Utility-based Resource Allocation Research Articles

Resource-utility approach to ensuring the technological security of the Republic of Belarus

The article is devoted to the development of the utility-based resource allocation approach to the analysis and management of socio-economic systems by extending it to the research, scientific, technical, and innovation spheres. One of the reasons for the inconsistency of scientific and technological progress is demonstrated to be the cost method for evaluating its effectiveness, which identifies costs with results and ignores the usefulness of scientific and technological progress achievements. Appropriate utility criteria and indicators for assessing and managing this area are proposed in terms of implementation of the technology recovery strategy for Belarus and its allied Russia. Based on the analysis of the dynamics of scientific, technical and technological development of a number of Western European countries, a significant positive impact on the scientific and technological progress resulted from using proposed utility assessing criteria oriented towards a more efficient use of limited resources has been proved. With this in mind, recommendations are given on the development and implementation of a technological recovery strategy as a tool not only to strengthen the technological security of the state, but also to overcome the global contradictions generated by scientific and technological progress.

Slice resource allocation based on Comprehensive Utility

Due to the increasing demand for quality of service in power communication services quality.smart grids require network slicing technology to provide differentiated services. However, the existing algorithms lack in-depth research on the comprehensive service, particularly in power services and system accessibility. To address these issues, this paper proposes an integrated utility-based resource allocation scheme. Under the premise of meeting user needs, the priority of slices is comprehensively considered to optimize system utility and access rate. By comparing to local optimization algorithms, the results show that the new algorithm can maximize the overall user utility of the system and optimize the slice access rate simultaneously.

Utility-Based Resource Allocation in Wireless-Powered Communication Networks

This paper investigates the joint optimization of time allocation and transmit power in wireless-powered communication networks (WPCNs), where the downlink wireless energy transfer happens in the first phase, and then uplink wireless information transfer takes place in the second phase. Considering the rate fairness among users, we formulate the resource allocation in WPCNs as a network utility maximization (NUM) problem that aims to maximize the sum of utilities over all users subject to the energy causality constraint, the quality of service constraint, and the physical constraint. We further prove that the NUM problems with different channel access schemes (the time-division multiple access scheme, the concurrent transmission scheme, and the nonorthogonal multiple access scheme) can be equivalently transformed to convex problems by deliberated math transformations. Simulations demonstrate the efficiency of this paper.

System utility based resource allocation for D2D multicast communication in software-defined cellular networks

Device-to-device (D2D) multicast communication is a useful way to improve the communication efficiency of local services. This study considers a scenario of D2D multicast communication in software defined cellular network and investigates the frequency resource allocation problem. Firstly, we build the system model and formulate the optimization problem. Secondly, a hierarchical scheme to achieve a suboptimal solution is proposed. To select appropriate user equipments (UEs) as potential D2D transmitters (PDTs), a social aware PDT selection method is proposed. Then, a resource allocation algorithm considering users’ priorities is proposed. Furthermore, to study the resource allocation for general system that UEs without priorities, a non-priority considered allocation algorithm is proposed also. Numerical simulation results show that the proposed schemes are effective in improving the system utility and reducing the resource consuming for D2D communications.

Dual Connectivity for LTE-NR Cellular Networks: Challenges and Open Issues

The Dual Connectivity (DC) technology has gained a lot of momentum in the LTE Release 12 as a means to enhance the per-user throughput and provide mobility robustness. Some studies in the literature have discussed the interworking between the LTE and the air interface of the upcoming Fifth Generation (5G) in a DC scenario. That integration may provide some benefits to meet the high throughput, reliability and availability requirements of the 5G networks. This work firstly presents a brief overview of the DC technology considering the integration between 4G e 5G. Then, we highlight some open issues and challenges for future investigation involving Radio Resource Management (RRM) in such a scenario, which includes: (i) user-cell association, (ii) interaction between base stations and (iii) resource allocation. After that, we propose an extension of a utility-based resource allocation algorithm for DC scenarios. Finally, a performance evaluation is conducted in multi-Radio Access Technology (RAT) LTE-NR scenarios considering the bearer split configuration of the DC technology, where the gains provided by the proposed algorithm are demonstrated.

A Utility-Based Joint Subcarrier and Power Allocation for Green Communications in Multi-User Two-Way Regenerative Relay Networks

In this paper, we investigate utility-based joint subcarrier and power allocation algorithms for improving the energy efficiency (EE) in multi-user two-way regenerative relay networks. With the objective of determining the best subcarrier allocation for each user pair, subcarrier pairing permutation, and power allocation to all the nodes, a network price is introduced to the power consumption as a penalty for the achievable sum rate, followed by the examination of its impact on the tradeoff between the EE and spectral efficiency. The formulated optimization problem is a non-convex mixed-integer nonlinear programming problem, and thus a concave lower bound on the objective function and a series of convex transformations are applied to transform the problem into a convex one. Through dual decomposition, we propose a utility-based resource allocation algorithm for iteratively tightening the lower bound and finding the optimal solution of the primal problem. By exploring the structure of the obtained optimal solution, an optimal price that enables green resource allocation is found from the perspective of maximizing EE. Additionally, a suboptimal algorithm is investigated to strike a balance between computational complexity and optimality. Simulation results evince the effectiveness of the proposed algorithms.

Utility-based resource allocation for interference limited OFDMA cooperative relay networks

This paper proposes a utility-based resource allocation algorithm for the uplink OFDMA Inter-cell Interference (ICI) limited cooperative relay network. Full channel state information (CSI) is assumed to be available at the resource controller at initial stage, then the work is extended to consider more realistic assumption, i.e., only partial channel state information (PCSI) is available. The proposed algorithm aims to maximize the total system utility while simultaneously satisfying the individual user’s minimum data rate requirements. In the proposed algorithm, relay selection is initially performed based on the consideration of ICI. Then, subcarrier allocation is performed to achieve maximum utility assuming equal power allocation. Finally, based on the amount of ICI, a modified water-filling power distribution algorithm is proposed and used to optimize the per-carrier power allocation across the allocated set of subcarriers. The results show that, compared to conventional algorithms, the proposed algorithm significantly improves system performance in terms of total sum data rate, outage probability and fairness.

Channel Occupancy-Based Dynamic Spectrum Leasing in Multichannel CRNs: Strategies and Performance Evaluation

Spectrum leasing has been proposed as an effective approach for enabling more flexible spectrum utilization in CRNs. In CRNs, a primary network (PN) which consists of multiple primary users (PUs) can lease part of the licensed spectrum to secondary users (SUs) in exchange for operational benefits. The focus of this study is to investigate how and to what extent the PN allows spectrum leasing in CRNs, considering the QoS requirements of the PN and the secondary network (SN). Correspondingly, we propose two dynamic spectrum leasing strategies, which can improve the QoS performance of SUs while ensuring sufficient remuneration for PUs. In order to dynamically adjust the portion of leased bandwidth, two forms of leasing algorithms, which are based on channel occupancy of the PN and the SN respectively are proposed. Analytical models are developed to characterize system centric performance metrics including capacity, blocking probability, and forced termination probability in both networks. Furthermore, a dynamic pricing scheme for spectrum leasing and utility-based resource allocation is introduced. Finally the performance of dynamic leasing is compared quantitatively with that of static leasing and approaches without leasing. Numerical results obtained from both analysis and simulations show that dynamically adjusted spectrum leasing improves network performance.

Utility-based efficient dynamic distributed resource allocation in buffer-aided relay-assisted OFDMA networks

In this paper, we study resource allocation in buffer-aided relay-assisted OFDMA networks. We consider utility-based stochastic optimization framework where there are constraints to be met either instantaneously or in average sense. Using the well-known Lyapunov drift-plus-penalty policy, we extract the instantaneous problem that needs to be solved in each slot to control the data admission and allocate the time slots, power, and subchannels. We propose the parameters that should be taken into account in utilizing the drift-plus-penalty policy in relay-assisted cellular networks, for providing fair data admission and satisfying the average power constraints. We introduce a low-complexity strategy for power and subchannel allocation and propose distributed and centralized algorithms to utilize it. Specifically, the proposed efficient dynamic distributed resource allocation (EDDRA) scheme is suitable for use in practice as it imposes less overhead on the system and splits the resource allocation tasks among the base station (BS) and the relays. Extensive simulation results show the effectiveness of the proposed parameters in meeting the objective and the constraints of the studied problem. We also show that the proposed EDDRA scheme has close performance to the proposed centralized one and outperforms an existing centralized scheme.

Utility Maximization Resource Allocation in Wireless Networks: Methods and Algorithms

In wireless networks, it is still a challenge to allocate the limited resources among users to meet their specific quality of service (QoS) requirements, especially when the users have different traffic types, i.e., the hard QoS traffic, the best effort traffic, and the soft QoS traffic. In this paper, we develop the utility-based resource allocation algorithms in the following three tasks: 1) resource allocation among the hard QoS traffic and the soft QoS traffic; 2) resource allocation among the best effort traffic and the soft QoS traffic; and 3) finally resource allocation among the hard QoS traffic, the best effort traffic, and the soft QoS traffic, by solving the network utility maximization problem using the Karush–Kuhn–Tucker condition. We develop a number of critical theorems to give the conditions that find the optimal solutions for the above three cases in a unified framework. These theorems then act as design guidelines for the three algorithms. The proposed algorithms take into account the traffic type, the total available resources and the users’ channel qualities. We evaluate the time complexity of the proposed algorithms, which comes out to be polynomial, and study the network performance by numerical examples. Numerical results demonstrate the bandwidth allocations, the fairness index and the total maximum utility under different channel qualities and resource situations.

OPTIMAL RESOURCE ALLOCATION FOR LAYERED VIDEO MULTICASTING IN WIMAX

Real-time video disseminations over wireless links such as video telephony, video conferencing and mobile TV have become fast growing applications. In order to cope with the increasing users’ demand, a new scalable and efficient wireless distribution platform is needed. With numerous advantages such as QoS assurance, high data rate and wide coverage, WiMAX has been regarded as an ideal choice to provide ubiquitous access for multimedia applications. However, how to achieve effective layered video multicasting in WiMAX networks is a challenging issue. In this study, we propose a utility-based resource allocation mechanism for layered video multicasting. In the proposed approach, Application-Layer Forward Error Correction (AL-FEC) is employed to deal with inevitable packet loss. Thus, for each video layer, WiMAX base stations can jointly choose a proper Modulation-and-Coding Scheme (MCS) and AL-FEC overheads to transmit. Our goal is to maximize the system utility which comprises two important factors, system reward and resource usage. System reward represents the summation of video quality of all subscribers while resource usage denotes the consumed WiMAX resources in terms of time slots. To efficiently obtain the optimal solution, we propose two heuristic algorithms, namely genetic algorithm and Layered Policy Iteration (LPI) algorithm and compared their performance to the optimal solution found by exhaustive search. We demonstrate the performance of the approaches via extensive simulations. The simulation results show that the LPI algorithm can provide great service quality and utilize system resource effectively.

Utility-Based Resource Allocation for Multi-Channel Decentralized Networks

The architecture of decentralization makes future wireless networks more flexible and scalable. However, due to the lack of the central authority (e.g., BS or AP), the limitation of spectrum resource, and the coupling among different users, designing efficient resource allocation strategies for decentralized networks faces a great challenge. In this paper, we address the distributed channel selection and power control problem for a decentralized network consisting of multiple users, i.e., transmit-receiver pairs. Particularly, we first take the users' interactions into account and formulate the distributed resource allocation problem as a non-cooperative transmission control game (NTCG). Then, a utility-based transmission control algorithm (UTC) is developed based on the formulated game. Our proposed algorithm is completely distributed as there is no information exchange among different users and hence, is especially appropriate for this decentralized network. Furthermore, we prove that the global optimal solution can be asymptotically obtained with the devised algorithm, and more importantly, in contrast to existing utility-based algorithms, our method does not require that the converging point is one Nash equilibrium (NE) of the formulated game. In this light, our algorithm can be adopted to achieve efficient resource allocation in more general use cases.

Utility-based resource allocation in high-speed railway wireless networks

AbstractIn this paper, we investigate the utility-based resource allocation problem at a base station in high-speed railway (HSR) wireless networks, jointly taking into account the power allocation along the time and the packet allocation among services. The problem to maximize the total utility under the average power constraint is formulated as a mixed-integer non-linear programming (MINLP) problem. Through the integer constraint relaxation, the MINLP problem can be simplified into a convex optimization problem. The detailed analysis reveals that the relaxed problem can be equivalently decomposed into power allocation problem along the time and packet allocation problem among services, which can reduce the problem size. When the optimality of the relaxed problem is achieved, the power allocation along the time and the packet allocation along the time for each service are both proportionally fair. Since the integer relaxation causes a non-integer solution not implementable in practice, a greedy algorithm is proposed to obtain a near-optimal integer solution of the MINLP problem. Finally, the performance of the proposed algorithm is analyzed by simulations under realistic conditions for HSR wireless networks.

A utility-based resource allocation scheme for IEEE 802.11 WLANs via a machine-learning approach

The problem of allocating resources in IEEE 802.11 wireless local area networks (WLAN) is challenging due to limited bandwidth, time-varying channel conditions, and especially the distributed channel-access manner. In this paper we propose an intelligent resource allocation scheme that dynamically adjusts medium-access-control (MAC) parameters to tune channel-access opportunities and maximize the total utility. "Intelligent" refers to the capability of our approach to regulate each 802.11 node's parameters automatically according to the changes of surrounding situations, e.g. channel conditions and number of nodes. Our intelligent allocation scheme uses neural networks to on-line learn the nonlinear function between the adopted MAC parameters and allocated throughput. Based on the learned knowledge, MAC parameters can therefore be dynamically adjusted toward the desired throughput allocation and consequently the maximal WLAN utility. Simulations results demonstrate the effectiveness of our allocation scheme in maximizing the system utility in a varying 802.11 WLAN environment.

Utility Based Resource Allocation Model for Cloud Services

Minimizing Resource allocation problems under the demand and price uncertainty in cloud computing environments is the motivation to explore a resource provisioning strategy for cloud consumers. In this paper a utilization-based optimal cloud (UBOC) algorithm is proposed to minimize the total cost for provisioning resources in a certain time period. To make an optimal decision, the demand uncertainty from cloud consumer side and price uncertainty from cloud providers are taken into account to adjust the tradeoff between on-demand and oversubscribed costs. Using this UBOC user can share cloud resources and pay based on the usage and the results show that this algorithm can minimize the total cost under uncertainty. It also provisions the resources to remove the demand uncertainty.

End-to-end utility-based resource allocation for multi-RAT nodes in heterogeneous cognitive wireless networks

This paper studies the problem of effective resource allocation for multi-radio access technologies (Multi-RAT) nodes in heterogeneous cognitive wireless networks (HCWNs). End-to-end utility, which is defined as the delay of end-to-end communication, is taken into account in this paper. In the scenario of HCWNs, it is assumed that the cognitive radio nodes have the ability of Multi-RAT and can communicate with each other through different paths simultaneously by splitting the arrival packets. In this paper, the problem is formulated as the optimization of split ratio and power allocation of the source cognitive radio node to minimize the delay of end-to-end communication, and a low complexity step-by-step iterative algorithm is proposed. Numerical results show good performance of the proposed algorithm over two other conventional algorithms.

Utility-based Resource Allocation with Bipartite Matching in OFDMA-based Wireless Systems

In order to efficiently utilize limited radio resources, resource allocation schemes in OFDMA-based wireless networks have gained intensive attention recently. Instead of improving the throughput performance, the utility is adopted as the metric for resource allocation, which provides reasonable methods to build up the relationship between user experience and various quality-of-service (QoS) metrics. After formulating the optimization problem by using a weighted bipartite graph, a modified bipartite matching method is proposed to find a suboptimal solution for the resource allocation problem in OFDMA-based wireless systems with feasible computational complexity. Finally, simulation results are presented to validate the effectiveness of the proposed method.

A Utility-Based Mechanism for Broadcast Recipient Maximization in WiMAX Multilevel Relay Networks

The radio resource allocation problem in wireless networks is a major research issue because the resource is scarce, and it must be shared among multiple network components. In this paper, we consider the resource allocation problem in downlink broadcast services in IEEE 802.16j WiMAX networks, where the objective is to serve as many mobile stations (MSs) as possible with the given resource budget. Solving the problem is not trivial because allocating different amounts of resources to the multihop relay base station (MR-BS) and relay stations (RSs) yields different network topologies and, thus, diverse routing paths from the MR-BS to the MSs. Existing approaches only provide inflexible heuristic resource allocation strategies or solutions based on a limited network scenario. More significantly, they focus on single-level relay networks (i.e., an RS can only relay data for MSs, not for other RSs). However, in practice, RSs can relay traffic from other RSs, and the resource allocation could be more efficient. This motivates us to formalize the problem as a multilevel broadcast receipt maximization (ML-BRM) problem. We show that the problem is NP-complete and propose a multilevel utility-based resource allocation (ML-URA) mechanism to solve the ML-BRM problem. In addition, we present a computation reduction mechanism that reduces the running time of the ML-URA substantially. The performance evaluation via simulations shows that the proposed ML-URA can serve more MSs than existing approaches, and the derived solution is very close to the optimum under different network scenarios and resource budgets. Finally, we show the approximation ratio between the ML-URA and the optimal solution.

Control of the trade-off between resource efficiency and user fairness in wireless networks using utility-based adaptive resource allocation

This work addresses the fundamental problem of the trade-off between resource efficiency and user fairness in wireless networks that use opportunistic radio resource allocation. The concept of managing the trade-off by controlling the system fairness index is applied. In order to do that, two adaptive utility-based resource allocation frameworks consisting of subcarrier assignment and power allocation algorithms are proposed. These frameworks are named utility-based alpha-rule and beta-rule, and are suitable for non-real-time and real-time services, respectively. Not only can both frameworks be designed to work as wellknown classic policies found in the literature, but also as adaptive policies, which are able to meet a desired system fairness target. System level simulations show that the proposed frameworks are powerful tools to the network operator, since they can decide in which trade-off point of the efficiency-fairness plane they want to operate the system.

A cross-layer resource allocation scheme for spatial multiplexing-based MIMO-OFDMA systems

We investigate the resource allocation problem for the downlink of a multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) system. The sum rate maximization itself cannot cope with fairness among users. Hence, we address this problem in the context of the utility-based resource allocation presented in earlier papers. This resource allocation method allows to enhance the efficiency and guarantee fairness among users by exploiting multiuser diversity, frequency diversity, as well as time diversity. In this paper, we treat the overall utility as the quality of service indicator and design utility functions with respect to the average transmission rate in order to simultaneously provide two services, real-time and best-effort. Since the optimal solutions are extremely computationally complex to obtain, we propose a suboptimal joint subchannel and power control algorithm that converges very fast and simplifies the MIMO resource allocation problem into a single-input single-output resource allocation problem. Simulation results indicate that using the proposed method achieves near-optimum solutions, and the available resources are distributed more fairly among users.