high-efficiency dual-input Doherty power amplifier is implemented according to this approach. The test results show that the peak power is 48.6-49.7dBm, and the 6-dB back-off efficiency is 51.0-67.0%, and the saturation efficiency is 52.4-74.6%. The digital pre-distortion correction is carried out at the frequency points of 1.8/2.1GHz, and the adjacent channel power ratio is lower than -54.5dBc. Simulation and experiment results can verify the effectiveness and correctness of the proposed method.

WiFi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features. The basic assumption of fingerprinting localization is that the received signal strength indication (RSSI) distance is accord with the location distance. Therefore, how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization. In this paper, a particle swarm optimization-extreme learning machine (PSO-ELM) algorithm is proposed on the basis of the original fingerprinting localization. Firstly, we collect the RSSI of the experimental area to construct the fingerprint database, and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives. Secondly, PSO algorithm is used to improve the bias and weight of ELM neural network, and the global optimal results are obtained. Finally, extensive simulation results are presented. It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor (KNN), Kmeans and Back-propagation (BP) algorithms.

Unmanned Aerial Vehicle (UAV) ad hoc network has achieved significant growth for its flexibility, extensibility, and high deployability in recent years. The application of clustering scheme for UAV ad hoc network is imperative to enhance the performance of throughput and energy efficiency. In conventional clustering scheme, a single cluster head (CH) is always assigned in each cluster. However, this method has some weaknesses such as overload and premature death of CH when the number of UAVs increased. In order to solve this problem, we propose a dual-cluster-head based medium access control (DCHMAC) scheme for large-scale UAV networks. In DCHMAC, two CHs are elected to manage resource allocation and data forwarding cooperatively. Specifically, two CHs work on different channels. One of CH is used for intra-cluster communication and the other one is for inter-cluster communication. A Markov chain model is developed to analyse the throughput of the network. Simulation result shows that compared with FM-MAC (flying ad hoc networks multi-channel MAC, FM-MAC), DCHMAC improves the throughput by approximately 20%$\sim$50\% and prolongs the network lifetime by approximately 40%.

The flexibility of unmanned aerial vehicles (UAVs) allows them to be quickly deployed to support ground users. Intelligent reflecting surface (IRS) can reflect the incident signal and form passive beamforming to enhance the signal in the specific direction. Motivated by the promising benefits of both technologies, we consider a new scenario in this paper where a UAV uses non-orthogonal multiple access to serve multiple users with IRS. According to their distance to the UAV, the users are divided into the close users and remote users. The UAV hovers above the close users due to their higher rate requirement, while the IRS is deployed near the remote users to enhance their received power. We aim at minimizing the transmit power of UAV by jointly optimizing the beamforming of UAV and the phase shift of IRS while ensuring the decoding requirement. However, the problem is non-convex. Therefore, we decompose it into two sub-problems, including the transmit beamforming optimization and phase shift optimization, which are transformed into second-order cone programming and semidefinite programming, respectively. We propose an iterative algorithm to solve the two sub-problems alternatively. Simulation results prove the effectiveness of the proposed scheme in minimizing the transmit power of UAV.

This paper investigates the age of information (AoI)-based multi-user mobile edge computing (MEC) network with partial offloading mode. The weighted sum AoI (WSA) is first analyzed and derived, and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment. Due to the non-analytic expression of the WSA w.r.t. the optimization variables and the unknowability of future network information, the problem cannot be solved with known solution methods. Therefore, an online Joint Partial Offloading and User Scheduling Optimization (JPO-USO) algorithm is proposed by transforming the original problem into a single-slot data assignment sub-problem and a single-slot user scheduling sub-problem and solving the two sub-problems separately. We analyze the computational complexity of the presented JPO-USO algorithm, which is of $\mathcal{O}(N)$, with $N$ being the number of users. Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods. It is shown that both the user's data assignment and the user's AoI should be jointly taken into account to decrease the system WSA when scheduling users.

In future optical transport networks, lightpath performance analysis is of great practical significance for fully automated management. In general, the quality of transmission (QoT) of lightpaths, measured by optical quality factor or optical signal-to-noise ratio, has a complex time-varying process, along with the interactions of the other lightpath state parameters (LSPs), such as transmit power, chromatic dispersion, polarization mode dispersion. Current studies are mostly focused on lightpath QoT estimation, but ignoring lightpath-level data analytics. In this case, our article proposes a novel lightpath performance analysis method considering recurrence plot (RP) and cross recurrence plot (CRP). Firstly, we give a detailed interpretation on the recurrence patterns of LSPs via a qualitative 2-D RP representation and its quantitative measure. It can potentially enable the accurate and fast lightpath failure detection with a low computational burden. On the other hand, CRP is devoted to modeling the relationships between lightpath QoT and LSPs, and the correlation degree is determined by a geometric mean of multiple indexes of cross recurrence quantification analysis. From the view of application, such CRP analysis can provide the effective knowledge sharing to guarantee more credible QoT prediction. Extensive experiments on a real-world optical network dataset have clearly demonstrated the effectiveness of our proposal.

Traditional wireless sensor networks (WSNs) are typically deployed in remote and hostile environments for information collection. The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks. Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes. The trust-based security mechanism is usually adopted to solve this problem in WSNs. However, the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes. Moreover, how to efficiently collect behavioral evidences are rarely discussed. To address these issues, in this paper, we present a trust management mechanism based on fuzzy logic and a cloud model. First, a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty. Then, the cloud model is introduced to estimate the trust values for sensor nodes. Finally, a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance. Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

In this paper, we aim to design a practical low complexity low-density parity-check (LDPC) coded scheme to build a secure open channel and protect information from eavesdropping. To this end, we first propose a punctured LDPC coded scheme, where the information bits in a codeword are punctured and only the parity check bits are transmitted to the receiver. We further propose a notion of check node type distribution and derive multi-edge type extrinsic information transfer functions to estimate the security performance, instead of the well-known weak metric bit error rate. We optimize the check node type distribution in terms of the signal-to-noise ratio (SNR) gap and modify the progressive edge growth algorithm to design finite-length codes. Numerical results show that our proposed scheme can achieve a lower computational complexity and a smaller security gap, compared to the existing scrambling and puncturing schemes.

In wireless sensor networks (WSNs), the performance of related applications is highly dependent on the quality of data collected. Unfortunately, missing data is almost inevitable in the process of data acquisition and transmission. Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data. However, in realistic application scenarios, it is very difficult to obtain these prior information from incomplete data sets. Therefore, we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information. By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix, a compressive sensing (CS) based missing data recovery model is established. Then, we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model. Furthermore, an improved fast matching pursuit algorithm is proposed to solve the model. Simulation results show that the proposed method can effectively recover the missing WSNs data.

In this paper, we explore a cooperative decode-and-forward (DF) relay network comprised of a source, a relay, and a destination in the presence of an eavesdropper. To improve physical-layer security of the relay system, we propose a jamming aided decode-and-forward relay (JDFR) scheme combining the use of artificial noise and DF relaying which requires two stages to transmit a packet. Specifically, in stage one, the source sends confidential message to the relay while the destination acts as a friendly jammer and transmits artificial noise to confound the eavesdropper. In stage two, the relay forwards its re-encoded message to the destination while the source emits artificial noise to confuse the eavesdropper. In addition, we analyze the security-reliability tradeoff (SRT) performance of the proposed JDFR scheme, where security and reliability are evaluated by deriving intercept probability (IP) and outage probability (OP), respectively. For the purpose of comparison, SRT of the traditional decode-and-forward relay (TDFR) scheme is also analyzed. Numerical results show that the SRT performance of the proposed JDFR scheme is better than that of the TDFR scheme. Also, it is shown that for the JDFR scheme, a better SRT performance can be obtained by the optimal power allocation (OPA) between the friendly jammer and user.

Directional modulation (DM) is one of the most promising secure communication techniques. However, when the eavesdropper is co-located with the legitimate receiver, the conventional DM has the disadvantages of weak anti-scanning capability, anti-deciphering capability, and low secrecy rate. In response to these problems, we propose a two-dimensional multi-term weighted fractional Fourier transform aided DM scheme, in which the legitimate receiver and the transmitter use different transform terms and transform orders to encrypt and decrypt the confidential information. In order to further lower the probability of being deciphered by an eavesdropper, we use the subblock partition method to convert the one-dimensional modulated signal vector into a two-dimensional signal matrix, increasing the confusion of the useful information. Numerical results demonstrate that the proposed DM scheme not only provides stronger anti-deciphering and anti-scanning capabilities but also improves the secrecy rate performance of the system.

Recently, anomaly detection (AD) in streaming data gained significant attention among research communities due to its applicability in finance, business, healthcare, education, etc. The recent developments of deep learning (DL) models find helpful in the detection and classification of anomalies. This article designs an oversampling with an optimal deep learning-based streaming data classification (OS-ODLSDC) model. The aim of the OS-ODLSDC model is to recognize and classify the presence of anomalies in the streaming data. The proposed OS-ODLSDC model initially undergoes pre-processing step. Since streaming data is unbalanced, support vector machine (SVM)-Synthetic Minority Over-sampling Technique (SVM-SMOTE) is applied for oversampling process. Besides, the OS-ODLSDC model employs bidirectional long short-term memory (BiLSTM) for AD and classification. Finally, the root means square propagation (RMSProp) optimizer is applied for optimal hyperparameter tuning of the BiLSTM model. For ensuring the promising performance of the OS-ODLSDC model, a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018, KDD-Cup 1999, and NSL-KDD datasets.

The distribution of data has a significant impact on the results of classification. When the distribution of one class is insignificant compared to the distribution of another class, data imbalance occurs. This will result in rising outlier values and noise. Therefore, the speed and performance of classification could be greatly affected. Given the above problems, this paper starts with the motivation and mathematical representing of classification, puts forward a new classification method based on the relationship between different classification formulations. Combined with the vector characteristics of the actual problem and the choice of matrix characteristics, we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point. Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine. We introduce the cost control to solve the problem of sample skew. Finally, based on the bi-boundary support vector machine, a two-step weight setting twin classifier is constructed. This can help to identify multitasks with feature-selected patterns without the need for additional optimizers, which solves the problem of large-scale classification that can't deal effectively with the very low category distribution gap.

The high-speed movement of satellites makes it not feasible to directly apply the mature routing scheme on the ground to the satellite network. DT-DVTR in the snapshot-based connection-oriented routing strategy is one of the representative solutions, but it still has room for improvement in terms of routing stability. In this paper, we propose an improved scheme for connection-oriented routing strategy named the Minimal Topology Change Routing based on Collaborative Rules (MTCR-CR). The MTCR-CR uses continuous time static topology snapshots based on satellite status to search for inter-satellite link(ISL) construction solutions that meet the minimum number of topology changes to avoid route oscillations. The simulation results in Beidou-3 show that compared with DT-DVTR, MTCR-CR reduces the number of routing changes by about 92\%, the number of path changes caused by routing changes is about 38\%, and the rerouting time is reduced by approximately 47\%.At the same time,in order to show our algorithm more comprehensively,the same experimental index test was also carried out on the Globalstar satellite constellation.

The underwater wireless optical communication (UWOC) system has gradually become essential to underwater wireless communication technology. Unlike other existing works on UWOC systems, this paper evaluates the proposed machine learning-based signal demodulation methods through the self-built experimental platform. Based on such a platform, we first construct a real signal dataset with ten modulation methods. Then, we propose a deep belief network (DBN)-based demodulator for feature extraction and multi-class feature classification. We also design an adaptive boosting (AdaBoost) demodulator as an alternative scheme without feature filtering for multiple modulated signals. Finally, it is demonstrated by extensive experimental results that the AdaBoost demodulator significantly outperforms the other algorithms. It also reveals that the demodulator accuracy decreases as the modulation order increases for a fixed received optical power. A higher-order modulation may achieve a higher effective transmission rate when the signal-to-noise ratio (SNR) is higher.

The rapid development of emerging technologies, such as edge intelligence and digital twins, have added momentum towards the development of the Industrial Internet of Things (IIoT). However, the massive amount of data generated by the IIoT, coupled with heterogeneous computation capacity across IIoT devices, and users' data privacy concerns, have posed challenges towards achieving industrial edge intelligence (IEI). To achieve IEI, in this paper, we propose a semi-federated learning framework where a portion of the data with higher privacy is kept locally and a portion of the less private data can be potentially uploaded to the edge server. In addition, we leverage digital twins to overcome the problem of computation capacity heterogeneity of IIoT devices through the mapping of physical entities. We formulate a synchronization latency minimization problem which jointly optimizes edge association and the proportion of uploaded non-private data. As the joint problem is NP-hard and combinatorial and taking into account the reality of large-scale device training, we develop a multi-agent hybrid action deep reinforcement learning (DRL) algorithm to find the optimal solution. Simulation results show that our proposed DRL algorithm can reduce latency and have a better convergence performance for semi-federated learning compared to benchmark algorithms.