Self-optimization of Random Access Channel in 3GPP LTE

Self-optimization of Random Access Channel in 3GPP LTE,10.1109/IWCMC.2011.5982742,Osman N. C. Yilmaz,Jyri Hamalainen,Seppo Hamalainen

Self-optimization of Random Access Channel in 3GPP LTE  
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In this paper, a mathematical framework for the Random Access Channel (RACH) modeling in Long-Term Evaluation (LTE) technology is presented on the basis of probability theory and statistics. The framework is exemplified by basic investigations on the self-optimization of RACH resource unit allocation and preamble split. Evaluations are performed by modeling a stochastic process in which outputs of a dynamic LTE network simulator are taken as inputs. Long-Term Evaluation (LTE) is a new technology in which radio interface is designed based on Orthogonal Frequency Division Multiple Access (OFDMA) by Third Generation Partnership Project (3GPP) (1). LTE enables higher peak data rates and more users per cell as well as lower control plane latency than previously deployed Third Generation (3G) technologies. In order to limit the growth of capital expenditures (CAPEX) and operational expenditures (OPEX), new Self- Organizing Network (SON) paradigm is introduced in 3GPP LTE standards evolving towards LTE-Advanced. From the operator's point of view, SON provides an important enabler for the cost reduction through automated network functions (2- 4). These automated functions should react dynamically for the variations of different parameters and performance indicators, optimizing the overall network performance and service quality. According to architectural categorization, the SON approach can be designed by using the following four different classes depending on the location of optimization algorithms: centralized SON, distributed SON, localized SON and hybrid SON (3). In practice a hybrid SON is usually required, in which all kinds of approaches are in use simultaneously coping with different needs of different use cases in a self-coordinated manner. Let us briefly introduce the main characters of the above-mentioned approaches: Centralized SON is a mechanism, where a large number of cells are involved in the optimization and slower parameter update rates are faced since long-term statistics are processed. In centralized solutions, SON functionality is located at a high level in the architecture such as Network Management System (NMS) or Element
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