Efficient Resource Allocation Algorithm Research Articles

Energy-Efficient Resource Allocation for Secure OFDMA Systems

In this paper, resource allocation for energy-efficient secure communication in an orthogonal frequency-division multiple-access (OFDMA) downlink network is studied. The considered problem is modeled as a nonconvex optimization problem that takes into account the sum-rate-dependent circuit power consumption, multiple-antenna eavesdropper, artificial noise generation, and different quality-of-service (QoS) requirements, including a minimum required secrecy sum rate and a maximum tolerable secrecy outage probability. The power, secrecy data rate, and subcarrier allocation policies are optimized for maximization of the energy efficiency of secure data transmission (bit/joule securely delivered to the users). The considered nonconvex optimization problem is transformed into a convex optimization problem by exploiting the properties of fractional programming, which results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm not only converges in a small number of iterations but maximizes the system energy efficiency and guarantees a nonzero secrecy data rate for the desired users as well. In addition, the obtained results unveil a tradeoff between energy efficiency and secure communication.

Efficient Resource Allocation in Resource Provisioning Policies Over Resource Cloud Communication Paradigm

Optimal resource utilization for executing tasks within the cloud is one of the biggest challenges. In executing the task over a cloud, the resource provisioner is responsible for providing the resources to create virtual machines. To utilize the resources optimally, the resource provisioner has to take care of the process of allocating resources to Virtual Machine Manager (VMM). In this paper, an efficient way to utilize the resources, within the cloud, to create virtual machines has been proposed considering optimum cost based on performance factor. This performance factor depends upon the overall cost of the resource, communication channel cost, reliability and popularity factor. We have proposed a framework for communication between resource owner and cloud using Resource Cloud Communication Paradigm (RCCP). We extend the CloudSim[2] adding provisioner policies and Efficient Resource Allocation (ERA) algorithm in VMM allocation policy as a decision support for resource provisioner.

Radio Resource Management with Proportional Rate Constraint in the Heterogeneous Networks

We study the radio resource management (RRM) in orthogonal frequency division multiple access (OFDMA) involved heterogeneous networks, to maximize the system sum-rate under the proportional user rate constraint. An analytical model which reflects the network access features is presented. Allowing multi-homing access and resource element sharing, the RRM problem can be formulated as a linear programming (LP) problem, and the optimal solution provides upper-bound performance. In order to analyze the network selection criterion, we consider an approximated RRM problem with average resource allocations. Two different multi-homing formulations are used, and both are proven to have the same solution, where the network selection is related to the users' rate ratios between different networks. Then, we propose a low complexity suboptimal RRM algorithm, which is composed of a basic part without multi-homing access and a supplementary part with multi-homing support. The basic part makes single network selection and resource allocations. The network selection algorithm is designed based on the criterion found in the approximated problem. After network selection, an efficient resource allocation algorithm is utilized in the OFDMA network, which can employ the multi-user time and frequency diversity well. If multi-homing is allowed, the supplementary part selects the multi-homing users and reallocates partial resources to further improve the performance. Our simulation results show that the proposed suboptimal algorithm can achieve considerable gains over the previous schemes with minor performance degradation compared with the optimal solution.

A Low-Complexity Resource Allocation Algorithm for MIMO-OFDMA Multicast Systems with Spectrum-Guarantee Provisioning

We study the important problem of resource allocation for the downlink of Multiple-Input Multiple output (MIMO) Multicast Wireless Systems operating over frequency-selective channels and we propose a low-complexity but efficient resource allocation algorithm for MIMO-enabled OFDMA systems. The proposed solution guarantees a minimum spectrum share for each user while also takes advantage of the multicast transmission mode. The presence of multiple antennas in both transmitter and receiver offers spatial diversity to the system along with the frequency diversity added by the OFDMA access scheme. The computational complexity is reduced from exponential to linear and validation of the proposed solution is achieved through various simulation scenarios in comparison with other multicast and unicast reference schemes used in MIMO-OFDMA systems. Numerical results and complexity analysis demonstrate the feasibility of the proposed algorithm.

Efficient resource allocation algorithm for uplink in multicarrier-based cognitive radio networks with fairness consideration

An efficient fairness-aware uplink resource allocation algorithm in orthogonal frequency-division multiplexing-based cognitive radio systems is presented. The proposed resource allocation algorithm is divided into two steps. The subcarriers to user assignment is first performed, and then the power is allocated to the different subcarriers. The objective is to maximise the total data rate while guaranteeing that the interference introduced to the primary system is under the prescribed interference temperature limit. The fairness among users is considered within the subcarrier allocation by reducing the probability of having users whose instantaneous rates are below a given minimum rate. Many factors will be considered during the resource allocation process like the channel quality, potential interference caused to the primary bands, instantaneous rate achieved by every user and the increment in the total data rate. Simulation results confirm the efficiency of the proposed algorithm.

Optimal and Efficient Graph-Based Resource Allocation Algorithms for Multiservice Frame-Based OFDMA Networks

This paper addresses the resource allocation problem in Orthogonal Frequency Division Multiple Access (OFDMA)-based wireless networks. The resource allocation problem is posed as an optimization problem with individual user constraints. This formulation provides a special structure that lends to efficient solution of the problem. We develop an optimal algorithm based on standard graph theory and Lagrangian relaxation. Based on the special structure of the problem, the proposed resource allocation algorithm attains the optimal solution at a much lower complexity compared to general-purpose optimization algorithms used by previous OFDMA resource allocation approaches. Moreover, the resource allocation problem solved by the proposed algorithm supports practical features such as discrete modulation set and multiple OFDM symbols per resource allocation decision. Furthermore, by assuming even power allocation across the OFDM subchannels, a suboptimal resource allocation algorithm with lower complexity is developed. The proposed algorithms enable the system designer to control the tradeoffs among system performance, system complexity, and the quality of service (QoS) experienced by the users. Extensive simulations are conducted to evaluate the performance and complexity of the proposed algorithms under different system operating conditions.

T-Alloc: A practical energy efficient resource allocation algorithm for traditional data centers

Even if the cloud computing data centers are emerging as new candidates for replacement, traditional data centers are still growing rapidly in both number and capacity to meet the increasing demands for highly responsive computing and massive storage. Making the data center more energy efficient is therefore a necessary task. A traditional data center has many distinguished features with heterogeneous hardware, heterogeneous workload, average load rate focused, intensive time and personal effort for administrative tasks. This paper will propose a way of saving energy for traditional data centers considering all the above features. The basic idea is rearranging the allocation in such a way that energy is saved with suitable human effort. The simulation results show the efficiency of the method.

Wireless physical layer security model and resource allocation algorithm in MISO-OFDMA

The problem of wireless security for the downlink of multiuser MISO-OFDMA systems operating over a frequency-selective channel is investigated. Considering interference suppression and resource allocation between different users, a physical-layer security model is proposed that exploits the redundancy of transmit antenna arrays to provide security by deliberate signal randomisation. Signal randomisation causes the receiver's signal to have lower power efficiency than the optimal transmit beamforming. To overcome this problem, a suboptimal, but efficient resource allocation algorithm based on spatial correlation and array redundancy is devised.

Efficient resource allocation algorithm for cognitive OFDM systems

In this letter the transmission power and interference threshold constraints for a cognitive OFDM system are converted to a normalized capacity of each OFDM subchannel. A simple but very efficient ...

Efficient resource allocation algorithm for cognitive OFDM systems

In this letter the transmission power and interference threshold constraints for a cognitive OFDM system are converted to a normalized capacity of each OFDM subchannel. A simple but very efficient algorithm is proposed to allocate bits of all subchannels, which always picks the bit with the least cost. Simulation results show the proposed algorithm can obtain optimum solution in most cases. The remarkable characteristic of the algorithm is the computational complexity is very low and asymptotically constant for given radio scenarios, which meets the requirement of practical communication systems perfectly.

A Radio Resource Allocation Algorithm for QoS Provision in PMP-based Systems

Based on the studies on downlink resource allocation in p oint to m ulti-point (PMP) mode in the 802.16e systems, an efficient downlink resource allocation algorithm with low complexity is proposed to maxim ize the system throughput, which takes the advantage of frequency-selective fading property in orthogonal frequency division multiple (OFDM) networks. The proposed algorithm not only provides the traffic rate as high as possible, but also satisfies the minimum reserved traffic rate for non-real-time services. The simulation shows that the proposed algorithm performs better in terms of assuring individual QoS and offering fairness among users at the cost of slight degradation in throughput.

Robust Power Allocation Algorithms for Wireless Relay Networks

Resource allocation promises significant benefits in wireless networks. In order to fully reap these benefits, it is important to design efficient resource allocation algorithms. Here, we develop relay power allocation (RPA) algorithms for coherent and noncoherent amplify-and-forward (AF) relay networks. The goal is to maximize the output signal-to-noise ratio under individual as well as aggregate relay power constraints. We show that these RPA problems, in the presence of perfect global channel state information (CSI), can be formulated as quasiconvex optimization problems. In such settings, the optimal solutions can be efficiently obtained via a sequence of convex feasibility problems, in the form of second-order cone programs. The benefits of our RPA algorithms, however, depend on the quality of the global CSI, which is rarely perfect in practice. To address this issue, we introduce the robust optimization methodology that accounts for uncertainties in the global CSI. We show that the robust counterparts of our convex feasibility problems with ellipsoidal uncertainty sets are semi-definite programs. Our results reveal that ignoring uncertainties associated with global CSI often leads to poor performance, highlighting the importance of robust algorithm designs in practical wireless networks.

Utility Optimization Based on MSE for Parallel Broadcast Channels: The Square Root Law

In order to design efficient online resource allocation algorithms, convexity of the underlying optimization problem is an important prerequisite. This paper covers two resource allocation problems: the sum-power constrained utility maximization and the sum-power minimization for minimum utility requirements for parallel broadcast channels. We derive a new class of utility functions for which both optimization problems can be transformed into convex representations and give necessary and sufficient conditions for the optimum solution of the original non-convex problems with regard to power. We thereby extend the known log-convexity class for which convex representations can be found and, by introducing the square-root criteria, present a straight forward test to check whether arbitrary utility functions belong to our class. For the new class of utility functions we present simple algorithms which operate in the non-convex domain, prove convergence to the global optimum and evaluate their performance by simulations. Besides, the paper reveals some insights on the general structure of the mean square error region and thereby disproves a former result.

Admission control and resource allocation in a heterogeneous OFDMA wireless network

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

Fairness based resource allocation for multiuser MISO-OFDMA systems with beamforming

Resource allocation problem in multiuser multiple input single output-orthogonal frequency division multiple access (MISO-OFDMA) systems with downlink beamforming for frequency selective fading channels is studied. The article aims at maximizing system throughput with the constraints of total power and bit error rate (BER) while supporting fairness among users. The downlink proportional fairness (PF) scheduling problem is reformulated as a maximization of the sum of logarithmic user data rate. From necessary conditions on optimality obtained analytically by Karush-Kuhn-Tucker (KKT) condition, an efficient user selection and resource allocation algorithm is proposed. The computer simulations reveal that the proposed algorithm achieves tradeoff between system throughput and fairness among users.

Utility-Based Rate-Controlled Parallel Wireless Transmission of Multimedia Streams with Multiple Importance Levels

Multimedia data often have different levels of importance such that more important bits are less error-tolerant. A new rate control method for transporting such multimedia data over parallel wireless links with heterogeneous reliability is proposed. Rate-controlled parallel transmissions (RCPT) of different layers of a multimedia stream with different levels of importance over a wireless channel that support multiple links with heterogeneous reliability can improve the efficiency in resource allocation while satisfying the quality of service requirement of the multimedia connection. To exploit RCPT, we present and evaluate a novel dynamic resource allocation method that decomposes the available radio resources into multiple sets of links with different levels of reliability. We mathematically formulate a rate control problem for the flexible RCPT scheme and develop an efficient real-time resource allocation algorithm with a remarkably fast rate of convergence. Simulation results show that the proposed method improves the utility and reduces the power consumed for delivery of a multimedia stream at the required quality of service, in comparison with a previous scheme, where different layers of each multimedia class are scheduled with dependency, and two schemes that provide homogeneous high or low reliability over all parallel links.

An efficient dynamic resource allocation algorithm for packet-switched communication networks based on Hopfield neural excitation method

Dynamic Resource Allocation (DRA) algorithms set up different connections over the same resources and perform a scheduling policy to distribute the resources usage. Recently, intelligent DRA techniques based on Hopfield Neural Networks computational methods have been proposed, showing their potential for solving this kind of complex optimization problems. However, the initial algorithms suffer from severe instability problems impacting performance. This paper addresses these specific limitations stressing the proper neuron dynamics and proposing an efficient energy formulation and an optimum calculation of the weighting coefficients. These changes result in a maximum resource utilization together with an optimized neural network convergence.

Editorial: QoS Aware Wireless Communications Technologies and Systems

Wireless networks are becoming an integral part of our every day life. Internet access through cellular networks and other wireless infrastructures (e.g. Wi-Fi) are becoming a pressing requirement for the operators who demand new equipment and new techniques from the manufacturers and from the research community to support the numerous emerging wireless IP based applications. The main challenge within this new wave of efforts is the ability to support QoS in the inherently difficult to control wireless networking environment. This claim is reflected in a broad range of subjects, spanning from wireless broadband access mechanisms, resource management and cross layer design to cognitive systems and spectrum management. This special issue1 presents papers that constitute important steps towards designing and developing QoS aware wireless communications technologies and systems. In particular the first article “Privileged Treatment of UMTS Subscribers in GSM Networks”, by R. Mullner et al., presents the effect of prioritization of UMTS subscribers in GSM networks by an efficient resource allocation algorithm. The proposed method proves to be a suitable and powerful quality enhancement for dual-RAT terminals. A comprehensive strategy has been developed for the prioritization of data services taking into consideration service and subscriber related aspects. Delay sensitive services such as streaming are obtaining the required resources for meeting the guaranteed bit rate regardless of the terminal’s RAT capability. The authors show that privileged treatment of UMTS subscribers in GSM net-

Tracker and signal processing for the benchmark problem with unresolved targets

Radar signal processing is particularly important in tracking closely spaced targets and targets in the presence of sea-surface-induced multipath. Closely spaced targets can produce unresolved measurements when they occupy the same range cell of the radar. These issues are the salient features of the benchmark problem for tracking unresolved targets combined with radar management, for which this paper presents the only complete solution to date. In this paper a modified version of a recently developed maximum likelihood (ML) angle estimator, which can produce two measurements from a single (unresolved) detection, is presented. A modified generalized likelihood ratio test (GLRT) is also described to detect the presence of two unresolved targets. Sea-surface-induced multipath can produce a severe bias in the elevation angle measurement when the conventional monopulse ratio angle extractor method is used. A modified version of a recently developed ML angle extractor, which produces nearly unbiased elevation angle measurements and significantly improves the track accuracy, is presented. Efficient radar resource allocation algorithms for two closely spaced targets and targets flying close to the sea surface are also presented. Finally, the IMMPDAF (interacting multiple model estimator with probabilistic data association filter modules) is used to track these targets. It is found that a two-model IMMPDAF performs better than the three-model version used in the previous benchmark. Also, the IMMPDAF with a coordinated turn model works better than the one using a Wiener process acceleration model. The signal processing and tracking algorithms presented here, operating in a feedback manner, form a comprehensive solution to the most realistic tracking and radar management problem to date.

A priority-based resource allocation strategy in distributed computing networks

In a distributed computing network, various types of resources attached at nodes, such as disks, main memory, resident programs, and printers, could be shared among applications. To promote the resource utilization, an efficient resource allocation algorithm for application requests should be developed. In this paper, our system model considers the factors such as the computation power of nodes, the upper limit of shared programs, the transmission bandwidth of peripherals, the channel bandwidth, and the quality of service (QoS) requirement of applications. Based on these factors, a priority-based resource allocation strategy is proposed and applied to different environments, respectively. The simulation results show that our strategy has a near optimal solution.