Multi-user indoor optical wireless communication system channel control using a genetic algorithm

A genetic algorithm controlled multispot transmitter is demonstrated that is capable of optimising the received power distribution for randomly aligned single element receivers in multiple fully diffuse optical wireless communications systems with multiple mobile users. Using a genetic algorithm to control the intensity of individual diffusion spots, system deployment environment changes, user movement and user alignment can be compensating for, with negligible impact on the bandwidth and root mean square delay spread. It is shown that the dynamic range, referenced against the peak received power, can be reduced up to 27% for empty environments and up to 26% when the users are moving. Furthermore, the effect of user movement, that can perturb the channel up to 8%, can be reduced to within 5% of the optimised case. Compared to alternative bespoke designs that are capable of mitigating optical wireless channel drawbacks, this method provides the possibility of cost-effectiveness for mass-produced receivers in applications where end-user friendliness and mobility are paramount

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems

Impact of Liquid Crystal Based Interference Mitigation and Precoding on the Multiuser Performance of VLC Massive MIMO Arrays

In visible light communication systems, the ability to suppress interference caused by other light sources is a major benefit towards performance improvements. Especially for large transmitter arrays or even multi-cell arrangements, the interference problem needs to be handled. In previous work, we have presented a liquid crystal display (LCD) used as an adaptive interference-suppression filter mounted in front of each photodetector. The display elements are switched on and off in such a way that light emitted by unwanted light sources ideally is blocked, but light emitted by desired light sources reaches the detector. The pattern generated by the LC display has strong impact on the system performance. In this paper, we propose combined precoding in conjunction with LCD-based interference suppression in order to increase the signal-to-interference-plus-noise ratio and to ensure user fairness in massive MIMO scenarios. The suggested precoding strategy uses a new heuristic optimization approach based on the Santa Claus problem on unrelated machines known from computer sciences, and employs only binary entries in the weighting matrix. Corresponding results are compared with a genetic evolutionary optimization strategy and with conventional zero-forcing precoding. Regarding performance evaluation, we perform numerical ray-tracing simulations and present a room-scale VLC testbed for experimental verification

Genetic algorithm optimisation methods applied to the indoor optical wireless communications channel

This thesis details an investigation into the application of genetic algorithms to indoor optical wireless communication systems. The principle aims are to show how it is possible for a genetic algorithm to control the received power distribution within multiple dynamic environments, such that a single receiver design can be employed lowering system costs. This kind of approach is not typical within the research currently being undertaken, where normally, the emphasis on system performance has always been linked with improvements to the receiver design. Within this thesis, a custom built simulator has been developed with the ability to determine the channel characteristics at all locations with the system deployment environment, for multiple configurations including user movement and user alignment variability. Based on these results an investigation began into the structure of the genetic algorithm, testing 192 different ones in total. After evaluation of each one of the algorithms and their performance merits, 2 genetic algorithms remained and are proposed for use. These 2 algorithms were shown capable of reducing the receiver power deviation by up to 26%, and forming, whilst the user perturbs the channel, through movement and variable alignment, a consistent power distribution to within 12% of the optimised case. The final part of the work, extends the use of the genetic algorithm to not only try to optimise the received power deviation, but also the received signal to noise ratio deviation. It was shown that the genetic algorithm is capable of reducing the deviation by around 12% in an empty environment and maintain this optimised case to within 10% when the user perturbs the channel

Interference Suppression in Massive MIMO VLC Systems

The focus of this dissertation is on the development and evaluation of methods and principles to mitigate interference in multiuser visible light communication (VLC) systems using several transmitters. All components of such a massive multiple-input multiple-output (MIMO) system are considered and transformed into a communication system model, while also paying particular attention to the hardware requirements of different modulation schemes. By analyzing all steps in the communication process, the inter-channel interference between users is identified as the most critical aspect. Several methods of suppressing this kind of interference, i.e. to split the MIMO channel into parallel single channels, are discussed, and a novel active LCD-based interference suppression principle at the receiver side is introduced as main aspect of this work. This technique enables a dynamic adaption of the physical channel: compared to solely software-based or static approaches, the LCD interference suppression filter achieves adaptive channel separation without altering the characteristics of the transmitter lights. This is especially advantageous in dual-use scenarios with illumination requirements. Additionally, external interferers, like natural light or transmitter light sources of neighboring cells in a multicell setting, can also be suppressed without requiring any control over them. Each user's LCD filter is placed in front of the corresponding photodetector and configured in such a way that only light from desired transmitters can reach the detector by setting only the appropriate pixels to transparent, while light from unwanted transmitters remains blocked. The effectiveness of this method is tested and benchmarked against zero-forcing (ZF) precoding in different scenarios and applications by numerical simulations and also verified experimentally in a large MIMO VLC testbed created specifically for this purpose