An adaptive resource sharing strategy for TDMA

A satellite-based TDMA network consisting of four stations within different rain climatic zones has been operated in the 20/30 GHz frequency range using a recently developed flexible TDMA system allowing for FEC code rate and transmission bit rate variation. In this paper a strategy is presented to counteract overall link degradations due to atmospheric attenuation by dynamic allocation of resources. A spare time slot within the TDMA frame as a ‘common resource’ for bit rate and code rate switching offers up to 12 dB gain, whereas up-link power control, as it is implemented in this configuration, can cope with fades of 8 dB at maximum. For an experimental network configuration the expected long-term performance in terms of system availability is estimated for a viable version of the resource sharing strategy. Thereby, a model to calculate the probability of concurrent attenuation at the individual earth-station sites (‘satellite based diversity’) has been applied and the resulting probability to exhaust the resources is considered as a function of the degrading correlation between attenuations. Simulations with measured data via a ‘channel simulator’ and satellite measurements during the summer months of 1994 with the adaptive TDMA system are planned to test the functionality of the fade countermeasure strategy. Long-term propagation measurements on large-scale site diversity are required to verify predictions on the effective utilization of common resources.     

一种基于资源管理的视频服务器自适应QoS控制中间件

介绍了一种基于资源管理的视频服务器自适应QoS控制中间件,提出了“软”资源预留机制,提高了服务器资源管理的可靠性和准确性。在此基础上,提出了主动QoS调整机制。实验表明该中间件可以有效地确保系统QoS。     

An efficient radio resource management strategy for adaptive OFDM cellular systems

This paper presents an efficient Radio Resource Management （RRM） strategy for adaptive Orthogonal Frequency Division Multiplexing （OFDM） cellular systems. In the proposed strategy, only those users who have the same distance from their base stations can reuse a same subcarrier. This can guarantee the received Carrier-to-Interference ratio （C/I） of each subcarrier to be acceptable as required by system planning. Then by employing different modulation scheme on each subcarrier according to its received C/I, system spectral efficiency can be gracefully increased. Analytical and simulation results show that the spectral efficiency is improved by 40% without sacrificing the Bit Error Rate （BER） performance and call blocking probability and system capacity of the proposed strategy is better than conventional systems.     

自适应OFDMA系统无线资源分配和分组数据调度算法的研究

在对无线资源分配和下行链路分组数据调度算法研究的基础上，提出了一种适应于自适应OFDMA系统的联合算法，即K＆H／MPF算法。理论分析和仿真结果表明：该算法在满足不同用户QoS要求的前提下，不但能够提供比多载波正比公平调度器更高的容量增益，而且以极大的灵活性实现了用户数据的公平发送。     

An optimal solution to resource allocation among soft QoS traffic in wireless network

Optimization theory and nonlinear programming method have successfully been applied into wire‐lined networks (e.g., the Internet) in developing efficient resource allocation and congestion control schemes. The resource (e.g., bandwidth) allocation in a communication network has been modeled into an optimization problem: the objective is to maximize the source aggregate utility subject to the network resource constraint. However, for wireless networks, how to allocate the resource among the soft quality of service (QoS) traffic remains an important design challenge. Mathematically, the most difficult comes from the non‐concave utility function of soft QoS traffic in the network utility maximization (NUM) problem. Previous result on this problem has only been able to find its sub‐optimal solution. Facing this challenge, this paper establishes some key theorems to find the optimal solution and then present a complete algorithm called utility‐based allocation for soft QoS to obtain the desired optimal solution. The proposed theorems and algorithm act as designing guidelines for resource allocation of soft QoS traffic in a wireless network, which take into account the total available resource of network, the users’ traffic characteristics, and the users’ channel qualities. By numerical examples, we illustrate the explicit solution procedures.Copyright © 2013 John Wiley & Sons, Ltd.     

An adaptive AQM to provide delay guarantees for differentiated services

RIO（RED with IN and OUT） is the primary queue management mechanism proposed for assured forwarding in the DiffServ （Differentiated Service） framework. Although RIO can generally provide bandwidth guarantees, its queuing delay is sensitive to the traffic load. This paper presents a qualitative explanation for its origin. As a solution, an Adaptive RIO for Delay （ARIO-D） is proposed to provide guaranteed delay for multimedia traffic. Simulation results show that by trading loss for delay, ARIO-D can effectively improve the robustness of RIO under different and dynamic traffic, and provide stable and differentiated performance of queuing delay without any degradation in performance of throughput.     

An adaptive multiscale inverse scattering approach to photothermal depth profilometry

Photothermal depth profilometry is formulated as a nonlinear inverse scattering problem. Starting with the one-dimensional heat diffusion equation, we derive a mathematical model relating arbitrary variation in the depth-dependent thermal conductivity to observed thermal wavefields at the surface of a material sample. The form of the model is particularly convenient for incorporation into a nonlinear optimization framework for is particularly convenient for incorporation into a nonlinear optimization framework for recovering the conductivity based on thermal wave data obtained at multiple frequencies. We develop an adaptive, multiscale algorithm for solving this highly ill-posed inverse problem. The algorithm is designed to produce an accurate, low-order representation of the thermal conductivity by automatically controlling the level of detail in the reconstruction. This control is designed to reflect both (1) the nature of the underlying physics, which says that scale should decrease with depth, and (2) the particular structure of the conductivity profile, which may require a sparse collection of fine-scale components to adequately represent significant features such as a layering structure. The approach is demonstrated in a variety of synthetic examples representative of nondestructive evaluation problems seen in the steel industry.The work of authors E. L. Miller and I. Yavuz was supported by a CAREER Award from the National Science Foundation MIP-9623721, an ODDR&E MURI under Air Force Office of Scientific Research contract F49620-96-1-0028, and the Army Research Office Demining MURI under grant DAAG55-97-1-0013. The work of authors L. Nicolaides and A. Mandelis was supported by a research contract from Material and Manufacturing Ontario (MMO).     

An adaptive heuristic algorithm to solve the network slicing resource management problem

Artificial intelligence-based (AI-based) network slicing bandwidth allocation enables the 5G/6G service providers to create multiple virtual networks atop a shared physical infrastructure while fulfilling varying end-user demands. Some researchers argue that AI-enable network may run the danger of having private information compromised. We still need a backup rule-based methodology to allocate bandwidth resource to each slice, if the AI-based method suddenly encounters security issues. To design such a rule-based methodology, this study attempts to answer two questions: (1) Is the network slicing bandwidth allocation problem the nondeterministic polynomial-time completeness (NP-completeness)? (2) Is there a heuristic methodology without any training process, which has equivalent performance compared to the AI-based methodology? This study first proves the classical network slicing bandwidth allocation problems is NP-completeness. This shows that the designed heuristic method is inescapably suboptimal to the network slicing bandwidth allocation problem. Secondly, this study proposes the Adaptive Hungarian Algorithm (AHA), which outperforms previous AI-empowered method and does not need any training process. The experiments demonstrate that AHA reached 93%–97% of the maximal system throughput by brute-and-force algorithm, compared to other methodologies only having at most 93% of the maximal system throughput. This also indicates that AHA is capable to solve the network slicing bandwidth allocation problem, if the telecommunication operators do not have sufficient sample complexity to train an AI model.     

IP语音包的自适应编码和封装算法的研究

IP电话与传统电话相比语音质量较差，其中最主要的原因是因特网的带宽变化较大，导致丢包率较大。该文根据因特网带宽变化的特点提出了1种应用在IP电话网关中的语音自适应编码与封装策略，采用该策略的编码器能根据网络的带宽变化动态调节语音编码速率和语音包封装大小。据此，本文提出了4种算法：一种基于RTP协议语音包丢失率的计算算法、变速率编码算法，不同长度IP语音包的封装算法和根据丢包率来调整编码速率和封装的自适应算法。     

An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction

Copy-move forgery detection (CMFD) is the process of determining the presence of copied areas in an image. CMFD approaches are mainly classified into two groups: keypoint-based and block-based techniques. In this paper, a new CMFD approach is proposed on the basis of both block and keypoint based approaches. Initially, the forged image is partitioned into non overlapped segments utilizing adaptive watershed segmentation, wherein adaptive H-minima transform is used for extracting the markers. Also, an Adaptive Galactic Swarm Optimization (AGSO) algorithm is used to select optimal gap parameter while selecting the markers for reducing the undesired regional minima, which can increase the segmentation performance. After that, the features from every segment are extracted as segment features (SF) using Hybrid Wavelet Hadamard Transform (HWHT). Then, feature matching is performed using adaptive thresholding. The false matches or outliers can be removed with the help of Random Sample Consensus (RANSAC) algorithm. Finally, the Forgery Region Extraction Algorithm (FREA) is utilized for detecting the copied portion from the host image. Experimental results indicate that the proposed scheme find out image forgery region with Precision = 92.45%; Recall = 93.67% and F1 = 92.75% on MICC-F600 dataset and Precision = 94.52%; Recall = 95.32% and F1 = 93.56% on Bench mark dataset at pixel level. Also, it outperforms the existing approaches when the image undergone certain geometrical transformation and image degradation.     

An efficient and flexible MPLS signaling framework for mobile networks

Multiprotocol Label Switching (MPLS) has gained momentum in recent years as an effective tool to provide Quality of Service (QoS) in a variety of networks. This has in turn created active interest in the area of recovery in MPLS based networks. A number of recovery schemes for MPLS domains have been proposed in recent years. However, the current schemes lack support for recovery in dynamic network topologies. In this paper, a new flexible signaling protocol for LSP rerouting in dynamic network environments is introduced. The signaling protocol recovers from node and link failures reactively, taking a local approach to LSP reestablishment. The performance of the signaling protocol is evaluated through simulations. Results indicate that the protocol can effectively and efficiently handle rerouting in dynamic networks with a low protocol signaling overhead as compared to contemporary MPLS rerouting protocols. This would enable the MPLS based IP-QoS support mechanisms to extend to dynamic network topologies. A preliminary version of this work was presented at the 2004 IEEE International Conference on Communications, Paris. Ramprasad Nagarajan has received his B.E. degree in Electronics and Telecommunications from Pune University, India in 1999. He received his M.S. degree in Electrical and Computer Engineering from the Ohio State University, Columbus, OH in 2004. Currently, he is a Wireless Network Engineer in Nortel Networks, specializing in the area of network architecture and design of wireless packet core networks. Ramprasad’s current research interests include the study of wireless network evolution trends, next generation wireless networks, network capacity planning, performance analysis, and optimization. He is a member of the IEEE. Eylem Ekici has received his B.S. and M.S. degrees in Computer Engineering from Bogazici University, Istanbul, Turkey, in 1997 and 1998, respectively. He received his Ph.D. degree in Electrical and Computer Engineering from Georgia Institute of Technology, Atlanta, GA, in 2002. Currently, he is an assistant professor in the Department of Electrical and Computer Engineering of the Ohio State University, Columbus, OH. Dr. Ekici’s current research interests include wireless sensor networks, vehicular communication systems, next generation wireless systems, and space-based networks, with a focus on routing and medium access control protocols, resource management, and analysis of network architectures and protocols. He also conducts research on interfacing of dissimilar networks.     

An improved recursive and adaptive median filter for high density impulse noise

An improved recursive and adaptive median filter (RAMF) for the restoration of images corrupted with high density impulse noise is proposed in the present paper. Adaptive operation of the filter is justified with the variation in size of working window which is centered at noisy pixels. Based on the presence of noise-free pixel(s), the size of working window changes. The noisy pixels are filtered through the replacement of their values using both noise-free pixels of the current working window and previously processed noisy pixels of that window. These processed noisy pixels are obtained recursively. The combined effort thus provides an improved platform for filtering high density impulse noise of images. Experimental results with several real-time noisy images show that the proposed RAMF outperforms other state-of-the-art filters quantitatively in terms of peak signal to noise ratio (PSNR) and image enhancement factor (IEF). The superiority of the filter is also justified qualitatively through visual interpretation.     

An adaptive delay and synchronization control scheme for Wi-Fi based audio/video conferencing

The prevalence of the IEEE 802.11b technology has made Wi-Fi based Audio/Video (AV) conferencing applications a viable service. However, due to the “best-effort” transport service and other unpredictable factors such as user mobility, location and background traffic, the transport channel behavior often fluctuates drastically. It thus becomes rather difficult to configure an appropriate de-jitter buffer to maintain the temporal fidelity of the AV presentation. We propose in this paper an adaptive delay and synchronization control scheme for AV conferencing applications over campus-wide WLANs. Making use of a distributed timing mechanism, the scheme monitors the synchronization errors and estimates the delay jitters among adjacent Media Data Units (MDUs) in real-time. It piece-wisely controls the equalization delay to compensate for the delay jitters experienced by MDUs in a closed-loop manner. We investigate the performance of the proposed scheme through trace-driven simulations. We collected network traces from a production campus-wide IEEE 802.11b WLAN by emulating real conferencing sessions. Simulation results show that the scheme is capable of dynamically balancing between synchronization requirements and latency requirements in all scenarios. Small synchronization phase distortions, low MDU loss percentages and low average end-to-end delay can be achieved simultaneously. In particular, compared with solutions using a static setting, the proposed scheme is able to achieve a reduction of around 100ms in end-to-end delay with the same amount of MDU losses under some media-unfriendly situations. Haining Liu Haining Liu is currently a graduate student researcher in the Donald Bren School of Information and Computer Sciences at the University of California, Irvine. He received the B.E degree and M.S.E. degree in Electrical Engineering from Tianjin University (China) in 1994 and 1997, respectively. After that, he was with the Beijing Posts and Telecommunications Design Institute of Ministry of Information Industry (China) as a telecommunications design engineer from 1997 to 1999. He received another M.S.E. degree in Systems Engineering from the University of Pennsylvania in 2000. His research interests include multimedia networking, wireless networks and real-time systems. Prof. Magda El Zarki Magda El Zarki received the B.E.E. from Cairo University, Cairo, Egypt in 1979 and the Ph.D. degree in Electrical Engineering from Columbia University, New York City, NY in 1981 in December 1987. Currently she holds the position of Professor in the Donald Bren School of Information and Computer Sciences at the University of California, Irvine, where she is involved in the telecommunication networks program. Prior to that she was an Associate Professor in the Department of Electrical Engineering at the University of Pennsylvania in Philadelphia where she also held the position of Director of the Telecommunications Program. From 1992 - 1996 she held the position of Professor of Telecommunications at the Technical University of Delft, Delft, The Netherlands. Ms. El Zarki is the co-author of a new text: Mastering Networks: An Internet Lab Manual. She is also on the editorial board of several journals in the telecommunications area, and is actively involved in many international conferences.     

An optimal scheduling and routing under adaptive spectrum-matching framework for MIMO systems

Multi-users (MUs) along the communication links cause noise and traffic in the channel. The prediction of availability and the optimal usage of channels are the main objectives of the multi-input multi-output (MIMO) system. Several optimisation algorithms select the optimal channel for the users effectively. But the high-error rate and the probability values are the two major problems in traditionally optimised channel selection methods. The bandwidth allotted for information transmission is minimum. Moreover, the outage probability values are maximum in traditional scheduling algorithms. This paper proposes the new optimisation algorithm that predicts the channels for transmission and adaptive spectrum matching concept to predict the suitable channel from allocated bands. Also, the prioritisation on high-spectrum intensity basis assures an efficient data delivery to the receiver. The scheduling of available channels and data prioritisation minimises the error probability rates. This paper investigates the effectiveness of proposed optimal channel utilisation against the different modulation schemes such as three-dimensional complementary codes, linear network coding with the quadrature phase shift keying in terms of the average block error probability and bit error rate.     

A signal adaptive diffusion filter for video coding: Mathematical framework and complexity reductions

In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. In this setting, several measures to reduce the coding complexity of the tool are introduced, discussed and tested thoroughly. The coding complexity is reduced by up to 70% while maintaining over 80% of the gain. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime complexity of 119% and 117% decoder runtime complexity. For individual test sequences, results of 7.36% for Random Access are accomplished.     

Novel adaptive color space transform and application to image compression

Various contemporary standards by Joint Picture Expert Group, which is used for compression, exploited the correlation among the color components using a component color space transform before the subband transform stage. The transforms used to de-correlate the colors are primarily the fixed kernel transforms, which are not suitable for large class of images. In this paper an image dependent color space transform (ID-CCT), exploiting the inter-channel redundancy optimally and which is very much suitable for compression has been proposed. Also the comparative performance has been evaluated and a significant improvement has been observed, objectively as well as subjectively over other quantifiable methods.     

Efficient allocation of LTE downlink spectral resource to improve fairness and throughput

For the current generation of cellular communication systems, long‐term evolution (LTE) has been the major protocol to support high‐speed data transmission. It is critical to allocate downlink spectral resource in LTE, namely, resource blocks (RBs), but the issue is not well addressed in the standard. Therefore, the paper develops an efficient RB allocation algorithm with 4 mechanisms to improve both fairness and throughput in LTE. For fairness concern, our RB allocation algorithm uses a resource‐reservation mechanism to prevent cell‐edge user equipments from starvation, and a credit‐driven mechanism to keep track of the amount of resource given to each user equipment. For throughput concern, it adopts both weight‐assignment and RB‐matching mechanisms to allocate each RB to a packet according to its flow type and length. Through simulations, we demonstrate that the proposed RB allocation algorithm can significantly increase both throughput and fairness while reducing packet dropping and delays of real‐time flows, as compared with previous methods.     

基于Bootstrap技术的Web服务QoS值的置信区间估计和预测

目前对Web服务QoS（Quality of Service）的预测研究,通常预测QoS的静态值,很少预测QoS值的置信区间.本文借助非参数统计学的Bootstrap技术,提出估计Web服务QoS值置信区间的方法;然后利用与当前Web用户相似的其他Web用户调用待预测Web服务的QoS历史数据,预测当前Web用户调用待预测Web服务的QoS值的置信区间.本文估计了WSDream数据集1中每个用户调用每个Web服务的QoS值的置信区间,实验发现这些置信区间的上下限近似服从重尾分布.通过随机选择WSDream数据集1中60%到90%的用户和Web服务作为训练集,预测另外10%到40%的用户和Web服务的QoS值,实验结果表明预测的QoS置信区间与估计的QoS置信区间的平均覆盖率超过70%,最高达76%.在服务选择或服务推荐时给用户提供一个估计的或预测的QoS置信区间,可以更好地满足用户的个性化需求.     

Three-dimensional switchbox multiplexing in emerging 3D-FPGAs to reduce chip footprint and improve TSV usage

Three-dimensional integration technology is proposed to break down long wires and increase integration level of emerging complex designs. However, efficiency of this technology heavily depends on the usage of Through-Silicon Vias. TSVs are key solutions for cooling the 3D-chips but they occupy considerable silicon area. Therefore, reducing the number of required TSVs in routing step is very critical in 3D-chips. In this paper, a TSV multiplexing approach is proposed to reduce the number of required routing TSV. We proposed two multiplexed 3D-switchbox architectures. In the first architecture, the TSVs inside the switchboxes are multiplexed while in the second architecture, TSVs are multiplexed between the switchboxes. Moreover, a routing algorithm is suggested to route the FPGA using the multiplexed switchboxes to evaluate the proposed architectures. Experimental results show that the presented architectures and algorithms reduce the number of used TSVs by 64.58% and 71.27% on average for the first and second architectures respectively, in cost of a negligible overheads in total wire length and auxiliary switches.     

A joint resource optimization and adaptive modulation framework for uplink single‐carrier frequency‐division multiple access systems

In this paper, we study joint resource allocation and adaptive modulation in single‐carrier frequency‐division multiple access systems, which is adopted as the multiple access scheme for the uplink in the 3GPP Long Term Evolution standard. We formulate an adaptive modulation and sum‐cost minimization (JAMSCmin) problem. Unlike orthogonal frequency‐division multiple access, in addition to the restriction of allocating a subchannel to one user at most, the multiple subchannels allocated to a user in single‐carrier frequency‐division multiple access systems should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for orthogonal frequency‐division multiple access, etc.) cannot help towards its optimal solution. We propose a novel optimization framework for the solution of this problem that is inspired from the recently developed canonical duality theory. We first formulate the optimization problem as binary‐integer programming (BIP) problem and then transform this BIP problem into continuous space canonical dual problem that is the concave maximization problem. Based on the solution of the canonical dual problem, we derive joint resource allocation and adaptive modulation algorithm, which has polynomial time complexity. We provide conditions under which the proposed algorithm is optimal. We compare the proposed algorithm with the existing algorithms in the literature. The results show a tremendous performance gain. Copyright © 2013 John Wiley & Sons, Ltd.