The work in this thesis is outlined in the following chapters. Each chapter has its own objects and conclusions that will be highlighted at their respective beginning and ends. Chapter 2 provides a detailed channel model for an indoor optical wireless communication channel. This chapter is vital for the analysis within the rest of the thesis, providing all fundamental equations and explanation of the channel’s performance characteristics. Within the chapter, a purpose built channel simulator is designed, that forms the platform for all results, which to prove their validity, is characterised in terms of accuracy and computational effort. The simulation plat- form also includes a purpose built object intersection algorithm, required for simulating user movement. Within this chapter, the first provisional analysis is conducted within the channel, illustrating, in a quantive fashion, the problems the system designer is facing.
Chapter 3, contains the theory required in understanding how the received power at a given lo- cation within the environment can be modified. It details how, by knowing the impulse response between a source and receiver, the GA is able to control it. Some simplifications to the model are then presented with a strong justification. This is followed by a theoretical introduction to the GA, covering all aspects needed for the work, before beginning the GA development. Once the GA framework has been established, provisional results are shown before a thorough analysis of the effectiveness of the GA with different receiver characteristics. At this point a generalised receiver is formed, for evaluation of the algorithms effectiveness in handling user movement in multiple environments.
Chapter 4, begins with the next stage of user induced channel perturbation analysis, in quanti- fying the effect of dynamic receiver orientation. The GA is then subjected to the perturbation, and shown to be effective in mitigating, to some extent, the effects of the user, not only ran-
domly aligning the receiver, but also moving in a further two environments. This chapter further contains results of the GA effectiveness at reducing received power deviation around a further two environments when there are multiple users moving. These tests were the toughest given the GA, and possibly the scenarios that show the highest level of realism to the scenarios.
Chapter 5, moves on from attempts to reduce the deviation in received power, to reducing the de- viation in the received signal to noise ratio. The received noise power is different in each location of the room, similar to the signal power, and as such the SNR is not uniform. It also affects the BER of a system. The work in this chapter is very slightly different as reducing the deviation in peak received power, when coupled with the change in received noise power, the change to SNR is not predictable. The GA is modified here with a new fitness function that accounts for SNR. In this chapter a detailed noise model is also presented that allows the SNR to be transposed to BER, which unfortunately as will be shown is not as suitable for the GA to optimise as required.
Chapter 6, then concludes the work, with a note to further work that may be suitable for in- vestigation given the results that are presented here. At the end of the chapters are several appendices, listing detailed tables of results of computational time and error results, a complete list of every GA algorithmic permutation tested, with results in the algorithm test room, and a complete appendix detailing more of the specifics regarding user movement positional attributes.
Chapter 2
The Optical Wireless Channel
2.1
Introduction
For a system designer a detailed knowledge of the channel is vital for the effective development and optimisation of the major components within the system such as transmitter, receiver and the modulation scheme. In this chapter a channel model is proposed with a detailed discussion and justification to the choice of simulation method, reflection model, accuracy or error levels and the model constraints and assumptions presumed. The design of the simulator is essential for the work presented within the thesis, and, as such, the research emphasis is placed upon development of this flexible platform. It is necessary to be able to determine the channel’s impulse response for a wide range of scenarios, including those with user movement, and so a custom ray object intersection algorithm is also proposed. Research emphasis will also need to be placed upon computational efficiency, and the mathematical and algorithmic optimisations that need to be accounted for are discussed because, without them, the future work could not be completed. Furthermore, there is one more capability that is required from the simulator that will be discussed further in chapter 3, in that the model must allow for dynamically variable source radiation emission powers.
Due to the importance of determining the channel impulse response, a variety of methods have been previously proposed, each with a respective set of objectives, advantages and disadvan- tages [82]. Closed form approximations [3], which provides the possibility to simply investigate basic configurations and conduct simple analysis on factors such as material reflectivity and the source intensity profile, are too complex when considering multiple reflections. Experimental characterisation [83, 84, 85], is an expensive and lengthy task that has to be done on a channel by channel basis [86].
A general simulation method, first proposed by Barry [87], using some techniques borrowed from the ray tracing community [88], provided the ability, with relative ease of implementation, to determine the impulse response for a system where the signal underwent any number of reflec- tions for any configuration of source and receiver inside an arbitrary empty rectangular room. However, due to the algorithm being recursive, the computational time is exponential, and the memory requirements were impractical beyond three reflections. A solution to overcoming the memory requirements, with a slight improvement in computation time, was proposed by L´opez- Hern´andez in [89], using a method of dividing the simulation into time, as opposed to reflections. A further refinement came from Carruthers [90, 91] when the algorithm was applied iteratively allowing for the computational time to be proportional to the square of the number reflections required. The method also allowed the possibility of simulating scenarios with multiple trans- mitters and receivers without considerable time penalties.
An alternative simulation approach, based on a statistical model, was proposed by P´erez-Jim´enez [92]. In this method the root mean square (RMS) delay spread, and mean excess delay are es- timated, based purely upon the known geometric factors of the system configuration such as transmitter and receiver positions and orientation. The values are then compared against an initial impulse responseh(t) and either a Rayleigh or Gamma function, adjusting theh(t) until it fits the distribution. The method is described as being faster than the work of Barry. How-
ever, no computational times were provided and the requirement for an initial impulse response makes this approach hard to justify for use. Moreover the authors presented shortly afterwards a new method based upon a mixed deterministic Monte Carlo ray tracing algorithm [93, 94, 95]. By sending out rays from the source in a pseudo random nature and literally tracing the rays through the reflections, the room does not need to be partitioned into elements, as was the case with the Barry method, thus the level of accuracy is determined not by the spacial segmenta- tion, but by the choice in the number of rays. The authors still base their impulse response calculations on the original Barry formulations, but the use of rays allows the unnecessary cal- culations from rays that would never be incident upon the receiver to be omitted. The method presented is potentially beneficial in terms of computational time, albeit through the choice of reduction in the number of rays, and would be convenient to implement on vectorised graphics processors. However, as the rays are generated statistically, each run of the simulation provided different results, and it has been shown that simulation of the same system configuration 100 times provides a relative error of between 5% and 20% between each run [96].
Finally there is one other statistically based simulation technique that was presented in [97, 98], called “random walk”. Here a ray is generated with a pseudo random direction and traced around the environment, determining on its path, if and when, it hits a surface. If it hits a surface it is reflected with a new path in a pseudo random direction. This process is continued until, eventually it reaches the receiver. The method has shown merit for the problems involving integrating sphere diffusers that are inherently small (5-100cm) and spherical reflective environ- ments. However, for larger, non spherical environments, such as the work presented here, the channel model simulation technique is not readily applicable.
After discussing the system model in section 2.2, the modified impulse response calculations, although largely based upon the work by Barry, will be presented in section 2.3. Section 2.4 provides the details of the object intersection algorithm implemented with an analyis of the
precision errors found in simulation. Section 2.5 provides justification for the model assump- tions implemented with an error analysis. Section 2.6 provides results that verify the simulator against known results and an analysis of the effects on the number of reflections, number of diffusion spots and the FOV of the receivers with regards to received power levels, bandwidth and RMS delay spread.