MITIGATING THE EFFECT OF TOPOLOGY IN CELLULAR MOBILE NETWORK USING MATLAB
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- NGN 3000
BACKGROUND OF THE STUDY
Wireless Ad hoc Networks are networks in which wireless nodes cooperate to establish network connectivity and perform routing functions in the absence of infrastructure using selforganization. Similarly, Mobile wireless Ad hoc Networks (MANETs) are networks in which nodes communicate through wireless links but move freely. Here, each node functions, when necessary, as a relay node so as to allow multi hop store-and-forward communications. A key problem associated with mobile wireless ad hoc networks is to conserve energy so as to prolong the battery life and to accommodate the movement of the network nodes. In a mobile wireless ad hoc network, the network topology is formed based on the nodes’ transmission ranges and routes of node movement. The objective in topology control is to reduce energy consumption so as to maintain a specified network topology such as 1-Connected (i.e. the network is connected). Sensor networks are a class of networks wherein multiple sensors are interconnected to realize a desired network topology (Akyildiz, 2012). These networks could be static in the sense that the sensor nodes of a network do not move once a desired topology is formed, and could be dynamic where in some or all of the involved sensor nodes are mobile. Further, the dynamic sensor networks could be ad hoc without having an explicit centralized node for command, control, and communication purposes. The ad hoc mobile networks pose tremendous design challenges. Three important aspects of ad hoc mobile wireless networks in general and sensor networks in particular are: (a) Location management; (b) Energy management; and (c) Topology management. Location is an important attribute of senor or ad hoc mobile wireless nodes. In a recent paper (Sridhar, 2008), they have described an approach for location management in a sensor network wherein not all the nodes are GPS-enabled. In another paper (Sridhar, 2007), they have described the issues related to energy management and have described how to achieve energy management at various levels, namely, component, system, and network levels. In this research, the researcher addresses the issues related to topology management and suggests an approach for maintaining the desired topology even under uncontrolled mobility of the involved sensor nodes. A smart antenna is an antenna array (or multiple antennas) that can adapt to the environment in which it operates (Litva, 2006). Smart antenna technology has been used to overcome signal impairments in wireless personal communications. When spatial signal processing achieved through a smart antenna is combined with temporal signal processing, the space-time processing can mitigate propagation distortion and interference to enable higher network capacity, coverage, and quality (Liberti, 2009). A smart antenna not only suppresses interference, but also combats multipath fading by combining multiple antenna signals. To process multiple antenna signals, two combining schemes—diversity combining and adaptive combining—can be employed. Diversity combining exploits the spatial diversity among multiple antenna signals and achieves higher performance. There are four classical diversity combining schemes: switched diversity, selection diversity, equal gain combining, and maximal ratio combining (MRC) (Jakes, 2000). After weighting each antenna signal proportional to its signal to noise ratio (SNR), MRC combines each signal, thus providing maximum output SNR. Adaptive combining is based on dynamic reconfiguration in that the antenna weights are dynamically adjusted to enhance the desired signal while suppressing interference signals to maximize signal to interference plus noise ratio (SINR). It achieves the same performance as the MRC without presence of interference. Because of concerns with high system complexity and high power consumption that can be caused by the network topology, adaptive antenna techniques have been considered primarily for base stations so far (Song, 2009). This increasing demand for high data rate mobile communication services, without a corresponding increase in radio frequency spectrum allocation, motivates the need for new techniques to improve spectrum efficiency. Adaptive antenna arrays have emerged as one of the most promising technologies for increasing the spectral efficiency and improving the performance of present and future wireless communication systems (Landrigan, 2012). This is an array of antennas which is capable of changing its antenna pattern dynamically to adjust to noise, interference and multipath. They are used to enhance received signals and may also be used to form beams for transmission. In an adaptive array, signals received by each antenna are weighted and combined using complex weights (magnitude and phase) in order to maximize a particular performance criterion e.g. the Signal to Interference plus Noise Ratio (SINR) or the Signal to Noise Ratio (SNR). Fully adaptive system use advanced signal processing algorithms to locate and track the desired and interfering signals to dynamically minimize interference and maximize intended signal reception (Santhi, 2008). Unlike conventional antennas, they confine the broadcast energy to a narrow beam. It optimizes the way the signals are distributed on a real time basis by focusing the signal to the desired user and ‘steering’ it away from the other users occupying the same channel in the same cell and adjacent or distant cell (Shubair, 2007). The beam forming is done digitally, and a main lobe is generated in the direction of the strongest signal component. In addition, side lobes are generated in the direction of multi path components and nulls in the direction of interferers. This technique will maximize the signal to interference and noise ratio (SINR).
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