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Padmaloshani, Palanisamy,Nirmala, Sivaraj Electronics and Telecommunications Research Instit 2020 ETRI Journal Vol.42 No.2
Inter-cell interference (ICI) is a major problem in heterogeneous networks, such as two-tier femtocell (FC) networks, because it leads to poor cell-edge throughput and system capacity. Dynamic ICI coordination (ICIC) schemes, which do not require prior frequency planning, must be employed for interference avoidance in such networks. In contrast to existing dynamic ICIC schemes that focus on homogeneous network scenarios, we propose a novel semi-distributed dynamic ICIC scheme to mitigate interference in heterogeneous network scenarios. With the goal of maximizing the utility of individual users, two separate algorithms, namely the FC base station (FBS)-level algorithm and FC management system (FMS)-level algorithm, are employed to restrict resource usage by dominant interference-creating cells. The distributed functionality of the FBS-level algorithm and low computational complexity of the FMS-level algorithm are the main advantages of the proposed scheme. Simulation results demonstrate improvement in cell-edge performance with no impact on system capacity or user fairness, which confirms the effectiveness of the proposed scheme compared to static and semi-static ICIC schemes.
Padmaloshani Palanisamy,Nirmala Sivaraj 한국전자통신연구원 2018 ETRI Journal Vol.40 No.3
Femtocell (FC) technology envisaged as a cost‐effective approach to attain better indoor coverage of mobile voice and data service. Deployment of FCs over macrocell forms a heterogeneous network. In urban areas, the key factor limits the successful deployment of FCs is inter‐cell interference (ICI), which severely affects the performance of victim users. Autonomous FC transmission power setting is one straightforward way for coordinating ICI in the downlink. Application of intelligent control using soft computing techniques has not yet explored well for wireless networks. In this work, autonomous FC transmission power setting strategy using Adaptive Neuro Fuzzy Inference System is proposed. The main advantage of the proposed method is zero signaling overhead, reduced computational complexity and bare minimum delay in performing power setting of FC base station because only the periodic channel measurement reports fed back by the user equipment are needed. System level simulation results validate the effectiveness of the proposed method by providing much better throughput, even under high interference activation scenario and cell edge users can be prevented from going outage.
Development of a Neuro Controller for a Negative Output Elementary Luo Converter
Ramanujam Kayalvizhi,Sirukarumbur Pandurangan Natarajan,Padmaloshani Palanisamy 전력전자학회 2007 JOURNAL OF POWER ELECTRONICS Vol.7 No.2
The negative output elementary Luo converter is a newly developed DC-DC converter. Due to the time-varying and switching nature of the above converter, its dynamic behavior becomes highly non-linear. Conventional controllers are incapable of providing good dynamic performance for such a converter and, hence, a neural network is utilized as a controller in this work. The performance of the chosen Luo converter using PI versus neuro controls is compared under load and line disturbances using MATLAB and TMS320F2407 DSP. The results validate the superiority of the developed neuro controller.
Development of a Neuro Controller for a Negative Output Elementary Luo Converter
Kayalvizhi Ramanujam,Natarajan Sirukarumbur Pandurangan,Palanisamy Padmaloshani The Korean Institute of Power Electronics 2007 JOURNAL OF POWER ELECTRONICS Vol.7 No.2
The negative output elementary Luo converter is a newly developed DC-DC converter. Due to the time-varying and switching nature of the above converter, its dynamic behavior becomes highly non-linear. Conventional controllers are incapable of providing good dynamic performance for such a converter and, hence, a neural network is utilized as a controller in this work. The performance of the chosen Luo converter using PI versus neuro controls is compared under load and line disturbances using MATLAB and TMS320F2407 DSP. The results validate the superiority of the developed neuro controller.