Delay-bounded Quality Of Service Research Articles

3D Location and Resource Allocation Optimization for UAV-Enabled Emergency Networks Under Statistical QoS Constraint

Unmanned aerial vehicles (UAVs) as aerial base stations have attracted great attention in emergency communication networks due to flexible deployment. With the popularization of smart devices, the demand for multimedia services is increasing in disaster relief. Therefore, it is very important to significantly improve throughput of unmanned aerial vehicle base station (UAV-BS) while ensuring quality-of-service (QoS) of multimedia traffic. In this paper, we consider a UAV-BS to serve a group of users in the downlink who have different statistical delay-bound QoS requirements in an emergency situation. We formulate a problem to maximize the sum statistical-QoS-guaranteed throughput (effective capacity) of all users by jointly optimizing the UAV's 3D location, power and bandwidth allocation under each user's statistical QoS requirement constraint. The formulated problem is a non-linear non-convex optimization problem, which is very difficult to solve. To this end, we propose an efficient iteration algorithm based on the block coordinate descent and successive convex optimization techniques to solve it. Specifically, we decouple the primary problem into three sub-problems, which can be approximated as easy-to-solve convex optimization problems. In each iteration, three sub-problems are alternately optimized. Finally, numerical simulation results prove the effectiveness of our proposed algorithm compared with benchmarks.

Caching and D2D assisted wireless emergency communications networks with statistical QoS provisioning

Along with natural disasters, the destruction of communication infrastructures leads to the congestion or failure of communication networks. Unmanned aerial vehicles (UAVs), which are with a high flexibility, can be employed as temporary base stations to establish emergency networks. To relieve the backhaul burden of UAVs, some imperative contents can be cached by terrestrial cache-enabled rescuers (CERs) and provide for victims with device-to-device (D2D) transmissions. To support the effectiveness and timeliness of emergency communication, the delay-bounded quality-of-service (QoS) requirement and network throughput are desired to be comprehensively considered, which imposes a new challenge for caching placement and CER deployment. In this paper, we focus on joint caching placement and CER deployment to maximize the effective capacity subject to delay-bounded QoS requirement. The overall non-convex problem is transformed into the caching placement and the CER deployment sub-problems. Then, we develop the QoS-aware caching placement scheme with fixed CER deployment density and obtain the QoS-aware CER deployment density with fixed caching placement. Based on the block-coordinate descent method, we also propose the joint caching placement and CER deployment scheme, which can not only effectively enhance average effective capacity but also guarantee the delay-bounded QoS requirement. Also, numerical simulations are conducted to show the performances of the proposed schemes.

Information-Centric Virtualization for Software-Defined Statistical QoS Provisioning Over 5G Multimedia Big Data Wireless Networks

The multimedia transmission represents a typical big data application in the fifth-generation (5G) wireless networks. However, supporting multimedia big data transmission over 5G wireless networks imposes many new and open challenges because multimedia big data services are both time-sensitive and bandwidth-intensive over time-varying wireless channels with constrained wireless resources. To overcome these difficulties, in this paper we propose the information-centric virtualization architectures for software-defined statistical delay-bounded quality of service (QoS) provisioning over 5G multimedia big data wireless networks. In particular, our proposed schemes integrate the three 5G-promising candidate techniques to guarantee the statistical delay-bounded QoS for multimedia big data transmissions: 1) information-centric network (ICN), to derive the optimal in-network caching locations for multimedia big data; 2) network functions virtualization (NFV), to abstract the PHY-layer infrastructures into several virtualized networks to derive the optimal multimedia data contents delivery paths; and 3) software-defined networks (SDNs), to dynamically reconfigure wireless resources allocation architectures through the SDN-control plane. Under our proposed architectures, to jointly optimize the implementations of NFV and SDN techniques under ICN architectures, we develop the three virtual network selection and transmit-power allocation schemes to: 1) maximize single user’s effective capacity; 2) jointly optimize the aggregate effective capacity and allocation fairness over all users; and 3) coordinate non-cooperative gaming among all users, respectively. By simulations and numerical analyses, we show that our proposed architectures and schemes significantly outperform the other existing schemes in supporting the statistical delay-bounded QoS provisioning over the 5G multimedia big data wireless networks.

Resource Virtualization for Customized Delay- Bounded QoS Provisioning in Uplink VMIMO-SC-FDMA Systems

Wireless network virtualization, which decouples the physical supply process and the service provisioning process, can abstract, isolate, and share the physical infrastructure network equipment. This paper studies the resource virtualization in virtual multiple-input multiple-output single-carrier frequency-division-multiple-access uplink systems, where resources are abstracted to hide the complex details of the fading channel and the link rates are virtualized using the statistical method. Furthermore, the virtual link rates are scheduled and instantiated to different slices with customized delay-bounded quality of service (QoS) provisioning. In this scheme, a physical mobile network operator (PMNO) is in charge of the network resource at the physical layer, while virtual mobile network operators (VMNOs) are responsible for the traffic admission and the slice management at the MAC layer. Furthermore, we build up the resource virtualization problem as a cross-layer Stackelberg game, which has the interactive dual processes based on the QoS exponent: top-to-down sub-game of leaders at the MAC layer and down-to-top sub-game of follower at the physical layer. Using the newly designed functions for PMNO and VMNOs, we develop an effective dynamic algorithm with an iterative dual update to meet the optimization targets of PMNO and VMNOs. Simulation results verify the superiority and stability of delay-bounded QoS guaranteed wireless resource virtualization algorithm developed in this paper in terms of convergence, access rate, and delay-outage probability.

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.

Statistical-QoS Driven Energy-Efficiency Optimization Over Green 5G Mobile Wireless Networks

Since the Information and Communications Technologies (ICT) were designed without taking the energy-saving into account, the unexpected excessive energy consumption of the fourth-generation (4G) and pre-4G wireless networks causes serious carbon dioxide emissions. To achieve green wireless networks, the fifth-generation (5G) wireless networks are expected to significantly increase the network energy efficiency while guaranteeing the quality of service (QoS) for time-sensitive multimedia wireless traffics. In this paper, we develop the statistical delay-bounded QoS driven green power allocation schemes to maximize the effective power efficiency (EPE), which is defined as the statistical-QoS-guaranteed throughput (effective capacity) per unit power, over single-input single-output (SISO) and multipleinput multiple-output (MIMO)-channels based 5G mobile wireless networks. For the SISO-channel based 5G wireless networks, our developed QoS-driven green power allocation scheme converges to the despicking water-filling scheme (despicking channel inversion scheme) when the QoS constraint becomes very loose (stringent). We further develop and analyze the statistical-QoS-driven green power allocation scheme to maximize the EPE over the multiplexing-MIMO based 5G mobile wireless networks. The obtained numerical results show that our developed statistical QoS-driven green power allocation schemes can optimize the EPE over 5G mobile wireless networks, thus enabling the effective implementation of green 5G wireless networks.

Battery-dynamics driven tdma mac protocols for wireless body-area monitoring networks in healthcare applications

We propose the cross-layer based battery-aware time division multiple access (TDMA) medium access control (MAC) protocols for wireless body-area monitoring networks in wireless healthcare applications. By taking into account the joint effect of electrochemical properties of the battery, time-varying wireless fading channels, and packet queuing characteristics, our proposed schemes are designed to prolong the battery lifespan of the wireless sensor nodes while guaranteeing the reliable and timely message delivery, which is critically important for the patient monitoring networks. In addition, we develop a Markov chain model to analyze the performance of our proposed schemes. Both the obtained analytical and simulation results show that our proposed schemes can significantly increase the battery lifespan of sensor nodes while satisfying the reliability and delay-bound quality of service (QoS) requirements for wireless body-area monitoring networks. Furthermore, the case study of the electrocardiogram (ECG) monitoring application shows that besides meeting the delay requirements, our proposed schemes outperform the IEEE 802.15.4 and Bluetooth protocols in terms of battery lifespan.

Cross-Layer-Model Based Adaptive Resource Allocation for Statistical QoS Guarantees in Mobile Wireless Networks

We propose a cross-layer-model based adaptive resource-allocation scheme for the diverse quality-of-service (QoS) guarantees over downlink mobile wireless networks. Our proposed scheme dynamically assigns power-levels and time- slots for heterogeneous real-time mobile users to satisfy the variation of statistical delay-bound QoS requirements. To achieve this goal, we apply Wu and Negi's effective capacity approach to derive the admission-control and power/time-slot allocation algorithms, guaranteeing the statistical delay-bound for heterogeneous mobile users. When designing such an algorithm, we study the impact of physical-layer issues such as adaptive power-control and channel-state information (CSI) feedback delay on the QoS provisioning performance. Through numerical and simulation results, we observe that the adaptive power adaptation has a significant impact on statistical QoS-guarantees. In addition, the analyses indicate that our proposed resource-allocation algorithms are shown to be able to efficiently support the diverse QoS requirements for various real-time mobile users over different wireless channels. Also, in an in-door mobile environment, e.g., the widely used wireless local-area networks (WLAN), our proposed algorithm is shown to be robust to the CSI feedback delay.

Cross-layer modeling for quality of service guarantees over wireless links

We propose a cross-layer approach to investigate the impact of physical-layer infrastructure on data-link-layer quality-of-service (QoS) performance over wireless links in mobile networks. At the physical layer, we take multiple-input-multiple-output (MIMO) diversity schemes as well as adaptive-modulation-and-coding (AMC) techniques into account. At the data-link layer, our focus is on how this physical-layer infrastructure influences the real-time multimedia delay-bound QoS performance. To achieve this goal, we first model the physical-layer service process as a finite-state Markov chain (FSMC). Based on this FSMC model, we then characterize the QoS performance at data-link-layer using the effective capacity approach, which turns out to be critically important for the statistical QoS guarantees over wireless links in mobile networks. We also investigate the impact of physical-layer power control and channel-state information (CSI) feedback delay on the QoS performance. The numerical results obtained demonstrate that our proposed cross-layer model can efficiently characterize the interactions between the physical-layer infrastructure and data-link-layer QoS performance.

Flow-Based Admission Control Algorithm in the DiffServ-Aware ATM-Based MPLS Network

This paper proposes a flow-based admission control algorithm through an Asynchronous Transfer Mode (ATM) based Multi-Protocol Label Switching (MPLS) network for multiple service class environments of Integrated Service (IntServ) and Differentiated Service (DiffServ). We propose the Integrated Packet Scheduler to accommodate IntServ and Best Effort traffic through the DiffServ-aware MPLS core network. The numerical results of the proposed algorithm achieve reliable delay-bounded Quality of Service (QoS) performance and reduce the blocking probability of high priority service in the DiffServ model. We show the performance behaviors of IntServ traffic negotiated by end users when their packets are delivered through the DiffServ-aware MPLS core network. We also show that ATM shortcut connections are well tuned with guaranteed QoS service. We validate the proposed method by numerical analysis of its performance in such areas as throughput, end-to-end delay and path utilization.