Quasi-Quadrature Modulation Method for Power-Efficient Video Transmission Over LTE Networks

Maksymyuk, Taras,Longzhe Han,Xiaohu Ge,Hsiao-Hwa Chen,Minho Jo IEEE 2014 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY Vol.63 No.5

<P>New emerging services, such as real-time video streaming or video on demand, are causing rapid growth in packet transmission over wireless networks. Unlike voice calls, for which the duration is usually not very long, video streaming applications require continuous transmission for a long time. Therefore, video streaming applications in mobile networks consume more energy compared with voice calls. Thus, the task of optimizing data transmission algorithms has become more important during the last few years. Apparently, the majority of multimedia traffic is video transmission. These applications consume much more power, compared with audio or general data transmission, because of higher throughput requirements. This paper addresses the problem of decreasing power consumption due to video transmission applications in Long-Term Evolution (LTE) networks. There are existing solutions for managing power consumption during video transmission. In particular, Third-Generation Partnership Project LTE Advanced (LTE-A) has defined the discontinuous reception/transmission (DRX/DRT) mechanism to allow devices to turn off their radio interfaces and go to sleep in various patterns. Some other similar solutions suggest DRX/DRT optimization to maximize the sleep periods of devices while guaranteeing quality of service in multimedia applications. However, existing solutions for packet transmission optimization are not very effective without physical-layer optimization. However, existing solutions for packet transmission optimization are not very effective without physical-layer optimization. We suggest a new method of modulation for improving energy efficiency of wireless video transmission. Four different schemes of quasi-quadrature modulation using multiple-input-multiple-output (MIMO) techniques with different quality of service performances are proposed in this paper. We simulate H.264/AVC video transmission. Results confirm the theoretical analysis. The proposed approach is able to improve energy efficiency while providing the same packet loss probability.</P>

Fractal Modeling for Multi-Tier Heterogeneous Networks with Ultra-High Capacity Demands

Taras Maksymyuk,Mykola Brych,Ihor Strykhalyuk,Minho Jo 한국산학기술학회 2015 SmartCR Vol.5 No.4

Technology evolution and business interest to emerging IoT concept are driving the rapid development of advanced applications. Multitude of Internet connected devices and things in combination with the cloud based data analytics and knowledge based technologies are major components for the future IoT concept. This results in big data demands that need to be transmitted in over mobile network and requires new intelligent approaches to the network design. In this paper, a new deterministic approach for analysis of multi-tier heterogeneous network is proposed based on the fractal geometry. Proposed solution is well tractable and suitable for every possible topology of heterogeneous network, providing exact information about network capacity and co-channel interference. Two fractal models were developed based on triangular and rectangular pattern and basic geometric equations were derived for multi-tier network topology. Frequency reuse problem was addressed based on the proposed fractal model for cellular and wireless backhaul spectrum and optimal tradeoff between cellular and backhaul capacity was found. Performance simulations show that proposed fractal modeling provides more accurate network analysis in terms of capacity and interference influence in multi-tier heterogeneous networks.

Stochastic Geometry Models for 5G Heterogeneous Mobile Networks

Taras Maksymyuk,Mykola Brych,Volodymyr Pelishok 한국산학기술학회 2015 SmartCR Vol.5 No.2

Next generation wireless networks are expected to provide thousand times higher capacity comparing to existing LTE (Long Term Evolution) networks. Increasing of network capacity can be achieved by combining both spatial and spectral network densification. Influence of spatial network densification on future tremendous capacity growth is very high due to limited spectral resources. Therefore, optimal network planning is an important challenge for future heterogeneous networks with high number of small cells. Network geometry modeling is the significant part of network design and analysis. Multi-tier heterogeneous networks are very complex in terms of topology that requires new advanced approaches to the network planning. In we study the most recent solutions on the stochastic network geometry and analyze their feasibility for different scenarios of heterogeneous network. Studied approached provides good tractability of the mobile network topology and behavior. Poisson point processes combining with Voronoi tessellation provides good approximation of network nodes deployment and coverage areas. We also study feasibility of stochastic models for different buildings environment, including hyper dense skyscrapers environment. Hybrid network model combining Poisson point process with K-means clustering method was developed for D2D (Device-to-Device) heterogeneous network. Proposed model reflects random user behavior and estimate available groups for D2D transmission. Performance simulation of single tier, multi-tier and D2D based heterogeneous network shows that heterogeneous network provides significantly higher performance in terms of throughput and signal-to-interference-plus-noise ratio. Future research directions for network geometry have been outlined in this paper including emerging hot topic of combing the stochastic and deterministic network modelling.

Study and Development of Next-Generation Optical Networks

Taras Maksymyuk,Stepan Dumych,Olena Krasko,Mykola Kaidan,Bohdan Strykhalyuk 한국산학기술학회 2014 SmartCR Vol.4 No.6

Next-generation optical networks are expected to provide tremendous capacity in order to support upcoming traffic increases. Many technologies are currently being developed for optical transport networks in order to increase throughput, improve energy efficiency and simplify network deployment. The most important problem in current optical networks is transmission of Internet protocol (IP) traffic. Regardless of the tremendous throughput with optical fibers, switching nodes still limit overall network performance. Recently, optical burst switching technology has been developed to overcome this problem. Optical burst switching combines the advantages of both circuit switching and packet switching networks and provides good performance in terms of packet data transmission. Even though optical burst switching networks provide a good mechanism for IP traffic transmission, overall performance is still limited because of access networks. Existing passive optical networks based on Ethernet technology are not fully compatible with optical burst switching, which results in bottlenecks on the border between transport and access networks. In this paper, we present a new method of optical wavelength time-division multiple access (OWTDMA) for passive optical networks. The proposed approach can provide outstanding scalability of network resources and can increase throughput of the optical access network. In addition, we propose implementation of OWTDMA in edge nodes of optical burst switching networks to eliminate bottlenecks between transport and access networks. Simulation results prove the advantage of our proposed approach.

Device-to-device-based heterogeneous radio access network architecture for mobile cloud computing

Minho Jo,Maksymyuk, Taras,Strykhalyuk, Bohdan,Choong-Ho Cho IEEE 2015 IEEE wireless communications Vol.22 No.3

<P>The emerging heterogeneous mobile network architecture is designed for an increasing amount of traffic, quality requirements, and new mobile cloud computing demands. This article proposes a hierarchical cloud computing architecture to enhance performance by adding a mobile dynamic cloud formed by powerful mobile devices to a traditional general static cloud. A mobile dynamic cloud is based on heterogeneous wireless architecture where device-to-device communication is used for data transmission between user devices. The main advantage of the proposed architecture is an increase in overall capacity of a mobile network through improved channel utilization and traffic offloading from Long Term Evolution-Advanced to device-to-device communication links. Simulations show that the proposed architecture increases the capacity of a mobile network by up to 10 percent depending on the conditions and amount of offloaded data. The offloading probability is also evaluated by taking into consideration the number of devices in the cloudlet and the content matching values. We have gained insight into how content similarity affects offloading probability much more than the number of devices in a cloudlet.</P>

Deep Learning based Loss Recovery Mechanism for Video Streaming over Mobile Information-Centric Network

( Longzhe Han ),( Taras Maksymyuk ),( Xuecai Bao ),( Jia Zhao ),( Yan Liu ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.9

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.