Energy efficiency (EE) has become a key design metric for the sustainable development of future wireless communications. Dynamic metasurface antennas (DMAs), with their compact size and low power consumption, are considered a promising solution for improving EE. In this letter, we investigate the energy-efficient beamforming design for downlink multiple-input single-output communication systems, where the DMA-equipped base station serves multiple users simultaneously. We aim to maximize the energy efficiency by jointly optimizing the DMA weight matrix and the digital precoder, subject to the constraints of power budget and Lorentzian-phase. To address this complicated non-convex optimization problem, we develop an alternating optimization algorithm based on manifold optimization and sequential convex approximation. Numerical results verify the effectiveness of the proposed method.

Current state-of-the-art joint entity and relation extraction framework is based on span-level entity classification and relation identification between pairs of entity mentions. However, while maintaining an efficient exhaustive search on spans, the importance of syntactic features is not taken into consideration. It will lead to a problem that the prediction of a relation between two entities is related based on corresponding entity types, but in fact they are not related in the sentence. In addition, although previous works have proven that extract local context is beneficial for the task, it still lacks in-depth learning of contextual features in local context. In this paper, we propose to incorporate syntax knowledge into multi-head self-attention by employing part of heads to focus on syntactic parents of each token from pruned dependency trees, and we use it to model the global context to fuse syntactic and semantic features. In addition, in order to get richer contextual features from the local context, we apply local focus mechanism on entity pairs and corresponding context. Based on applying the two strategies, we perform joint entity and relation extraction on span-level. Experimental results show that our model achieves significant improvements on both Conll04 and SciERC dataset compared to strong competitors.

In order to improve multicast's spectrum energy-efficient of elastic optical network configured with Colorless, Directionless and Contentionless-Flexible Reconfigurable Optical Add/Drop Multiplexer (CDC-F ROADM) nodes, an All-optical Multicast Energy Efficiency Scheduling Algorithm (AMEESA) is proposed. In the routing phase, considering both energy consumption and link spectrum resource utilization, the link cost function is designed to establish the multicast tree with the least cost. In the spectrum allocation phase, a spectrum conversion method based on High Spectral Resolution (HSR) is designed by changing the spectrum slot index of adjacent links according to links availability of spectrum blocks. And an energy-saving spectrum conversion scheme is selected to allocate spectrum block resources for the multicast tree. Simulation analysis shows that the proposed algorithm can effectively improve the network energy efficiency and reduce the bandwidth blocking probability of IP multicast.

In this paper, we focus on the problem of forecasting the trend of patents in different technologies. Different from other time series forecasting datasets, the length of series in our dataset is much shorter with fewer instances. So, other forecasting models which treat the time series as long high-dimension embedding do not perform well enough on this problem. Those models require a number of parameters. That is hard to trained by our dataset. And this kind of models can only applied on specified number of time series. While new technology emerges, the number of time series increases. That increases the dimension, so the model should be trained again. At the same time, not only do we value the error of forecasting, we also value the trend in our forecasting. So we develop a novel model that is trained by all the series to find the common patterns and generates corresponding prediction to deal with the trend. And we use a more appropriate index to evaluate trend that the model predicts. Treating the evolution of every technology as a time series, Convolution Neural Network (CNN) is used to capture the patterns among series. Therefore, our model requires less parameters and can be trained incrementally. Then, Recurrent Neural Network (RNN) is used to encode the information into an intermediate representation. With decoding the intermediate representation into values in multiple steps, the trend can be forecast. Finally, we test our model on other datasets. It achieves better results on some other datasets with that kind of characteristics.

Due to the probabilistic failure of the optical fiber of the underlying network in the virtual environment, traditional full protection configures one protection path at least which leads to high resource redundancy and low acceptance rate of the virtual network. In this paper, a Security Awareness-based Diverse Virtual Network Mapping (SA-DVNM) strategy is proposed to provide security guarantee in the event of failures. In SA-DVNM, the physical node weight formula is designed by considering the hops between nodes and the bandwidth of adjacent links, besides, a path-balanced link mapping mechanism is proposed to minimize the overloaded link. For improving the acceptability of virtual network, SA-DVNM strategy designs a resource allocation mechanism that allows path cut when a single path is unavailable for low security. Considering the difference of time delay to ensure the security of delay-sensitive services, a multipath routing spectrum allocation method based on delay difference is designed to optimize the routing and spectrum allocation for SA-DVNM strategy. The simulation results show that the proposed SA-DVNM strategy can improve the spectrum utilization and virtual optical network acceptance rate in the probabilistic fault environment, and reduce the bandwidth blocking probability.

To address the problems of low spectrum utilization and high energy consumption caused by physical impairment in elastic optical networks, a service differentiated energy efficiency routing strategy with Link Impairment-Aware Spectrum Partition (LI-ASP) is proposed. For reducing the nonlinear impairment between different channels, a path weight formula jointly considering the link spectrum state and transmission impairment is designed to balance the load. A modulation level-layered auxiliary graph is constructed according to traffic’s spectrum efficiency and maximum transmission distance. Starting from the highest modulation in the auxiliary graph, the K link-disjoined maximum weight paths are selected for high quality requests, and the K link-disjoined shortest energy efficiency paths are selected for low quality requests. Then, LI-ASP strategy divides spectrum partition according to requests rate ratio. The First-Fit (FF) and Last-Fit (LF) spectrum allocation policies are used to reduce cross-phase modulation between the requests with different rates. The simulation results show that the proposed LI-ASP strategy can reduce the bandwidth blocking probability and energy consumption effectively.

Nowadays, diversified demand of different users is becoming a focus for improving network performance. Traditional network can't meet the demand, so we turn to Software-Defined Network architecture, which realizes virtualization and abstraction of underlying hardware resource via separation of control and data planes. First, we propose a virtual network mapping algorithm to allocate link resources, using Ant Colony algorithm to find the optimal solution. Then we develop a virtual network fault recovery mechanism to satisfy the need of end users with different fault tolerance. The mechanism is achieved by the failure recovery algorithm named NumMap Algorithm, which provide varied network reliability levels for users of varied priority. By the end of paper, we conduct simulation experiments to evaluate the algorithms with performance metrics such as failure repairing ratio, success running ratio, and working link resource utilization. The results demonstrate the superiority of the proposed algorithm compared with ResRemap and ResBackup algorithms.

OPC UA (Object Linking and Embedding for Process Control Unified Architecture) has drawn extensive attention in Industrial Internet of Things (IIOT), for it enables secure data exchanges between different operating systems and devices. In the field of industrial control, the applications of OPC UA are gradually extended to the field layer, which adopt the popular client-server interaction model. However, as the number of underling servers increases, the management of the upper layer becomes more difficult. Moreover, there are few kinds of software supporting OPC UA multi-server aggregation currently and they lack effective cache management capability. In order to solve these problems, we design an OPC UA multi-server aggregator with cache management, which enables centralized management of underlying data. Test results show that the proposed aggregator effectively improves the communication efficiency.

This paper proposes a deep neural network model for jointly modeling Natural Language Understanding and Dialogue Management in goal-driven dialogue systems. There are three parts in this model. A Long Short-Term Memory (LSTM) at the bottom of the network encodes utterances in each dialogue turn into a turn embedding. Dialogue embeddings are learned by a LSTM at the middle of the network, and updated by the feeding of all turn embeddings. The top part is a forward Deep Neural Network which converts dialogue embeddings into the Q-values of different dialogue actions. The cascaded LSTMs based reinforcement learning network is jointly optimized by making use of the rewards received at each dialogue turn as the only supervision information. There is no explicit NLU and dialogue states in the network. Experimental results show that our model outperforms both traditional Markov Decision Process (MDP) model and single LSTM with Deep Q-Network on meeting room booking tasks. Visualization of dialogue embeddings illustrates that the model can learn the representation of dialogue states.

Cochannel pulse radar and long term evolution (LTE) deployments are considered by the FCC citizen broadband radio service (CBRS) and also 5G spectrum sharing. This paper proposes a radar spectrum sharing method based on LTE reduced-power almost blank subframe (RP-ABS) mechanism, which is used to mitigate interference to pulse radar while maintaining high LTE throughput. For LTE, payload bits are first packed into nonradar interfered time domain subframes (SFs), and then RP-ABS or even zero-power almost blank subframe (ABS) are adopted in the rest SFs. For pulse radar, target detection performance is guaranteed, thanks to the decreased interference caused by LTE RP-ABS power. We also formulate and solve an optimization problem for radar and RP-ABS-based LTE spectrum sharing, which consider tradeoffs between RP-ABS-based LTE performance (bit error rate and throughput) and radar detection probability concurrently. Using 3rd generation partnership project (3GPP) LTE sensitivity test as a benchmark, 28.83% throughput gain can be obtained for RP-ABS-based method. A hardware-in-the-loop testbed is also designed to prove spectrum sharing between RP-ABS-based LTE and radar viable.

The combination of traditional processors and Field Programmable Gate Arrays (FPGA) is shaping the future networking platform for intensive computation in resource-constrained networks and devices. These networks present two key challenges of security and resource limitations. Lightweight ciphers are suitable to provide data security in such constrained environments. Implementing the lightweight PRESENT encryption algorithm in a reconfigurable platform (FPGAs) can offer secure communication service and flexibility. This paper presents hardware acceleration of security primitives in SDN using NETFPGA-10G. We implement an efficient design of the PRESENT algorithm for faster, smaller and lower power consumption hardware circuit using Verilog. We evaluate the performance of the hardware and software implementations of PRESENT. Experimental results prove that the proposed hardware design is a viable option for use in resource constrained devices in future networks and their applications.

Recent innovations in network virtualization and elastic optical networks enable the flexible deployment of optical networks. However, the spectrum fragmentation and virtual optical links being embedded to the longer lightpaths decrease the spectrum efficiency of elastic optical networks. To address this issue, a coordinated virtual optical network embedding algorithm based on resources availability-aware (RA-CVONE) is proposed. Firstly, we design the node importance metric to embed the neighboring virtual nodes to the closer physical nodes, and then a coordinated embedding approach is adopted, which can further shorten the length of the lightpaths to which virtual optical links are embedded. Moreover, we jointly evaluate the resources availability of a physical lightpath and its node, then select the candidate lightpaths with less spectrum fragmentations and abundant resources for a virtual optical link. Based on the resources availability, a path selection strategy is proposed to determine a physical lightpath of which the vacant spectrum resources can be effectively used. Simulation results show that the RA-CVONE algorithm can significantly shorten the length of the physical lightpaths which are selected to embed virtual optical links, reduce the bandwidth blocking probability of virtual optical network requests, and improve the spectrum utilization of elastic optical networks.

In order to address the problems of the high bandwidth blocking probability and imbalance resources consumption in physical network during virtual optical network mapping, Fragmentation-Aware based on time and spectrum domain of Virtual Network Mapping (FA-VNM) algorithm is proposed. In the FA-VNM algorithm, the fragments problem in the time domain and the spectrum domain is considered. Fragment formula jointly considering the time fragment and spectrum fragment is devised to minimize the spectrum fragments. Further, in order to balance the network resources consumption, based on the FA-VNM, Load Balancing based on degree of Virtual Network Mapping (LB-VNM) algorithm is proposed. In the stage of node mapping, physical node average resource carrying capacity is introduced and the physical node with larger average resources carrying capacity is mapped first. In order to balance the resource consumption in physical path, weight value of physical path is calculated in the stage of link mapping. Then, according to the weight value of each physical path, virtual links are mapped to achieve the purpose of load balancing for reduce the blocking rate. Simulation results show that the algorithms can effectively reduce the blocking rate and improve the resources utilization.

针对通信信号业务种类识别问题,提出利用机器学习领域的线性回归算法和多项式拟合模型提取信号功率谱的多项式拟合因子作为信号的统一特征来构建训练集,并在深度学习平台keras上构建了全连接的神经网络分类器模型.相比传统的方法,具有对无线电信号统一表征而无需对业务逐个提取个性化特征的优点.选取实际无线电监测数据中的码分多址(CDMA)上行、CDMA下行、增强型全球移动通信系统(EGSM)上行、EGSM下行、无线局域网(WLAN)以及长期演进(LTE)6种信号的功率谱数据作为数据集,通过验证得到了97％的分类准确率,证明了该方法的可行性.

Compared with elastic optical networks, spectrum contention problem may become more serious in space-division multiplexing elastic optical networks. Spectrum converters (SCs) can effectively resolve the problem of spectrum contention without additional delays, but SCs have the disadvantages of complicated technologies and high costs. To resolve the two problems aforesaid, taking into account the alleviated spectrum continuity constraint caused by the space dimension and the node flexibility introduced by architecture on demand (AoD), a space-frequency joint contention scheduling algorithm (SFJSA) based on AoD is proposed in this paper. SFJSA divides the contention scheduling process into two phases: scheduling in space domain and in frequency domain. It gives priority to resolve contention in the spatial domain, and then considers using spectrum converters in spectrum domain. In the stage of space switching, a weight formula is designed to balance the costs of spectrum selective switches and the loads of cores. In the stage of spectrum conversion, a concept of spectrum compactness is introduced to further optimize the utilization of spectrum resources and spectrum converters. The simulation results indicate that the proposed algorithm can reduce the number of spectrum converters required while improving the blocking performance, which means it achieves better blocking performance in a more cost-effective manner.

Based on the Synopsys physical design tool IC Compiler, taking the design of BES6799 chip under SMIC 0.18 mu m Logic 1P5M process as an example, this paper analyzed and compared the clock skew and clock cell area of clock trees which are built by three methods, such as inserting inverter, buffer, combination of both. It was found that the clock tree completed with buffers has a better clock skew and the clock cell areas are almost the same. So buffer is selected as the clock delay cell to build clock tree. In order to further reduce the clock skew and then make timing closure, the clock tree synthesis method which the important clocks are synthesized first is proposed. It was shown that the method can effectively reduce the clock skew and clock cell area. Based on the above methods, the chip was finally taped out in SMIC.

To satisfy the enormous data transmission demands, caching popular files into memories at user terminals (UTs) is a promising solution, which can alleviate the heavy burden on backhaul links and shorten the transmission delay. In this paper, we study the user caching strategy by exploiting the effect of user mobility. The contact of mobile users is modeled as the Poisson process. Both Zipf distributed and uniform distributed file demands are considered in the caching strategy. To improve user's quality of experience (QoE) for delay sensitive services, we define the user satisfaction metric in terms of the delay time, and maximize it through the proposed caching placement strategy. The mixed equality-inequality constrained optimization problem is solved by the multiplier penalty function (MPF) method. Numerical results reveal that the maximal average user satisfaction is achieved when the file caching coordinates with the file demand.

Delay-tolerant networks (DTNs) are wireless mobile networks that exhibit frequent intermittent connectivity and large transmission delay among nodes. Research results have shown that quota-controlled routing protocols can strike a reasonable balance between routing performance and cost, where quota is a value to control the number of message copies. However, the question of how to set the optimal quota dynamically in order to achieve the lower bound of routing cost is still open. In this paper, we model the optimization of quota control as an extremal functional problem and analyze it by a classic mathematical method called Calculus of Variations (CoV) for the first time. The function of time-variant quota with minimal average number of message copies is obtained in closed form, and an optimal quota control algorithm is proposed under practical routing design considerations. Both the numerical and simulation results show that the proposed model and algorithm are effective and efficient.

As virtualization is becoming a promising way to support various emerging application, efficient virtual network embedding (VNE) algorithm is becoming more and more important. In this paper, we investigate the VNE problem from a new perspective. We propose two concepts of strongly connected graph and weakly connected graph in undirected graph witch are similar to the concepts in directed graph. With the arrival of VN requests, the connectivity of residual substrate network becomes smaller and smaller and the residual substrate network is divided into several strongly connected graphs. And there are weak connections among these strongly connected graphs. Then, we embed each VN request onto each strongly connected graphs with load balancing. Load balancing could avoid blocked or bottlenecked area of substrate network. Simulation experiments show compared with other algorithms in large-scale network, acceptance ratio and revenue can be obviously improved and cost can be reduced by our algorithm. It also show the relationship between the connectivity of residual substrate network and the performance metrics.

Based on SDN (Software Defined Network) theory, this paper constructs a topology modeling for deliberate attacks. Then this paper analyzes the network vulnerability anti deliberate attacks and topological characteristics and with the distribution coefficient, then proposed the flow uniformity scheme to enhance the invulnerability of complex network, and by verified by simulation.