Optimal Bandwidth Allocation Research Articles

Passive Wireless Sensor Network‐Based In‐Transit Health Status Monitoring for Railway Transportation

In this paper, passive wireless sensor network technology is used to conduct in‐depth research and analysis on the monitoring of the in‐transit health status of railway transportation. Safety detection sensors, train communication networks, and other onboard devices organically constitute a train sensor network with comprehensive state sensing, information aggregation, and business collaboration. First, this paper analyzes the three‐layer architecture of the sensing layer, network layer, and application layer of the train operation status safety detection sensor network. Based on analyzing the feasibility of Ethernet application in an onboard communication network, ring, multiring, and ladder‐type sensor network structure schemes are designed. Next, the real‐time performance and reliability of various sensor network structures are analyzed. First, the characteristics of bandwidth demand, priority, and importance of information transmission of each monitoring object are analyzed; then, the system utility function is established according to the bandwidth usage efficiency and communication demand of information transmission of each monitoring object; finally, the bandwidth allocation optimization model is solved by using a particle swarm optimization algorithm, and the remaining bandwidth resources are dynamically rationed on demand while ensuring the complete transmission of basic information of each monitoring object. Rail transit technology has profoundly affected and changed the travel and lifestyle of residents in the new era and will provide strong infrastructure support for the smooth advancement of new urbanization construction and development strategies. The remaining bandwidth resources are dynamically allocated on the premise of ensuring the complete transmission of basic information of each monitoring object. In summary, this paper presents a complete study on the optimal allocation of resources for wireless sensor networks dedicated to rail transit condition monitoring, starting from the perspectives of routing protocol optimization, data fusion rate optimization, and bandwidth dynamic allocation optimization to achieve the efficient use of limited resources. The achieved research results enrich the research content in the field of wireless monitoring of rail transit system conditions at home and abroad in a certain sense and will also provide theoretical and empirical support for practical practitioners, managers, and scholars of service condition monitoring of rail transit systems.

Deep Learning for Radio Resource Allocation With Diverse Quality-of-Service Requirements in 5G

To accommodate diverse Quality-of-Service (QoS) requirements in 5th generation cellular networks, base stations need real-time optimization of radio resources in time-varying network conditions. This brings high computing overheads and long processing delays. In this work, we develop a deep learning framework to approximate the optimal resource allocation policy that minimizes the total power consumption of a base station by optimizing bandwidth and transmit power allocation. We find that a fully-connected neural network (NN) cannot fully guarantee the QoS requirements due to the approximation errors and quantization errors of the numbers of subcarriers. To tackle this problem, we propose a cascaded structure of NNs, where the first NN approximates the optimal bandwidth allocation, and the second NN outputs the transmit power required to satisfy the QoS requirement with given bandwidth allocation. Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks. Simulation results validate that the cascaded NNs outperform the fully connected NN in terms of QoS guarantee. In addition, deep transfer learning can reduce the number of training samples required to train the NNs remarkably.

Experimental Analysis of Optimal Bandwidth Allocation in Computer Vision Systems

This paper considers computer vision (CV) systems in which a central monitoring station receives and analyzes the video streams captured and delivered wirelessly by multiple cameras. It addresses how the bandwidth can be allocated to various cameras by presenting a cross-layer solution that optimizes the overall detection or recognition accuracy. In further contrast with prior work, it presents and develops a real CV system and subsequently provides a detailed experimental analysis of cross-layer optimization. Other unique features of the developed solution include employing the popular HTTP streaming approach, utilizing homogeneous cameras as well as heterogeneous ones with varying capabilities and limitations, and including a new algorithm for estimating the effective medium airtime. The results show that the proposed solution significantly improves the CV accuracy.

Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning

In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication resources, device scheduling is crucial to the convergence rate of FL. In this paper, we propose a joint device scheduling and resource allocation policy to maximize the model accuracy within a given total training time budget for latency constrained wireless FL. A lower bound on the reciprocal of the training performance loss, in terms of the number of training rounds and the number of scheduled devices per round, is derived. Based on the bound, the accuracy maximization problem is solved by decoupling it into two sub-problems. First, given the scheduled devices, the optimal bandwidth allocation suggests allocating more bandwidth to the devices with worse channel conditions or weaker computation capabilities. Then, a greedy device scheduling algorithm is introduced, which selects the device consuming the least updating time obtained by the optimal bandwidth allocation in each step, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy. Experiments show that the proposed policy outperforms state-of-the-art scheduling policies under extensive settings of data distributions and cell radius.

Multi-Agent Deep Reinforcement Learning-Based Flexible Satellite Payload for Mobile Terminals

Information dissemination in mobile networks turns out to be a problem when the network is sparse. Mobile networks begin to establish a separate cluster attributable to the limited communication range of terminals. The multi-beam satellite communication systems can play a significant role in providing direct-to-user satellite mobile services and connecting the separated clusters. This paper focuses on how to efficiently schedule limited satellite-based radio resources to enhance transmission efficiency and meet the requested traffic with low complexity. Taking the inter-beam interference and resource utilization variance into consideration, we build a game-theoretic based model for bandwidth allocation in the forward link. As the size of satellite beams increases, the size of the action space for deep reinforcement learning based on a single agent becomes large, resulting in high time complexity. Thus, we extend the single-agent deep reinforcement learning to the multi-agent context and then propose a cooperative multi-agent deep reinforcement learning method to achieve the optimal bandwidth allocation strategy. Each beam works as a player who is willing to satisfy the request traffic with flexible payloads. We built a multi-beam satellite platform using real historical data. The experimental results show that this approach is capable of enhancing transmission efficiency and can be flexible to achieve the desired goal with low complexity.

Deep Reinforcement Learning-Based Joint Scheduling of eMBB and URLLC in 5G Networks

To satisfy tight latency constraints, ultra–reliable low latency communications (URLLC) traffic is scheduled by overlapping the on–going enhanced mobile broad band (eMBB) transmissions (i.e., puncturing approach), which causes eMBB users unprecedented rate loss and hence degraded quality–of–service (QoS). To tackle this issue, this letter proposes to achieve QoS tradeoff between eMBB and URLLC in 5G networks. We jointly optimize bandwidth allocation and overlapping positions of URLLC users’ traffic with deep deterministic policy gradient algorithm observing channel variations and URLLC traffic arrivals. Simulation results show that the proposed system–wide tradeoff method achieves the best tradeoff performance.

Optimal bandwidth allocation for web crawler systems with time constraints

Web crawler is an important tool to obtain information from the Internet in a timely manner. In a typical web crawler system with limited bandwidth, many websites are crawled with different time constraints. Existing studies regarding web crawler systems do not consider the bandwidth allocation in such a complex environment; hence, the time constraints may not be satisfied. In this study, we investigate the bandwidth allocation approaches for such a web crawler system. The approaches are designed for two scenarios, i.e., when the number of websites exceeds or does not exceed the maximum number of web crawlers that the system can execute simultaneously. For the latter situation, we propose approaches to control the bandwidth for web crawlers to minimize the maximum complete time or minimize the sum of execution times of all web crawlers, considering assumptions of both sufficient and insufficient bandwidths. For the former situation, we propose a round-based reallocation approach to schedule both the sequence and bandwidth allocation of the web crawlers. Extensive simulations are conducted to validate the proposed approaches, and the results show that our approaches satisfy the time constraints well and achieve desirable execution performances in various scenarios.

On accounting for screen resolution in adaptive video streaming: QoE‐driven bandwidth sharing framework

SummaryScreen resolution along with network conditions are main objective factors impacting the user experience, in particular for video streaming applications. User terminals on their side feature more and more advanced characteristics resulting in different network requirements for good visual experience. Previous studies tried to link mean opinion score (MOS) to video bitrate for different screen types (e.g., Common Intermediate Format [CIF], Quarter Common Intermediate Format [QCIF], and High Definition [HD]). We leverage such studies and formulate a Quality of Experience (QoE)‐driven resource allocation problem to pinpoint the optimal bandwidth allocation that maximizes the QoE over all users of a network service provider located behind the same bottleneck link, while accounting for the characteristics of the screens they use for video playout. For our optimization problem, QoE functions are built using curve fitting on datasets capturing the relationship between MOS, screen characteristics, and bandwidth requirements. We propose a simple heuristic based on Lagrangian relaxation and Karush Kuhn Tucker (KKT) conditions to efficiently solve the optimization problem. Our numerical simulations show that the proposed heuristic is able to increase overall QoE up to 20% compared to an allocation with a TCP look‐alike strategy implementing max‐min fairness.

Performance Model for Video Service in 5G Networks

Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.

Optimal Power and Bandwidth Allocation for Multiuser Video Streaming in UAV Relay Networks

We all have encountered unsatisfactory video viewing experience with mobile devices in hot spot areas where a crowd of people are demanding video streaming simultaneously. One possible solution to relieve the problem is to use the unmanned aerial vehicle (UAV) as a relay to improve the quality-of-experience (QoE). In this paper, we focus on the allocation of bandwidth and transmission power for multiuser video streaming in UAV relay networks. Our goal is to maximize the total long-term QoE of users, which are determined by the freezing time and the bit rate of video. We employ the Lyapunov optimization theory to transform the original problem with long-term average form into the drift-plus-penalty problem, and propose a decentralized allocation scheme using alternative ascending clock auction to derive the optimal solution. Simulations are conducted to demonstrate the superiority of the proposed method.

Joint altitude, power control, and bandwidth allocation optimization for unmanned aerial vehicle‐enabled reliable communications

AbstractUnmanned aerial vehicles (UAVs) as aerial relays have found many applications in the current communication network. Since the air‐to‐ground (AtG) link differs from the terrestrial link, ensuring a reliable AtG transmission remains to be a meaningful issue. This article investigates that a UAV works as an aerial relay to forward information from multiple access points to multiple remote base stations in an emergency situation. Using the full‐duplex decode‐and‐forward relaying mode, we formulate a problem to minimize the maximum outage probability among all links, jointly optimizing the UAV altitude, power control, and bandwidth allocation. The outage probability function takes into account the line‐of‐sight probability‐based AtG fading and the self‐interference introduced by full‐duplex relaying, both of which make our formulated problem nonconvex. We reformulate the outage probability function into a more tractable function. Then, we decouple the original problem into two subproblems, that is, altitude optimization as well as power control and bandwidth allocation. Each subproblem is solved by the successive convex optimization technique, and an overall iteration algorithm is proposed to solve the original problem based on the block coordinate descent theory. Simulation results reveal the reliability gain of our proposed algorithm compared with our designed benchmark. Besides, the optimal UAV three‐dimensional location that minimizes the global min‐max outage probability is found by numerical simulations. Finally, the average throughput gain can also be obtained by our proposed algorithm.

Development of a Hybrid Algorithm for User Association and Resource Allocation to Improve Load Balancing and Energy Efficiency in 5G HetNet

This paper presents the development and integration of a power control algorithm into the User Association Algorithm with Optimal Bandwidth Allocation (UAAOBA) to form a Hybrid Algorithm for User Association and Resource Allocation (HAUARA). The power control algorithm updates the transmit power of the Base Stations (BSs) towards a minimum transmit power that satisfies the minimum data rate requirement (1 Gbps) of the User Equipment UEs. The power update is achieved using the Newton Rhapson’s method and it adapts the transmit powers of the BSs to the number of their connected UEs. The developed HAUARA provides an optimal solution for user associations, bandwidth allocation, and transmit powers to UEs concurrently. This maximizes the network energy efficiency by coordinating the load fairness of the network while guaranteeing the quality of service requirement of the UEs. The network energy efficiency performance of the developed HAUARA is compared with that of the UAAOBA. The results show that the developed algorithm has network energy efficiency improvement of 12.36%, 10.58%, and 13.44% with respect to UAAOBA for increase number of macro BS antennas, pico BSs, and femto BSs, respectively. Also, the network load balancing performance of the developed HAUARA is compared with that of the UAAOBA. The results show that the developed algorithm has network load balancing improvement of 12.62%, 10.04%, and 10.34% with respect to UAAOBA for increase number of macro BS antennas, pico BSs, and femto BSs, respectively. This implies that the developed algorithm outperforms the UAAOBA in terms of network energy efficiency and load balancing.

Bandwidth allocation and resource adjustment for stability enhancement in complex networks

We introduce the discrete Green’s function to elucidate how resource fluctuations determine flow fluctuations in a network optimizing a global cost function. To enhance the robustness of the network against fluctuations, we develop the schemes of optimal bandwidth allocation in links and optimal resource adjustment in nodes. With the total bandwidth of the network fixed, the approach of optimal bandwidth allocation is to increase the bandwidth in links such that the number of overloaded links or the amount of excess flows in networks under fluctuations can be minimized. Similarly, the approach of optimal resource adjustment is to minimize the number of overloaded links in networks under fluctuations with the total resource change in the network fixed. Compared with the conventional approach of proportionate bandwidth assignment or resource reduction, it is found that the optimized bandwidth allocation or resource adjustment can highly enhance the stability of the networks against fluctuations. The changes of loads and currents prescribed by the optimal bandwidth allocation and resource adjustment schemes are correlated with each other, except for some nodes that exhibit relay effects.

Optimal resource allocation in networked control systems

This study proposes an optimal bandwidth allocation algorithm for a Networked Control System (NCS) that includes a time-driven sensor, event-driven controller and random channels. The authors predict resource demands at the next time step for the random channels, then calculate the resource demands from the event-driven and the time-driven channels. They allocate bandwidths for each channel by solving a convex optimisation problem based on the resource demands. When the total traffic exceeds the total capacity of the network, only the random and event-driven network bandwidths are allocated optimally. For time-driven channels, they increase the sampling period to reduce traffic volume and satisfy the total capacity constraint. This maintains sufficiently slow sampling to improve the quality of service in NCS. An upper bound on the sampling period is set to guarantee sufficiently fast sampling for control dynamics. Simulation results show that the proposed approach minimises network congestion.

Backhaul-Aware Optimization of UAV Base Station Location and Bandwidth Allocation for Profit Maximization

Unmanned Aerial Vehicle Base Stations (UAVBSs) are envisioned to be an integral component of the next generation Wireless Communications Networks (WCNs) by dynamically moving the supply towards the demand. A significant drawback of the state-of-the-art have been designing a WCN in which the service-oriented performance measures (e.g., throughput) are optimized without considering different relevant decisions such as determining the location and allocating the resources, jointly. In this study, we address the UAVBS location and bandwidth allocation problems together to optimize the total network profit. In particular, a Mixed-Integer Non-Linear Programming (MINLP) formulation is developed, in which the location of a single UAVBS and bandwidth allocations to users are jointly determined. The objective is to maximize the total profit without exceeding the backhaul and access capacities. The profit gained from a specific user is assumed to be a piecewise-linear function of the provided data rate level, where higher data rate levels would yield higher profit. Due to high complexity of the MINLP, we propose an efficient heuristic algorithm with lower computational complexity. We show that, when the UAVBS location is determined, the resource allocation problem can be reduced to a Multidimensional Binary Knapsack Problem (MBKP), which can be solved in pseudo-polynomial time. To exploit this structure, the optimal bandwidth allocations are determined by solving several MBKPs in a search algorithm. We test the performance of our algorithm with two heuristics and with the MINLP model solved by a commercial solver. Our numerical results show that the proposed algorithm outperforms the alternative solution approaches and would be a promising tool to improve the total network profit.

Bandwidth Distribution Algorithm of Applications in LTE-Femtocell Network

The wireless communication has become the essential of today’s Internet of Things (IoT) and mobile applications which is increasing exponentially in near future. These applications demand high data rate to speed up the communication among IoT or internet applications. The data rate requirement of individual application is different. To fulfill these demands of every application Long Term Evolution (LTE) integrated with femtocell technology is identified as suitable wireless technology. The running applications on a user equipment changes dynamically. Hence in dense deployment of femtocell and IoT devices, the bandwidth distribution to many active applications becomes more complicated. For multiple running applications per device with available bandwidth constraint, optimal bandwidth allocation to applications has become the most challenging issue. We include the bandwidth allocation to applications during handover of user equipment in LTE-femtocell network. The handover procedure needs efficient cell selection criteria for target cell selection. Hence in our research we focus on bandwidth distribution among applications by proposing “Efficient Bandwidth Distribution” (EBD) mechanism and efficient cell selection by “Dynamic Cell Selection” (DCS) mechanisms to mitigate above mentioned issues.

Capacity of Backscatter Communication With Frequency Shift in Rician Fading Channels

This letter investigates the capacity performance of backscatter communication (BackCom) with frequency shift in Rician fading channels, where each BackCom user passively modulates its own signal over an externally-generated continuous wave (CW) carrier. Besides, a sub-carrier is actively generated in each BackCom user for frequency shift so that BackCom users are allocated to non-overlapping sub-channels for interference-free transmission. The ergodic capacity with the optimal bandwidth allocation is analyzed, where the exact closed-form expression is not attainable and an approximation is obtained for convenience. For fair comparison, uniform bandwidth allocation and no bandwidth allocation schemes are analyzed, where approximated ergodic capacities are derived in closed-form expressions. Simulation results confirm the tightness of analytical results, and demonstrate that the superiority of the optimal bandwidth allocation and the sub-optimality of the uniform bandwidth allocation scheme when the number of BackCom users is small.

Buffer-Aware Streaming in Small-Scale Wireless Networks: A Deep Reinforcement Learning Approach

Buffer-aware video streaming, which exploits the available storage space in user device to store the prefetched video data in good channels for video data use in poor channels, has been proved to have the potential to reduce the impact of fluctuating wireless channels on user-perceived video performance. However, in practical wireless networks, due to the unknown channel state and video rate, providing buffer-aware video streaming service to wireless user is a challenging problem. In this paper, with the aim to design an autonomous wireless video streaming system, we apply the deep reinforcement learning approach to dynamic resource optimization for wireless buffer-aware video streaming under unknown channel state and video rate. Specifically, we define a reward function for buffer-aware video streaming as the effective video streaming time when neither video-playback overflow nor video-playback underflow occurs. We propose a Markov decision process based problem formulation of the joint bandwidth allocation and buffer management for maximizing the effective video streaming time of all users. The optimal bandwidth allocation and buffer management policy is learned from training a deep neural network based on a deep reinforcement learning algorithm. We simulate the proposed algorithm in Tensorflow. Simulation results verify that the proposed deep reinforcement learning approach is effective for buffer-aware video streaming in wireless networks.

Distributed Bottleneck-Aware Coflow Scheduling in Data Centers

With the booming development of data parallel frameworks, the coflow abstraction has been greatly favored by data center transport designs, for its prominent ability in capturing application-level semantics. To accelerate job completion, coflow completion time (CCT) is a most important metric, and coflow scheduling is the most effective and widely-adopted means of optimizing CCT. However, most existing coflow scheduling mechanisms neglect the ubiquitous in-network bottlenecks and schedule coflows based on non-blocking giant switch hyperthesis. Such a practice is likely to result in undesired link contention inside the fabric, finally impairing CCT performance. To address this problem, we propose the Distributed Bottleneck-Aware coflow scheduling algorithm called DBA, which approximates the minimum remaining time first (MRTF) heuristic on all fabric-wide links. In this way, core link bandwidths are allocated to coflows as expected and the CCT performance will not be violated. As an evolutionary algorithm, DBA enhances the traditional dual decomposition method thus converges to the optimal bandwidth allocation very fast. Extensive simulations verify DBA's outstanding CCT performance as well as high link utilization. Furthermore, DBA introduces very little overhead and is robust to routing strategies, parameter variations and computation delays.

METHOD OF SCHEDULING AND ACTIVE QUEUES MANAGEMENT ON ROUTERS INTERFACES OF TELECOMMUNICATION NETWORKS

The subject matter of the article is the method of scheduling and active queues management on routers interfaces of telecommunication networks. The goal of the work is to develop and improve the method related to the optimization of queues management processes on routers interfaces of telecommunication networks and aimed at improving the indicators of the quality of service. The article solves optimization problems of Congestion Management, Resource Allocation and Congestion Avoidance on routers interfaces of telecommunication networks in the framework of the two-stage calculation method. Research methods are based on the results of analytical calculations obtained in the course of using the Optimization Toolbox package of the Mat lab environment. Wherein in the course of improving the queue management method on routers interfaces of telecommunication networks, a mathematical programming apparatus was used, containing linear programming functions. The following results were obtained – the improved method of scheduling and active queues management on routers interfaces of telecommunication networks with the coordinated, sequential and two-stage solution of such interface problems as Congestion Management, Resource Allocation, and Congestion Avoidance. Conclusions : using the proposed method of scheduling and active queue management had made to achieve an agreed solution to Congestion Management, Resource Allocation, and Congestion Avoidance problems. In the course of solving the optimization problem of Congestion Management, which was conditionally related to the first stage of calculations, it was possible to achieve optimal aggregation and allocation of packet flows over queues formed on the router interface based on the commensurability of their classes. In the course of solving the optimization problems of resource allocation, and congestion avoidance, which were conditionally related to the second stage of calculations, it was possible to achieve an optimal bandwidth allocation of the router interface between the queues formed on it, and minimize possible denials of service.