Mobile Cellular Communication By Gottapu Pdf 20 1
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In mobile radio networks and digital communication systems, the statistical properties of the signal received by the receiver are crucial for designing and optimizing the performance of these systems. However, the received signal is often affected by various factors such as distance, terrain, obstacles, and interference that cause it to vary in amplitude and phase over time and space. This paper aims to separate the received mobile signal into two components: slow variations and fast variations. Slow variations are caused by shadowing effects, which occur when large objects such as buildings or hills block or reflect the signal. Fast variations are caused by multipath effects, which occur when the signal arrives at the receiver through different paths with different delays and phases. To achieve this separation, this paper proposes a moving average filter that smooths out the fast variations and preserves the slow variations. By applying this filter to the received mobile signal, this paper can perform an independent study of shadowing and multipath effects and their impact on mobile radio networks and digital communication systems.
The moving average filter is a simple and widely used technique for smoothing out noisy signals. It works by taking the average of a fixed number of consecutive samples of the signal and using it as the output value. The number of samples used for averaging is called the window size. The larger the window size, the smoother the output signal will be, but also the more delay it will introduce. The choice of the window size depends on the characteristics of the signal and the application requirements.
In this paper, the moving average filter is applied to the received mobile signal in order to separate slow variations and fast variations. The window size is chosen such that it is large enough to eliminate most of the fast variations caused by multipath effects, but small enough to preserve the slow variations caused by shadowing effects. The output of the filter is then used as an estimate of the slow variations, while the difference between the input and output of the filter is used as an estimate of the fast variations. By doing this, this paper can isolate and analyze the effects of shadowing and multipath on the received mobile signal.
The paper evaluates the performance of the moving average filter using real-world data collected from a mobile radio network. The data consists of received signal strength measurements taken at different locations and times by a mobile receiver. The paper compares the results obtained by applying the moving average filter with those obtained by using other methods such as log-normal fitting and Rayleigh fading models. The paper also discusses the advantages and limitations of the moving average filter and suggests possible improvements and extensions for future work. 061ffe29dd