Yazar "Li, Mingzhe" seçeneğine göre listele

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    Interference coordination for millimeter wave communications in 5G networks for performance optimization
    (Springeropen, 2019) Wang, Jiao; Weitzen, Jay; Bayat, Oğuz; Sevindik, Volkan; Li, Mingzhe
    To address the increasing data rate demands for future wireless networks, a dense deployment of base stations or access points is the most promising approach; however, doing so may cause high intercell interference (ICI). Numerous interference coordination (IC) approaches have been proposed to reduce ICI. Conducting 5G communication on millimeter wave (mmWave) bands is more complex because of its higher propagation losses and greater attenuation variance, all of which depend on environment change. Massive antenna arrays with beamforming techniques can be used to overcome high propagation loss, reduce interference, deliver performance gains of coordination without a high overhead, and deliver high network capacity with multiplex transmitters. The central challenge of a massive antenna array that uses beamforming techniques is coordinating the users and beams for each transmitter within a large network. To address this challenge, we propose a novel two-level beamforming coordination approach that partitions a large network into clusters. At the intracluster level, this approach performs intracluster coordination similar to the user selection algorithms in a multiuser multiple input and multiple output (MU-MIMO); doing this maximizes the utility function or minimizes the signal-to-interference-plus-noise ratio (SINR) function within a cluster. A dynamic time domain IC approach is employed at the intercluster level, collecting interference information for cluster-edge user equipment (UE) and allocating the UE dynamically among the clusters to reduce the intercluster interference for a switched-beam system (SBS). Simulation results show that the proposed two-level IC approach achieves a higher edge user performance or cell capacity than with the current uncoordinated/coordinated approaches.
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    Joint interference coordination approach in femtocell networks for QoS performance optimization
    (Wiley, 2017) Wang, Jiao; Weitzen, Jay; Bayat, Oğuz; Sevindik, Volkan; Li, Mingzhe
    Future heterogeneous networks with dense cell deployment may cause high intercell interference. A number of interference coordination (IC) approaches have been proposed to reduce intercell interference. For dense small-cell deployment with high intercell interference between cells, traditional forward link IC approaches intended to improve edge user throughput for best effort traffic (ie, file transfer protocol download), may not necessarily improve quality of service performance for delay sensitive traffic such as voice over long-term evolution traffic. This study proposes a dynamic, centralized joint IC approach to improve forward link performance for delay-sensitive traffic on densely deployed enterprise-wide long-term evolution femtocell networks. This approach uses a 2-level scheme: central and femtocell. At the central level, the algorithm aims to maximize network utility (the utility-based approach) and minimize network outage (the graphic-based approach) by partitioning the network into clusters and conducting an exhaustive search for optimized resource allocation solutions among femtocells (femto access points) within each cluster. At the femtocell level, in contrast, the algorithm uses existing static approaches, such as conventional frequency reuse (ReUse3) or soft frequency reuse (SFR) to further improve user equipment quality of service performance. This combined approach uses utility-and graphic-based SFR and ReUse3 (USFR/GSFR and UReUse3/GReUse3, respectively). The cell and edge user throughput of best effort traffic and the packet loss rate of voice over long-term evolution traffic have been characterized and compared using both the proposed and traditional IC approaches.
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    Performance model for factory automation in 5G networks
    (2022) Wang, Jiao; Weitzen, Jay; Bayat, Oğuz; Sevindik, Volkan; Li, Mingzhe
    The fifth generation (5G) of mobile networks is emerging as a key enabler of modern factory automation (FA) applications that ensure timely and reliable data exchange between network components. Network slicing (NS), which shares an underlying infrastructure with different applications and ensures application isolation, is the key 5G technology to support the diverse quality of service requirements of modern FA applications. In this article, an end-to-end (E2E) NS solution is proposed for FA applications in a 5G network. Regression approaches are used to construct a performance model for each slice to map the service level agreement to the network attributes. Interference coordination approaches for switched beam systems are proposed to optimize radio access network (RAN) performance models. A case study of a non-public network is used to show the proposed NS solution. Simulation result shows that for services with different QoS requirements, different IC approaches should be used as optimization methods. Design prediction using regression approach has been evaluated and shows that the prediction successful rate increases when more existing data are used.
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    Performance model for video service in 5G networks
    (Mdpi, 2020) Wang, Jiao; Weitzen, Jay; Bayat, Oğuz; Sevindik, Volkan; Li, Mingzhe
    Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users' activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.