Channel prediction in wireless communications

Abstract

Knowledge of the channel over which signals are sent is of prime importance in modern wireless communications. Inaccurate or incomplete channel information leads to high error rates and wasted bandwidth and energy. Although active channel measurement is commonly used to gain channel knowledge, it can only accurately represent the channel at the time the measurement was taken, makes energy and bandwidth demands, and adds significant complexity to the radio system. Due to the highly time variant nature of wireless channels, active measurements become invalid almost as soon as they are taken, making alternative approaches to predicting future behaviour highly attractive. Such systems would allow maximum advantage to be taken of the limited bandwidth available and make significant power savings. This thesis investigates a number of complementary technologies, leading towards a channel prediction scheme suitable for mobile devices. As a first step towards channel prediction, anomaly detection is investigated within periodic wireless signals to establish when radical changes in the channel occur. In pre- vious experiments, long monotonic sequences had been observed to coincide with certain anomalies but not others when using Kullback-Leibler Divergence (KLD) analysis, possibly allowing the characterisation of anomaly types. An investigation is described to explain the origin of these features in a rigorous mathematical sense. A proof is given for the causes of the monotonic sequences, followed by a discussion of the types of signal anomaly which would underly such a feature and the value of this information. The second part describes a novel channel characterisation method which uses a class of Recurrent Neural Network (RNN) called an Echo State Network (ESN). Using this tool, a channel characterisation system can be constructed without an explicit statistical or mathematical model of the wireless environment, relying instead on observed data. This approach is much more convenient than existing models which require detailed information about the wireless system's parameters and also allows for new channel classifications to be added easily. It is able to achieve double the correct classification rate of a conventional statistical classifier, and is computationally simple to implement, making it ideal for inclusion on low-power mobile devices. Following their successful use in characterisation, ESNs are used in the final part in an investigation into channel prediction in a number of different scenarios. They were however found to be unable to produce useful predictions for all but the most trivial channel models. An alternative method is described for indoor environments using an approach inspired by ray tracing. It is simple and computationally lightweight to implement, again making it suitable for mobile devices. Simulation results show that it can outperform pilot-assisted methods by a significant margin, while not wasting bandwidth on channel measurement.