Victim Aware AP-PF CoMP Clustering for Resource Allocation in Ultra-Dense Heterogeneous Small-Cell Networks

Heterogeneous networks with dense deployment of femto cells has provided the promising solution to enhance the system throughputs for the next generation wireless communication. When the large number of heterogeneous networks are overlapped, then traditional intercell interface technique failed to mitigate the interference in between the cells. So, to mitigate the interference, it requires advanced approach for improving the cell edge throughputs and spectral efficiency. For this, the paper presents a frame work to allocate the efficient resource among the users in dense networks. We proposed affinity propagation unsupervised learning to form the cluster with center and then regularized the cluster for effectively allocated the resource. Users on the cluster edge has suffering the inter cluster interface, a victim aware and coordination multipoint mechanism is further proposed to allocated the required resources for these victimized users. We analyzed the performance of our proposed framework with proportional fair based criteria. The total throughputs, edge throughput and spectral efficiency of the system are significantly enhanced in our simulation results through this proposed framework.

[1]  Mohsen Guizani,et al.  Cooperation for spectral and energy efficiency in ultra-dense small cell networks , 2016, IEEE Wireless Communications.

[2]  Markus Rupp,et al.  The Vienna LTE-Advanced Simulators , 2016 .

[3]  Sanjay Kumar Biswash,et al.  Efficient Resource Management by Exploiting D2D Communication for 5G Networks , 2016, IEEE Access.

[4]  Shin-Ming Cheng,et al.  On exploiting cognitive radio to mitigate interference in macro/femto heterogeneous networks , 2011, IEEE Wireless Communications.

[5]  Shahrokh Valaee,et al.  Mobility-Based Clustering in VANETs Using Affinity Propagation , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[6]  Glauber Brante,et al.  Distributed Fuzzy Logic-Based Relay Selection Algorithm for Cooperative Wireless Sensor Networks , 2013, IEEE Sensors Journal.

[7]  Li-Chun Wang,et al.  Data-Driven Resource Management for Ultra-Dense Small Cells: An Affinity Propagation Clustering Approach , 2019, IEEE Transactions on Network Science and Engineering.

[8]  Tommy Svensson,et al.  Downlink Radio Resource Allocation for Coordinated Cellular OFDMA Networks , 2010, IEICE Trans. Commun..

[9]  Lajos Hanzo,et al.  Energy Efficient OFDMA Networks Maintaining Statistical QoS Guarantees for Delay-Sensitive Traffic , 2016, IEEE Access.

[10]  Gerhard Fettweis,et al.  Coordinated Multi-Point in Mobile Communications: From Theory to Practice , 2011 .

[11]  Dong Liu,et al.  Semi-dynamic User-Specific Clustering for Downlink Cloud Radio Access Network , 2016, IEEE Transactions on Vehicular Technology.

[12]  Ramjee Prasad,et al.  OFDM for Wireless Multimedia Communications , 1999 .

[13]  Rosdiadee Nordin,et al.  Semi-Clustering of Victim-Cells Approach for Interference Management in Ultra-Dense Femtocell Networks , 2017, IEEE Access.

[14]  Mikko Valkama,et al.  System-level performance of LTE-Advanced with joint transmission and dynamic point selection schemes , 2012, EURASIP J. Adv. Signal Process..

[15]  Andreas F. Molisch,et al.  Downlink Base Station Cooperative Transmission Under Limited-Capacity Backhaul , 2013, IEEE Transactions on Wireless Communications.

[16]  Lars Thiele,et al.  Coordinated multipoint: Concepts, performance, and field trial results , 2011, IEEE Communications Magazine.

[17]  Jinbo Liu,et al.  Affinity Propagation Based CoMP Clusters for Dense Small Cell Networks with Backhaul Constraints , 2017, Wirel. Pers. Commun..

[18]  Rahim Tafazolli,et al.  Dynamic Clustering Framework for Multi-Cell Scheduling in Dense Small Cell Networks , 2013, IEEE Communications Letters.

[19]  Satoshi Nagata,et al.  Coordinated multipoint transmission and reception in LTE-advanced: deployment scenarios and operational challenges , 2012, IEEE Communications Magazine.

[20]  Ali Imran,et al.  Coordinated Multi-Point Clustering Schemes: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[21]  Holger Claussen,et al.  On femto deployment architectures and macrocell offloading benefits in joint macro-femto deployments , 2010, IEEE Communications Magazine.

[22]  Sanjeev Kumar,et al.  Performance Analysis of Resource Scheduling Techniques in Homogeneous and Heterogeneous Small Cell LTE-A Networks , 2020, Wirel. Pers. Commun..

[23]  Stefano Pantaleoni,et al.  Bone Mineral Density at Diagnosis of Celiac Disease and after 1 Year of Gluten-Free Diet , 2014, TheScientificWorldJournal.

[24]  Holger Claussen,et al.  Towards 1 Gbps/UE in Cellular Systems: Understanding Ultra-Dense Small Cell Deployments , 2015, IEEE Communications Surveys & Tutorials.

[25]  Jie Zhang,et al.  Data-Driven Deployment and Cooperative Self-Organization in Ultra-Dense Small Cell Networks , 2018, IEEE Access.

[26]  Tommy Svensson,et al.  The role of small cells, coordinated multipoint, and massive MIMO in 5G , 2014, IEEE Communications Magazine.

[27]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[28]  Kwang-Cheng Chen,et al.  Cognitive and Game-Theoretical Radio Resource Management for Autonomous Femtocells with QoS Guarantees , 2011, IEEE Transactions on Wireless Communications.

[29]  Ramjee Prasad,et al.  Wideband indoor channel measurements and BER analysis of frequency selective multipath channels at 2.4, 4.75, and 11.5 GHz , 1996, IEEE Trans. Commun..

[30]  Hui Liu,et al.  A Practical Semidynamic Clustering Scheme Using Affinity Propagation in Cooperative Picocells , 2015, IEEE Transactions on Vehicular Technology.

[31]  Hamed S. Al-Raweshidy,et al.  Radio Over Fiber Technologies for Mobile Communications Networks , 2002 .

[32]  Mahamod Ismail,et al.  A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network , 2014, TheScientificWorldJournal.