QoS-Driven Energy-Efficient Power Control With Random Arrivals and Arbitrary Input Distributions

This paper studies optimal power control policies that maximize the achievable rates of underlay cognitive radio systems with arbitrary input distributions under both peak/average transmit power and peak/average interference power constraints for general fading distributions. In particular, optimal power adaptation schemes are formulated and low-complexity optimal power control algorithms are proposed. Additionally, simpler approximations of optimal power control policies in the low-power regime are determined. By considering gamma distributed channel power gains of the interference link between the secondary transmitter and the primary receiver and of the transmission link between the secondary transmitter and the secondary receiver, closed-form expressions for the maximum achievable rate attained with optimal power control in the low-power regime are provided. Through numerical results, the impact of the fading severity of both interference and transmission links and transmit power and interference power constraints on the maximum achievable rate of the cognitive user for different practical constellations and Gaussian signals are investigated.

In this paper, secure communication over a buffer-aided two-hop communication link, in which a source sends data to a destination with the aid of a trusted half-duplex relay node in the presence of a passive eavesdropper, is studied. It is assumed that there is no direct link between the source and the destination, and the buffer-aided relay forwards the information to the destination by employing the decode-and-forward (DF) scheme. Both the source and the relay transmissions are assumed to be vulnerable to the eavesdropper. The perfect channel side information (CSI) of the network is assumed to be available at the source and the relay. Initially, conventional relaying protocols where equal partition of the time for the reception and transmission of the relay is considered. Then, the optimal time fraction with the optimal power control policy that maximizes the secrecy throughput is identified. Subsequently, the optimal joint link selection and power control policy that maximizes the secrecy throughput is derived. Numerical results demonstrate that the joint link selection and power control scheme can significantly improve the achievable secrecy throughput of the buffer-aided two-hop communication system.

This paper considers the joint design problem of linear precoder, decision feedback equalizer (DFE) and bit allocation for multi-input multi-output (MIMO) broadcast (BC) channels. The problem is formulated as the power minimization problem when the target bit rate and per stream symbol error rate (SER) are specified. For the two-user case, a particular class of joint triangularization (JT) can be applied to obtain the optimal design for the joint optimization problem with quality of service (QoS) constraints, namely, the Minimum Power JT broadcast DFE transceiver (MPJT). For arbitrary number of users, a suboptimal design, the Power Minimized QR DFE broadcast transceiver (PMQR), is also proposed. Numerical results are presented to demonstrate the performance of MPJT and PMQR transceivers under different QoS constraints. 1

For the grid workflow system include a large of candidate services, the activity scheduling in workflow’s ex-ecution base on quality of service (QoS) constraints can pro-vide optimal service for users. This paper established the QoS’s parameter system with applied technology of grid workflow, and introduced the each parameter’s calculation model of activity’s QoS, the grid work-flow’s QoS estimation method based on the basic flow control structures defined by the abstract grid workflow language (AGWL). The experimental results show that the calculation method in this paper can estimate stably QoS parameter values of grid workflow combining with the multi candidate services, and, can provide a reasonable basis for process scheduling based on QoS constraints.

We propose a Quality-of-Service (QoS) driven power- and rate-control scheme in mobile wireless networks. Specifically, the proposed scheme aims at maximizing the system throughput subject to a given delay QoS constraint. Over the block fading channel, we derive the optimal adaptation policy by integrating information theory with the concept of effective capacity. The analyses demonstrate that there exists a fundamental tradeoff between spectral efficiency and QoS provisioning. When the QoS constraint is loose, the optimal power control policy converges to the well-known water-filling scheme, where the Shannon capacity can be achieved. On the other hand, when the QoS constraint is stringent, the optimal policy converges to the total channel inversion such that the system operates with a constant service rate.

A QoS-guaranteed multicast routing protocol

Enforcing QoS in scientific workflow systems enacted over Cloud infrastructures

Intelligent reflecting surface (IRS) is an emerging technology that is able to reconfigure the wireless channel via tunable passive signal reflection and thereby enhance the spectral and energy efficiency of wireless networks cost-effectively. In this paper, we study an IRS-aided multiuser multiple-input single-output (MISO) wireless system and adopt the two-timescale (TTS) transmission to reduce the signal processing complexity and channel training overhead as compared to the existing schemes based on the instantaneous channel state information (I-CSI), and at the same time, exploit the multiuser channel diversity in transmission scheduling. Specifically, the long-term passive beamforming is designed based on the statistical CSI (S-CSI) of all links, while the short-term active beamforming is designed to cater to the I-CSI of all users' reconfigured channels with optimized IRS phase shifts. We aim to minimize the average transmit power at the access point (AP), subject to the users' individual quality of service (QoS) constraints. The formulated stochastic optimization problem is non-convex and difficult to solve since the long-term and short-term design variables are complicatedly coupled in the QoS constraints. To tackle this problem, we propose an efficient algorithm, called the primal-dual decomposition based TTS joint active and passive beamforming (PDD-TJAPB), where the original problem is decomposed into a long-term problem and a family of short-term problems, and the deep unfolding technique is employed to extract gradient information from the short-term problems to construct a convex surrogate problem for the long-term problem. The proposed algorithm is proved to converge to a stationary solution of the original problem almost surely. Simulation results are presented which demonstrate the advantages and effectiveness of the proposed algorithm as compared to benchmark schemes.

With the rapid development of e-commerce over Internet, web services have attracted much attention in recent years. Nowadays, enterprises are able to outsource their internal business processes as services and make them accessible via the Web. Then they can dynamically combine individual services to provide new value-added services. With the increasing number of web services having equivalent functionality, the binding procedure is driven by some non-functional, Quality of Service (QoS) criteria, such as the money cost, response time, reputation, reliability or a trade-off between them. Thus, an important problem is, given QoS constraints, how to aggregate and leverage individual service’s QoS information to derive the optimal QoS of the composite service. In this paper, we propose a novel QoS model for performing flexible service selection. The key idea of the model is to relax users’ QoS constraints and try to find the most possible services satisfying users’ QoS requirements. Based on the proposed QoS framework, we develop various algorithms for making service selection on individual and composite services. We also introduce a top-k ranking strategy to reflect a user’s personalized requirements. Experimental evaluation shows the proposed QoS model is efficient and practical.

MPI-QMULInformation Systems Research Centre, Macau Polytechnic InstituteMacau SAR,P. R. ChinaDepartment of Electronic Engineering, Queen MaryUniversity of LondonE-mail: xuy@mpi-gmul.org/john.bigham@elec.gmul.ac.ukAbstract: In this paper, we solve the call admission control(CAC) problem in a cellular network via a form ofNeuroEvolution algorithm. The results are compared withComplete Sharing scheme, which allows all types of traffic toaccess all available channels at all time, and show that ourproposed CAC scheme CN can reduce the handoff failuresubject to several predefined Quality of Service (QoS)constraints: the handoff dropping probability (CDR) andintra-class fairness are all guaranteed within the predefinedconstraints. Additionally, the numerical results shows thatunder the same handoff failure constraint, better fairnesscauses higher new setup calls blocking probabilities (CBR) andlower CDR, therefore the fairness and maximizing revenue aretwo opposite goals, and the fairness and CDR do not conflictwith each other.Keywords: CAC,NEAT, QoS, Fairness.

In this paper, the tradeoff between spectrum efficiency (SE) and energy efficiency (EE) is investigated in terms of interference management and power allocation for heterogeneous networks (HetNets) with non-orthogonal multiple access (NOMA). The EE and SE tradeoff is modeled as a multi-objective problem (MOP) under the maximum power and quality of service (QoS) constraints, which is non-convex. The MOP is relaxed into a convex single objective problem (SOP) by adopting a weighted sum strategy with the hypograph transformation. The SOP is solved in two steps. In the first step, we propose a power allocation technique based on non-cooperative (NC) game for EE and SE in NOMA HetNets. In the proposed NC game, the macro base station (MBS) and the small BSs (SBSs) compete with an equal priority in order to optimize their transmit powers towards maximizing the weighted sum of SE and EE. In the second step, a closed-form formula is proposed to control the power allocated to users while taking into account both QoS constraint and successive interference cancellation (SIC) condition. From simulations, the proposed technique can, in some dedicated settings, considerably improve the tradeoff between EE and SE over conventional techniques.

In this paper, performance of transmission in cognitive radio systems over time-selective flat fading channels is studied under quality of service (QoS) constraints and channel uncertainty. Cognitive secondary users are assumed to initially perform sensing over the transmission channel to detect the activities of the primary users. Then, depending on the channel sensing result, they choose their transmission power policies and perform channel estimation. Following the training phase, they transmit data through the channel. The activities of the primary users are modeled as a two-state Markov process. A state transition model is constructed to model the cognitive transmissions. Statistical limitations on the buffer lengths are imposed to take into account the QoS constraints, and an average power constraint on the secondary users is considered to limit the interference to the primary users. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. Numerical results are provided for the power and rate policies.

In this paper, performance of cognitive transmission over time-selective flat fading channels is studied under quality of service (QoS) constraints and channel uncertainty. Cognitive secondary users (SUs) are assumed to initially perform channel sensing to detect the activities of the primary users, and then attempt to estimate the channel fading coefficients through training. Energy detection is employed for channel sensing, and different minimum mean-square-error (MMSE) estimation methods are considered for channel estimation. In both channel sensing and estimation, erroneous decisions can be made, and hence, channel uncertainty is not completely eliminated. In this setting, performance is studied and interactions between channel sensing and estimation are investigated. Following the channel sensing and estimation tasks, SUs engage in data transmission. Transmitter, being unaware of the channel fading coefficients, is assumed to send the data at fixed power and rate levels that depend on the channel sensing results. Under these assumptions, a state-transition model is constructed by considering the reliability of the transmissions, channel sensing decisions and their correctness, and the evolution of primary user activity which is modeled as a two-state Markov process. In the data transmission phase, an average power constraint on the secondary users is considered to limit the interference to the primary users, and statistical limitations on the buffer lengths are imposed to take into account the QoS constraints of the secondary traffic. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. Numerical results are provided for the power and rate policies.

Using SOA, service processes can be composed statically or dynamically using services provided by different service providers. Some services may become faulty at runtime and cause the service process to violate the end-to-end quality of service (QoS) constraint. We propose an efficient approach for replacing faulty services to ensure that the reconfigured service process still meets the original end-to-end QoS constraint. We use an iterative algorithm to identify reconfiguration regions that have a small number of services, some faulty and some healthy, in order to have more service selection options. By reconfiguring services in these selected regions rather than the whole service process, the computational complexity is significantly reduced. Simulation study has shown that our approach is efficient as most service processes can be repaired by replacing only a small number of services.

In this paper, the fading broadcast channel with confidential messages is studied in the presence of statistical quality of service (QoS) constraints in the form of limitations on the buffer length. We employ the effective capacity formulation to measure the throughput of the confidential and common messages. We assume that the channel side information (CSI) is available at both the transmitter and the receivers. Considering average power constraints at the transmitter side, we first define the effective secure throughput region, and prove that the throughput region is convex. Then, we obtain the optimal power control policies that achieve the boundary points of the effective secure throughput region.