Performance analysis in heterogeneous cloud radio access networks with non-uniform device-to-device deployment

2016 ◽  
Author(s):  
Munzali Ahmed Abana ◽  
Mugen Peng ◽  
Zhongyuan Zhao
Keyword(s):  
Performance Analysis ◽  
Access Networks ◽  
Cloud Radio Access Networks ◽  
Radio Access Networks ◽  
Radio Access ◽  
Device To Device ◽  
Device Deployment ◽  
Heterogeneous Cloud

scholarly journals Heterogeneous Cloud Radio Access Networks: Enhanced Time Allocation for Interference Mitigation

2018 ◽  
Vol 2018 ◽  
pp. 1-17
Author(s):  
Imad Al-Samman ◽  
Reham Almesaeed ◽  
Angela Doufexi ◽  
Mark Beach
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Interference Mitigation ◽  
Base Station ◽  
Small Cell ◽  
Access Networks ◽  
Cloud Radio Access Networks ◽  
Radio Access Networks ◽  
Radio Access ◽  
Heterogeneous Cloud

Responding to the unprecedented challenges imposed by the 5G technologies, mobile operators have given significant attention to Heterogeneous Cloud Radio Access Networks (H-CRAN) due to their beneficial features of performing optimization, cost effectiveness, and improving spectral and energy efficiency performance. H-CRAN inherits the attractive benefits of Heterogeneous Networks (HetNet) and the cloud computing by facilitating interference mitigation, scalability, and radio resource control. Consequently, H-CRAN is proposed in this article as a cost-effective potential solution to alleviate intertier interference and improve cooperative processing gains in HetNets by employing cloud computing. H-CRAN can provide efficient resource sharing at the spectrum, network, and infrastructure levels. Therefore, this article proposes H-CRAN cooperative interference mitigation method that enhances the time sharing among Radio Remote Heads (RRH) users. The study proposes an enhanced Almost Blank Subframe (ABSF) technique to increase the SINR and throughput of the small-cell (low power base station) and macrocell users. Simulation results show that the proposed Dynamic Programming-Diverse Almost Blank Subframe (ABSF) Pattern (DP-DAP) scheme improved the macro- and small-cell users up to 56% and 35%, respectively, as compared to other state-of-the-art ABSF schemes.

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