Statistical Quality Of Service Provisioning Research Articles

Secure Rate Control and Statistical QoS Provisioning for Cloud-Based IoT Networks

The Internet of Things (IoT) facilitates physical things to detect, interact, and execute activities on-demand, enabling a variety of applications such as smart homes and smart cities. However, it also creates many potential risks related to data security and privacy vulnerabilities on the physical layer of cloud-based Internet of Things (IoT) networks. These can include different types of physical attacks such as interference, eavesdropping, and jamming. As a result, quality-of-service (QoS) provisioning gets difficult for cloud-based IoT. This paper investigates the statistical QoS provisioning of a four-node cloud-based IoT network under security, reliability, and latency constraints by relying on the effective capacity model to offer enhanced QoS for IoT networks. Alice and Bob are legitimate nodes trying to communicate with secrecy in the considered scenario, while an eavesdropper Eve overhears their communication. Meanwhile, a friendly jammer, which emits artificial noise, is used to degrade the wiretap channel. By taking advantage of their multiple antennas, Alice implements transmit antenna selection, while Bob and Eve perform maximum-ratio combining. We further assume that Bob decodes the artificial noise perfectly and thus removes its contribution by implementing perfect successive interference cancellation. A closed-form expression for an alternative formulation of the outage probability, conditioned upon the successful transmission of a message, is obtained by considering adaptive rate allocation in an ON-OFF transmission. The data arriving at Alice’s buffer are modeled by considering four different Markov sources to describe different IoT traffic patterns. Then, the problem of secure throughput maximization is addressed through particle swarm optimization by considering the security, latency, and reliability constraints. Our results evidence the considerable improvements on the delay violation probability by increasing the number of antennas at Bob under strict buffer constraints.

Statistical Delay and Error-Rate Bounded QoS Provisioning for SWIPT Over CF M-MIMO 6G Mobile Wireless Networks Using FBC

As a new and dominating type of time-sensitive traffic over 6G wireless networks, massive ultra-reliable and low latency communications (mURLLC) has attracted considerable research attention, while raising several new challenges, including massive connectivity, ultra-low latency, super-reliability, and high energy efficiency. Several promising 6G enablers, including statistical delay and error-rate bounded quality-of-service (QoS) provisioning, cell-free (CF) massive multi-input multi-output (m-MIMO), simultaneous wireless information and power transfer (SWIPT), etc., have been developed to support mURLLC. Specifically, CF m-MIMO can significantly enhance QoS performance of SWIPT by boosting data rate and energy efficiency. On the other hand, finite blocklength coding (FBC) has been proposed to support various massive access techniques while reducing access latency and guaranteeing stringent QoS. However, how to efficiently integrate SWIPT with CF m-MIMO using FBC for statistical delay and error-rate bounded QoS to support mURLLC has posed many new challenges not encountered before. To overcome these difficulties, in this paper we develop FBC based statistical delay and error-rate bounded QoS provisioning schemes over SWIPT-enabled CF m-MIMO 6G wireless networks. First, we establish SWIPT-enabled CF m-MIMO system models using FBC. Then, we optimize the tradeoffs between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -effective capacity and harvested energy for our proposed statistical QoS provisioning. Finally, our simulated results validate and evaluate our developed schemes.

On Delay-Aware Resource Control With Statistical QoS Provisioning By Dual Connectivity in Heterogeneous Aeronautical Network

In this paper, we explore the effect of dual connectivity (DC) on delay-aware resource control with statistical Quality of Service (QoS) provisioning in heterogeneous aeronautical networks. Firstly, the Software Defined Networking (SDN) controller is introduced to support the DC for aircraft by centralized management of different base stations. In light of the delay analysis of user traffic, the delay-aware resource control of SDN controller is established as a multi-objective stochastic optimization problem to guarantee the statistical QoS provisioning. Secondly, the multi-objective stochastic optimization problem is transformed into a weighted single-objective optimization and relaxed by the Lagrangian relaxation. Then, the Lyapunov optimization theory is utilized to solve the relaxed stochastic optimization for optimal resource control. Finally, the performance of resource control under DC mode is verified in two aeronautical scenarios.

Heterogeneous Statistical QoS Provisioning Over Wireless Powered Sensor Networks

The wireless energy transfer, which is a promising technology for the wireless sensor networks (WSNs), can efficiently solve the energy scarcity that results from the application boosting. However, not only the energy scarcity, but also the quality of service (QoS) guarantees need to be taken into account for the WSNs. Different types of applications in the WSNs impose the new challenge on heterogeneous QoS provisioning for the WSNs. To solve the above problem, in this paper, we develop the joint downlink energy assignment and uplink power control scheme with the heterogeneous statistical QoS provisioning (HeP) for wireless powered sensor networks (WPSNs). In particular, we build up the HeP model, where the aggregate effective capacity (AEC) is defined as the aggregate throughput under the statistical QoS constraints for the WPSNs. Based on the mode, we formulate the AEC maximization problems for uniformed time division and dynamic time allocation scenarios, respectively. For the uniformed time division scenario, we divide the AEC maximization problem into the hybrid access point determined downlink energy assignment problem and the sensor node determined uplink power control problem. Then, we solve these problems and obtain the corresponding closed-form solutions. For the dynamic time allocation scenario, we develop the joint time allocation, downlink energy assignment, and uplink power control scheme to maximize the AEC and iteratively derive the scheme. Extensive simulations are conducted to demonstrate the effect of heterogeneous statistical QoS on our developed resource allocation schemes for WPSNs. The results show that the HeP resource allocation schemes are superior to the schemes with homogeneous statistical QoS guarantees.

Energy-Efficient Resource Allocation for MIMO-OFDM Systems Serving Random Sources With Statistical QoS Requirement

This paper optimizes resource allocation that maximizes the energy efficiency (EE) of wireless systems with statistical quality of service (QoS) requirement, where a delay bound and its violation probability need to be guaranteed. To avoid wasting energy when serving random sources over wireless channels, we convert the QoS exponent, a key parameter to characterize statistical QoS guarantee under the framework of effective bandwidth and effective capacity, into multi-state QoS exponents dependent on the queue length. To illustrate how to optimize resource allocation, we consider multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. A general method to optimize the queue length based bandwidth and power allocation (QRA) policy is proposed, which maximizes the EE under the statistical QoS constraint. A closed-form optimal QRA policy is derived for massive MIMO-OFDM system with infinite antennas serving the first order autoregressive source. The EE limit obtained from infinite delay bound and the achieved EEs of different policies under finite delay bounds are analyzed. Simulation and numerical results show that the EE achieved by the QRA policy approaches the EE limit when the delay bound is large, and is much higher than those achieved by existing policies considering statistical QoS provision when the delay bound is stringent.

A Pricing Power Control Scheme with Statistical Delay QoS Provisioning in Uplink of Two-tier OFDMA Femtocell Networks

Femtocell is a promising technique to enhance indoor coverage and improve network capacity. Nevertheless, because of the random and co-channel deployment of femtocells, the macrocell will suffer serious cross-tier interference from femtocells in two-tier femtocell networks. Thus, interference mitigation in femtocell networks has been an indispensable task. Meanwhile, with the explosive popularity of smart terminals, especially smart phones and tablets, the wireless networks have loaded a mount of data services with diverse delay quality of service (QoS) requirements. However, due to the stochastically varying nature of wireless physical channel, it is extremely difficult to offer a deterministic delay guarantee in wireless networks. Therefore, the effective capacity of femtocell users (FU) has been introduced to provide a statistical delay QoS provisioning. For that reason, in this paper, we will study the interference mitigation with statistical delay QoS guarantee in uplink two-tier orthogonal frequency division multiple access (OFDMA) femtocell networks. In order to mitigate the cross-tier interference at macrocell base station (MBS), we adopt a price-based power control strategy, in which the MBS protects itself by pricing the interference from FU. Additionally, to guarantee the statistical delay QoS for each FU, effective capacity is introduced into their utility functions. Then, a Stackelberg game is formulated to study the joint utility maximization of the MBS and the FUs subject to a maximum tolerable interference power constraint at the MBS. Subsequently, based on the mathematical analysis of the equilibrium of the formulated Stalkeberg game, a particle swarm optimization (PSO) aided power allocation (PSOPA) algorithm is proposed to solve this optimization problem. At last, simulation results show that our proposed PSOPA algorithm can not only improve significantly the average effective capacity of each FU and guarantee their statistical delay QoS, but also converge successfully.

A probabilistic approach to statistical QoS provision of event detection in sensor networks

It is of significant importance to provide network-wide Quality of Service (QoS) for a wide range of event detection applications in wireless sensor networks. This paper investigates the important problem of QoS provision to abnormal event detection. For event detection applications, there are two key performance metrics, i.e., detection probability and detection latency. This paper considers both metrics, aiming to provide statistical QoS for abnormal event detection. It is, however, a challenging issue because of stringent resource constraints of sensor nodes and unpredictable randomness of physical events. We propose a probabilistic approach to statistical QoS provision for event detection in sensor networks. We propose a distributed algorithm that iteratively determines the active probability of each sensor node. The probability is kept small for energy efficiency but sufficiently large to provide the required detection QoS. Our approach is flexible and can detection QoS customized by applications. Comprehensive simulation experiments have been conducted, which demonstrate that our approach is able to provide the required detection QoS for event detection and achieves considerably longer the system lifetime compared with other competing schemes.

Energy-Efficient Uplink Resource Allocation in LTE Networks With M2M/H2H Co-Existence Under Statistical QoS Guarantees

Recently, energy efficiency in wireless networks has become an important objective. Aside from the growing proliferation of smartphones and other high-end devices in conventional human-to-human (H2H) communication, the introduction of machine-to-machine (M2M) communication or machine-type communication into cellular networks is another contributing factor. In this paper, we investigate quality-of-service (QoS)-driven energy-efficient design for the uplink of long term evolution (LTE) networks in M2M/H2H co-existence scenarios. We formulate the resource allocation problem as a maximization of effective capacity-based bits-per-joule capacity under statistical QoS provisioning. The specific constraints of single carrier frequency division multiple access (uplink air interface in LTE networks) pertaining to power and resource block allocation not only complicate the resource allocation problem, but also render the standard Lagrangian duality techniques inapplicable. We overcome the analytical and computational intractability by first transforming the original problem into a mixed integer programming (MIP) problem and then formulating its dual problem using the canonical duality theory. The proposed energy-efficient design is compared with the spectral efficient design along with round robin (RR) and best channel quality indicator (BCQI) algorithms. Numerical results, which are obtained using the invasive weed optimization (IWO) algorithm, show that the proposed energy-efficient uplink design not only outperforms other algorithms in terms of energy efficiency while satisfying the QoS requirements, but also performs closer to the optimal design.

QoS-Aware Power Allocations for Maximizing Effective Capacity Over Virtual-MIMO Wireless Networks

To enable multiple mobile users to transmit their signals simultaneously over the same sub-channels, the virtual multiple-input multiple-output (V-MIMO) techniques can exploit the multiple-input multiple-output (MIMO) spectrum efficiency gain. Traditional V-MIMO transmission schemes mainly focus on maximizing the throughput of grouped mobile users without taking into account the quality-of-service (QoS) provisionings. In this paper, we propose the optimal power allocation schemes with statistical QoS provisionings to maximize the effective capacity of non-collaborative/collaborative V-MIMO wireless networks, respectively. For non-collaborative V-MIMO wireless networks, the mobile users in one V-MIMO group transmit signals independently over the same sub-channels. In the view point of existing mobile users, they solely occupy the sub-channels. Thus, the existing mobile users employ the QoS-driven single-user power allocation scheme to maximize their effective capacity. By converting the non-collaborative V-MIMO transmission optimization problem into a strictly convex optimization problem, we derive the QoS-driven power allocation scheme for the newly added mobile users to maximize their effective capacity. For collaborative V-MIMO wireless networks where the mobile users in one group can collaboratively transmit their signals, we derive the QoS-driven collaborative power allocation schemes for both the existing and the newly added mobile users. Also conducted is a set of simulation evaluations, showing that our proposed power allocation schemes for V-MIMO wireless networks outperform the other existing schemes.

Joint Spectrum and Power Efficiencies Optimization for Statistical QoS Provisionings Over SISO/MIMO Wireless Networks

Spectrum and power efficiencies are both crucial to design efficient wireless networks. In past two decades, spectrum and power efficiencies of wireless networks are optimized separately. However, to increase the spectrum efficiency while reducing the energy consumption, it is necessary to jointly optimize spectrum and power efficiencies of wireless networks. Supporting the statistical quality of service (QoS) provisionings for real-time traffic is crucial, but imposes new challenges, in the next generation wireless networks. In this paper, we propose an efficient framework to jointly optimize effective spectrum efficiency (ESE) and effective power efficiency (EPE) under different statistical QoS guarantees constraints to support the real-time traffic over wireless networks. In particular, we derive the relationship between ESE and EPE under statistical QoS provisioning constraint. Based on this relationship, we obtain the mutually beneficial (MB) region and the contention-based (CB) region. In the MB region, we propose a novel strategy to achieve the joint effective spectrum and power efficiencies optimization using the average transmit power control. In the CB region, we propose the wireless-relay-based strategy to jointly optimize the effective capacity and power efficiency. In both MB and CB regions, we develop the dynamic transmit-power control strategy and the MIMO-based strategy to jointly maximize the effective spectrum and power efficiencies. Also conducted is a set of numerical evaluations showing that our proposed strategies can achieve superior joint spectrum and power efficiencies optimization for the diverse statistical QoS provisionings.

Statistical QoS provisionings for wireless unicast/multicast of multi-layer video streams

Due to the time-varying wireless channels, deterministic quality of service (QoS) is usually difficult to guarantee for real-time multi-layer video transmissions in wireless networks. Consequently, statistical QoS guarantees have become an important alternative in supporting real-time video transmissions. In this paper, we propose an efficient framework to model the statistical delay QoS guarantees, in terms of QoS exponent, effective bandwidth/capacity, and delay-bound violation probability, for multi-layer video transmissions over wireless fading channels. In particular, a separate queue is maintained for each video layer, and the same delay bound and corresponding violation probability threshold are set up for all layers. Applying the effective bandwidth/capacity analyses on the incoming video stream, we obtain a set of QoS exponents for all video layers to effectively characterize this delay QoS requirement.We then develop a set of optimal adaptive transmission schemes to minimize the resource consumption while satisfying the diverse QoS requirements under various scenarios, including video unicast/multicast with and/or without loss tolerance. Simulation results are also presented to demonstrate the impact of statistical QoS provisionings on resource allocations of our proposed adaptive transmission schemes.

Resource allocation for statistical quality of service provision in buffered crossbar switches

AbstractThe buffered crossbar switch is a promising switching architecture that plays a crucial role for providing quality of service (QoS) in computer networks. Sufficient amount of resources—bandwidth and buffer space—must be allocated in buffered crossbar switches for QoS provision. Resource allocation based on deterministic QoS objectives might be too conservative in practical network operations. To improve resource utilization in buffered crossbar switches, we study the problem of resource allocation for statistical QoS provision in this paper. First, we develop a model and techniques for analyzing the probabilistic delay performance of buffered crossbar switches, which is described by the delay upper bound with a prescribed violation probability. Then, we determine the required amounts of bandwidth and buffer space to achieve the probabilistic delay objectives for different traffic classes in buffered crossbar switches. In our analysis, we apply the effective arrival envelope to specify traffic load in a statistical manner and characterize switch service capacity by using the service curve technique. Instead of just focusing on one specific type of scheduler, the model and techniques developed in this paper are very flexible and can be used for analyzing buffered crossbar switches with a wide variety of scheduling algorithms. Copyright © 2007 John Wiley &amp; Sons, Ltd.

Cross-layer cooperation for accurate admission control decisions in mobile ad hoc networks

Mobile ad hoc networks (MANETs) are unpredictable by nature. Providing any kind of reliability for quality of service (QoS) in such networks is challenging. Quantifying available resources accurately, avoiding interference with ongoing QoS traffic and adapting to QoS violations caused by nodes&apos; mobility are the main concerns for the design of an efficient admission control protocol in MANETs. Adaptive admission control (AAC), a novel admission control protocol which uses robust and accurate resource estimation and prediction techniques for relevant admission decisions has been proposed. Furthermore, AAC uses statistical QoS provision to counteract the QoS threatening mobility. Through simulations, we show that our proposed scheme outperforms existing approaches in terms of correctness and overall performance.