Underwater wireless optical communication (UWOC) is a key technology for supporting information transmission in the Internet of Underwater Things (IoUT). Recently, semantic communication has been introduced into UWOC systems to enhance transmission efficiency and robustness. However, existing underwater wireless optical semantic communication (UWOSC) systems overlook the bandwidth limitation imposed by practical optoelectronic devices, which hinders high-speed semantic information transmission. In this paper, we introduce orthogonal frequency division multiplexing (OFDM) modulation into the UWOSC system for the first time, and design a novel OFDM-based UWOSC system with symbol and power loading (SPL). In the proposed system, shifted-window-based hierarchical vision transformer blocks are employed to extract semantic features and encode them into frequency-domain baseband symbols for OFDM transmission. Furthermore, an SPL scheme, comprising an entropy model-driven symbol reordering strategy and a lightweight power allocation network, is designed to adaptively allocate subcarriers and power based on semantic importance and channel conditions. Experiments conducted on an emulated UWOC platform demonstrate that the proposed OFDM-based UWOSC system achieves superior performance compared with baseline schemes under high transmission bandwidth scenarios. Moreover, the introduction of the SPL module further improves semantic spectrum efficiency, effectively mitigating the adverse impact of low-pass characteristic inherent in bandwidth-limited UWOSC systems. These results demonstrate that the proposed system has strong potential to enable high-speed semantic communication in the IoUT.

In this paper, we study the theory of four-wave mixing type noises over multicore fiber (MCF) and analyze the impacts of the noises on quantum key distribution (QKD) in the quantum and classical coexistence system. The models about four-wave mixing type noises proposed in this paper include forward inter-core four-wave mixing (FIC-FWM), backward four-wave mixing (B-FWM) and backward inter-core four-wave mixing (BIC-FWM). a) The model of FIC-FWM based on discrete change model (FIC-FWM-DCM) is proposed to obtain the precise power of FIC-FWM. Simultaneously, we further propose the model of FIC-FWM based on extended coupled-power theory (FIC-FWM-ECPT) which can obtain similar results to FIC-FWM-DCM but with lower computational complexity. b) The generation mechanism of B-FWM is proposed, and the model is built by combining forward four-wave mixing and backward Rayleigh scattering. c) The generation mechanism of BIC-FWM is proposed, and the model is built by combining FIC-FWM and B-FWM. Based on the above three models of four-wave mixing type noises, we analyze the impacts of the noises on QKD in the quantum and classical coexistence systems over MCF. The results show that the proposed models of FIC-FWM can significantly improve the simulation accuracy in the coexistence system. B-FWM noise can generate a serious impact on QKD when the guard bandwidth between quantum and classical channels is less than 250 GHz with the launch power of 2 mW. BIC-FWM noise can generate a significant impact on QKD when the frequency spacing is less than or equal to 25 GHz and the launch power is larger than 2 mW. Finally, the experiments are carried out to measure FIC-FWM, and the results show the proposed model of FIC-FWM can obtain the consistent results with experiments.

The rapid development of large language model (LLM) has established distributed training as the dominant paradigm, yet communication efficiency remains a critical challenge. Existing training cluster(e.g., Rail-Optimized architecture) based on electrical packet switch (EPS), suffering from high costs, excessive power consumption, and inefficient bandwidth utilization. While optical circuit switch (OCS) offers a promising alternative with its high bandwidth, low latency, and power efficiency, its rigid connectivity struggles to accommodate dynamic multi-task workloads, and its per-unit cost remains prohibitive at scale. To address these limitations, we propose ReFly, a reconfigurable architecture for LLM training based on OCS. By modeling GPU communication requirements, we design a Cycle Decomposition (CD) scheme for cluster construction and an Alternating Decomposition (AD) algorithm to dynamically schedule multiple OCS. Experimental results demonstrate that ReFly reduces deployment costs by 77% and power consumption by 98% compared to state-of-the-art Rail-Optimized architecture while achieving comparable performance, and a 229% higher bus bandwidth than Fat-Tree. These advancements position ReFly as an efficient and cost-effective solution for next-generation LLM training clusters.

The explosive growth of artificial intelligent data centers (AIDCs) and AI-driven workloads has led to massive, bursty, and dynamic inter-AIDC traffic, placing unprecedented demands on optical transport networks for capacity, flexibility, and ultra-low latency. To meet these challenges, multi-band wavelength-division multiplexing (MB-WDM) systems extend the spectrum beyond the conventional C-band. However, the interaction between nonlinear effects and dynamic traffic patterns leads to nonlinear bandwidth fragmentation (NBF) and degraded efficiency. Effective defragmentation requires modeling inter-service dependencies, as reallocating one service may impact others through nonlinear coupling. To address this, we propose TransMACB, a Transformer-enhanced multi-agent contextual bandit framework for globally optimized reassignment. Each service is assigned a dedicated agent, and the Multi-Agent Advantage Decomposition Theorem is applied to factorize the joint optimization problem. This enables a Transformer encoder-decoder to capture structured inter-agent dependencies and generate context-aware policies. Simulations demonstrate that TransMACB significantly improves overall network performance, highlighting the necessity of combining multi-agent intelligence with Transformer-based coordination in managing NBF.

Driven by the diverse requirements of industrial internet of things (IIoT) services, passive optical network (PON) has emerged as a promising technology for Industrial Internet, owing to its advantages in multi-service support, immunity to electromagnetic interference (EMI) and cost-effective deployment. However, due to the highly dynamic nature of industrial services and their diverse demands, it poses great challenges to guarantee the deterministic latency demand of time sensitive services while avoiding inevitable conflicts among various services. To address this problem, we propose a flexible-grouping based collaborative dynamic bandwidth allocation (FGC-DBA) scheme. Specifically, FGC-DBA characterizes a cluster-based peak-valley compensation algorithm (CPVC) to achieve deterministic latency of highly dynamic services by fluctuation counterbalance. It aims to find a group of services with complementary demand, thereby dynamic nature only exists within intra-group rather than inter-group and the latency fluctuation is fixed as the size of each group. Furthermore, we present a fine-coarse collaborative transmission window allocation strategy (FCCTA) to accurately allocate transmission window for various services based on the CPVC algorithm. It aims to reduce the conflicts and meet the deterministic latency demand simultaneously when considering the various services’ demand. In the experiment, we demonstrate that compared to existing schemes, FGC-DBA scheme reduces jitter by at least 75% for cyclic time-sensitive (CTS) services and brings queuing time close to zero, while reducing jitter by up to 50% for non-cyclic time-sensitive (TS) services. Notably, all of these improvements are achieved with only a 5% increase in running time.

In this article, we propose and experimentally demonstrate an optical transmitter system with chaotic fingerprint for identity authentication in physical-layer of optical networks. The construction and identification of chaotic fingerprints are introduced in detail. The illegal optical transmitter can be detected through fingerprint identification. We compared the recognition performance of five neural networks (NNs), among which 2D-CNN has the best performance. The simulation results show that the recognition accuracy can reach 100%, and the initial values will not affect the accuracy. Moreover, the experimental results show that the recognition accuracy of legal transmitters is 99.2%, and the detection accuracy of illegal transmitters is 95.7%. Compared to other fingerprint schemes, chaotic fingerprints have higher security, larger fingerprint space, stronger anti-noise ability, and better flexibility. However, the chaotic fingerprint and user signal are separated. To prevent illegal attackers from stealing fingerprints, we use fractional Fourier transform (FRFT) for encryption. The FRFT module can not only achieve the secure connection between the chaotic fingerprint and the user signal but also realize the concealment of time-delay signatures (TDSs) of the fingerprint, which ensures the security of fingerprints. To sum up, the proposed system can resist eavesdropping and injection attacks simultaneously, which provides security for optical networks.

Recently, underwater wireless optical communication (UWOC) has emerged to support the high data rate requirements of oceanic exploration. In this paper, we propose an accurate and closed-form UWOC channel model to understand the effects of dynamic ocean environment on optical signal propagation. The model takes into account the impairments induced by oceanic path-loss, oceanic turbulence, pointing error loss and link interruption due to angle-of-arrival (AoA) fluctuations jointly. We further derive analytical expressions for various outage performance metrics. To boost the system robustness to dynamic ocean environment, we design a probabilistic shaping (PS)-based strategy with unipolar pulse amplitude modulation (PAM), which maximizes the ergodic constellation constrained capacity. Furthermore, considering the limitation of computational resources in real ocean environment, we simplify the PS-based scheme to alleviate the problem. Numerical results verify the accuracy of the proposed channel model and the outage performance analysis. Moreover, the simplified PS-based unipolar M-PAM scheme is validated to be a promising solution for the development and deployment of high speed adaptive UWOC systems.

To accommodate ever-growing traffic, network operators are actively deploying high-degree reconfigurable optical add/drop multiplexers (ROADMs) to build large-capacity optical networks. High-degree ROADM-based optical networks have multiple parallel fibers between ROADM nodes, requiring the adoption of ROADM nodes with a large number of inter-/intra-node components. However, this large number of inter-/intra-node optical components in high-degree ROADM networks increases the likelihood of multiple failures simultaneously, and calls for novel methods for accurate localization of multiple failed components. To the best of our knowledge, this is the first study investigating the problem of multi-failure localization for high-degree ROADM-based optical networks. To solve this problem, we first provide a description of the failures affecting both inter-/intra-node components, and we consider different deployments of optical power monitors (OPMs) to obtain information (i.e., optical power) to be used for automated multi-failure localization. Then, as our main and original contribution, we propose a novel method based on a rules-informed neural network (RINN) for multi-failure localization, which incorporates the benefits of both rules-based reasoning and artificial neural networks (ANN). Through extensive simulations and experimental demonstrations, we show that our proposed RINN algorithm can achieve up to around 20% higher localization accuracy compared to baseline algorithms, incurring only around 4.14 ms of average inference time.

Multi-band transmission has been considered a competitive solution for expanding optical network capacity in the near term. Research on multi-band transmission has currently been extensive, covering various aspects. Nevertheless, many studies focus solely on end-to-end transmission systems and overlook the dynamic nature of networks, employing simple static methods for all scenarios. To address these challenges, we propose an online transmission optimization method based on multi-stage programmable Raman amplifiers (PRAs). The proposed method includes designing optimal gain profiles based on real-time feedback of channel conditions in dynamic networks and quickly predicting pump settings through an inverse mapping model. Additionally, it dynamically adjusts the pump settings in response to various scenarios, achieving high-precision gain profiles within four iterations at most, thus leading to efficient transmission optimization. Experimental simulation results indicate that, when utilizing 400G QPSK modulation, the optimization method can achieve an excellent average generalized signal-to-noise ratio (GSNR) improvement, reaching up to 2.45 dB compared to traditional methods. We have further validated the benefit of the optimization method under various service scenarios, with an average GSNR improvement exceeding 2.1 dB, demonstrating the adaptation for dynamic service demands.

For enhancing the security of coherent optical communication systems, an optical transmitter identification method based on constellation diagrams is proposed. Addressing the limitations of existing optical transmitter identification methods in adapting to higher-order modulation formats, the constellation diagram is employed as the optical transmitter fingerprint. Different optical transmitters can be distinguished by the amplitude features and phase features in the constellation diagram. Four CNN networks are applied for extracting these features to classify optical transmitters, including CX-LeNet, CX-AlexNet, VGG-16 and ResNet-18. Recognition performance of a network is evaluated with the help of recognition accuracy and PR curves. The employment of the grad-CAM explainer reflects the attention of the network. In the back-to-back system, the accuracy of classification decreases with the increasing symbol rate and increases with the increasing number of symbols. In the 100 km coherent optical communication system with the symbol rate of 25 GBaud, the accuracy of identifying 6 optical transmitters transmitting 16QAM signals with the VGG-16 network is 100%. Further simulation proves that the proposed method is still feasible in high-speed and long-distance coherent optical communication systems. Effective detection of unauthorized samples proves the ability of the proposed method to enhance the security of coherent optical communication systems.

Edge–cloud collaboration integrated with network virtualization is indispensable for diversified edge services. Meanwhile, the multilayer elastic optical network (ML-EON) is a promising underlying network for virtual network requests (VNRs) customized for edge–cloud collaborative services. However, the joint allocation of computing resources and high-dimensional ML-EON resources in virtual network embedding (VNE) will pose great computational complexity for online service deployment. In this article, we propose an approximately lossless model compression mechanism to ease the computing burden of the VNE over ML-EON for edge–cloud collaborative services. An integer quadratic constraint programming (IQCP) model is established for the problem. Model compression based on virtual link mapping cost estimation (VLMCE) is investigated to shield the variables and constraints related to ML-EON. In particular, the resource metric and topology metric are introduced into VLMCE to cope with resource contentions among virtual links in the same VNR, and improve estimation accuracy. The model solving relies on Hopfield neural network (HNN) is further studied, where optimizing the compressed model is losslessly converted to minimizing the energy function of HNN. The experimental results reveal that the proposed mechanism guarantees an approximately lossless algorithm performance and a high-time efficiency compared with the original IQCP model. The performances of VNR cost and blocking ratio are also promoted compared with the benchmarks.

With the increasing demand of network slices in vertical industries, slice resource provisioning in transport networks has encountered two challenges, one is efficient slice resource provisioning in the presence of traffic uncertainty of slices, and another is flexible slice resource isolation for customizable isolation needs. In this paper, we propose an innovative flexible hybrid isolation model to support any customized resource isolation from complete isolation to full sharing, and solve the slice resource provisioning problem named Hybrid Slicing Minimum Bandwidth (HSMB) by considering traffic prediction error to mitigate the negative impact of traffic uncertainty in the proposed model. After analyzing the HSMB problem, 1) we first try to solve the problem in steps and decompose the HSMB problem into grouping sub-problem and adjusting sub-problem, 2) we then propose a low-complexity dynamic programming grouping algorithm and a fast iterative adjustment algorithm for the two sub-problems based on probabilistic feature-based analysis, 3) we combine the algorithms of the two sub-problems and further propose a linking algorithm for the potential insufficient resource dilemma and high computational complexity dilemma to improve the efficiency of the solution. The numerical results show that the proposed flexible hybrid isolation model with different factors can facilitate flexible slice isolation with customized isolation demands, while the proposed algorithm can realize efficient slice resource provisioning with a probabilistic guarantee. The comparison result shows the proposed algorithms outperform the other benchmark algorithms.

The commercialization of 5G and the explosive emergence of new applications stimulate the exponential growth of network traffic and diversification of services. It is promising to integrate multi-band elastic optical network (MBEON) and network virtualization for large volume traffic transmission and highly diverse services. However, performing virtual network embedding (VNE) for network virtualization over MBEON faces the challenge of severe inter-channel stimulated Raman scattering (ISRS) effect, which complicates the underlying physical layer effect in the substrate network. In this paper, we investigate the ISRS-aware VNE over MBEON, where a cross-matching mechanism is proposed for virtual node mapping (VNM) and a hypergraph is introduced for parallel virtual link mapping (VLM). A lightpath-level integer linear programming model is first formulated. To integrate the cost and availability of VLM, which significantly affect the performance of the VNE under the ISRS effect, into the VNM process, the “virtual node-substrate node” mapping pairs are specifically evaluated through the cross-matching mechanism. Moreover, to tackle the couplings among multiple lightpaths induced by the wide spectrum ISRS effect, hypergraphs are used to model the ISRS effect-aware quality of transmission (QoT) constraints among multiple lightpaths. A hypergraph maximal weight independent set heuristic is presented for lightpath selection, which guarantees the obedience of basic constraints and generates near-optimal solutions. Experimental results show that the proposed methods decrease blocking ratio by more than 30% compared with the benchmarks with similar computational complexity.

With the rapid development, artificial intelligence (AI) technology may occupy the job positions which require only simple knowledge. Autonomous learning ability has been one of the core abilities that needs to be cultivated in engineering education. However, how to leverage students’ autonomous learning in the practice session of curriculum is still facing several challenges. In view of these challenges, we develop an AI empowered remote experiment platform, which has independent intelligent algorithm center for students to deploy their own algorithms into the real experiment scenarios. Also, in this platform, there is a digital-twins engine to give real-time feedback to the students, helping them to improve their algorithms and even the whole projects. Based on this experiment platform, we propose a teacher-guided autonomous learning practice teaching mode, including autonomous goal setting, autonomous practice process and autonomous feedback optimization, in which the teachers will be a guide to guarantee the learning objective is achieved. A survey was conducted, showing that under this practice teaching mode, students have a deeper understanding of theoretical knowledge, more obvious cultivation of autonomous learning ability, and higher satisfaction with the course.

The integration of Internet of Things (IoT) and industry reveals the industrial manufacturing developments, resulting in Industry IoT (IIoT), which is to provide a general interconnect system for the access of various industry devices. However, as the amount and type of terminal increase, the creditability and privacy protection of terminal devices are hard to be guaranteed in IIoT, since the data and digital identity of access devices are nearly transparent for more devices in networks. It is a critical issue for the security of IIoT whether the access and service of device are trustworthy. In this article, we present a novel private blockchain-enabled trusted anonymous access (BlockTrust) architecture for IIoT, where the distributed cloud radio and optical access networks (C-RONs) are considered to provide a risk reduction of privacy leakage. Based on the BlockTrust architecture, a blockchain-enabled tripartite anonymous identification trusted service provisioning (TriTrustServ) scheme is further proposed to guarantee a balanced tradeoff among the credibility, confidentiality, and efficiency in IIoT, including digital identity generation, anonymous access identification, and trusted resource provisioning. Note that for the sake of a high credibility in IIoT networks, a tripartite authentication is presented in this article with the first time among device manufacturer, devices, and network operator for the access process of device in IIoT networks. The feasibility and efficiency of BlockTrust architecture are experimentally verified in the realistic testbed, and the performances of the TriTrustServ scheme are evaluated by comparing with two benchmark schemes in the terms of average mistrust rate, resource utilization, and identification cost.

Due to the threat of failure and the discrete distribution of data center users, the research of distributed cloud data center provides real-time cloud services with robustness, reliability and security. Faced with data center interconnection, network failures cause mass services delay and interruption, which do a great damage to cloud computing. Many researchers have studied fault location methods in data center interconnection, which are easy to trap in local optimum limited by search capability and reduce the accuracy of location, especially when confronted with large-scale alarm information. In this article, the deep neural evolution network is introduced to extract deep-hidden fault features from massive collected alarm information in cloud data center interconnection. It has the prominent capacity of global search without the constraint of gradient to realize the breakthrough of fault location accuracy. The fault location method based on deep neural evolution network (FL-DNEN) is applied which uses the alarm set and suspicious scope of fault getting from fault propagation model as input and export deterministic faults accurately. The emulations demonstrate that the proposed method dramatically improves the accuracy of fault location to 92 percent with large-scale alarm information, which improves the resilience of cloud data center interconnection dramatically.

With the explosive growth of edge applications in the 5G/B5G era, edge-cloud collaboration (ECC) is playing a prominent role in edge service provisioning. For highly diversified edge-cloud collaborative services (ECSs), the joint allocation of heterogeneous computing resources in heteroge-neous servers and multi-dimensional underlying optical network resources should be conducted. In this paper, we investigate the heterogeneous task scheduling for ECSs over multilayer elastic optical network (ML-EON), which involves the joint allocation of heterogeneous computing resources in edge and cloud servers and high-dimensional network resources. We propose a Task-Node Matching Score (TNMS) based method, which evaluates the fitness for each mapping tuple between each task in ECS and each substrate node in ML-EON, and adaptively generates a specific matching score for each task-node pair. Furthermore, TNMS is extended with a pre-allocation mechanism (TNMS-Pre) to estimate the costs of multi-dimensional resources in ML-EON for virtual link (VL) mapping. The estimated VL mapping costs are integrated into the matching scores to guide the task placement to be cost-efficient. To guarantee the feasibility, a maximal weight matching (MWM) based method is presented to determine the task placement schemes. Simulation results demonstrate the effectiveness of the adaptive scoring for heterogeneous task placement and the pre-allocation mechanism for reducing the ML-EON costs.

The intelligent service function chains (SFCs) provisioning is of great significance for agile deployments of 5G vertical applications. However, the heterogeneities of entities in substrate network (SNet) and SFCs hinder deep learning (DL) models to fully integrate the information of SNet and SFCs. Furthermore, the potential infeasibility of output policies and difficulty in training data acquisition also pose challenges to DL methods. To overcome the above limitations, we propose a Differentiated Weight Graph Convolutional Neural Network (DWGCN) model, which configures different weights for different kinds of entities, to predict the optimal virtual network function (VNF) placements. Moreover, the model is integrated with maximal weight matching to enhance the feasibility of VNF placement policies. A transfer learning method is further introduced to reduce the required training data with knowledge transfer. Experimental results demonstrate the effectiveness of the proposed methods in SFC mapping cost, high time efficiency, and knowledge transferability.

We theoretically study the issues for long-distance transmission of quantum key distribution (QKD) coexisting with classical signals. The recently proposed phase-matching QKD protocol can drastically improve the transmission distance of QKD. However, in the coexistence system, the noise generated by classical signals, especially spontaneous Raman scattering noise, is a big challenge. Moreover, the classical optical amplifier, which is necessary for the realistic long-distance classical communication, makes the noise more serious. In view of this, we establish the unified Raman noise model for three discrete-variable QKD protocols (BB84, measurement-device-independent and phase-matching QKD protocols) in the presence of classical optical amplifiers, which can be applied to both single-core single-mode fiber and multicore fiber. Then, we derive the key rate of the three QKD protocols coexisting with classical signals using the proposed unified Raman noise model. Finally, simulation results show that multicore fiber is promising for simultaneous long-distance transmission of QKD and the dense wavelength division multiplexing system.

Mobile/multi‐access edge computing (MEC) takes advantage of its proximity to end‐users, which greatly reduces the transmission delay of task offloading compared to mobile cloud computing (MCC). Offloading computing tasks to edge servers with a certain amount of computing ability can also reduce the computing delay. Meanwhile, device‐to‐device (D2D) cooperation can help to process small‐scale delay‐sensitive tasks to further decrease the delay of tasks. But where to offload the computing tasks is a critical issue. In this article, we integrate MEC and D2D cooperation techniques to optimize the offloading decisions and resource allocation problem in D2D‐enabled three‐tier MEC networks for Internet of Things (IoT). Mobile devices (MDs), edge clouds, and central cloud data center (DC) make up these three‐tier MEC networks. They cooperate with each other to finish the offloading tasks. Each task can be processed by MD itself or its neighboring MDs at device tier, by edge servers at edge tier, or by remote cloud servers at cloud tier. Under the maximum energy cost constraints, we formulate the cooperative offloading problem into a mixed‐integer nonlinear problem aiming to minimize the total delay of tasks. We utilize the alternating direction method of multipliers (ADMM) to speed up the computing process. The proposed scheme decomposes the complicated problem into 3 smaller subproblems, which are solved in a parallel fashion. Finally, we compare our proposal with D2D and MEC networks in simulations. Numerical results validate that the proposed D2D‐enabled MEC networks for IoT can significantly enhance the computing abilities and reduce the total delay of tasks.

With the traffic growth with different deterministic transport and isolation requirements in radio access networks (RAN), Flexible Ethernet (FlexE) over wavelength division multiplexing (WDM) network is as a candidate for next generation RAN transport, and the security issue in RAN transport is much more obvious, especially the eavesdropping attack in physical layer. Therefore, in this work, we put forward a cross-layer design for security enhancement through leveraging universal Hashing based FlexE data block permutation and multiple parallel fibre transmission for anti-eavesdropping in end-to-end FlexE over WDM network. Different levels of attack ability are considered for measuring the impact on network security and resource utilization. Furthermore, the trade-off problem between efficient resource utilization and guarantee of higher level of security is also explored. Numerical results demonstrate the cross-layer defense strategies are effective to struggle against intruders with different levels of attack ability.

Magnetic MIMO is a wireless power transfer (WPT) system that employs multiple magnetic resonance coils to provide high efficient wireless power in the near field. Magnetic energy beamforming is a typical scheme to control the currents or voltages of the transmitter coils in order to achieve some objectives. Thus, the magnetic channel information is essential to magnetic beamforming (MagBF), and it needs complicated circuits and communication protocols to feedback such information. Such information may be not available due to the circuit limits or privacy concerns. In addition, the performance will be degraded with imperfect channel estimation in the noisy and mobile dynamic environment. In this case, only some limited feedback information is available, e.g., received power. In this article, we propose a random MagBF method to achieve maximum received power efficiency and simplify the system architecture. This scheme employs iterative Monte Carlo sampling and resampling to search an optimal beamforming solution based on the received power feedbacks. We design an online training protocol to implement the proposed scheme. It is computationally light and requires only limited feedback information, which avoids complex channel estimation or AC measurements. The simulation and real experimental results indicate that our algorithm can effectively increase the received power and approach the optimal performance with a fast convergent rate.

The centralized radio access network (C-RAN) is an effective architecture to promote CAPEX/OPEX reduction and cell cooperation derived from its centralized baseband processing. However, there is a contradiction between centralization gain and transport resource saving, which hinders the vision of a resource-efficient and cost-effective RAN deployment. Advanced RAN architectures with functional splits are then introduced to cope with this challenge. Distinguished with other studies, we are intended to investigate whether a fine-grained functional split architecture could benefit to the RAN evolution, and how it impacts on the converged optical-wireless access networks. To this end, we establish a quantitative model to analyze the performance of this architecture. With the fine-grained split, baseband unit (BBU) is divided into a set of fine-grained units (FU) to be placed in desired processing pools (PP) as a service chain. To analyze the placement performance, we propose a mixed-integer linear programming model (MILP) considering the PP selection, routing, wavelength and bandwidth assignment, as well as latency control to minimize the number of PPs, bandwidth, latency, and functions deployment cost. We compare its performance with other two coarse-grained split architectures, i.e., SBBU (adopt low-PHY split like BBU in 4G) and recently emerged DU-CU in both small-scale and large-scale network scenarios. Our analyses provide insights into the modeling and design of efficient converged optical-wireless access networks in 5G and beyond.

In 5G access network, a hybrid free space optic (FSO)/millimeter-wave (mmW) system is a promising fronthaul solution for dense urban area. In the hybrid FSO/mmW system, mmW in-band self-fronthaul is more efficient because the spectrum of radio access and fronthaul are allocated in the same radio band. However, it incurs inefficient usage of spectrum and transmission power. The allocation of resource blocks and power for user access and fronthaul transmission should consider dynamic weather conditions to improve energy efficiency of system. In this paper, we consider the downlink of 5G fronthaul. An intelligent resource reuse and allocation algorithm based on the constrained differential evolution is proposed to maximize the energy efficiency. The spectrum of mmW is partitioned into two parts, one for resource blocks in radio access, the other for data transmission over fronthaul. Also, transmission power in access and fronthaul are jointly considered to improve the energy efficiency. Simulation results show a considerable benefit on energy efficiency for different weather conditions compared with traditional water-filling baseline algorithm.

Using IoT devices to locate targets is widely applied in many scenarios. However, replacing the batteries of these devices is time and labor consuming. In this article, we propose a wireless power-driven positioning system (WP2S) that employs MIMO-based wireless power transfer access points to supply energy to batteryless anchors. In this case, the IoT localization devices will have unlimited power. We formulate the equivalent Fisher information matrix (EFIM) as a fundamental tool to analyze the system performance. Then, we propose resource allocation schemes for optimal location estimation and energy efficiency problems by relaxing the objectives as semidefinite programming problems. In addition, we also analyze the impacts of channel uncertainty, anchor uncertainty, and NLOS for the performances of location estimation and energy consumption. The robust algorithms are developed according to uncertainty models. Both the analysis and simulations demonstrate that the estimation accuracy relies heavily on the transmitted power and the uncertainty models will consume more power to meet the location requirements.

An appearance model is critical for most modern trackers. While numerous novel appearance models have been proposed with demonstrated success, challenges such as occlusion and drifting are still not well addressed. In this paper, we propose a novel contextual bag-of-words (CBOW) discriminative appearance model that appropriately handles drifting and occlusion. Specifically, a contextual region containing both the target and its surroundings is explored to construct a compact representation with two bags-of-words. Each word carries discriminative appearance information that is learned by Bayesian inference. An adaptive updating approach, where the background BOWs of the CBOW model acts as a "sentinel" to prevent the integration of the background appearance with the object model, is introduced to alleviate the drifting problem. Based on CBOW, visual tracking is posed within a Bayesian framework. Moreover, an explicit detection method is employed to handle severe occlusions, which further reduces drifting. Two trackers based on the same CBOW model are implemented using either handcrafted color/texture or deep convolutional features. Our trackers are evaluated based on the popular OTB50 and VOT2015 benchmarks and perform competitively against the current state of the art. In addition, they outperform two recent BOWs trackers by a large margin using the currently available figures of merit. To take into account a tracking breakdown, we propose a new figure of merit called the mean maximum-tracked-frame ratio (MTFR) that evaluates a tracker's temporal persistence without any interruption. Experiments with OTB50 demonstrate the superior robustness of our tracker compared with all other evaluated trackers on the basis of MTFR.

In this letter, we propose a novel discriminative bag-of-words (DBoW) model that can both adapt to appearance variations over time and reduce the commonly observed drifting problem in online tracking. Specifically, a contextual region containing both the object and its surroundings is explored to construct a compact representation with two bags-of-words. Each visual word is learned to carry discriminative appearance cues for the object. In order to alleviate the drifting problem, an adaptive updating approach is introduced to prevent the integration of the background into the object model. Based on DBoW model, a robust and near real-time tracker is proposed, where tracking is accomplished by searching the candidate that best matches to the maintained DBoW model. Extensive experimental results demonstrate competitive performance of the proposed method to state-of-the-art algorithms.

In the practical crowdsourcing systems, there exist many cooperative tasks, each of which requires a group of users to perform together, such as finding the shortest multi-hop path or obtaining the media resources from a set of hosts. In this paper, we tackle the problem of how to truthfully and fairly schedule or allocate sufficient users who join mobile crowd-sourcing applications with their smartphones. Moreover, the cooperation among users is taken into account. Thus, we present a novel Cooperative Crowdsourcing (C2) auction mechanism for crowdsourcing multiple cooperative tasks. C2 contains two parts: user selection and payment computation. In the first part, we first prove that users selection with the minimum social cost is NP hard problem and design a greedy algorithm to achieve near-optimal solution in polynomial time. The other part is that the server determines the payments of selected users to avoid the bidder's cheating behavior through a pricing algorithm that if and only if users honestly bid their cost, they can obtain the maximum utility. Both theoretical analysis and extensive simulations demonstrate that C2 auction achieves not only truthfulness, individual rationality and high computational efficiency, but also low overpayment ratio.

Cloud radio access network (C-RAN) has become a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing using cloud BBUs. In this paper, we propose a novel multistratum resources optimization (MSRO) architecture for cloud-based radio over optical fiber networks with software defined networking. Additionally, a global evaluation strategy (GES) is introduced in the proposed architecture. The MSRO can enhance the responsiveness to end-to-end user demands and globally optimize radio frequency, optical spectrum and BBU processing resources effectively to maximize radio coverage. The overall feasibility and efficiency of the proposed architecture with GES strategy are experimentally verified on OpenFlow-enabled testbed in terms of resource occupation rate and path provisioning latency.

In this paper, we propose a fast single image dehazing algorithm using domain transformation-based edge-preserving filter and weighed quadtree subdivision. The proposed algorithm first estimates the atmospheric light in the minimal component of the haze image based on the weighed quadtree subdivision. Since the dark channel prior is invalid when the scene objects are with similar intensity to the atmospheric light, we then modify the transmission map to improve the adaptability of the algorithm. Due to the observation that haze is widely spread in the hazy image, the transmission map should be smoothly changed over the scene. The proposed algorithm utilizes the domain transformation-based edge-preserving filter to smooth the details and preserve edges and corners, aiming to obtain the refined transmission map. Experimental results demonstrate that the proposed algorithm produces comparative or even better results compared to the state-of-the-art methods but with much higher computational efficiency.

Due to the presence of bad weather conditions, images captured in outdoor environments are usually degraded. In this paper, a novel single image dehazing method is proposed to enhance the visibility of such degraded images. Since the property of haze is widely spread, the estimated transmission should be smoothly changed over the scene. The fast wavelet transform (FWT) is introduced to estimate the smooth transmission in our work. To preserve more details and correct the color distortion, a solution based on weighted image fusion strategy is provided. Compared with the state-of-the-art single image dehazing methods, our method based on FWT with weighted image fusion (FWTWIF) produces similar or even better results with lower complexity. In order to verify the high visibility restoration and efficiency of our method, comparative experiments are conducted at the end of this paper.

The key current challenges for the industrial application of all optical switching networks are energy consumption, transmission rate, spectrum efficiency, and switching throughput. The energy consumption problem is mainly researched in this paper. From the perspective of components and modules, node equipment, and network levels, different enabling technologies are proposed to overcome this problem, which are also evaluated through different experimental demonstrations. First, high-sampling-rate digital-to-analog converters (DACs) and WSS-based ROADM modules are demonstrated as components and modules for energy-efficient all optical switching networks. Then, an all optical transport network test-bed consisting of 10 Pbit/s level all optical switching nodes based on multi-level and multi-planar switching architecture is experimentally demonstrated for the first time, which can reduce power consumption by 43%. A control architecture for energy-efficient all optical switching networks is built with OpenFlow based software defined networking (SDN), and experimental results are given to verify the performance of this control architecture. Finally, we describe an All Optical Networks Innovation (AONI) project in China, which aims to explore transmission, switching, and networking technologies in all optical switching networks, and then two application scenarios are forecast based on the technical breakthroughs of this project.

For the color fadedness and the contrast reduction of hazy images, we propose a novel method based on digital total variation (TV) filter with color transfer (DTVFCT) for single color image dehazing. The estimation of atmospheric veil is a filtering problem on the minimal component image and the digital TV filter is applied to preserve the edges and gradients of images to avoid halo artifacts. To obtain a quality vision of a resulting image, color transfer is utilized to protect the final haze-free image's color from high dynamic. The proposed method enjoys a celerity because of fast convergence of digital TV filter. The experiment results show the vivid color and the accuracy of the restored edges and gradients in the comparative study.

Visual tracking under illumination changes is a challenging task for various computer vision systems. In this paper, we propose a robust and efficient tracking algorithm based on a novel feature descriptor called joint gradient-intensity histogram, which exploits both the pixel intensity and gradient orientation features. Unlike the traditional histogram based algorithms, the joint gradient-intensity histogram is extracted efficiently by employing the integral histogram, which is calculated recursively in the specially designed local image region, making the exhaustively searching process in real time. Experiments with the state-of-art tracking methods demonstrate superior performance of the proposed tracking algorithm under illumination changes.

Direct reporting, partial nodes selection and compressive sensing are three common types of data gathering in wireless sensor networks. Integrating the above three techniques together for a better performance has been paid many attentions. Hybrid-CS is an effective way to save energy for data gathering, which adopts direct reporting method instead of compressive sensing in the area near the leaf nodes. This paper presents a higher-level hybrid scheme that integrates partial nodes selection into compressive sensing by using a threshold, so as to extend the area before compressive sensing. For this idea, we give a completed theoretical analysis of the relationship between energy consumption and different parameters settings, then we present the influence of different parameters acting on network performance under three general scenarios. At last, we run extensive simulations on the real data trace of Citysee system and the results show that the proposed integrating scheme can realize the purpose of network performance optimization.

In this letter, we propose a robust and real-time visual tracking algorithm via a novel kernel based multiple cue adaptive appearance model (KBMCAAM). In particular, the appearance model is constructed with a naive Bayes classifier which is trained utilizing sparse multi-scale Haar-like features weighted by a spatial kernel function. Moreover, multiple image cues are integrated to improve the model's discriminative capacity. Experimental results demonstrate the superior performance of our proposed method to many state-of-art algorithms.

In low-duty-cycle wireless sensor networks, wireless nodes usually have two states: active state and dormant state. The necessary condition for a successful wireless transmission is that both the sender and the receiver are awake. In this paper, we study the problem: How fast can raw data be collected from all source nodes to a sink in low-duty-cycle WSNs? To address this, we define the Minimum Data Collection Delay (MDCD) problem, and give both the lower and upper tight bounds on the minimum delay for data collection when interfering links are eliminated. Furthermore, a novel concept, Virtual Grid Network (VGN) is introduced to successfully convert the MDCD problem into max-flow problem, and present a MDCD algorithm enlightened by the Ford-fulkerson max-flow method, which is able to find an optimal solution in polynomial time and achieves the lower bound. Extensive simulations are conducted and the results show that the proposed MDCD algorithm significantly outperforms the Shortest Path Routing algorithm (up to 32%) and achieves the lower bound.

In this paper we present a novel and robust framework that decomposes continuous people detection into three parts, including off-line detection, tracking and learning. We introduce temporal coherence and spatial constraints into off-line detection phase by collecting a dynamical model from tracker which is estimated and updated by the learning algorithm. This method makes the detector aim at regions where a potential target will appear in the next frame, capable of handling pedestrians with occlusions and variety of scales, which as result greatly improves performance of pedestrian detection. We carry out a quantitative and qualitative evaluation on the public datasets.

Exemplar-based methods have been widely used in human action recognition. To analyze human action in monocular video has always been a challenging problem, due to depth information loss and ambiguities. In this paper we presented a method applying skin color detection and then calculating relative positions of face and hands to solve self-occlusions and to eliminate ambiguities. Then we applied 2D shape analysis to classify basic human actions. Several low level features were used to describe shapes, which needs less computation and can improve recognition speed to real-time level. We testified our method on a public action database and got satisfying results.

Currently, Wireless Sensor Networks (WSN) mostly operate on the fixed and unlicensed Industrial, Scientific and Medical (ISM) spectrum. With the increasing spectrum shortage in WSN, the Cognitive Radio Sensor Networks (CRSN) is proposed to employ the Cognitive Radio (CR) technology to utilize the spectrum resources dynamically (i.e., Dynamic Spectrum Access, DSA). However, it's a challenge to increase the delivery rate and the throughput in the CRSN because of the power and process restrictions. In this paper, with the consideration of the spectrum idle condition and communication capability, a novel DSA strategy based on the Real-time Usability (DSARU) is presented. And the energy-saving updating algorithm of the spectrum real-time usability is proposed to sense the spectrum changes. Based on the IEEE 802.15.4, DSARU simulation model and experiment system are set up to evaluate the transmission performances. Both simulation and experiment results demonstrate that the DSARU strategy can efficiently increase the delivery rate and throughput, especially under the condition of spectrum interference.

With the emergence of various broadband services, a lot of bandwidth fragments will be generated during the operation of services being mapped into optical channels, which will waste too many bandwidth resources and decrease the transmission performance of optical networks. Traffic grooming strategy in dynamic optical networks can optimise the utilisation of bandwidth resources and reduce the blocking probability. However, in the distributed control plane of automated switched optical networks, all the traffic grooming strategies are implemented in each control node, and resource collision will still occur because the same link resource may be used by two path computation requests or traffic engineering information flooded by open shortest path first-traffic engineering protocol may be not synchronous at each control node or signalling delay time is too long. To reduce the collision of resource, a unified control plane is designed based on path computation element (PCE) for Internet protocol over wavelength division multiplexing networks, and a novel PCE-based traffic grooming strategy is proposed in the framework of unified control plane. Based on this strategy, four PCE-based traffic grooming algorithms are proposed and compared with the distributed traffic grooming strategy without PCE on a simulation platform implemented using disperse event simulation tool OMNET++.

Multi-granularity optical cross-connect (MG-OXC)-based optical network is a promising optical network architecture as it is capable of flexible switching at different granularity levels. In MG-OXC-based optical networks, wavelength conversion (WC) capability and the number of usable add/drop ports of the nodes are two key factors affecting its performance. Two analytical models of blocking probability for MG-OXC-based optical networks both without WC capability and with sparse WC capability are proposed, exploiting Erlang's loss formula and birth–death process. Based on the models and simulation, the impact of WC capability and the number of add/drop ports on the blocking probability are investigated. Three kinds of granularities (i.e. fibre, waveband and wavelength) are considered in MG-OXC nodes to reduce the complexity and size of switch fabric. Both the analytical and simulation results are given on two network topologies under dynamic traffic patterns. Simulation results show that the proposed models are accurate and effective for the analysis of blocking probability in MG-OXC-based optical networks.

This paper considers the comparison between optical and electrical interconnect system from a parallel computing performance view. The motivation of the work is based on increasing demand on parallel computing tasks and the rapid development of optical chip-to-chip interconnects techniques. To meet the increasing demand of large-scale parallel or multiprocessor computing tasks, an analytic method to evaluate the computing performance of interconnect systems is proposed in this paper. The bandwidth-limit model and full-bandwidth model are both under our investigation. The paper characterizes the influence of unit processing time, communication overhead, processor number and the transmission time. The speedup and efficiency are selected to represent the parallel performance of an interconnect system. The analytic expressions of these two indexes are derived in the paper. Deploying the proposed models, we depict the performance gap between the optical and electrical interconnect systems. Results show that the large communication bandwidth optical chip-to-chip system has an obvious speedup gain, which is up to 93%. However, the existence of efficiency peak point indicates that the immoderate pursuing of high-bandwidth have no use for system efficiency improvement.

In this paper, a practical dynamic resource allocation scheme for real-time variable bit rate (VBR) video transmission is investigated. This scheme uses time-domain adaptive linear prediction, instead of the conventional size prediction of I-, P- and B- frames, to forecast future bandwidth requirement in time. Media delivery index (MDI) is used here as QoS measurement and complementary reference for adjustment. Besides, considering that the practical multi-user application may arouse adjustment collision, an adjustment priorities classified strategy based on the user-level classifications is put forward. Last, a test-bed based on the proposed scheme is constructed and the experimental results show that the bandwidth effective utilization has increased by 20%-60% compared to a fixed service rate with QoS guaranteed and no collisions.

Traditionally, researches of the multicasting in the all-optical networks are focused on the issues of the parallel-mode multicasting scheme (PM), which can produce multiple simultaneous copies of the optical signals by an optical power splitter or other devices. However, when all the copies exported simultaneously, since the conflicts of the optical signals during the process of switching are hardly to be avoided, it is quite difficult to guarantee that all of them can be transmitted successfully, especially when there is a high network load. In this paper, an all-optical serial-mode multicasting scheme (SM), which can be implemented in the packet-based all-optical networks, also refers to the optical packet switched networks (OPSN), is researched experimentally. With the experimental results, we discuss the limitations of this scheme. Then, with the computer simulations, we compare its performance with the PM scheme. A conclusion can be drawn that compared with the PM scheme, the SM scheme can increase the multicast success ratio and reduce the multicast retransmission times at the costs of some signal impairments and some extra transmission latency.

In hierarchical networks, the topology and QoS parameters of a domain have to be first aggregated before being propagated to other domains. However, topology aggregation may distort useful information. This paper focuses on minimizing the distortion caused by reducing a full-mesh representation to a spanning tree. An optimization method of minimizing the distortion of additive parameters caused by spanning tree aggregation is presented. Based on this new method, two approximation algorithms are proposed. Simulation results show that both algorithms perform much better than the traditional way of decoding the spanning tree with upper or lower bounds.