Showing papers on "Resource management published in 2014"

Human Resources and the Resource Based View of the Firm

Abstract: The resource-based view RBV of the firm has influenced the field of strategic human resource management SHRM in a number of ways

Reducing the Risk of Supply Chain Disruptions

Abstract: The article discusses the management of supply chains, arguing that risk management solutions that are developed as possible ways to address supply chain disruptions should be evaluated for their cost efficiency as of 2014. Topics include supply chain efficiency, segmentation, regionalization, centralization, and resource management.

Energy-Efficient Resource Sharing for Mobile Device-to-Device Multimedia Communications

Abstract: Device-to-device (D2D) communications bring significant benefits to mobile multimedia services in local areas. However, these potential advantages hinge on intelligent resource sharing between potential D2D pairs and cellular users. In this paper, we study the problem of energy-efficient uplink resource sharing over mobile D2D multimedia communications underlaying cellular networks with multiple potential D2D pairs and cellular users. We first construct a novel analytical model of energy efficiency for different sharing modes, which takes into account quality-of-service (QoS) requirements and the spectrum utilization of each user. Then, we formulate the energy-efficient resource sharing problem as a nontransferable coalition formation game, with the characteristic function that accounts for the gains in terms of energy efficiency and the costs in terms of mutual interference. Moreover, we develop a distributed coalition formation algorithm based on the merge-and-split rule and the Pareto order. The distributed solution is characterized through novel stability notions and can be adapted to user mobility. From it, we obtain the energy-efficient sharing strategy on joint mode selection, uplink reusing allocation, and power management. Extensive simulation results are provided to demonstrate the effectiveness of our proposed game model and algorithm.

Social-aware D2D communications: qualitative insights and quantitative analysis

Abstract: With emerging demands for local area services, device-to-device communication is conceived as a vital component for the next-generation cellular networks to improve spectral reuse, bring hop gains, and enhance system capacity. Ripening these benefits depends on efficiently solving several main technical problems, including mode selection, resource allocation, and interference management. Aiming to establish a new paradigm for solving these challenging problems in D2D communication, in this article we propose a social-aware enhanced D2D communication architecture that exploits social networking characteristics for system design. By developing a profound understanding of the interplay between social networks' properties and mobile communication problems, we qualitatively analyze how D2D communications can benefit from social features, and quantitatively assess the achievable gains in a social-aware D2D communication system.

Adaptive Resource Provisioning for the Cloud Using Online Bin Packing

Abstract: Data center applications present significant opportunities for multiplexing server resources. Virtualization technology makes it easy to move running application across physical machines. In this paper, we present an approach that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers actively used. We abstract this as a variant of the relaxed on-line bin packing problem and develop a practical, efficient algorithm that works well in a real system. We adjust the resources available to each VM both within and across physical servers. Extensive simulation and experiment results demonstrate that our system achieves good performance compared to the existing work.

Small islands, valuable insights: systems of customary resource use and resilience to climate change in the Pacific

Abstract: Understanding how social-ecological systems are and can be resilient to climate change is one of the world's most crucial problems today. It requires knowledge at local and global scales, the integration of natural and social sciences, and a focus on biocultural diversity. Small Pacific Islands and the knowledge-practice-belief systems of their peoples have a long history of resilience to environmental variability and unpredictability, including in areas with marginal habitats and with periodic, severe disturbance (e.g., drought, flood, storms, and tsunami). We review the state of research on these knowledge systems as it pertains to resilience and adaptation, and we highlight critical research needs to address the interrelated areas of: (1) local-scale expertise and observations of change with regard to weather, life-history cycles, and ecological processes; (2) customary resource management institutions and practices (i.e., with agroforests and the nearshore marine environment); and (3) the roles of leaders, social institutions, and social networks in the context of disturbance and change. We conclude that these knowledge systems can contribute high-resolution observations, benchmark data, and insights into practices that enhance resilience and adaptive capacity in integrated terrestrial and marine systems. Community-based and participatory approaches can complement and ground-truth climate models and direct culturally appropriate resource management, research, and adaptation measures. Although most islands in the Pacific are small, their knowledge systems include valuable insights on seasonal cycles, ecological processes, and the management of biocultural diversity that are relevant at a broad scale for understanding resilience and adaptability to the social-ecological effects of climate change.

A survey on resource allocation strategies in cloud computing

Abstract: Cloud computing provides user-requested services that are reliable, dynamic, flexible and efficient. In order to offer such guaranteed services to cloud users, effective resource allocation strategies must be implemented. The methodology used should also confirm to the Service Level Agreement (SLA) drawn between the customer and the service provider. This work presents a study of such resource allocation strategies in cloud computing. The strategies include resource requirements prediction algorithms and resource allocation algorithms. This works studies the various resource allocation techniques utilized in cloud computing and makes a comparative study of the merits and demerits of each technique. This study aims to identify an efficient resource allocation strategy that utilizes resources effectively in the resource constrained environment of cloud computing.

In-Network Cache Management and Resource Allocation for Information-Centric Networks

Abstract: We introduce the concept of resource management for in-network caching environments. We argue that in Information-Centric Networking environments, deterministically caching content messages at predefined places along the content delivery path results in unfair and inefficient content multiplexing between different content flows, as well as in significant caching redundancy. Instead, allocating resources along the path according to content flow characteristics results in better use of network resources and therefore, higher overall performance. The design principles of our proposed in-network caching scheme, which we call ProbCache, target these two outcomes, namely reduction of caching redundancy and fair content flow multiplexing along the delivery path. In particular, ProbCache approximates the caching capability of a path and caches contents probabilistically to: 1) leave caching space for other flows sharing (part of) the same path, and 2) fairly multiplex contents in caches along the path from the server to the client. We elaborate on the content multiplexing fairness of ProbCache and find that it sometimes behaves in favor of content flows connected far away from the source, that is, it gives higher priority to flows travelling longer paths, leaving little space to shorter-path flows. We introduce an enhanced version of the main algorithm that guarantees fair behavior to all participating content flows. We evaluate the proposed schemes in both homogeneous and heterogeneous cache size environments and formulate a framework for resource allocation in in-network caching environments. The proposed probabilistic approach to in-network caching exhibits ideal performance both in terms of network resource utilization and in terms of resource allocation fairness among competing content flows. Finally, and in contrast to the expected behavior, we find that the efficient design of ProbCache results in fast convergence to caching of popular content items.

A Survey of Energy Efficient Resource Management Techniques for Multicell Cellular Networks

Abstract: This paper surveys the recent findings in the area of energy efficient radio resource management in cellular networks. The primary objective is to identify and evaluate the key techniques that have the highest energy saving potential to be developed in the context of Green Networks while serving as a guideline for future research endeavours. The focus of the paper is targeted towards multicell networks which are composed of multiple BSs co-existing in the same area sharing the available radio resources. Due to this, greater emphasis is given towards the techniques that take inter-cell interference (ICI) into account while allocating the resources and, in the process, maximize the energy efficiency (EE). The resource management solutions presented in the paper are classified under three network domains namely homogeneous, heterogeneous, and cooperative networks. Furthermore, the analytical techniques for characterizing the EE of multicell networks are discussed in terms of the stochastic geometry framework. Finally, the paper outlines the current challenges and open issues in the area of energy efficient resource management for multicell cellular networks.

Fuxi: a fault-tolerant resource management and job scheduling system at internet scale

Abstract: Scalability and fault-tolerance are two fundamental challenges for all distributed computing at Internet scale. Despite many recent advances from both academia and industry, these two problems are still far from settled. In this paper, we present Fuxi, a resource management and job scheduling system that is capable of handling the kind of workload at Alibaba where hundreds of terabytes of data are generated and analyzed everyday to help optimize the company's business operations and user experiences. We employ several novel techniques to enable Fuxi to perform efficient scheduling of hundreds of thousands of concurrent tasks over large clusters with thousands of nodes: 1) an incremental resource management protocol that supports multi-dimensional resource allocation and data locality; 2) user-transparent failure recovery where failures of any Fuxi components will not impact the execution of user jobs; and 3) an effective detection mechanism and a multi-level blacklisting scheme that prevents them from affecting job execution. Our evaluation results demonstrate that 95% and 91% scheduled CPU/memory utilization can be fulfilled under synthetic workloads, and Fuxi is capable of achieving 2.36T-B/minute throughput in GraySort. Additionally, the same Fuxi job only experiences approximately 16% slowdown under a 5% fault-injection rate. The slowdown only grows to 20% when we double the fault-injection rate to 10%. Fuxi has been deployed in our production environment since 2009, and it now manages hundreds of thousands of server nodes.

Resource Allocation Under Channel Uncertainties for Relay-Aided Device-to-Device Communication Underlaying LTE-A Cellular Networks

Abstract: Device-to-device (D2D) communication in cellular networks allows direct transmission between two cellular devices with local communication needs. Due to the increasing number of autonomous heterogeneous devices in future mobile networks, an efficient resource allocation scheme is required to maximize network throughput and achieve higher spectral efficiency. In this paper, performance of network-integrated D2D communication under channel uncertainties is investigated where D2D traffic is carried through relay nodes. Considering a multi-user and multi-relay network, we propose a robust distributed solution for resource allocation with a view to maximizing network sum-rate when the interference from other relay nodes and the link gains are uncertain. An optimization problem is formulated for allocating radio resources at the relays to maximize end-to-end rate as well as satisfy the quality-of-service (QoS) requirements for cellular and D2D user equipments under total power constraint. Each of the uncertain parameters is modeled by a bounded distance between its estimated and bounded values. We show that the robust problem is convex and a gradient-aided dual decomposition algorithm is applied to allocate radio resources in a distributed manner. Finally, to reduce the cost of robustness defined as the reduction of achievable sum-rate, we utilize the chance constraint approach to achieve a trade-off between robustness and optimality. The numerical results show that there is a distance threshold beyond which relay-aided D2D communication significantly improves network performance when compared to direct communication between D2D peers.

A Mechanism Design Approach to Resource Procurement in Cloud Computing

Abstract: We present a cloud resource procurement approach which not only automates the selection of an appropriate cloud vendor but also implements dynamic pricing. Three possible mechanisms are suggested for cloud resource procurement: cloud-dominant strategy incentive compatible (C-DSIC), cloud-Bayesian incentive compatible (C-BIC), and cloud optimal (C-OPT). C-DSIC is dominant strategy incentive compatible, based on the VCG mechanism, and is a low-bid Vickrey auction. C-BIC is Bayesian incentive compatible, which achieves budget balance. C-BIC does not satisfy individual rationality. In C-DSIC and C-BIC, the cloud vendor who charges the lowest cost per unit QoS is declared the winner. In C-OPT, the cloud vendor with the least virtual cost is declared the winner. C-OPT overcomes the limitations of both C-DSIC and C-BIC. C-OPT is not only Bayesian incentive compatible, but also individually rational. Our experiments indicate that the resource procurement cost decreases with increase in number of cloud vendors irrespective of the mechanisms. We also propose a procurement module for a cloud broker which can implement C-DSIC, C-BIC, or C--OPT to perform resource procurement in a cloud computing context. A cloud broker with such a procurement module enables users to automate the choice of a cloud vendor among many with diverse offerings, and is also an essential first step toward implementing dynamic pricing in the cloud.

Energy Efficient Uplink Resource Allocation in LTE Networks with M2M/H2H Co-existence under Statistical QoS Guarantees

Abstract: 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.

Exploring Saturation of Themes and Spatial Locations in Qualitative Public Participation Geographic Information Systems Research

Abstract: Use of public participation geographic information systems (PPGIS) studies that collect local knowledge in a spatial format is increasing as a tool in natural resources management. Qualitative PPGIS studies have been conducted as individual interviews, as workshops, and in focus groups. As the number of qualitative PPGIS studies increases, so does the need to understand their quality. Saturation, the point when the researcher determines that the collection of additional data will provide minimal new information as it relates to a particular issue, directly reflects on the validity of the study. While the concept of saturation is well established, it is still inconsistently assessed and reported. Furthermore, how saturation applies to qualitatively collected spatial data has not been addressed. A method is presented to assess spatial saturation of qualitative PPGIS data from 19 focus groups that were conducted to investigate important places for recreation, livelihoods, and the environment in the Florida P...

Analysis, Modeling and Simulation of Workload Patterns in a Large-Scale Utility Cloud

Abstract: Understanding the characteristics and patterns of workloads within a Cloud computing environment is critical in order to improve resource management and operational conditions while Quality of Service (QoS) guarantees are maintained. Simulation models based on realistic parameters are also urgently needed for investigating the impact of these workload characteristics on new system designs and operation policies. Unfortunately there is a lack of analyses to support the development of workload models that capture the inherent diversity of users and tasks, largely due to the limited availability of Cloud tracelogs as well as the complexity in analyzing such systems. In this paper we present a comprehensive analysis of the workload characteristics derived from a production Cloud data center that features over 900 users submitting approximately 25 million tasks over a time period of a month. Our analysis focuses on exposing and quantifying the diversity of behavioral patterns for users and tasks, as well as identifying model parameters and their values for the simulation of the workload created by such components. Our derived model is implemented by extending the capabilities of the CloudSim framework and is further validated through empirical comparison and statistical hypothesis tests. We illustrate several examples of this work's practical applicability in the domain of resource management and energy-efficiency.

Maximizing throughput of overprovisioned HPC data centers under a strict power budget

Abstract: Building future generation supercomputers while constraining their power consumption is one of the biggest challenges faced by the HPC community. For example, US Department of Energy has set a goal of 20 MW for an exascale (1018 flops) supercomputer. To realize this goal, a lot of research is being done to revolutionize hardware design to build power efficient computers and network interconnects. In this work, we propose a software-based online resource management system that leverages hardware facilitated capability to constrain the power consumption of each node in order to optimally allocate power and nodes to a job. Our scheme uses this hardware capability in conjunction with an adaptive runtime system that can dynamically change the resource configuration of a running job allowing our resource manager to re-optimize allocation decisions to running jobs as new jobs arrive, or a running job terminates. We also propose a performance modeling scheme that estimates the essential power characteristics of a job at any scale. The proposed online resource manager uses these performance characteristics for making scheduling and resource allocation decisions that maximize the job throughput of the supercomputer under a given power budget. We demonstrate the benefits of our approach by using a mix of jobs with different power response characteristics. We show that with a power budget of 4:75 MW, we can obtain up to 5:2X improvement in job throughput when compared with the SLURM scheduling policy that is power-unaware. We corroborate our results with real experiments on a relatively small scale cluster, in which we obtain a 1:7X improvement.

Resource Efficiency: A New Paradigm on Energy Efficiency and Spectral Efficiency Tradeoff

Abstract: Spectral efficiency (SE) and energy efficiency (EE) are the main metrics for designing wireless networks. Rather than focusing on either SE or EE separately, recent works have focused on the relationship between EE and SE and provided good insight into the joint EE-SE tradeoff. However, such works have assumed that the bandwidth was fully occupied regardless of the transmission requirements and therefore are only valid for this type of scenario. In this paper, we propose a new paradigm for EE-SE tradeoff, namely the resource efficiency (RE) for orthogonal frequency division multiple access (OFDMA) cellular network in which we take into consideration different transmission-bandwidth requirements. We analyse the properties of the proposed RE and prove that it is capable of exploiting the tradeoff between EE and SE by balancing consumption power and occupied bandwidth; hence simultaneously optimizing both EE and SE. We then formulate the generalized RE optimization problem with guaranteed quality of service (QoS) and provide a gradient based optimal power adaptation scheme to solve it. We also provide an upper bound near optimal method to jointly solve the optimization problem. Furthermore, a low-complexity suboptimal algorithm based on a uniform power allocation scheme is proposed to reduce the complexity. Numerical results confirm the analytical findings and demonstrate the effectiveness of the proposed resource allocation schemes for efficient resource usage.

Managing resources using resource expiration data

Abstract: Resource management techniques, such as cache optimization, are employed to organize resources within caches such that the most requested content (e.g., the most popular content) is more readily available. A service provider utilizes content expiration data as indicative of resource popularity. As resources are requested, the resources propagate through a cache server hierarchy associated with the service provider. More frequently requested resources are maintained at edge cache servers based on shorter expiration data that is reset with each repeated request. Less frequently requested resources are maintained at higher levels of a cache server hierarchy based on longer expiration data associated with cache servers higher on the hierarchy.

Resource Allocation Techniques in Cooperative Cognitive Radio Networks

Abstract: In the past decade, cognitive radio and cooperative communication techniques have been proposed in the literature for efficiently utilizing the radio resources. Cognitive radio is an emerging technology intended to enhance the utilization of the radio frequency spectrum. The cooperative communication system, with the same total power and bandwidth of legacy wireless communication systems, can increase the data rate of the future wireless communication system. A combination of cognitive radio with cooperative communication can further improve the future wireless network performance. Efficient resource allocation in cooperative cognitive radio network (CRN) is essential in order to meet the challenges of future wireless networks. In this article, a survey of resource allocation in cooperative CRN is presented. We discuss the taxonomy of objectives and protocols used in the literature for resource allocation in cooperative CRN. This paper also highlights the use of power control, cooperation types, network configurations and decision types used in cooperative CRN. Finally, directions for future research are outlined.

An Integrated Approach to Snowmelt Flood Forecasting in Water Resource Management

Abstract: Water scarcity and floods are the major challenges for human society both present and future. Effective and scientific management of water resources requires a good understanding of water cycles, and a systematic integration of observations can lead to better prediction results. This paper presents an integrated approach to water resource management based on geoinformatics including technologies such as Remote Sensing (RS), Geographical Information Systems (GIS), Global Positioning Systems (GPS), Enterprise Information Systems (EIS), and cloud services. The paper introduces a prototype IIS called Water Resource Management Enterprise Information System (WRMEIS) that integrates functions such as data acquisition, data management and sharing, modeling, and knowledge management. A system called SFFEIS (Snowmelt Flood Forecasting Enterprise Information System) based on the WRMEIS structure has been implemented. It includes operational database, Extraction-Transformation-Loading (ETL), information warehouse, temporal and spatial analysis, simulation/prediction models, knowledge management, and other functions. In this study, a prototype water resource management IIS is developed which integrates geoinformatics, EIS, and cloud service. It also proposes a novel approach to information management that allows any participant play the role as a sensor as well as a contributor to the information warehouse. Both users and public play the role for providing data and knowledge. This study highlights the crucial importance of a systematic approach toward IISs for effective resource and environment management.

Self-adaptive workload classification and forecasting for proactive resource provisioning

Abstract: As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining an importance as a foundation for online capacity planning and resource management. Time series analysis offers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting QoS metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real-world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared with statically applied forecasting methods, for example, in an exemplary scenario on average by 37%. In a case study, between 55 and 75% of the violations of a given service level objective can be prevented by applying proactive resource provisioning based on the forecast results of our implementation. Copyright © 2014 John Wiley & Sons, Ltd.

Toward increased engagement between academic and indigenous community partners in ecological research

Abstract: Ecological research, especially work related to conservation and resource management, increasingly involves social dimensions. Concurrently, social systems, composed of human communities that have direct cultural connections to local ecology and place, may draw upon environmental research as a component of knowledge. Such research can corroborate local and traditional ecological knowledge and empower its application. Indigenous communities and their interactions with and management of resources in their traditional territories can provide a model of such social-ecological systems. As decision-making agency is shifted increasingly to indigenous governments in Canada, abundant opportunities exist for applied ecological research at the community level. Despite this opportunity, however, current approaches by scholars to community engaged ecological research often lack a coherent framework that fosters a respectful relationship between research teams and communities. Crafted with input from applied scholars and leaders within indigenous communities in coastal British Columbia, we present here reflections on our process of academic-community engagement in three indigenous territories in coastal British Columbia, Canada. Recognizing that contexts differ among communities, we emerge with a generalizable framework to guide future efforts. Such an approach can yield effective research outcomes and emergent, reciprocal benefits such as trust, respect, and capacity among all, which help to maintain enduring relationships. Facing the present challenge of community engagement head-on by collaborative approaches can lead to effective knowledge production toward conservation, resource management, and scholarship.

Virtual Network Embedding with Opportunistic Resource Sharing

Abstract: Network virtualization has emerged as a promising approach to overcome the ossification of the Internet. A major challenge in network virtualization is the so-called virtual network embedding problem, which deals with the efficient embedding of virtual networks with resource constraints into a shared substrate network. A number of heuristics have been proposed to cope with the NP-hardness of this problem; however, all of the existing proposals reserve fixed resources throughout the entire lifetime of a virtual network. In this paper, we re-examine this problem with the position that time-varying resource requirements of virtual networks should be taken into consideration, and we present an opportunistic resource sharing-based mapping framework, ORS, where substrate resources are opportunistically shared among multiple virtual networks. We formulate the time slot assignment as an optimization problem; then, we prove the decision version of the problem to be NP-hard in the strong sense. Observing the resemblance between our problem and the bin packing problem, we adopt the core idea of first-fit and propose two practical solutions: first-fit by collision probability (CFF) and first-fit by expectation of indicators' sum (EFF). Simulation results show that ORS provides a more efficient utilization of substrate resources than two state-of-the-art fixed-resource embedding schemes.

Cognitive radio resource management for future cellular networks

Abstract: The heterogeneous network (HetNet) architecture, device-to-device (D2D) communications, and coexistence with existing wireless systems have been regarded as new communication paradigms introduced in LTE-A/LTE-B cellular networks. To facilitate these paradigms, considerable research has shown promise of the cognitive radio (CR) technology, particularly the cognitive radio resource management (CRRM) on the top of resource allocation to control Layer-1 and Layer-2 radio operations, thus eliminating the concerns of potential system impacts and operation unreliability to bridge the gap between cellular and CR technologies. To support diverse communication paradigms with different challenges, a variety of CRRM schemes have been recently proposed, which however significantly perplexes the system implementation. To provide a general reconfigurable framework, in this article, we reveal a software-defined design of the CRRM. Through proper configurations, this software- defined design is able to adapt to diverse communication paradigms in LTE-A/LTE-B, and provides transmission reliability in terms of quality- of-service guarantees via the optimum control of the design. Supporting diverse CRRM schemes, this design substantially simplifies the system realization to bring the development of the CRRM to the next stage of practice for the fifth generation (5G) cellular network.

An analysis of power dynamics within innovation platforms for natural resource management

Abstract: Innovation systems thinking is increasingly influencing approaches to sustainable agricultural development in developing world contexts. This represents a shift away from technology transfer towards recognition that agricultural change entails complex interactions among multiple actors and a range of technical, social and institutional factors. One option for practically applying innovation systems thinking involves the establishment of innovation platforms (IPs). Such platforms are designed to bring together a variety of different stakeholders to exchange knowledge and resources and take action to solve common problems. Yet relatively little is known about how IPs operate in practice, particularly how power dynamics influence platform processes. This paper focuses on a research-for-development project in the Ethiopian highlands which established three IPs for improved natural resource management. The ‘power cube’ is used to retrospectively analyse the spaces, forms and levels of power within these platfo...

Radio Resource Allocation for Downlink Non-Orthogonal Multiple Access (NOMA) Networks Using Matching Theory

Abstract: In this paper, we study the resource allocation and scheduling problem for a downlink non- orthogonal multiple access (NOMA) network where the base station (BS) allocates the spectrum resources and power to the set of users. We aim to optimize the sub-channel assignment and power allocation to achieve a balance between the number of scheduled users and total sum-rate maximization. To solve the above problem, we propose a many-to-many two-sided user-subchannel matching algorithm in which the set of users and sub-channels are considered as two sets of players pursuing their own interests. The algorithm converges to a pair-wise stable matching after a limited number of iterations. Simulation results show that the proposed algorithm can approach the performance of the upper bound and greatly outperforms the OFDMA scheme.