The hop-limited adaptive routing (HLAR) mechanism and its enhancement (EHLAR), both tailored for the packet-switched non-geostationary (NGEO) satellite networks, are proposed and evaluated. The proposed routing mechanisms exploit both the predictable topology and inherent multi-path property of the NGEO satellite networks to adaptively distribute the traffic via all feasible neighboring satellites. Specifically, both mechanisms assume that a satellite can send the packets to their destinations via any feasible neighboring satellites, thus the link via the neighboring satellite to the destination satellite is assigned a probability that is proportional to the effective transmission to the destination satellites of the link. The satellite adjusts the link probability based on the packet sending information observed locally for the HLAR mechanism or exchanged between neighboring satellites for the EHLAR mechanism. Besides, the path of the packets are bounded by the maximum hop number, thus avoiding the unnecessary over-detoured packets in the satellite networks. The simulation results corroborate the improved performance of the proposed mechanisms compared with the existing in the literature.

Multi-layer transport networks that utilize sub-lambda paths over a wavelength path have been shown to be effective in accommodating traffic with various levels of granularity. For different service requirements, a virtualized network was proposed where the infrastructure is virtually sliced to accommodate different levels of reliability. On the other hand, network reconfiguration is a promising candidate for quasi-dynamic and multi-granular traffic. Reconfiguration, however, incurs some risks such as service disruption and fluctuations in delay. There has not yet been any study on accommodating and reconfiguring paths according to different service classes in multi-layer transport networks. In this paper, we propose differentiated reconfiguration to address the trade-off relationship between accommodation efficiency and disruption risk in virtualized multi-layer transport networks that considers service classes defined as a combination of including or excluding a secondary path and allowing or not allowing reconfiguration. To implement the proposed network, we propose a multi-layer redundant path accommodation design and reconfiguration algorithm. A reliability evaluation algorithm is also introduced. Numerical evaluations show that when all classes are divided equally, equipment cost can be reduced approximately by 6%. The proposed reconfigurable networks are shown to be a cost effective solution that maintains reliability.

Multi-tenant datacenter networking, with which multiple customer networks (tenants) are virtualized and consolidated in a single shared physical infrastructure, has recently become a promising approach to reduce device cost, thanks to advances of virtualization technologies for various networking devices (e.g., switches, firewalls, load balancers). Since network devices are configured with low-level commands (no context of tenants), network engineers need to manually manage the context of tenants in different stores such as spreadsheet and/or configuration management database (CMDB). The use of CMDB is also effective in increasing the ‘visibility’ of tenant configurations (e.g., information sharing among various teams); However, different from the ideal use, only limited portion of network configuration are stored in CMDB in order to reduce the amount of ‘double configuration management’ between device settings (running information) and CMDB (stored information). In this present work, we aim to bridge the gap between CDMB and device status. Our basic approach is to automatically analyze per-device configuration settings to recover per-tenant network-wide configuration (running information) based on a graph-traversal technique applied over abstracted graph representation of device settings (to handle various types of vendor-specific devices); The recovered running information of per-tenant network configurations is automatically uploaded to CMDB. An implementation of this methodology is applied to a datacenter environment that management of about 100 tenants involves approximately 5,000 CMDB records, and our practical experiences are that this methodology enables to double the amount of CMDB records. We also discuss possible use cases enabled with this methodology.

The pilot contamination is a serious problem which hinders the capacity increasing in the massive MIMO system. Similar to Fractional Frequency Reuse (FFR) in the OFDMA system, Fractional Pilot Reuse (FPR) is proposed for the massive MIMO system. The FPR can be further classified as the strict FPR and soft FPR. Meanwhile, the detailed FPR schemes with pilot assignment and the mathematical models are provided. With FPR, the capacity and the transmission quality can be improved with metrics such as the higher Signal to Interference and Noise Ratio (SINR) of the pilots, the higher coverage probability, and the higher system capacity.

The advanced front-end (AFE) for automatic speech recognition (ASR) was standardized by the European Telecommunications Standards Institute (ETSI). The AFE provides speech enhancement realized by an iterative Wiener filter (IWF) in which a smoothed FFT spectrum over adjacent frames is used to design the filter. We have previously proposed robust time-varying complex Auto-Regressive (TV-CAR) speech analysis for an analytic signal and evaluated the performance of speech processing such as F0 estimation and speech enhancement. TV-CAR analysis can estimate more accurate spectrum than FFT, especially in low frequencies because of the nature of the analytic signal. In addition, TV-CAR can estimate more accurate speech spectrum against additive noise. In this paper, a time-invariant version of wide-band TV-CAR analysis is introduced to the IWF in the AFE and is evaluated using the CENSREC-2 database and its baseline script.

The next generation high efficiency video coding (HEVC) standard achieves high performance by extending the encoding block to 64×64. There are some parallel tools to improve the efficiency for encoder and decoder. However, owing to the dependence of the current prediction block and surrounding block, parallel processing at CU level and Sub-CU level are hard to achieve. In this paper, focusing on the spatial motion vector prediction (SMVP) and temporal motion vector prediction (TMVP), parallel improvement for spatio-temporal prediction algorithms are presented, which can remove the dependency between prediction coding units and neighboring coding units. Using this proposal, it is convenient to process motion estimation in parallel, which is suitable for different parallel platforms such as multi-core platform, compute unified device architecture (CUDA) and so on. The simulation experiment results demonstrate that based on HM12.0 test model for different test sequences, the proposed algorithm can improve the advanced motion vector prediction with only 0.01% BD-rate increase that result is better than previous work, and the BDPSNR is almost the same as the HEVC reference software.

In this paper, a real-valued reweighted l1 norm minimization method based on data reconstruction in monostatic multiple-input multiple-output (MIMO) radar is proposed. Exploiting the special structure of the received data, and through the received data reconstruction approach and unitary transformation technique, a one-dimensional real-valued received data matrix can be obtained for recovering the sparse signal. Then a weight matrix based on real-valued MUSIC spectrum is designed for reweighting l1 norm minimization to enhance the sparsity of solution. Finally, the DOA can be estimated by finding the non-zero rows in the recovered matrix. Compared with traditional l1 norm-based minimization methods, the proposed method provides better angle estimation performance. Simulation results are presented to verify the effectiveness and advantage of the proposed method.

Most P2PTV systems select a neighbor peer in an overlay network using RTT or a random method without considering the underlying network. Streaming traffic is shared over a network without localization awareness, which is a serious problem for Internet Service Providers. In this paper, we present a novel scheme to achieve P2PTV traffic localization by inserting delay into P2P streaming packets, so that the length of the inserted delay depends on the AS hop distance between a peer and its neighbor peer. Experiments conducted on a real network show that our proposed scheme can perform efficient traffic localization.

This letter presents a method for active noise cancelation (ANC) for headphone application. The method improves the performance of ANC by deriving a flexible independent component analysis (ICA) algorithm in a hybrid structure combining feedforward and feedback configurations with correlation-based wind detection. The effectiveness of the method is demonstrated through simulation.

This paper proposes fast repairing methods that uses hierarchical software defined network controllers for recovering from massive failure in a large-scale IP over a wavelength-division multiplexing network. The network consists of multiple domains, and slave controllers are deployed in each domain. While each slave controller configures transport paths in its domain, the master controller manages end-to-end paths, which are established across multiple domains. For fast repair of intra-domain paths by the slave controllers, we define the optimization problem of path configuration order and propose a heuristic method, which minimizes the repair time to move from a disrupted state to a suboptimal state. For fast repair of end-to-end path through multiple domains, we also propose a network abstraction method, which efficiently manages the entire network. Evaluation results suggest that fast repair within a few minutes can be achieved by applying the proposed methods to the repairing scenario, where multiple links and nodes fail, in a 10,000-node network.

Gentle AdaBoost is widely used in object detection and pattern recognition due to its efficiency and stability. To focus on instances with small margins, Gentle AdaBoost assigns larger weights to these instances during the training. However, misclassification of small-margin instances can still occur, which will cause the weights of these instances to become larger and larger. Eventually, several large-weight instances might dominate the whole data distribution, encouraging Gentle AdaBoost to choose weak hypotheses that fit only these instances in the late training phase. This phenomenon, known as “classifier distortion”, degrades the generalization error and can easily lead to overfitting since the deviation of all selected weak hypotheses is increased by the late-selected ones. To solve this problem, we propose a new variant which we call “Penalized AdaBoost”. In each iteration, our approach not only penalizes the misclassification of instances with small margins but also restrains the weight increase for instances with minimal margins. Our method performs better than Gentle AdaBoost because it avoids the “classifier distortion” effectively. Experiments show that our method achieves far lower generalization errors and a similar training speed compared with Gentle AdaBoost.

This paper presents a new connected component labeling algorithm. The proposed algorithm scans image lines every three lines and processes pixels three by three. When processing the current three pixels, we also utilize the information obtained before to reduce the repeated work for checking pixels in the mask. Experimental results demonstrated that our method is more efficient than the fastest conventional labeling algorithm.

An effective time-step control method is proposed for the damped pseudo-transient analysis (DPTA). This method is based on the idea of the switched evolution/relaxation method which can automatically adapt the step size for different circuit states. Considering the number of iterations needed for the convergence of the Newton-Raphson method, the new method adapts the suitable time-step size with the status of previous steps. By numerical examples, it is proved that this method can improve the simulation efficiency and convergence for the DPTA method to solve nonlinear DC circuits.

Amplify-and-forward (AF) relay multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems can achieve high data rate and high quality communications. On the other hand, it has to estimate all channels between the source-relay and relay-destination nodes in the destination node. In MIMO/OFDM systems, high time resolution carrier interferometry (HTRCI) has been proposed to achieve an accurate channel estimation (CE) with a small number of pilot signals. However, since it has many interferences, an accurate CE is not obtained and the system performance is degraded in AF relay MIMO/OFDM systems. Therefore, in this paper, we propose the complex HTRCI (C-HTRCI) pilot signal and the enhanced minimum mean square error (E-MMSE) equalization to achieve an accurate CE and to improve the system performance for AF relay MIMO/OFDM systems.

Based on a unified representation of generalized cyclotomic classes, every generalized cyclotomic sequence of order d over $Z_{p_{1}^{e_{1}}p_{2}^{e_{2}}cdots p_{r}^{e_{r}}}$ is shown to be a sum of d-residue sequences over $Z_{p_{s}^{e_{s}}}$ for $sin {1,2,cdots,r }$. For d=2, by the multi-rate approach, several generalized cyclotomic sequences are explicitly expressed by Legendre sequences, and their linear complexity properties are analyzed.

An L2-in-L3 tunneling technology plays an important role in network virtualization based on the concept of Software-Defined Networking (SDN). VXLAN (Virtual eXtensible LAN) and NVGRE (Network Virtualization using Generic Routing Encapsulation) protocols are being widely used in public cloud datacenters. These protocols resolve traditional VLAN problems such as a limitation of the number of virtual networks, however, their network performances are low without dedicated hardware acceleration. Although STT (Stateless Transport Tunneling) achieves far better performance, it has pragmatic problems in that STT packets can be dropped by network middleboxes like stateful firewalls because of modified TCP header semantics. In this paper, we propose yet another layer 4 protocol (Segment-oriented Connection-less Protocol, SCLP) for existing tunneling protocols. Our previous study revealed that the high-performance of STT mainly comes from 2-level software packet pre-reassembly before decapsulation. The SCLP header is designed to take advantage of such processing without modifying existing protocol semantics. We implement a VXLAN over SCLP tunneling and evaluate its performance by comparing with the original VXLAN (over UDP), NVGRE, Geneve, and STT. The results show that the throughput of the proposed method was comparable to STT and almost 70% higher than that of other protocols.

Heterogeneous hetworks (HetNets) have been introduced as an emerging technology in order to meet the increasing demand for mobile data. HetNets are a combination of multi-layer networks such as macrocells and small cells. In such networks, users may suffer significant cross-layer interference. To manage this interference, the 3rd Generation Partnership Project (3GPP) has introduced enhanced Inter-Cell Interference Coordination (eICIC) techniques. Almost Blank SubFrame (ABSF) is one of the time-domain techniques used in eICIC solutions. We propose a dynamically optimal Signal-to-Interference-and-Noise Ratio (SINR)-based ABSF framework to ensure macro user performance while maintaining small user performance. We also study cooperative mechanisms to help small cells collaborate efficiently in order to reduce mutual interference. Simulations show that our proposed scheme achieves good performance and outperforms the existing ABSF frameworks.

Crowd collectiveness, i.e., a quantitative metric for collective motion, has received increasing attention in recent years. Most of existing methods build a collective network by assuming each agent in the crowd interacts with neighbors within fixed radius r region or fixed k nearest neighbors. However, they usually use a universal r or k for different crowded scenes, which may yield inaccurate network topology and lead to lack of adaptivity to varying collective motion scenarios, thereby resulting in poor performance. To overcome these limitations, we propose a compressive sensing (CS) based method for measuring crowd collectiveness. The proposed method uncovers the connections among agents from the motion time series by solving a CS problem, which needs not specify an r or k as a priori. A descriptor based on the average velocity correlations of connected agents is then constructed to compute the collectiveness value. Experimental results demonstrate that the proposed method is effective in measuring crowd collectiveness, and performs on par with or better than the state-of-the-art methods.

This paper proposes a new method that combines signal modulation and FDTD (Finite-Difference Time-Domain) simulations to reduce the computation time in multiple-antenna analysis. In this method, signals are modulated so as to maintain orthogonality among the excited signals; multiple antennas are excited at the same time. This means just one FDTD simulation is needed whereas the conventional method demands as many simulations as there are transmitting antennas. The simulation of a 2×2 multi-antenna system shows that the proposed method matches the performance of the conventional method even though its computation time is much shorter.

With the growth of cloud-based services, cloud data centers are experiencing large growth. A key component in a cloud data center is the network technology deployed. In particular, Ethernet technology, commonly deployed in cloud data centers, is already envisioned for 10 Tbps Ethernet. In this paper, we study and analyze the makespan, workload execution times, and virtual machine migrations as the network speed increases. In particular, we consider homogeneous and heterogeneous data centers, virtual machine scheduling algorithms, and workload scheduling algorithms. Results obtained from our study indicate that the increase in a network's speed reduces makespan and workloads execution times, while aiding in the increase of the number of virtual machine migrations. We further observed that the number of migrations' behaviors in relation to the speed of the networks also depends on the employed virtual machines scheduling algorithm.