A New Intelligent Approach Based on Genetic Algorithm for Finding Optimal Base Stations Configuration in the Dynamic Scenario Used in SON
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The problem of automatic selection and configuration of base station sites in the dynamic scenario is investigated. We propose a intelligent approach based on Genetic Algorithm used to adjust the level of the antenna power and select the base stations’ site from the candidate sites. According to the users’ density the base stations adjust their antenna radiated power automatically. Results are given that most of base stations are powered off when the density becomes very low, and show that it will save lots of energy resources. Since this algorithm can adjust the status of base stations intelligently and automatically, it can be used in SON.Keywords:
Base (topology)
Selection algorithm
Base station (BS) selection algorithms that dynamically adapt to teletraffic variations in the microcellular environment are introduced. Though overlapping areas usually exist between BS coverage, we redirect teletraffic away from heavily loaded cells. We present three algorithms. The first is the directed retry with handover priority algorithm that recognises blocking of a call and searches for alternative BSs. The second is the BS load equalization algorithm that directs new calls initiated in the overlapping area to be made to the BS with most available channels. The third is the rearrangement upon blocking algorithm that clears a connection from the current saturated BS in order to allow the mobile station in trouble to be served. The performance of these algorithms is evaluated using event driven computer simulations in addition to an analytical queueing model. It is found that in situations where radio coverage of adjacent cells is overlapped, the proposed algorithms yield higher system capacity.
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For the problem of passive location in mobile cellular network, base stations (BSs) selection can improve positioning accuracy. Through the analysis of base station layout in cellular networks, using Geometric Dilution of Precision (GDOP) as the optimization objective, we propose a Dynamic Base Stations Selection (DBSS) method in a cellular unit. This method enables the system to dynamically select the positioning base station when positioning target in the detection area. DBSS mainly include three steps: nearest base station calculation, layout of base stations analysis, and base station selection based on the target location. We mainly focus on the derivation of four-base station dynamic selection (DBSS4) and five-base station dynamic selection (DBSS5) algorithms. In simulation experiments, DBSS4 algorithm and DBSS5algorithm were compared with the state-of-the-art of BSs selection methods. The results show that our proposed method can achieve the exhaustive search in cellular cells and reduce more than 20% of the GDOP cumulative positioning error compared with the fixed four-base station selection algorithm. Meanwhile, the proposed method is more efficient, requires less running time and floating-point operations (FLOPs) than other comparison algorithm, and is independent of localization algorithms.
Dilution of precision
Selection algorithm
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The problem of automatic selection and configuration of base station sites in the dynamic scenario is investigated. We propose a intelligent approach based on Genetic Algorithm used to adjust the level of the antenna power and select the base stations’ site from the candidate sites. According to the users’ density the base stations adjust their antenna radiated power automatically. Results are given that most of base stations are powered off when the density becomes very low, and show that it will save lots of energy resources. Since this algorithm can adjust the status of base stations intelligently and automatically, it can be used in SON.
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Selection algorithm
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Channel capacity formula of MIMO system before and after antenna selection is derived. A modified dynamic antenna selection algorithm is presented. This algorithm selects the optimal k antennas based on Water Pouring principle, then the selected k transmitting antennas are redistributed equal transmit power. To verify the algorithm, it is applied to full-rank and rank deficient channel. Numerical results show the algorithm could select the optimal antenna subset in the two cases in a high degree of accuracy without redundant computation time.
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When the mobile station locates in the detective range of several base stations in the wireless location system based on CDMA network, an issue about base station selection is put forward. The optimized base station selection can reduce the use of system resources, and influence the performance of location system directly. This paper presents an algorithm in which HDOP is applied to base station selection. The field trials show that the algorithm has higher stability, higher positioning precision and capability of real time. The algorithm can be also used to guide the layout of base station and the design of location system.
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In this paper,a new antenna selection algorithm is proposed for multiple-input multiple-output(MIMO) wireless communication systems.It is assumed that the channel state information(CSI) is exactly known at the receiver;hence the antenna selection is considered only at the receiver.The goal of antenna selection is to find a tradeoff between the maximization of the channel capacity and the minimization of the pair-wise error probability(PEP),which turns out that the scheme of antenna selection plays an important part when dealing with high antenna correlation.It is shown that the proposed scheme for a system with two-transmit antennas and five-receive antennas offers better PEP performance than the algorithm based on the maximization capacity with little capacity loss.The simulation results indicate that the proposed scheme can meet the capacity maximization and the PEP minimization.
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Minification
Selection algorithm
Channel state information
Spatial correlation
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Aiming at the high hardware cost brought by multiple antenna that needs multiple RF chains,this paper analyzes the necessity for MIMO antenna subset selection with low complexity,and introduces the increasing antenna selection algorithm, decreasing antenna selection algorithm,and improved increasing and decreasing antenna selection algorithm.Simulation results show that increasing and decreasing algorithm can quickly select the antenna subset that makes the biggest system capability.This algorithm is simple and available,and the simulation result is very close to the result by exhaustive search method.
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Reconfigurable antenna
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In this work, a new antenna selection algorithm is proposed for multiple-input multiple-output (MIMO) wireless communication systems. It is assumed that the channel state information (CSI) is exactly known at the receiver, and hence the antenna selection is considered only at the receiver. The goal of antenna selection is to find a tradeoff between the maximization of the channel capacity and the minimization of the pair-wise error probability (PEP). It turns out that the scheme of antenna selection plays an important part when dealing with high antenna correlation. It is shown that our proposed scheme for a system with two-transmit antennas and five-receive antennas offers better PEP performance than the algorithm based on the maximization capacity, but has little capacity loss. The simulation results indicate that our proposed scheme can jointly meet the capacity maximization and the PEP minimization
Maximization
Selection algorithm
Minification
Channel state information
Spatial correlation
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A major problem faced by machine type communication (MTC) devices in machine to machine (M2M) communication is the congestion and traffic overloading when incorporating into LTE Advanced networks. In this paper, we present an approach to tackle this problem by providing an efficient way for multiple access in the network and minimizing network overload. We consider the random access network (RAN) between the LTE base stations and MTC devices in the cell. We propose an unsupervised learning algorithm, based on Q-learning, as a means of base station selection scheme where MTC devices continuously adapt to changing network traffic and decide which base station is to be selected on the basis of QoS parameters. Simulation results demonstrate that the proposed algorithm helps MTC devices achieve better performance and, therefore, enhances the M2M communication performance.
LTE Advanced
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Random access
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In this paper, we present a cell selection algorithm for Tetra trunk based professional mobile radio systems. The users are attached the base stations by considering the C1 path loss parameter and the cell load indicator broadcasted by each base station. The simulation results are obtained for various scenarios. It is observed that proposed cell selection algorithm gives better performances than distance based cell selection algorithm in terms of fair distribution of users among base stations and the signal to interference plus noise ratio belonging to attached users.
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Tetra
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Cellular radio
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