Global Bandwidth Research Articles

A metasurface-based high gain circularly polarized patch antenna with wide global bandwidth

This paper introduces a novel metasurface (MS)-based circularly polarized (CP) microstrip patch antenna. Utilizing a square microstrip patch with plus- and diagonal-shaped slots on a 1.6 mm thick FR-4 substrate as the host antenna, the design incorporates a 4 × 4 square array MS on another 1.6 mm thick FR-4 substrate, separated by a 3.4 mm thick Teflon layer. The antenna offers a −10-dB impedance bandwidth from 5.45 to 7.55 GHz and an axial ratio (AR) bandwidth between 6.02 and 6.35 GHz, achieving a maximum realized gain of 6.3 dBic at 6.25 GHz, exhibiting right-handed circular polarization (RHCP). An equivalent circuit model is developed to validate impedance response, and the fabricated antenna closely matches simulated results. With a low profile of 0.42 λ o × 0.42 λ o × 0.033 λ o at 6.25 GHz, this CP antenna is suitable for applications like satellite communication for their uplinks and WLAN application.

Improved estimation of drift coefficients using optimal local bandwidths

Stochastic differential equations (SDEs) are commonly used to model various systems. Data-driven methods have been widely used to estimate the drift and diffusion terms of a Langevin equation. Among the most commonly used estimation methods is the Nadaraya–Watson estimator, which is a non-parametric data-driven approach. In this study, we propose a method to improve the estimation of the drift coefficient of a stochastic process using optimal local bandwidths that minimize the error of the approximation of the first conditional moments of a univariate system. This approach is compared to a global bandwidth estimation and an estimation based on a fixed number of nearest neighbors. The proposed method has the potential to reduce the error of the drift estimation, thereby improving the accuracy of the model.

Multisite gaming streaming optimization over virtualized 5G environment using Deep Reinforcement Learning techniques

The massive growth of live streaming, especially gaming-focused content, has led to an overall increase in global bandwidth consumption. Certain services see their quality diminished at times of peak consumption, degrading the quality of the content. This trend generates new research related to optimizing image quality according to network and service conditions. In this work we present a gaming streaming use case optimization on a real multisite 5G environment. The paper outlines the virtualized workflow of the use case and provides a detailed description of the applications and resources deployed for the simulation. This simulation tests the optimization of the service based on the addition of Artificial Intelligence (AI) algorithms, assuring the delivery of content with good Quality of Experience (QoE) under different working conditions. The AI introduced is based on Deep Reinforcement Learning (DRL) algorithms that can adapt, in a flexible way, to the different conditions that the multimedia workflow could face. That is, adapt, through corrective actions, the streaming bitrate, in order to optimize the QoE of the content on a real-time multisite scenario. The results of this work demonstrate how we have been able to minimize content losses, as well as the fact of obtaining high audiovisual multimedia quality results with higher bitrates, compared to a service without an optimizer integrated in the system. In a multi-site environment, we have achieved an improvement of 20 percentage points in terms of blockiness efficiency and also 15 percentage points in block loss.

Analysis of MIMO methods for 5G and subsequent technologies: advantages and disadvantages

Formulation of the problem. The global bandwidth shortage in the wireless communications sector has prompted the study and research of wireless access technology, known as mass Multiple Input/output (MIMO) technology. Mass MIMO is one of the key technologies for next-generation networks, which combines antennas in both the transmitter and receiver to provide high spectral and energy efficiency using relatively simple processing. Gaining a better understanding of the massive MIMO system to overcome the fundamental challenges of this technology is vital for the successful deployment of 5G and higher networks to implement various intelligent sensor system applications. Thus, in this paper, fully digital, analog and hybrid structures are analyzed and the method of multilevel massive MIMO transmission is described in detail. Purpose. Analysis of the main technologies needed to meet the demand for data transmission expected for 5G and 6G networks, with the allocation of transmission methods necessary for the mass implementation of MIMO. Practical significance. Evaluation of the MIMO system bandwidth, hardware and computational complexity, as well as energy efficiency, it is shown that massive MIMO circuits can rely on asymptotic linear independence when designing signal processing, given the large number of antennas used. Classical MIMO methods are not able to meet the technical requirements, so hybrid architectures use a combination of analog and digital areas to use the spatial resolution provided by a large number of antenna elements, but at the same time the number of energy-consuming and expensive radio frequency circuits remains within reasonable limits, and computational complexity is controlled. It becomes clear that there is no single structure/algorithm that would provide the best compromise between complexity and performance in all possible scenarios, rather, there is a need to adapt them to the characteristics of the application and channel when designing the system. As for optimizing the energy efficiency of the RF power supply, this is achievable using solutions such as multilayer massive MIMO.

A reliable alternating multi‐path transmission control protocol scheduling for bandwidth aggregation performance

SummaryThe Multi‐Path Transmission Control Protocol is a new generation protocol for the transport layer which allows multihomed devices to exploit simultaneously their own interfaces. This feature would normally lead to some high‐performance achievement in regard to regular TCP. However, the heterogeneous paths might cause the occurrence of the 'out‐of‐order' problem which requires the intervention of a more efficient scheduler to reach a high level of global bandwidth utilization. Since scheduling plays a key role in performance improvements, several studies have been implemented exploiting this idea, namely those that adopt the reactive approach and those others adopting the proactive one. In this paper, our goal is to improve the multipath throughput. The testbed experimentation consists primarily in the evaluation and analysis of the behaviour of different recent scheduling approaches with an asymmetric paths topology. Then, we propose an alternating scheduling approach which shed the light on bandwidth utilization. The results show that this approach is a more efficient solution than taking the reactive and proactive ones separately as it is based on a preliminary study that we conducted on the main performance and reliability parameters and has been implemented in the asymmetric multipath networks. This technique has led to a smart scheduling that provides a high level of throughput aggregation.

AoI-Aware Optimization of Service Caching-Assisted Offloading and Resource Allocation in Edge Cellular Networks

The rapid development of the Internet of Things (IoT) has led to computational offloading at the edge; this is a promising paradigm for achieving intelligence everywhere. As offloading can lead to more traffic in cellular networks, cache technology is used to alleviate the channel burden. For example, a deep neural network (DNN)-based inference task requires a computation service that involves running libraries and parameters. Thus, caching the service package is necessary for repeatedly running DNN-based inference tasks. On the other hand, as the DNN parameters are usually trained in distribution, IoT devices need to fetch up-to-date parameters for inference task execution. In this work, we consider the joint optimization of computation offloading, service caching, and the AoI metric. We formulate a problem to minimize the weighted sum of the average completion delay, energy consumption, and allocated bandwidth. Then, we propose the AoI-aware service caching-assisted offloading framework (ASCO) to solve it, which consists of the method of Lagrange multipliers with the KKT condition-based offloading module (LMKO), the Lyapunov optimization-based learning and update control module (LLUC), and the Kuhn-Munkres (KM) algorithm-based channel-division fetching module (KCDF). The simulation results demonstrate that our ASCO framework achieves superior performance in regard to time overhead, energy consumption, and allocated bandwidth. It is verified that our ASCO framework not only benefits the individual task but also the global bandwidth allocation.

Measurement‐based delay, angular dispersion and propagation loss characteristics of outdoor propagation in beam domain and multi‐beam operation at 38 GHz for 5‐G communication systems

Owing to the global bandwidth shortage in frequency bands below 6 GHz, higher frequency bands such as millimetre-wave bands will play integral roles in fifth-generation (5-G) communication systems. Beamforming is necessary for high-frequency bands to maintain system coverage and increase system capacity through spatial division multiple access. Moreover, more than one beam might be used simultaneously to enhance signal quality and prevent blockages. Recent studies have investigated, focussed on power loss characteristics in multi-beam operation, and compared the channel characteristics for single-beam operation. To provide robust channel characteristics, knowledge of the multipath characteristics in multi-beam operation is required in the design of 5-G communication systems. To fill this gap such as the multipath dispersion characteristics of outdoor-to-outdoor propagation in delay and angular domains, the present study analysed outdoor data measured at 38 GHz and presented the propagation losses (path losses and shadowing), root-mean-square delay spread, and angular spread in multi-beam operation. We believe that the dispersion statistics (delay and angular spreads) and propagation loss characteristics in multi-beam operation would be useful in the development of 5-G communication systems in high-frequency bands.

Efficient Resource Allocation for Multi-Beam Satellite-Terrestrial Vehicular Networks: A Multi-Agent Actor-Critic Method With Attention Mechanism

With the rapid development of intelligent transportation systems, there is an increasing demand for a variety of vehicular services, such as automated driving assistance, emergency alert, infotainment, etc. However, in some situations (e.g., remote areas or maritime scenarios), the terrestrial networks alone cannot serve the vehicular applications very well due to the infrastructure deployment and maintenance issues. Satellite networks have become an effective supplement to terrestrial networks, which complement well in terms of coverage, flexibility, reliability, and availability. In this paper, we consider the low orbit multi-beam satellite-terrestrial networks to serve for vehicles. We model this problem as a cooperative multi-agent reinforcement learning process, where each beam acts as an agent, and the global bandwidth is cooperatively shared among all the agents. A multi-agent actor-critic method with attention mechanism is proposed to allocate resources for vehicles with strict delay requirements and minimum bandwidth consumption. When allocating bandwidth, the channel efficiency, the angle of the beams and the priorities of requests in different regions are also considered. Centralized training and distributed execution is performed in the training of the agents. Extensive simulation results verify the effectiveness of our proposed method, where all the agents can well cooperative to achieve efficient resource allocation on-demand for the vehicles under strictly limited bandwidth resources.

Virtualized High Throughput Satellite Gateway with a Global Bandwidth Management Method

With the development of new satellite payload technology, in order to improve the utilization of system resources, research is based on software-defined network (SDN) and network function virtualization (NFV) gateway architecture. Based on this architecture, the system realizes global resource management and overall data distribution, which can solve the problem of resource allocation and maximum/minimum rate guarantee between different VNO terminals under different beams, different gateways, and different satellites. For this, a global bandwidth management method can be used which is mainly a process of management to control the traffic on a communication link. The proposed global resource management and control method can be based on the rate guarantee value of the VNO/terminal configured in the system as the basic limiting condition and reallocate the rate guarantee value limiting parameter according to the resource application status of the online terminal. The method can maximize the resource utilization of the entire satellite communication system and satisfy the resource request of the user terminal as much as possible.

L 2 consistency of the kernel quantile estimator

Let F be a continuous distribution function and let Q be its associated quantile function. Let Fh be the kernel estimator of F and Qh that of Q. In this article the L 2 right inversion distance between Qh and Q is introduced. It is shown that this distance can be represented in terms of Fh and F, more precisely it is established that the right inversion distance is equal to the conventional integrated squared error between Fh and F. This representation shows that any good bandwidth for Fh is a reasonable bandwidth for Qh and, this fact enables us to suggest methods to choose the smoothing parameter of Let be the kernel estimator of Q equipped with the global crossvalidation bandwidth designed for Let be the linear kernel estimator of Q, being the plug-in bandwidth function. A small scale simulation study presented in this paper contains some examples of distributions for which appears to be superior to This paper also contains some properties of the classical L 2 distance between Qh and Q.

A Metasurface-Based Wide-Bandwidth and High-Gain Circularly Polarized Patch Antenna

A metasurface-based circularly polarized (CP) microstrip patch antenna with high performance in terms of the global bandwidth and gain is presented. The antenna configuration was a square-modified microstrip patch that was sandwiched between a metasurface and the ground plane. The metasurface comprised a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> array of square patches, while the single-feed radiating microstrip patch had axial ratio (AR) tuning stubs and a crossed slot. Under excitation, the microstrip patch and crossed slot, in conjunction with the metasurface, generated multiple resonances and AR minimum points that combined to produce large global bandwidths with regard to the impedance, AR, and 3 dB gain. Using a stacked layout, a low-profile design with the dimensions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$54\,\,\text {mm} \times 54\,\,\text {mm} \times 3.1$ </tex-math></inline-formula> mm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\,\,\lambda _{\text {o}} \times 1.2\,\,\lambda _{\text {o}}\times 0.07\,\,\lambda _{\text {o}}$ </tex-math></inline-formula> at 6.8 GHz) was obtained, which generated a highly stable broadside gain above 10 dBic throughout the entire operating AR bandwidth. This antenna had a measured −10 dB impedance bandwidth of 5.62–11.04 GHz (65.06%) and a 3 dB AR bandwidth of 5.64–7.89 GHz (33.25%). In addition, it had a measured 3 dB gain bandwidth of 5.17–8.2 GHz (45.32%), with a measured peak gain of 12.17 dBic.

CAVLCU: an efficient GPU-based implementation of CAVLC

CAVLC (Context-Adaptive Variable Length Coding) is a high-performance entropy method for video and image compression. It is the most commonly used entropy method in the video standard H.264. In recent years, several hardware accelerators for CAVLC have been designed. In contrast, high-performance software implementations of CAVLC (e.g., GPU-based) are scarce. A high-performance GPU-based implementation of CAVLC is desirable in several scenarios. On the one hand, it can be exploited as the entropy component in GPU-based H.264 encoders, which are a very suitable solution when GPU built-in H.264 hardware encoders lack certain necessary functionality, such as data encryption and information hiding. On the other hand, a GPU-based implementation of CAVLC can be reused in a wide variety of GPU-based compression systems for encoding images and videos in formats other than H.264, such as medical images. This is not possible with hardware implementations of CAVLC, as they are non-separable components of hardware H.264 encoders. In this paper, we present CAVLCU, an efficient implementation of CAVLC on GPU, which is based on four key ideas. First, we use only one kernel to avoid the long latency global memory accesses required to transmit intermediate results among different kernels, and the costly launches and terminations of additional kernels. Second, we apply an efficient synchronization mechanism for thread-blocks (In this paper, to prevent confusion, a block of pixels of a frame will be referred to as simply block and a GPU thread block as thread-block.) that process adjacent frame regions (in horizontal and vertical dimensions) to share results in global memory space. Third, we exploit fully the available global memory bandwidth by using vectorized loads to move directly the quantized transform coefficients to registers. Fourth, we use register tiling to implement the zigzag sorting, thus obtaining high instruction-level parallelism. An exhaustive experimental evaluation showed that our approach is between 2.5times and 5.4times faster than the only state-of-the-art GPU-based implementation of CAVLC.

Selection of the Bandwidth Matrix in Spatial Varying Coefficient Models to Detect Anisotropic Regression Relationships

The commonly used Geographically Weighted Regression (GWR) fitting method for a spatial varying coefficient model is to select a bandwidth h for the geographic location (u, v), and assign the same weight to the two dimensions. However, spatial data usually present anisotropy. The introduction of a two-dimensional bandwidth matrix not only gives weight from two dimensions separately, but also increases the direction of kernel smoothness. The adaptive bandwidth matrix is more flexible. Therefore, in this paper, a two dimensional bandwidth matrix is introduced into the spatial varying coefficient model for parameter estimation. Through simulation experiments, the results obtained under the adaptive bandwidth matrix are compared with those obtained under the global bandwidth matrix, indicating the effectiveness of introducing the adaptive bandwidth matrix.

Optimum Averaging of Superimposed Training Schemes in OFDM Under Realistic Time-Variant Channels

The current global bandwidth shortage in orthogonal frequency division multiplexing (OFDM)-based systems motivates the use of more spectrally efficient techniques. Superimposed training (ST) is a candidate in this regard because it exhibits no information rate loss. Additionally, it is very flexible to deploy and it requires low computational cost. However, data symbols sent together with training sequences cause an intrinsic interference. Previous studies, based on an oversimplified channel (a quasi-static channel model) have solved this interference by averaging the received signal over the coherence time. In this paper, the mean square error (MSE) of the channel estimation is minimized in a realistic time-variant scenario. The optimization problem is stated and theoretical derivations are presented to attain the optimum amount of OFDM symbols to be averaged. The derived optimal value for averaging is dependent on the signal-to-noise ratio (SNR) and it provides a better MSE, of up to two orders of magnitude, than the amount given by the coherence time. Moreover, in most cases, the optimal number of OFDM symbols for averaging is much shorter, about 90% reduction of the coherence time, thus it provides a decrease of the system delay. Therefore, these results match the goal of improving performance in terms of channel estimation error while getting even better energy efficiency, and reducing delays.

A New Kernel Estimator Based on Scaled Inverse Chi-Squared Density Function

In this work, a new kernel estimator based on scaled inverse chi-squared distribution is proposed to estimate densities having nonnegative support. The optimal rates of convergence for the mean squared error (MSE) and the mean integrated squared error (MISE) are obtained. Adaptive Bayesian bandwidth selection method with Lindley approximation is used for heavy tailed distributions. Simulation studies are performed to compare the performance of the average integrated square error (ISE) by using the bandwidths obtained from the global least squares cross-validation bandwidth selection method and the bandwidths obtained from adaptive Bayesian method with Lindley approximation. Finally, real data sets are presented to illustrate the findings.

L1 Properties of the Nadaraya Quantile Estimator

Let X be a real random variable having f as density function. Let F be its cumulative distribution function and Q its quantile function. For h > 0, let Fh and Qh denote respectively the Nadaraya kernel estimator of F and Q. In the first part of this paper the almost sure convergence of the conventional L1 distance between Qh and Q is established. In the second part, the L1 right inversion distance is introduced. The representation of this L1 right inversion distance in terms of Fh and F is given. This representation allows us to suggest ways to choose a global bandwidth for the estimator Qh.

Novel Node-Ranking Approach for SDN-Based Virtual Network Embedding

Network virtualization is considered as a key technology for the future network. The emergence of software-defined network (SDN) provides a platform for the research and development of network virtualization. One of the key challenges in network virtualization is virtual network embedding (VNE). Some of the previous VNE algorithms perform virtual node embedding, which combines the nodes’ resource attributes and local topology attributes by arithmetic operations. On the one hand, it is not easy to distinguish the topological differences between SN and VN only by simple topology metrics. On the other hand, it is easy to ignore the different weight impacts of different metrics using only arithmetic operations, which will lead to an unbalanced embedding solution. To deal with these issues, we propose a novel node-ranking approach based on topology-differentiating (VNE-NRTD) for SDN-based virtual network embedding. Owing to the topological difference between SN and VN, different node metrics are used to quantify the substrate nodes and virtual nodes, respectively. Then, the nodes are ranked using the modified set pair analysis (SPA) method to avoid the unbalanced embedding solution. On this basis, we introduce the global bandwidth of the network topology into node-ranking to further improve the efficiency of node embedding. The simulation results show that the VNE-NRTD algorithm proposed in this paper outperforms other latest heuristic algorithms in terms of the VNR acceptance ratio, long-term average R/C ratio, substrate node utilization, and substrate link utilization.

Technological and geographic heterogeneity in broadband markets: The challenge for regulation

When the telecommunications industry was liberalised in Europe and North America in the 1980s and 1990s, it inherited a legacy of monopoly providers whose footprint was national or multi-regional in its character. The regulatory framework, particularly that adopted in EU member states, reflected this pattern of relatively homogeneous deployment achieved, in part, by decades of cross-subsidised pricing and universal service goals. Perhaps because of this legacy, telecommunications regulators have often adopted the presumption that relevant markets are national in character, unless proven otherwise Although geographically-variegated regulatory remedies have been permitted (even in the face of allegedly national relevant markets) and adopted in many member states, many regulators have never done so, and overly cautious thresholds for permitting geographically based forbearance suggest a continued bias towards presuming national markets and remedies. We find that this presumption of uniformity and the tendency to aggregate geographic markets together is not supported by first principles of antitrust analysis, although there may have been strong practical reasons to apply this presumption in the past circumstances of the telecommunications and broadband industries.On the ground, however, there has arguably never been as much heterogeneity across geographies and across technological solutions that provide effective ultra-fast broadband speeds. Both technological (i.e., product market) and geographic heterogeneity are likely to increase with the advent of mobile 5G networks. With their deployment, a cautious regulatory stance towards geographic variation and a cautious regulatory stance towards inter-technology or inter-modal competition may result in regulation that could exceed what is required to ensure effective competition and could instead distort the incentives to enter of facilities-based actors. This may also result in higher-cost and inefficient investment. A more geographically varied and technologically agnostic regulatory framework may satisfy the principle of proportionate and focused regulation—with the possibility that the locus of regulation shifts from the access network to bottleneck facilities such as fibre, ducts and poles.This discussion is especially germane when one considers the highly speculative nature of forecasts and projections about future demand, and the competing claims of proponents of 5G and fibre. While there is some scepticism about the performance of mobile networks, we note that pure mobile and fixed 5G services may have synergies in deployment, and that the idea of competing with residential broadband services is a core strategy of very influential large-scale industry actors. In terms of a future research agenda, regulatory decisions could benefit from much more research into the relationship between domestic and global bandwidth constraints and their influence on development of software and application, as well as much more quantitative research by academics on the drivers of bandwidth demand. The risks associated with promoting investment that results in large-scale wasted resources should also be central to the regulatory agenda.

Adaptive local polynomial estimations for heterogeneously variational regression functions

A novel local polynomial estimator for regression functions is given. Traditional local polynomial estimators adjust the smoothness of estimations using a global constant bandwidth. The methods with local variable bandwidths, some of them select the bandwidths by considering only the inhomogeneous distribution of inputs but not outputs, and the others usually require some complicated pre-estimations. We construct an adaptive local polynomial estimator, which considers both the variations in the outputs and the arrangements of the inputs by using a new local variable bandwidth and needs no additional computational burden. The asymptotic bias, variance and mean squared error of the new estimations are compared with traditional local polynomial estimations. Simulation work shows that the new method outperforms the traditional local polynomial estimator and some other methods in estimating heterogeneously variational regression functions.

Massive MIMO Systems for 5G and Beyond Networks-Overview, Recent Trends, Challenges, and Future Research Direction.

The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues of this technology is vital for the successful deployment of 5G—and beyond—networks to realize various applications of the intelligent sensing system. In this paper, we present a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. We discuss all the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in a massive MIMO system and discuss some state-of-the-art mitigation techniques. We outline recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems. Additionally, we discuss crucial open research issues that direct future research in massive MIMO systems for 5G and beyond networks.