Heterogeneous Quality Of Service Requirements Research Articles

On Performance of NOMA-Based Wireless Powered Communication Networks Assisted With Power Beacons and PPP Distributed IN

This work studies the performance of power beacon (PB) assisted downlink cooperative non-orthogonal multiple access (NOMA) in a wireless powered communication network (WPCN) with realistic non-linear energy harvesting (EH) model. The paper proposes an analytical methodology for improving performance at blocked user at the edge of network via an intermediate node (IN). IN deployed in the coverage area of the base station (BS), acts as decode-and-forward relay to perform cooperative transmission. IN assists the BS in communicating with the blocked Internet of Things (IoT) device after harvesting energy from PB mounted on the BS. IN employs NOMA to serve IoT devices with heterogeneous quality of service requirements in order to achieve higher spectral and energy efficiency. A practical non-linear EH model is employed at IN to power the cooperative transmission to the IoT devices. The performance of the proposed scheme is compared with the system employing orthogonal multiple access and it is shown that the proposed scheme attains improved performance in terms of outage probability, system throughput, and energy efficiency.

DeSlice: An Architecture for QoE-Aware and Isolated RAN Slicing.

Network slicing is considered a key feature of 5G and beyond cellular systems. It opens the door for new business models of mobile operators, enables new services, reduces costs with advanced infrastructure-sharing techniques, and improves heterogeneous traffic service. With slicing, the operators can tailor the network resources to the requirements of specific verticals, applications, and corresponding traffic types. To satisfy the heterogeneous quality of service (QoS) requirements of various slices, efficient virtualization and resource allocation algorithms are required. Such algorithms are especially crucial for the radio access network (RAN) because of the spectrum scarcity. This article develops DeSlice, a novel architecture for RAN slicing. DeSlice enables efficient real-time slicing algorithms that satisfy heterogeneous QoS requirements of the slices and improve the quality of experience for their end users. The article illustrates the advantages of DeSlice by considering the problem of the joint service of cloud VR, video, and web traffic. It develops the algorithms using DeSlice architecture and application-to-network communication. With simulations, it shows that, together, the architecture and the algorithms allow greatly improving the QoE for these traffics significantly.

Energy Efficiency Optimization for MISO-NOMA SWIPT System with Heterogeneous QoS Requirements

This letter solves the energy efficiency optimization problem for the simultaneous wireless information and power transfer (SWIPT) systems with non-orthogonal multiple access (NOMA), multiple input single output (MISO) and power-splitting structures, where each user may have different individual quality of service (QoS) requirements about information and energy. Nonlinear energy harvesting model is used. Alternate optimization approach is adopted to find the solution, which shows a fast convergence behavior. Simulation results show the proposed scheme has higher energy efficiency than existing dual-layer iteration and throughput maximization methods.

Service Provisioning in Sliced Cloud Radio Access Networks

Network slicing- (NS-) based cloud radio access networks (C-RANs) have emerged as a key paradigm to support various novel applications in 5G and beyond networks. However, it is still a challenge to allocate resources efficiently due to heterogeneous quality of service (QoS) requirements of diverse services as well as competition among different network slices. In this paper, we consider a service provisioning allocation framework to guarantee resource utilization while ensuring the QoS of users. Specifically, an inter/intraslice bandwidth optimization strategy is developed to maximize the revenue of the system with multiple network slices. The proposed strategy is hierarchically structured, which decomposes into network-level slicing and packet scheduling level slicing. At the network level, resources are allocated to each slice. At the packet scheduling level, each slice allocates physical resource blocks (PRBs) among users associated with the slice. Numerical results show that the proposed strategy can effectively improve the revenue of the system while guaranteeing heterogeneous QoS requirements. For example, the revenue of the proposed strategy is 21% higher than that of the average allocation strategy.

Harvesting Devices' Heterogeneous Energy Profiles and QoS Requirements in IoT: WPT-NOMA vs BAC-NOMA

The next generation Internet of Things (IoT) exhibits a unique feature that IoT devices have different energy profiles and quality of service (QoS) requirements. In this paper, two energy and spectrally efficient transmission strategies, namely wireless power transfer assisted non-orthogonal multiple access (WPT-NOMA) and backscatter communication assisted NOMA (BAC-NOMA), are proposed by utilizing this feature of IoT and employing spectrum and energy cooperation among the devices. Furthermore, for the proposed WPT-NOMA scheme, the application of hybrid successive interference cancelation (SIC) is also considered, and analytical results are developed to demonstrate that WPT-NOMA can avoid outage probability error floors and realize the full diversity gain. Unlike WPT-NOMA, BAC-NOMA suffers from an outage probability error floor, and the asymptotic behaviour of this error floor is analyzed in the paper by applying the extreme value theory. In addition, the effect of a unique feature of BAC-NOMA, i.e., employing one device's signal as the carrier signal for another device, is studied, and its impact on the diversity gain is revealed. Simulation results are also provided to compare the performance of the proposed strategies and verify the developed analytical results.

Auto-Scaling Network Service Chains Using Machine Learning and Negotiation Game

Network Function Virtualization (NFV) enables Network Operators (NOs) to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) running on commercial off-the-shelf servers are easy to deploy, update, monitor, and manage. Such virtualized services are often deployed as Service Chains (SCs), which require in-sequence placement of computing and memory resources as well as routing of traffic flows. Due to the ongoing migration towards cloudification of networks, the concept of auto-scaling which originated in Cloud Computing, is now receiving attention from networks professionals too. Prior studies on auto-scaling use measured load to dynamically react to traffic changes. Moreover, they often focus on only one of the resources (e.g., compute only, or network capacity only). In this study, we consider three different resource types: compute, memory, and network bandwidth. In prior studies, NO takes auto-scaling decisions, assuming tenants are always willing to auto-scale, and Quality of Service (QoS) requirements are homogeneous. Our study proposes a negotiation-game-based auto-scaling method where tenants and NO both engage in the auto-scaling decision, based on their willingness to participate, heterogeneous QoS requirements, and financial gain (e.g., cost savings). In addition, we propose a proactive Machine Learning (ML) based prediction method to perform SC auto-scaling in dynamic traffic scenario. Numerical examples show that our proposed SC auto-scaling methods powered by ML present a win-win situation for both NO and tenants (in terms of cost savings).

Client-Centric Access Device Selection for Heterogeneous QoS Requirements in Beyond 5G IoT Networks

The expected manifolds increase in the traffic and the massive number of connected Internet of Things (IoT) devices would be a challenge to ensure a certain Quality of Service (QoS) in Beyond 5G (B5G) networks. Due to the increased availability of processing and energy resources and heterogeneous QoS requirements in modern IoT nodes, a client-centric access device selection approach for QoS provisioning in multiple Radio Access Technologies (RATs) scenario is proposed in this paper. The proposed algorithm provides the ability to specify node-specific QoS requirements at each node, a better access device selection, and improved network scalability. For experimental evaluation, a hybrid indoor network consisting of Wireless Fidelity (WiFi) and Light Fidelity (LiFi) RATs has been considered. Experimental results show that the proposed technique outperforms several conventional client-based access device selection approaches by up to 32.66% in network emulation experiments and up to 50% in hardware experimentation. The contribution of this paper includes the proposed algorithm, its complexity and game-theory based convergence analyses, evaluation of the proposed algorithm using network emulation and hardware-based setups, and LiFi channel analysis. The analyses and results imply that the proposed algorithm could be utilized in next-generation IoT networks as it performs better than conventionally used access device selection techniques for QoS provisioning.

Joint Power and Resource Block Allocation for Mixed-Numerology-Based 5G Downlink Under Imperfect CSI

Fifth-generation (5G) of wireless networks are expected to accommodate different services with contrasting quality of service (QoS) requirements within a common physical infrastructure in an efficient way. In this article, we address the radio access network (RAN) slicing problem and focus on the three 5G primary services, namely, enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC) and massive machine-type communications (mMTC). In particular, we formulate the joint allocation of power and resource blocks to the heterogeneous users in the downlink targeting the transmit power minimization and by considering mixed numerology-based frame structures. Most importantly, the proposed scheme does not only consider the heterogeneous QoS requirements of each service, but also the queue status of each user during the scheduling of resource blocks. In addition, imperfect Channel State Information (CSI) is considered by including an outage probabilistic constraint into the formulation. The resulting non-convex problem is converted to a more tractable problem by exploiting Big-M formulation, probabilistic to non-probabilistic transformation, binary relaxation and successive convex approximation (SCA). The proposed solution is evaluated for different mixed-numerology resource grids within the context of strict slice-isolation and slice-aware radio resource management schemes via extensive numerical simulations.

5MART: A 5G SMART Scheduling Framework for Optimizing QoS Through Reinforcement Learning

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of Service (QoS) requirements of various applications have put significant pressure on the underlying network infrastructure and represent an important challenge even for the very anticipated 5G networks. In this context, the solution is to employ smart Radio Resource Management (RRM) in general and innovative packet scheduling in particular in order to offer high flexibility and cope with both current and upcoming QoS challenges. Given the increasing demand for bandwidth-hungry applications, conventional scheduling strategies face significant problems in meeting the heterogeneous QoS requirements of various application classes under dynamic network conditions. This paper proposes 5MART , a 5G smart scheduling framework that manages the QoS provisioning for heterogeneous traffic. Reinforcement learning and neural networks are jointly used to find the most suitable scheduling decisions based on current networking conditions. Simulation results show that the proposed 5MART framework can achieve up to 50% improvement in terms of time fraction (in sub-frames) when the heterogeneous QoS constraints are met with respect to other state-of-the-art scheduling solutions.

LoRaWAN Modeling and MCS Allocation to Satisfy Heterogeneous QoS Requirements.

LoRaWAN infrastructure has become widely deployed to provide wireless communications for various sensor applications. These applications generate different traffic volumes and require different quality of service (QoS). The paper presents an accurate mathematical model of low-power data transmission in a LoRaWAN sensor network, which allows accurate validation of key QoS indices, such as network capacity and packet loss ratio. Since LoRaWAN networks operate in the unlicensed spectrum, the model takes into account transmission attempt failures caused by random noise in the channel. Given QoS requirements, we can use the model to study how the performance of a LoRaWAN network depends on the traffic load and other scenario parameters. Since in LoRaWAN networks the transmissions at different modulation and coding schemes (MCSs) typically do not collide, we use the model to assign MCSs to the devices to satisfy their QoS requirements.

On the Impact of Time-Correlated Fading for Downlink NOMA

This paper investigates the performance of non-orthogonal multiple access (NOMA) systems over time-correlated Rayleigh fading channels, where the users have heterogeneous quality of service requirements, e.g., a latency-critical user with a low target rate and a delay-tolerant user with a large target rate. In order to meet the different requirements of the users, two partial hybrid automatic repeat request (HARQ) schemes, including partial HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR), are proposed. The closed-form expressions of outage probabilities for NOMA with and without re-transmission are derived. With the developed outage probabilities, a condition on the superiority of NOMA to orthogonal multiple access (OMA) is obtained. In particular, the condition is characterized by the transmit powers for NOMA without re-transmission and is obtained by using the bisection method in the case with re-transmission. To further improve the performance of the HARQ enabled NOMA schemes, we consider an average transmit power minimization problem by optimizing the transmit power among different transmission rounds with outage constraints. However, due to the complexity of the developed outage probabilities, the formulated problem is non-convex and challenging to solve. Then we approximate the original problem by deriving the upper-bound approximations of the outage probabilities and solve it by using the geometric programming method. Simulation results demonstrate the accuracy of the developed analytical results. It is shown that the performance of NOMA is superior to OMA, only when the obtained condition is satisfied. HARQ-CC and HARQ-IR can enhance the outage performance of NOMA over time-correlated fading channels and the HARQ-IR has excellent performance in terms of energy efficiency.

Dynamic Priority-Based Resource Provisioning for Video Transcoding With Heterogeneous QoS

Video transcoding is widely adopted in online video services to transcode videos into multiple representations for dynamic adaptive bitrate streaming. This solution may consume significant resources and incur intolerable processing delays. Meanwhile, different videos have different quality-of-service (QoS) requirements for transcoding. Delay-sensitive videos must be transcoded within a strict deadline, whereas delay-tolerant videos are not required to be transcoded immediately. Some intelligent policies are required for provisioning the right amount of resources in the transcoding system to meet the heterogeneous QoS requirements, especially under dynamic workloads. To this end, we develop a robust dynamic priority-based resource provisioning scheme for video transcoding. We adopt the preemptive resume priority discipline to design a multiple-priority transcoding mechanism. The system performs the transcoding for delay-tolerant videos by utilizing idle resources for improving resource utilization while not affecting the transcoding for delay-sensitive videos. We adopt the model predictive control framework to design an online algorithm for dynamic resource provisioning to accommodate time-varying workloads by predicting future workloads. To seek performance robustness against prediction noise, we improve the performance of our online algorithm via robust design . The experimental results demonstrate that our proposed method can satisfy the heterogeneous QoS requirements while significantly reducing computing resource consumption.

Maximizing Sleeping Capacity Based on QoS Provision for Information-Centric Internet of Things

Unlike the IP-address-centric network, heterogeneous devices in the information-centric Internet of Things (IC-IoT) have heterogeneous quality of service (QoS) requirements. Employing sleeping technology in IC-IoT while satisfying the heterogeneous QoS requirements for IoT devices is challenging. In this paper, considering the IoT devices’ heterogeneous QoS requirements, we investigate a sleeping capacity maximizing problem in IC-IoT. First, a content-based heterogeneous network architecture is proposed to empower IC-IoT and decouple the heterogeneous content transmission functions. Specifically, macro base stations (MBSs) are used to guarantee the control coverage and transmit the low QoS requirement services, whereas small base stations (SBSs) are utilized to transmit the high QoS requirement services. The QoS requirement levels are differentiated by the outage constraints which are related to the achievable data rate. Then, an optimization problem is formulated to maximize the sleeping probability and available bandwidth while the outage probability and achievable data rate constraints are guaranteed. In the formulated optimization problem, sleeping probability plays the roles of both an optimization variable and an optimization objective, which makes the optimization problem challenging to be solved. In order to decouple sleeping probability from the optimization values, we first analyze the parameters related to the sleeping probability and the available bandwidth and then propose a bisection-based nonlinear programming algorithm. This algorithm divides the original problem into two subproblems. One is related to the bandwidth reallocation. The other one involves obtaining the optimal sleeping probability. Last but not least, analytical results are concluded due to the simulation results, which indicate the following facts: 1) optimal sleeping probability is linear to the available bandwidth, and inversely proportional to the devices’ density, and; 2) sleeping probability is more sensitive to the changes of channel quality than those of QoS provision.

Heterogeneous Statistical QoS Provisioning Over Airborne Mobile Wireless Networks

Airborne mobile wireless networks (AMWNs), which use spacecrafts and aircrafts such as satellites, airships, airplanes, unmanned aerial vehicles, and other high/medium/low-altitude platforms (HAPs/MAPs/LAPs) can efficiently support high dynamic network topologies and weakly connected communication links. Due to the dramatic dynamics of the AMWNs, it is very difficult to provide the deterministic delay-bounded quality of service (QoS) provisioning for time-sensitive real-time traffics (such as video and audio) over the AMWNs. Alternatively, the statistical delay-bounded QoS provisioning provides an efficient way for guaranteeing delay-bounded QoS requirements for real-time traffics over the AMWNs. On the other hand, because of the diversity of real-time traffics, it is highly demanded to consider the heterogeneity of delay-bounded QoS requirements for distinct real-time services under different HAPs/MAPs/LAPs in the AMWNs. In this paper, we establish the heterogeneous statistical QoS provisioning framework to support the diverse real-time services over the AMWNs. In particular, we formulate the optimization problem to maximize the aggregate effective capacity subject to heterogeneous statistical delay-bounded QoS requirements for both downlink and uplink transmissions-based AMWNs groups (AMWNGs) in the AMWNs. We solve the aggregate effective capacity maximization problems and derive the optimal heterogeneous statistical QoS-driven power allocation schemes for the AMWNs. The numerical analyses we obtained verify that our developed optimal heterogeneous statistical QoS-driven power allocation schemes can significantly increase the aggregate effective capacity for the AMWNs than the other existing schemes.

A framework for joint resource allocation of MapReduce and web service applications in a shared cloud cluster

The ongoing uptake of cloud-based solutions by different business domains and the rise of cross-border e-commerce in the EU require for additional public and private cloud solutions. Private clouds are an alternative for e-commerce sites to host not only Web Service (WS) applications but also Business Intelligence ones that consist of batch and/or interactive queries and resort to the MapReduce (MR) programming model.In this study, we take the perspective of an e-commerce site hosting its WS and MR applications on a fixed-size private cloud cluster. We assume Quality of Service (QoS) guarantees must be provided to end-users, represented by upper-bounds on the average response times of WS requests and on the MR jobs execution times, as MR applications can be interactive nowadays. We consider multiple MR and WS user classes with heterogeneous workload intensities and QoS requirements. Being the cluster capacity fixed, some requests may be rejected at heavy load, for which penalty costs are incurred. We propose a framework to jointly optimize resource allocation for WS and MR applications hosted in a private cloud with the aim to increase cluster utilization and reduce its operational and penalty costs. The optimization problem is formulated as a non linear mathematical programming model. Applying the KKT conditions, we derive an equivalent problem that can be solved efficiently by a greedy procedure. The proposed framework increases cluster utilization by up to 18% while cost savings go up to 50% compared to a priori partitioning the cluster resources between the two workload types.

Internet of Things, future networks, and the economics of virtual networks

The Internet of Things (IoT) gains momentum. Developments regarding smart grids, intelligent transportation systems, and low-power networks for smart cities constitute significant drivers in the evolution of network industries. IoT creates an array of new requirements for information and communications technology (ICT) data transmission: In addition to real-time and data geopositioning, new service characteristics result from the change of the traditional sender–receiver perspective of Transmission Control Protocol/Internet Protocol (TCP/IP) to content relevancy for many users (e.g. cloud computing) and dynamic changes of the state of devices. The future development and success of IoT hinges critically on the provision of heterogeneous quality of service (QoS) requirements which cannot be provided by best-effort TCP/IP Internet. It is thus not surprising that both in the US and the EU network neutrality regulations are currently being reconsidered. Alternative network logistics (virtual networks) dealing with heterogeneous QoS requirements of network traffic may require fundamental deviations from traditional Internet architectures. Corresponding logistics operating over joint physical infrastructures gain increasing attention under the heading of future networks (FNs). The goal of this article is to focus on the economic mechanisms of how the potentials of QoS differentiation in the context of FNs can be fully exploited and incentivized within innovative all-IP-based QoS traffic architectures.

Coordinated Scheduling Algorithm for System Utility Maximization With Heterogeneous QoS Requirements in Wireless Relay Networks

In this paper, we focus on the radio resource management (RRM) issues in the two-hop Orthogonal Frequency Division Multiple Access (OFDMA) system with heterogeneous quality of service (QoS) requirements. To solve the RRM problem, we first propose a new two-hop frame structure to support the data relay operation from the base station to the relay station and cooperative transmission for the edge users. Then, a two-hop coordinated scheduling algorithm is proposed, which utilizes the proposed two-hop frame structure and considers the corresponding interference status. The proposed two-hop coordinated scheduling algorithm solves the RRM problem of the two-hop OFDMA system with heterogeneous QoS requirements in an iterative manner, and so maximizes the system utility.

Resource Allocation for Heterogeneous Applications With Device-to-Device Communication Underlaying Cellular Networks

Mobile data traffic has been experiencing a phenomenal rise in the past decade. This ever-increasing data traffic puts significant pressure on the infrastructure of state-of-the-art cellular networks. Recently, device-to-device (D2D) communication that smartly explores local wireless resources has been suggested as a complement of great potential, particularly for the popular proximity-based applications with instant data exchange between nearby users. Significant studies have been conducted on coordinating the D2D and the cellular communication paradigms that share the same licensed spectrum, commonly with an objective of maximizing the aggregated data rate. The new generation of cellular networks, however, have long supported heterogeneous networked applications, which have highly diverse quality-of-service (QoS) specifications. In this paper, we jointly consider resource allocation and power control with heterogeneous QoS requirements from the applications. We closely analyze two representative classes of applications, namely streaming-like and file-sharing-like , and develop optimized solutions to coordinate the cellular and D2D communications with the best resource sharing mode. We further extend our solution to accommodate more general application scenarios and larger system scales. Extensive simulations under realistic configurations demonstrate that our solution enables better resource utilization for heterogeneous applications with less possibility of underprovisioning or overprovisioning.

Multi-Item Spectrum Auction for Recall-Based Cognitive Radio Networks With Multiple Heterogeneous Secondary Users

In this paper, we consider a spectrum auction system among heterogeneous secondary users (SUs) with various quality-of-service (QoS) requirements and a recall-based primary base station (PBS) that could recall channels after auction to deal with a sudden increase in its own demand. Beginning with proposing a recall-based single-winner spectrum auction (RSSA) algorithm, we further extend our work to allow multiple winners in order to improve the spectrum utilization and propose a recall-based multiple-winner spectrum auction (RMSA) algorithm. A combinatorial auction model is then formulated, and Vickrey-Clarke-Groves (VCG) mechanism is applied in the payment function. Moreover, the proposed RMSA algorithm focuses on a fair spectrum allocation among heterogeneous SUs and the increase in the PBS's auction revenue. Both theoretical and simulation results show that the proposed spectrum auction algorithm can improve the spectrum utilization with guarantees on SUs' heterogeneous QoS requirements.

When cellular meets WiFi in wireless small cell networks

The deployment of small cell base stations, SCBSs, overlaid on existing macrocellular systems is seen as a key solution for offloading traffic, optimizing coverage, and boosting the capacity of future cellular wireless systems. The next generation of SCBSs is envisioned to be multimode (i.e., capable of transmitting simultaneously on both licensed and unlicensed bands). This constitutes a cost-effective integration of both WiFi and cellular radio access technologies that can efficiently cope with peak wireless data traffic and heterogeneous quality of service requirements. To leverage the advantage of such multimode SCBSs, we discuss the novel proposed paradigm of cross-system learning by means of which SCBSs self-organize and autonomously steer their traffic flows across different RATs. Cross-system learning allows the SCBSs to leverage the advantage of both the WiFi and cellular worlds. For example, the SCBSs can offload delay-tolerant data traffic to WiFi, while simultaneously learning the probability distribution function of their transmission strategy over the licensed cellular band. This article first introduces the basic building blocks of cross-system learning and then provides preliminary performance evaluation in a Long-Term Evolution simulator overlaid with WiFi hotspots. Remarkably, it is shown that the proposed cross-system learning approach significantly outperforms a number of benchmark traffic steering policies.