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The popularity of video services such as video call or video on-demand has made it impossible for people to live without them in their daily lives. It can be anticipated that the explosive growth of vehicular communication owing to the widespread use of in-vehicle video infotainment applications in the future will result in increasing fragmentation and congestion of the wireless transmission spectrum. Accordingly, effective bandwidth management algorithms are demanded to achieve efficient communication and stable scalability in next-generation vehicular networks. To the best of our current knowledge, a noticeable gap remains in the existing literature regarding the application of the latest advancements in network communication technologies. Specifically, this gap is evident in the lack of exploration regarding how cutting-edge technologies can be effectively employed to optimize bandwidth allocation, especially in the realm of video service applications within the forthcoming vehicular networks. In light of this void, this paper presents a seamless integration of cutting-edge 6G communication technologies, such as terahertz (THz) and visible light communication (VLC), with the existing 5G millimeter-wave and sub-6 GHz base stations. This integration facilitates the creation of a network environment characterized by high transmission rates and extensive coverage. Our primary aim is to ensure the uninterrupted playback of real-time video applications for vehicle users. These video applications encompass video conferencing, live video, and on-demand video services. The outcomes of our simulations convincingly indicate that the proposed strategy adeptly addresses the challenge of bandwidth competition among vehicle users. Moreover, it notably boosts the efficient utilization of bandwidth from less crowded base stations, optimizes the fulfillment of bandwidth prerequisites for various video applications, and elevates the overall video quality experienced by users. Consequently, our findings serve as a successful validation of the practicality and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

In recent years, the use of broadband bandwidth and redundant links for stable transmission in networks has resulted in excessive energy consumption and reduced transmission efficiency. In this paper, we propose an optimization algorithm that reduces the number of transmission links and minimizes transmission energy by removing redundant traffic in networks where traffic redundancy is allowed. The optimization algorithm proposed in this paper uses the meta-heuristic method using Tabu search algorithm. The proposed optimization algorithm minimizes transmission energy by designing a neighborhood generation method that efficiently routes overlapping traffic. The performance evaluation of the proposed optimization algorithm was performed in terms of the number of links used to transmit all traffic generated in the network and the transmission energy consumed. From the performance evaluation results, it was confirmed that the proposed algorithm is superior to other algorithms previously proposed. [ABSTRACT FROM AUTHOR]

Power amplifier is one of the main components in the RF transmitters. It must provide various stringent features that can lead to complicating the design. In this paper, a new optimizing method based on the inclined planes system optimization algorithm is presented for the design of a discrete power amplifier. It is evaluated in a 2.4-3 GHz power amplifier, which is designed based on "Cree's CGH40010F GaN HEMT". The optimization goals are input and output return losses, Power Added Efficiency, and Gain. Large signal simulation of the optimized power amplifier shows a good performance across the bandwidth. In this frequency range, the input and output return losses are about lower than - 10 dB, the Power Added Efficiency is greater than 51%, while the Gain is higher than 13.5 dB. A two-tone test with a frequency space of 1 MHz is applied for the linearity evaluation of the designed power amplifier. The obtained result shows that the power amplifier has good linearity with a low memory effect. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive synthesis using the particle swarm optimization (PSO) for implementations of resonator-coupled filters is proposed. The coupling matrix of in-band filtering response is achieved and optimized by the PSO-based synthesis. Meanwhile, the stopband coupling matrix of the filter is predicted based on the combination of extra resonant nodes representing stopband spurious and parasitic effect of input/output ports. In addition, an enhanced accurate prediction of filter performance is calculated by the proposed approach, considering the practical fabrication tolerance on filter design. To verify principles mentioned earlier, various resonator-coupled filters are implemented. The calculation, EM-simulation, and measurement of filters show good agreements in both passband and stopband. [ABSTRACT FROM AUTHOR]

In this paper, an evolutionary optimization procedure is presented to generate band-pass metasurface-based filters in terahertz regime. As a measure of novelty, pass-band, transition, and out-band characteristics are investigated separately, all of which result in different metasurfaces for filtering applications. The presented approach is defined based on random hill climbing algorithm, regarding the established link between Matlab and HFSS software. A metasurface-based filter with specific properties is considered as the main problem to be solved by the optimization method. Moreover, the fuzzy theory, mean square method, and weighting coefficient procedure are considered to define an efficient fitness function evaluation approach. Also, a step-by-step procedure is used to generate desired structures with a great note of efficiency. The final generated structure has magnificent characteristics including sharp transitions together with transmittance around 0.68 and less than 0.04 at pass-band and out-band regions, respectively. Also, the generated metasurface benefits from wide bandwidth (65%) and great compactness compared to other previous works. [ABSTRACT FROM AUTHOR]

In space communication environments, most studies in delay tolerant networks (DTNs) adopt experimental approaches and they have shown that the utilization efficiency of link bandwidth can be affected by changing packet sizes at the bundle layer and the convergence layer. However, the lack of theoretical work makes it difficult to find the optimal packet sizes in general scenarios. In this paper, we focus on this important issue by performing a theoretical analysis to solve the packet size optimization problems in both one-hop and multihop DTNs. As our analysis is based on the widely used DTN architecture with Bundle Protocol (BP) and Licklider Transmission Protocol (LTP), the theoretical results can be applied to any DTN scenario using this architecture. We formulate packet size optimization problems in DTNs as nonconvex optimization problems. Furthermore, a goodput enhancement algorithm (GEA) is also proposed to find the optimal packet sizes in order to maximize goodput in DTNs. We conduct numerical analysis as well as simulations to validate our theoretical analysis and study the factors that determine the optimal packet sizes for BP and LTP. Numerical results show that GEA achieves significant performance improvement compared with schemes where packet size optimization on BP and LTP is not jointly considered. [ABSTRACT FROM PUBLISHER]

This paper considered a wireless communication network with multiple UAV base stations and multiple users.In the network,due to the limited spectrum resource,the backhaul links of the UAV base stations and the data links of the users shared the same spectrum.In order to use the spectrum resource efficiently and improve the communication performance of the users,this paper jointly optimized the bandwidth and power of the backhaul and data links as well as the flight trajectory of the UAV base stations,to maximize the minimum average rate of all users.This joint optimization was subject to the UAVs’ mobility,the total spectrum bandwidth,and the total transmit power,and the involved joint optimization problem was non-convex,thus it was difficult to find the optimal solution.This paper proposed an efficient algorithm to obtain a high-quality suboptimal solution of it.Simulation results show that the minimum user rate achieved by the proposed joint optimization algorithm is significantly higher than that of the benchmark schemes. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient job-dispatching algorithm for transcoding as a service (TaaS) in a multimedia cloud. We aim to minimize the energy consumption of service engines in the cloud while achieving low delay for TaaS. We formulate the job-dispatching problem as a constrained optimization problem under the framework of Lyapunov optimization. Using the drift-plus-penalty function, we propose an online algorithm that dispatches the transcoding jobs to service engines, with an objective to Reduce Energy consumption while achieving the QUEue STability (REQUEST). We first characterize the fundamental tradeoff between energy consumption and queue delay for the REQUEST algorithm numerically and obtain its performance bound theoretically. Second, we study the robustness of the REQUEST algorithm, with numerical results indicating that the REQUEST algorithm is robust to the inaccuracy of estimating the transcoding time. Third, we compare the performance of the REQUEST algorithm with the other two algorithms, i.e., the Round Robin and Random Rate algorithms. By simulation and real trace data, we show that by appropriately choosing the control variable, the REQUEST algorithm outperforms the Round Robin and Random Rate algorithms, with smaller time average energy consumption and time average queue length. The proposed REQUEST algorithm can be applied in cloud-assisted multimedia transcoding service. [ABSTRACT FROM PUBLISHER]

In this paper, we present an algorithm which automatically maps a given set of intellectual property onto a generic regular network-on-chip (NoC) architecture and constructs a deadlock-free deterministic routing function such that the total communication energy is minimized. At the same time, the performance of the resulting communication system is guaranteed to satisfy the specified design constraints through bandwidth reservation. As the main theoretical contribution, we first formulate the problem of energy- and performance-aware mapping in a topological sense, and show how the routing flexibility can be exploited to expand the solution space and improve the solution quality. An efficient branch-and-bound algorithm is then proposed to solve this problem. Experimental results show that the proposed algorithm is very fast, and significant communication energy savings can be achieved. For instance, for a complex video/audio application, 51.7% communication energy savings have been observed, on average, compared to an ad hoc implementation. [ABSTRACT FROM AUTHOR]

With the development of multi-/many-core processors, applications need to be written as parallel programs to improve execution efficiency. For data-intensive applications that use multiple threads to read/write files simultaneously, an I/O sub-system can easily become a bottleneck when too many of these types of threads exist; on the contrary, too few threads will cause insufficient resource utilization and hurt performance. Therefore, programmers must pay much attention to parallelism control to find the appropriate number of I/O threads for an application. This paper proposes a parallelism control mechanism named IOPA that can adjust the parallelism of applications to adapt to the I/O capability of a system and balance computing resources and I/O bandwidth. The programming interface of IOPA is also provided to programmers to simplify parallel programming. IOPA is evaluated using multiple applications with both solid state and hard disk drives. The results show that the parallel applications using IOPA can achieve higher efficiency than those with a fixed number of threads. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) theory indicates that the optimal reconstruction of an unknown sparse signal can be achieved from limited noisy measurements by solving a sparsity-driven optimization problem. For inverse synthetic aperture radar (ISAR) imagery, the scattering field of the target is usually composed of only a limited number of strong scattering centers, representing strong spatial sparsity. This paper derives a new autofocus algorithm to exploit the sparse apertures (SAs) data for ISAR imagery. A sparsity-driven optimization based on Bayesian compressive sensing (BCS) is developed. In addition, we also propose an approach to determine the sparsity coefficient in the optimization by using constant-false-alarm-rate (CFAR) detection. Solving the sparsity-driven optimization with a modified Quasi-Newton algorithm, the phase error is corrected by combining a two-step phase correction approach, and well-focused image with effective noise suppression is obtained from SA data. Real data experiments show the validity of the proposed method. [ABSTRACT FROM PUBLISHER]

Several types of low-power passive equalizer are investigated and optimized in this paper. The equalizer topologies include T-junction, parallel R-C, and series R-L structures. These structures can be inserted either at the driver or the receiver side at both the chip and package level to improve the channel bandwidth of central processing unit (CPU)-memory links. Using the eye area as the objective function to be maximized, we optimize these equalizers for the CPU–memory interconnection of an IBM POWER6 system with and without practical constraints on the RLC parameter values. An efficient optimization flow combined with an algorithm predicting the worst case eye diagram is proposed and employed to optimize 42 equalizer schemes. Simulation results show that, without employing any equalizer, the data eye is closed for the bit rate of 6.4 Gb/s, while the equalized schemes can work at the bit rate of 8 Gb/s. Very promising improvements in eye height and jitter are observed with little power overhead. Simulation results also show the sensitivity of the equalization schemes to the RLC values and the effect of coupling noise. [ABSTRACT FROM PUBLISHER]

In this paper, a cross-layer analytical framework is proposed to analyze the throughput and packet delay of a two-hop wireless link in wireless mesh network (WMN). It considers the adaptive modulation and coding (AMC) process in physical layer and the traffic queuing process in upper layers, taking into account the traffic distribution changes at the output node of each link due to the AMC process therein. Firstly, we model the wireless fading channel and the corresponding AMC process as a finite state Markov chain (FSMC) serving system. Then, a method is proposed to calculate the steady-state output traffic of each node. Based on this, we derive a modified queuing FSMC model for the relay to gateway link, which consists of a relayed non-Poisson traffic and an originated Poisson traffic, thus to evaluate the throughput at the mesh gateway. This analytical framework is verified by numerical simulations, and is easy to extend to multi-hop links. Furthermore, based on the above proposed cross-layer framework, we consider the problem of optimal power and bandwidth allocation for QoS-guaranteed services in a two-hop wireless link, where the total power and bandwidth resources are both sum-constrained. Secondly, the practical optimal power allocation algorithm and optimal bandwidth allocation algorithm are presented separately. Then, the problem of joint power and bandwidth allocation is analyzed and an iterative algorithm is proposed to solve the problem in a simple way. Finally, numerical simulations are given to evaluate their performances. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes a scheme for bandwidth allocation in wireless ad hoc networks. The quality-of-service (QoS) levels for each end-to-end flow are expressed using a resource-utility function, and our algorithms aim to maximize aggregated utility. The shared channel is modeled as a bandwidth resource defined by maximal cliques of mutual interfering links. We propose a novel resource allocation algorithm that employs an auction mechanism in which flows are bidding for resources. The bids depend both on the flow's utility function and the intrinsically derived shadow prices. We then combine the admission control scheme with a utility-aware on-demand shortest path routing algorithm where shadow prices are used as a natural distance metric. As a baseline for evaluation, we show that the problem can be formulated as a linear programming (LP) problem. Thus, we can compare the performance of our distributed scheme to the centralized LP solution, registering results very close to the optimum. Next, we isolate the performance of price-based routing and show its advantages in hotspot scenarios, and also propose an asynchronous version that-is more feasible for ad hoc environments. Further experimental evaluation compares our scheme with the state of the art derived from Kelly's utility maximization framework and shows that our approach exhibits superior performance for networks with increased mobility or less frequent allocations. [ABSTRACT FROM AUTHOR]

Consider directed acyclic graph (DAG) scheduling for a large heterogeneous system, which consists of processors with varying processing capabilities and network links with varying bandwidths. The search space of possible task schedules for this problem is immense. One possible approach for this optimization problem, which is NP-hard, is to start with the best task schedule found by a fast deterministic task scheduling algorithm and then iteratively attempt to improve the task schedule by employing a general random guided search method. However, such an approach can lead to extremely long search times, and the solutions found are sometimes not significantly better than those found by the original deterministic task scheduling algorithm. In this paper, we propose an alternative strategy, termed Push-Pull, which starts with the best task schedule found by a fast deterministic task scheduling algorithm and then iteratively attempts to improve the current best solution using a deterministic guided search method. Our simulation results show that given similar runtimes, the Push-Pull algorithm performs well, achieving results similar to or better than all of the other algorithms being compared. [ABSTRACT FROM AUTHOR]

Finding a disjoint path pair is an important component in survivable networks. Since the traffic is carried on the active (working) path most of the time, it is useful to find a disjoint path pair such that the length of the shorter path (to be used as the active path) is minimized. In this paper, we first address such a Min-Min problem. We prove that this problem is NP-complete in either single link cost (e.g., dedicated backup bandwidth) or dual link cost (e.g., shared backup bandwidth) networks. In addition, it is NP-hard to obtain a K-approximation to the optimal solution for any K > 1. Our proof is extended to another open question regarding the computational complexity of a restricted version of the Min-Sum problem in an undirected network with ordered dual cost links (called the MSOD problem). To solve the Min-Min problem efficiently, we introduce a novel concept called conflicting link set which provides insights into the so-called trap problem, and develop a divide-and-conquer strategy. The result is an effective heuristic for the Min-Min problem called COnflicting Link Exclusion (COLE), which can outperform other approaches in terms of both the optimality and running time. We also apply COLE to the MSOD problem to efficiently provide shared path protection and conduct comprehensive performance evaluation as well as comparison of various schemes for shared path protection. We show that COLE not only processes connection requests much faster than existing integer linear programming (ILP)-based approaches but also achieves a good balance among the active path length, bandwidth efficiency, and recovery time. [ABSTRACT FROM AUTHOR]

Temporal blocking is a class of algorithms which reduces the required memory bandwidth ( B/F ratio) of a given stencil computation, by “blocking” multiple time steps. In this paper, we prove that a lower limit exists for the reduction of the B/F attainable by temporal blocking, under certain conditions. We introduce the PiTCH tiling, an example of temporal blocking method that achieves the optimal B/F ratio. We estimate the performance of PiTCH tiling for various stencil applications on several modern CPUs. We show that PiTCH tiling achieves 1.5<2 times better B/F reduction in three-dimensional applications, compared to other temporal blocking schemes. We also show that PiTCH tiling can remove the bandwidth bottleneck from most of the stencil applications considered. [ABSTRACT FROM AUTHOR]

This paper describes a systematic investigation on the use of the genetic algorithm (GA) to accomplish ultra-low reflective multilayer coating designs for optoelectronic device applications. The algorithm is implemented using LabVIEW as a programming tool. The effects of the genetic operators, such as the type of crossover and mutation, as well as algorithm parameters, such as population size and range of search space, on the convergence of design-solution were studied. Finally, the optimal design is obtained in terms of the thickness of each layer for the multilayer AR coating using optimized genetic operators and algorithm parameters. The program is successfully tested to design AR coating in NIR wavelength range to achieve average reflectivity (R) below 10 −3 over the spectral bandwidth of 200 nm with different combinations of coating materials in the stack. The random-point crossover operator is found to exhibit a better convergence rate of the solution than single-point and double-point crossover. Periodically re-initializing the thickness value of a randomly selected layer from the stack effectively prevents the solution from becoming trapped in local minima and improves the convergence probability. [ABSTRACT FROM AUTHOR]

Increasingly more internet-applications require the supports of multiple multicasts, while existing multicast routing algorithms only aim to establish a single multicast routing tree (MRT). Sequentially optimizing the co-existing MRTs each is a common practice of handling the situation of multiple multicast session being concurrent, but it results in link-congestion on low-cost routes, or leads to non-optimal solution even if bandwidth reservation strategy is adopted. This paper proposes to optimize multiple co-existing MRTs as a whole via one-off optimization instead of sequentially optimizing each in isolation. To carry out the one-off optimization, a discrete artificial fish school algorithm (DAFSA) is proposed. The simulation results show that the proposed DAFSA is capable of optimally packing co-existing MRTs and exhibits remarkably better ability than several the most representative state-of-the-art algorithms in the sense of avoiding the link-congestion and minimizing the overall tree cost. The running time of the proposed DAFSA fully meets the requirements of the practical IP multicasting. Besides, Monte Carlo test proves that the convergence of the proposed DAFSA is not sensitive to its parameters. [ABSTRACT FROM AUTHOR]

The cloud-based media streaming service is a promising paradigm for multimedia applications. It is attractive to media streaming service providers, who wish to deploy their media server clusters in a media cloud at reduced cost. Since the real-time live streaming service is both a bandwidth-intensive and quality-sensitive application, how to optimize the internal bandwidth utilization of a data center network (DCN) as well as guarantee the external bandwidth of the real-time live streaming application, is a key issue of deploying virtual machine (VM)-hosted media server cluster in a media cloud. Therefore, in this study, we propose an external-bandwidth-guaranteed media server cluster deployment scheme in media cloud. The approach simultaneously considers the outside bandwidth requirement of a tree-based media server cluster for live streaming and the intra-bandwidth consumption of a DCN. The proposed scheme models the optimal problem as a new terminal-Steiner-tree-like problem and provides an approximate algorithm for placing the media servers. Our evaluation results show that the proposed scheme guarantees the external bandwidth requirement of a real-time live streaming application, at the same time, greatly reduces the intra-bandwidth utilization of a media cloud with different DCN structures. [ABSTRACT FROM AUTHOR]

The increasing demand for network applications, such as teleconferencing, multimedia messaging and mobile TV, which have diverse requirements, has resulted in the introduction of Long Term Evolution (LTE) by the Third Generation Partnership Project (3GPP). LTE networks implement resource allocation algorithms to distribute radio resource to satisfy the bandwidth and delay requirements of users. However, the scheduling algorithm problem of distributing radio resources to users is not well defined in the LTE standard and thus considerably affects transmission order. Furthermore, the existing radio resource algorithm suffers from performance degradation under prioritised conditions because of the minimum data rate used to determine the transmission order. In this work, a novel downlink resource allocation algorithm that uses quality of service (QoS) requirements and channel conditions to address performance degradation is proposed. The new algorithm is formulated as an optimisation problem where network resources are allocated according to users’ priority, whereas the scheduling algorithm decides on the basis of users’ channel status to satisfy the demands of QoS. Simulation is used to evaluate the performance of the proposed algorithm, and results demonstrate that it performs better than do all other algorithms according to the measured metrics. [ABSTRACT FROM AUTHOR]

Micro-perforated panel (MPP) absorber with a single uniform air cavity is regarded as a promising sound-absorbing structure; however, MPP absorption performance remains unfortunately limited due to the Helmholtz resonance mechanism, which results in more and more attention being placed on composite MPP absorbers. This study focuses on acoustic properties of different composite MPP absorbers, including three types of composite MPP absorbers which are coupled in serial, parallel and serial-parallel modes. Their mathematical models of the normal absorption coefficient are established by utilizing the acoustic electric analogy method. The single-cavity MPP absorber and three composite MPP absorbers are preliminarily designed to verify the equivalent circuit models and perform a pilot analysis of their sound absorption characteristics. Moreover, the particle swarm optimization algorithm is selected to optimize the absorbers so that optimal combination of structure parameters within a prescribed frequency range can be obtained. In addition, the absorbers are made based on the optimized parameters for experimental investigation. The results show that a wider absorption bandwidth may be achieved by composite MPP absorbers through introducing additional absorption peaks with reference to that for the conventional single-cavity MPP absorber, and there are more absorption peaks for the serial-parallel-coupled MPP absorber rather than the simply serial- or parallel-coupled MPP absorber so that better sound absorption effect may be achieved. [ABSTRACT FROM AUTHOR]

Finite-difference frequency-domain technique in conjunction with the particle swarm optimization algorithm is presented as an effective procedure for ridged waveguides design optimization. A suitable objective function is furthermore able to deal with the conflicting requirements of wide bandwidth and high power handling capability. Different configurations have been analyzed, and the influence of the algorithm parameters on the optimized structure has been investigated. [ABSTRACT FROM AUTHOR]

A study of the implementation patterns among massively threaded applications for many-core GPUs reveals that each of the seven most commonly used algorithm and data optimization techniques can enhance the performance of applicable kernels by 2 to 10× in current processors while also improving future scalability. The featured Web extra is a video interview with author John Stratton, who describes how implementation patterns can improve future scalability. YouTube URL: http://youtu.be/fgn9LJbInMw [ABSTRACT FROM PUBLISHER]

Many network operators offer some type of tiered service, in which users may select only from a small set of service levels (tiers). Such a service has the potential to simplify a wide range of core network functions, allowing the providers to scale their operations efficiently. In this work, we study a number of problem variants related to service tier selection. Our contributions include: 1) a faster algorithm for obtaining optimal service tiers; 2) a new formulation and optimal algorithm to optimize jointly the number and magnitude of each service tier; and 3) the concept of "TDM emulation" in which all service tiers are multiples of the same (software-configurable) bandwidth unit, and a suite of algorithms to select jointly the basic unit and service tiers. Our work provides a systematic framework for reasoning about and tackling algorithmically the general problem of service tier selection, and has applications to a number of networking contexts, including access networks (e.g., determining the tiers for ADSL, cable modem networks or PONs) and core networks (e.g., LSP sizing for MPLS networks). [ABSTRACT FROM AUTHOR]

We prove that the XCP equilibrium solves a constrained max-min fairness problem by identifying it with the unique solution of a hierarchy of optimization problems, namely those solved by max-min fair allocation, but solved by XCP under an additional constraint. This constraint is due to the "bandwidth shuffling" necessary to obtain fairness. We describe an algorithm to compute this equilibrium and derive a lower and upper bound on link utilization. While XCP reduces to max-min allocation at a single link, its behavior in a network can be very different. We illustrate that the additional constraint can cause flows to receive an arbitrarily small fraction of their max-min fair allocations. We confirm these results using ns2 simulations. [ABSTRACT FROM AUTHOR]

A newly developed evolutionary computing algorithm, Jumping Genes Evolutionary Algorithm is applied to optimize the dimensions of a folded patch antenna with double shorting walls. In the optimized design, an impedance bandwidth (VSWR ≤2) of 101% is recorded, which shows an improvement by 29% comparing with its original design. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 706–709, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22234 [ABSTRACT FROM AUTHOR]