The mobility that wireless communication offers to users, added to the ease of installation have increased the demand on such communication systems. However, the main drawback of wireless communication is the degradation of the signal as it travels through the channel due to the different propagation mechanisms the signal undergoes. To minimise the effect of the channel and get the best service, the base stations must be appropriately located within the environment. This requires proper knowledge of the channel characteristics. Ray tracing software is used throughout this work to generate the channel characteristics of an indoor environment. After getting the channel characteristics, a novel cost function is defined based on the path loss values and it is then optimised. Once the optimal base stations’ positions are found, the minimal amount of power required to cover a predefined percentage of the possible receivers’ locations is calculated. On the other hand, a receiver’s position acquiring enough field strength does not necessarily enjoy the service. This depends on the time dispersion parameters values relative to the symbol rate. The time dispersion parameters have always been ignored in the literature while finding the optimal base stations’ locations. Three cost functions that take into consideration both the path loss and rms delay spread, for the first time in the literature, are therefore defined. The cost functions are optimised and their corresponding results are compared. Furthermore, indoor environments have always been considered static which is never realistic. They are subject to continuous changes such as opening doors and windows as well as the presence of people. The first detailed analysis and quantified results of the effect of a dynamic environment on the optimal base stations’ positions and minimal emitted power are presented. It is shown that the optimal base stations’ locations and minimal emitted power are sensitive to such environment changes. The environment changes can also disturb the service for active receivers. Three techniques to overcome the effect of environment changes and bring the disturbed service back to receivers are proposed. The first two techniques rely on increasing the emitted power or changing the antenna polarisation. The third technique is a novel technique that gives the base station the ability to automatically move in various directions within a limited distance. The techniques are tested and their efficiency and limitations are discussed.

In this report we focus on the use of an economical way on how Scalar Network Analyzer (SNA) works instead of Vector Network Analyzer (VNA) to estimate the phase angle of signals in indoor channel. This is detailed in RMS delay theory and simulation section, experimental is designed in the according Experiment Design section, where we also state the required measurements known from the math part. In our work, data are recorded both from two different channel characteristics. Method of achieving amplitude is by using deconvolution theory. The condition of applying Hilbert transform are highlighted as impulse response h(t) in time domain should be causal.  The recorded data amplitude is computed by Hilbert Transform, and therefore validate the condition using Inverse Discrete Fourier Transform (IDFT) back to time domain to achieve h(t). Power delay profile P(t) is therefore presented afterwards. In paper calculations of rms delay τrms  of the channel which is the most important variable are also performed, the results calculated from different windowing truncation and the LOS and NLOS characteristics are compared in discussion and conclusion section, it also includes Opinions of window functions chosen for the phase estimation.

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A channel model is a mathematical or conceptual representation employed to describe the behavior and characteristics of a communication channel through which signal or data can be transferred from the transmitter (Tx) to the receiver (Rx) or between two transceivers. In wireless communication, the channel model represents the wireless medium with parameters like pathloss, impulse response, and multipath effects. A2A channel poses various challenges when UAVs operate at a higher altitude greater than 1000 ft (305 m). This thesis involves experiments having a range of altitudes from 20 m to 2 km and distances between two transceivers from 5 m to 3 km. This thesis aims to introduce A2A channel by considering and analyzing inherent channel characteristics such as pathloss in terms of line-of-sight (LOS) and non-line-of-sight (NLOS), multipath fading, delay spread, and power delay profile (PDP). These characteristics depend on frequency, altitude of transmitter (Tx) and receiver (Rx), distance between two transceivers, antenna properties, paths taken by the signals, and obstacles. Pathloss, RMS delay spread, and power delay profile have been discussed with the simulated graphs by varying the distances and altitudes. These channel characteristics have been analyzed for different conditions like varying building heights of the city, changing building material, and also changing both building height and material at the same time. Two empirical models, the EL model and the CI model, have been presented along with simulations. Simulation results using mmWave frequency have been shown. The simulations have been performed by Wireless Insite software.