Optimal Bandwidth Allocation Research Articles

Delay-Sensitive Task Offloading With D2D Service-Sharing in Mobile Edge Computing Networks

This letter considers a mobile edge computing (MEC) system where some mobile users (MUs) have not pre-installed the service program required to process their task data. Depending on the availability of the service program, and thus the ability to process data locally, users are tagged as eligible users (EUs) and ineligible users (IUs), respectively. Other than offloading all the task data to the edge server under a stringent spectrum, we consider a device-to-device (D2D) enabled service sharing method that allows IUs to obtain the service program from its adjacent EUs, such that some tasks can be processed locally at the user devices. To fully utilize the stringent wireless spectrum, we propose a spectrum-aggregation scheme that an EU who shares its service program to an IU can occupy the bandwidth originally allocated to both users. We aim to minimize the execution latency of all the MUs by jointly optimizing the service sharing, computation offloading, and bandwidth allocation. We formulate the problem as a mixed integer non-linear programming (MINLP), where the major difficulties are the combinatorial service sharing and task offloading decisions at UEs and the strong coupling between the integer decisions and system bandwidth allocation. To deal with this problem, we propose an efficient algorithm named MOP that first obtains the combinatorial decisions by formulating a service matching game, and accordingly derives the closed-form solution of the optimal bandwidth allocation. Simulation results show that MOP achieves less than 2.8% optimality gap compared to exhaustive search method, and offers substantial performance gain over the considered benchmark algorithms.

Premium quality or guaranteed fluidity? Client-transparent DASH-aware bandwidth allocation at the radio access network

In this paper we propose a novel client-transparent Dynamic Adaptive Streaming over HTTP (DASH)-aware bandwidth allocation strategy. The approach, while being application layer transparent, guarantees fluidity to all users but it provides priority-based services to premium users, identified by their Willingness-To-Pay (WTP) profiles. Since different service qualities can be accommodated using WTP, the approach can be extended to XR services, immersive videos, live uplink streaming. To achieve this goal, the allocation problem is firstly formulated as a classical Game Theory problem whose closed form solution is clearly understood in the literature. In a nutshell, the WTP-based, Game Theoretically Optimal Bandwidth Allocation (WTP-GTOBA), firstly satisfies the minimum bandwidth needs of each user and then fairly distributes the residual bandwidth. WTP-GTOBA can be approximately implemented by a greedy, application layer transparent algorithm to be implemented at a DASH-aware network element managing different neighbouring radio access stations. Thereby, the proposed method is suitable for integration of WTP and resource management among multiple service providers and heterogeneous client groups. Numerical simulations carried out under a realistic scenario show that the proposed approach outperforms state-of-the-art application-layer transparent competitors, providing premium quality and/or guaranteed fluidity to different users based on their WTP.

Transaction Throughput Optimization for Integrated Blockchain and MEC System in IoT

The integration of blockchain and mobile edge computing (MEC), as a secure, efficient, and reliable edge computing paradigm, has been widely applied in many applications, such as large-scale Internet of Things (IoT), Internet of Vehicles (IoV), and smart grid. However, due to the restricted transaction throughput of blockchain, the combination of blockchain and MEC in most existing works cannot support applications with frequent transaction requirements. In this paper, we propose an integrated blockchain and MEC (IBM) framework based on a space-structured ledger to meet the transaction demands for IoT applications. In the framework, a collaborative mining process is designed, where we consider the cooperation between mobile devices (MDs) and MEC servers. To promote mining efficiency, we further develop a high-performance consensus mechanism called reputation-based proof of work (Re-PoW), in which differentiated mining targets are assigned according to the reputation of MDs. In the Re-PoW consensus mechanism, heterogeneous capabilities and historical behaviors of MDs are all considered for accurately evaluating their reputation. In addition, we present an alternating optimization algorithm by jointly optimizing bandwidth allocation and computation resource allocation to further enhance the performance of the proposed scheme. Simulation results show that the proposed approach can achieve significant throughput improvement.

Machine Learning-Aided Energy Efficiency Strategy for Multiuser Cooperative Networks

Cooperative communication is widely seen as a promising key technology for improving the energy efficiency of battery-driven multiple mobile terminals (MTs). In this study, we investigate the use of machine learning (ML) in multiuser cooperative access networks. Because MT cooperation and bandwidth allocation are considered two main issues in such networks, we design an ML-aided method to solve the bandwidth issues so that the proposed method can maximize the network’s energy efficiency. Specifically, we use machine learning with artificial neural network (ANN) trained at base station (BS) (a) to decide whether MTs in the heterogeneous access network should cooperatively communicate and (b) to determine the optimal bandwidth allocation for this communication by distributing the trained ANN to all MTs. The computer simulation results show that under the described communication environment in this paper, the proposed method can provide 99.8% correct prediction for MT cooperation and output the optimal bandwidth allocation with at least 88% accuracy, which demonstrates the effectiveness of the proposed method. Besides, the simulations also show that the proposed method can provide about 14%–25% power consumption reduction, which validates the EE performance of the proposed method.

Backhaul-Aware Drone Base Station Placement and Resource Management for Fso Based Drone Assisted Mobile Networks

In drone assisted mobile networks, drones mounted small cell base stations (DBSs) are responsively and flexibly deployed over any Places of Interest (PoI), such as sporadic hotspots and disaster-struck areas, where the existing mobile network infrastructure is unable to provide wireless coverage. Here, a DBS is a relay node to relay traffic between a nearby macro base station (MBS) and the users. In addition, Free-space optics (FSO) is applied as the backhauling solution to significantly increase the capacity of the backhaul link between an MBS and a DBS in a drone assisted mobile network. Most of the existing DBS placement solutions assume the FSO based backhaul link provides sufficient link capacity, which may not be true, especially when a DBS is placed far away from an MBS (e.g., > 10 km in disaster-struck areas) or in a bad weather condition. In this paper, we formulate a problem to jointly optimize bandwidth allocation and DBS placement by considering the FSO based backhaul link capacity constraint. A Backhaul awaRe bandwidth allOcAtion and DBS placement (BROAD) algorithm is designed to efficiently solve the problem, and the performance of the algorithm is demonstrated via extensive simulations.

Cell on Wheels-Unmanned Aerial Vehicle System for Providing Wireless Coverage in Emergency Situations

Due to natural disasters, unmanned aerial vehicles (UAVs) can be deployed as aerial wireless base stations when conventional cellular networks are out of service. They can also supplement the mobile ground station to provide wireless devices with improved coverage and faster data rates. Cells on wheels (CoWs) can also be utilized to provide enhanced wireless coverage for short-term demands. In this paper, a single CoW cooperates with a single UAV in order to provide maximum wireless coverage to ground users. The optimization problem is formulated to find the following: (1) the optimal 2D placement of the CoW, (2) the optimal 3D placement of the UAV, (3) the optimal bandwidth allocation, (4) the percentage of the available bandwidth that must be provided to the CoW and UAV, and (5) the priority of wireless coverage; which maximizes the number of covered users. We utilize the exhaustive search (ES) and particle swarm optimization (PSO) algorithms to solve the optimization problem. The effectiveness of the proposed algorithms is validated using simulation results.

Security enhancement of UAV swarm enabled relaying systems with joint beamforming and resource allocation

The high mobility of unmanned aerial vehicles (UAVs) could bring abundant degrees of freedom for the design of wireless communication systems, which results in that UAVs, especially UAV swarm, have attracted considerable attention. This paper considers a UAV Swarm enabled relaying communication system, where multiple UAV relays are organized via coordinated multiple points (CoMP) as a UAV swarm to enhance physical layer security of the system in the presence of an eavesdropper. In order to maximize achievable secrecy rate of downlink, we jointly optimize the beamforming vector of the virtual array shaped by the UAV swarm and bandwidth allocation on it for receiving and forwarding, and both amplify-and-forward (AF) and decode-and-forward (DF) protocols are considered on the UAV swarm. Due to the non-convexity of the joint optimization problem, we propose an alternating optimization (AO) algorithm to decompose it into two subproblems utilizing block coordinate descent technique, then each subproblem is solved by successive convex optimization method. Simulation results demonstrate that DF has competitive performance advantage compared with AF and the superiority of the proposed secure transmission strategy with optimal beamforming and bandwidth allocation compared with benchmark strategies.

A novel bandwidth allocation scheme for OTSS-enabled flex-grid intra-datacenter networks

Optical circuit switching networks have been recognized as a promising solution for inter-datacenter networks. However, for intra-datacenter networks, they may fall short in efficiently provisioning traffic requests due to their relatively coarse-grained channel assignment and special intra-datacenter traffic patterns. Optical time slice switching (OTSS) has been recently proposed as an optical-switching technique that can provide flexible and transparent optical circuits by extending the merit of flex-grid switching to the time domain, thus achieving much finer granularity. As OTSS requires nanosecond speed optical switches which are expensive, it might not be economically viable to make a one-time upgrade for the entire datacenter. Thus, we expect fine-grained OTSS-enabled and coarse-grained flex-grid-enabled optical switching techniques to co-exist in the foreseeable future. In this study, we investigate an OTSS-enabled flex-grid (OTSS-FG) architecture for intra-datacenter networks. For scenarios where traffic flows are given, we develop a Mixed Integer Linear Program to study the optimal bandwidth allocation scheme in an OTSS-FG architecture. When traffic flows are generated in real time, by leveraging machine-learning techniques to detect flow types, we propose a flow-aware bandwidth allocation (FABA) scheme and a dynamic version of FABA, called “D-FABA” scheme. Numerical simulations show that proposed bandwidth allocation scheme can outperform benchmark schemes in terms of average delay and blocking probability.

Optimal resource allocation in networked control systems using viterbi algorithm

This paper presents an optimal bandwidth allocation method for a networked control system (NCS) which includes time-driven sensor, event-driven controller and random channels. A hidden markov model (HMM) with a discretized state space is formulated for the random traffic to predict the network states using a suitable data window. Network bandwidth is allocated based on the predicted traffic state subject to bounds on the deterministic traffic that guarantee acceptable NCS performance and do not exceed hardware limitations. Bandwidth allocation uses minimization of unmet bandwidth demand. A stability condition is derived for a variable but bounded sampling period interval. Computer simulation results show the effect of varying the number of discrete states for the HMM and the window width on bandwidth allocation. The results compare favorably with a published approach based on fuzzy logic.

Resource Allocation for Millimeter-Wave Train-Ground Communications in High-Speed Railway Scenarios

With the development of wireless communication, higher requirements arise for train-ground wireless communications in high-speed railway (HSR) scenarios. The millimeter-wave (mm-wave) frequency band with rich spectrum resources can provide users in HSR scenarios with high performance broadband multimedia services, while the full-duplex (FD) technology has become mature. In this paper,we study train-ground communication system performance in HSR scenarios with mobile relays (MRs) mounted on rooftop of train and operating in the FD mode. We formulate a nonlinear programming problem to maximize network capacity by allocation of spectrum resources. Then, we develop a sequential quadratic programming (SQP) algorithm based on the Lagrange function to solve the bandwidth allocation optimization problem fortrack-side base station (BS) and MRs in this mm-wave train-ground communication system. Extensive simulation results demonstrate that the proposed SQP algorithm can effectively achieve high network capacity for train-ground communication in HSR scenarios while being robust to the residual self-interference (SI).

Joint multipath flow and layer allocation for scalable video streaming

Projections estimating future online traffic reveal that better techniques are required to meet the expected demand. An emerging network paradigm, software defined networking (SDN) provides effective central management in routing and resource management. In this article, we define and solve a jointly optimal bandwidth allocation and multipath routing problem for SDN-based delay/jitter constrained transmission of scalable videos. We also develop a heuristic approach that can easily be implemented in an SDN controller. Two different configurations of the problem are simulated. Numerical evaluations show that proposed multipath routing solutions provide considerable improvement in both configurations when compared to benchmark algorithms. Mininet emulation results also agree with the theoretical results.

Joint Location, Bandwidth and Power Optimization for THz-Enabled UAV Communications

In this letter, the problem of unmanned aerial vehicle (UAV) deployment, power allocation, and bandwidth allocation is investigated for a UAV-assisted wireless system operating at terahertz (THz) frequencies. In the studied model, one UAV can service ground users using the THz frequency band. However, the highly uncertain THz channel will introduce new challenges to the UAV location, user power, and bandwidth allocation optimization problems. Therefore, it is necessary to design a novel framework to deploy UAVs in the THz wireless systems. This problem is formally posed as an optimization problem whose goal is to minimize the total delays of the uplink and downlink transmissions between the UAV and the ground users by jointly optimizing the deployment of the UAV, the transmit power and the bandwidth of each user. The communication delay is crucial for emergency communications. To tackle this nonconvex delay minimization problem, an alternating algorithm is proposed while iteratively solving three subproblems: location optimization subproblem, power control subproblem, and bandwidth allocation subproblem. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 59.3%, 49.8% and 75.5% respectively compared to baseline algorithms that optimize only UAV location, bandwidth allocation or transmit power control.

Coflow Scheduling in Data Centers: Routing and Bandwidth Allocation

In distributed computing frameworks like MapReduce, Spark, and Dyrad, a coflow is a set of flows transferring data between two stages of a job. The job cannot start its next stage unless all flows in the coflow finish. To improve the execution performance of such a job, it is crucial to reduce the completion time of a coflow, as it can contribute more than 50 percent of the job completion time. While several coflow schedulers have been proposed, we observe that routing, as a factor greatly impacting the Coflow Completion Time (CCT), has not been well considered. In this article, we focus on the coflow scheduling problem and jointly consider routing and bandwidth allocation. We begin by providing an analytical solution to the problem of optimal bandwidth allocation with pre-determined routes. In the following, we formulate the problem of scheduling a single coflow as a Non-linear Mixed Integer Programming problem and present its relaxed convex optimization problem. We further propose two algorithms, CoRBA and its simplified version: CoRBA-fast that solve the single coflow scheduling problem with a joint consideration of routing and bandwidth allocation. Lastly, to address multiple coflows in online scheduling, we propose an online scheduler named OnCoRBA. By comparing with the start-of-the-art algorithms and schedulers via simulations, we demonstrate that CoRBA and CoRBA-fast reduce the CCT by 30-400 percent and the OnCoRBA scheduler reduces the average online CCT by 20-230 percent. In addition, CoRBA-fast can be hundreds times faster than CoRBA with around 8 percent performance degradation compared to CoRBA, which makes the use of CoRBA-fast very appropriate in practice.

Cooperative Spectrum Sharing in Spectrum Domain with Discrete Time Energy Harvesting for Primary User

In this paper, we propose a spectrum sharing protocol for cognitive radio networks with an energy-constraint primary transmitter (PT), which performs energy harvesting from the received radio frequency (RF) signals broadcasting by two secondary users (SUs). After the amount of harvested energy at the PT is sufficient for data transmission, a SU will cooperatively forward PT's signal using amplify-and-forward (AF) scheme along with transmitting its own signal to increase the spectrum efficiency of the system. To be specific, the data transmission block can be divided into two phases. During the first phase, the PT will optimally select a relay from nodes S1 and S2 in order to further improve the overall throughput, then the PT transmits primary signal to the selected SU and primary receiver (PR), respectively, while the non-selected SU transmits signal to secondary receiver (SR). In the second phase, the selected SU transmits signals of both the primary and secondary systems simultaneously. A discrete Markov chain is used to simulate the charging and discharging processes of the PT's battery. In order to avoid mutual interference between the primary and secondary systems, a fraction of spectrum is used to transmit primary data and the residual spectrum is served for data transmission of the secondary system. An appropriate bandpass filter (BF) is deployed at the receive front-end of each receiver to extract the desired signals from assigned bandwidth. Based on this, the exact expressions of the outage probabilities for both the primary and secondary systems are derived. Moreover, the optimal bandwidth allocation coefficient is determined by utilizing convex optimization to maximize the achievable rate of the secondary system while guaranteeing the achievable rate of the secondary system in information transmission mode. Numerical results show that the proposed spectrum sharing protocol outperforms to other scheme and non-cooperative scenario in terms of average spectrum efficiency.

Optimizing Bandwidth Allocation in GPON as a Backhaul to Fulfill 5G Latency Requirement

To achieve full deployment of 5G services, a backhaul network solution is required to connect the central office to the BTS (Base Station). Some of the current alternatives used for backhaul solutions implement Wireless technology (Radio IP) and wire-line (copper and fiber). The use of fiber network can be used with Metro Ethernet or GPON network approach. The focus of GPON is based on the problems encountered when the requirement of latency is fixed at below 5 ms. Based on the findings of this research, in order to meet the 5G latency requirement of 5ms, the most suitable capacity allocation is 70% with average latency combined of upstream and downlink is 2.8ms. Furthermore, the average optimum bandwidth capacity is 72.10 % and 72.59 % for GPON and XGSPON respectively

Latency minimization for the MEC-aided vehicular network under Doppler Effect

In this paper, we consider a heterogeneous Mobile Edge Computing (MEC)-aided multi-layer vehicular network consisting of multiple vehicles as Edge Device (EDs), a road side unit (RSU), and a cloud center (CC). The raw data produced by the vehicles can be processed at the three nodes separately. EDs transmit the data to the RSU via wireless communication link, and a wired link is considered between the RSU and the CC. Taking the mobility of vehicles and the limited amount of resources into account, we jointly discuss the task offloading and resource allocation in this three-layer MEC-aided vehicular network. Specifically, the interactions of Doppler Effect and the bandwidth provided by RSU to different vehicles are investigated. Aiming at minimizing the latency while increasing the utilization of resource, we design an iterative task assignment and resource allocation (ITR) algorithm. The simulation results indicate that: 1) The average latency of all tasks is significantly decreased, and the optimal bandwidth allocation scheme to the EDs is achieved; 2) Due to the Doppler Effect of vehicles, it is important to consider both the mobility of vehicles and the length of communication links when allocating resource.

UAV-Enabled Wireless Backhaul Networks Using Non-Orthogonal Multiple Access

Owing to their potential mobility and agility, unmanned aerial vehicles (UAVs) have captured predominant interests in sustaining 5G wireless communication and beyond. In this paper, we scrutinize the downlink transmission of UAV-enabled wireless backhaul networks in which non-orthogonal multiple access is incorporated to boost up the massive connectivity and high spectra efficiency. More precisely, our aim is to maximize the worst ground user's achievable rate by optimizing bandwidth allocation, UAV's power allocation and placement. The formulated problem is non-convex and not easy to solve optimally. Consequently, to deal with the complexity and non-convexity of our problem, we develop a path following procedure and generate a less-onerous algorithm that is iteratively run till convergence. The simulation results are executed to validate not only the effectiveness, but also the convergence of the proposed method. In addition, a comparison with other alternative schemes is depicted to divulge the outperformance of our proposed algorithm.

A Novel Successive-Interference-Cancellation- Aware Design for Wireless Networks Using Software-Defined Networking

In traditional CSMA/CA-based MAC designs supporting successive interference cancellation (SIC), channel utilization is inefficient, because the inherited CSMA/CA-style contention approach often forces the whole channel to remain idle and the early released channel is not utilized. In this paper, we propose Multi-CQ, which is the first software-defined networking (SDN)-based MAC design for wireless LANs that supports SIC and significantly improves the channel utilization. Multi-CQ, adopting SDN’s functional separation idea and OFDMA, makes contention and data transmission be executed over two subchannels independently and concurrently, where the superposition coding are used for decoding combined signals, and multiple CQs (contention queues) are introduced to coordinate the concurrency. This concurrent execution greatly reduces the waste in channel contention. Next, adopting SDN’s central control idea, Multi-CQ controller selects nodes who can finish their frame transmissions in almost equal time, thereby improving the channel utilization. Finally, we develop a theoretical model to optimize bandwidth allocation for channel contention and data transmission. Extensive simulations verify the effectiveness of our design and the accuracy of our theoretical model. This study is also helpful to better design wireless MAC protocols supporting other advanced functionalities such as MU-MIMO.

Group Perceptual Quality Optimization for Multi-Channel Image Encoding Systems Based on Adaptive Hyper Networks

Images and short videos that produced by social networks surge in recent years. Image/Video encoders, such as JPEG and H.264, are indispensably involved to reduce the transmitting bandwidth. However, based on our observation, the encoding parameters and their candidates are often preset to fixed values (or fixed candidate values) in real-world scenarios, which might not be the optimal bandwidth allocation strategy. Considering that, we propose an efficient group quality optimization (GQO) framework for multi-channel image/video encoding systems in which the encoding parameters are configured in a perceptual-quality-driven manner. The GQO framework employs adaptive hyper network to predict the relationships between encoding parameters, transmitting resources, and perceptual qualities, i.e., just taking the pristine image as input, the adaptive hyper network could accurately yield a global overview of perceptual quality and transmitting resource varied along encoding parameters. A step-by-step optimization procedure is then employed to search the optimal encoding parameter for each channel so that overall perceptual quality could be maximized under limited transmitting resource. Experimental results demonstrate the proposed GQO framework could achieve higher perceptual quality whilst maintain the same bandwidth compared to traditional allocation strategies where encoding parameters are preset.

Mission-Critical Resource Allocation With Puncturing in Industrial Wireless Networks Under Mixed Services

As the manufacturing industry gradually expands and tends to be intelligent, the industrial networks have been greatly applied and are of paramount importance. Furthermore, taking the advantages of wireless networks into account, such as flexible deployment, low cost, and easy maintenance, etc., this paper is dedicated to the research of industrial wireless networks. Facing the problem of limited radio resource, in order to make full use of frequency band, this paper proposes a resource scheduling scheme with puncturing, which guarantees the reliability of subsequent ultra-reliable and low-latency communications (URLLC) services under the conditions of existing enhanced mobile broadband (eMBB) services. By optimizing the bandwidth pre-allocation for eMBB services, puncture weight and transmit power, this scheme can effectively meet the needs of the system. Specifically, the purpose of this paper is to minimize the decoding error rate of the devices carrying URLLC service while ensuring the demand for throughput of eMBB services. The problem proposed is a nonlinear stochastic optimization problem involving complex Q-function. The closed-form expression of the objective function is obtained through linear approximation and probability theory, which is further decoupled into three sub-problems. To this end, a block coordinate descent optimization (BCDO) algorithm is proposed to obtain the optimal bandwidth allocation, puncture weight and transmit power. The simulation results verify the rationality of the theoretical analysis and the effectiveness of the proposed algorithm, and also elaborate the influence of different parameters on the decoding error rate of the URLLC devices.