Interference Mitigation in Cognitive Small Cell Networks

The increasing demand for high-speed data service triggers researchers investigating and developing mobile wireless technology for indoor services. Some types of small cellular networks (small cells) are designed for indoor and random deployment with minimal operator involvement. The random deployment feature raises the probability of both co-tier and cross-tier interference. It enforces the small cells to have a feature of self-organisation. Hence, the research question going to be solved is "how to mitigate interference in small cells subject to the spectrum scarcity, random deployment, dynamic wireless channel, and complexity of the heterogeneous cellular networks (HetNet)?" Considering the complexity problem, researchers consider a concept with a comprehensive approach to addressing those problems, e.g. cognitive small cell or cognitive interference management. To simplify and speed up information exchange among base-stations (BSs) and to consider channel gain for resource allocation, some methods called spectrum splitting based-cognitive interference management (SSCIM) are proposed in this thesis. The methods start with recognising subchannel gain, in which each BS broadcasts pilot signals. Then each user terminal will receive the pilot and transmit back to its serving BS. Base on the pilot, the macro cellular BS (macro-BS) will identify and classify the resource blocks based on an assigned threshold, map and schedule the resource allocation. Subsequently, the macro-BS broadcasts the control channel and followed by data broadcasting. Meanwhile, small-BSs sense and analyse the macro-BS's control channel and then calculate and decide to occupy the idle spectra by using some power allocation techniques. The simulation results show that SSCIM methods outperform both non-interference management and interfering resource blocking-based-CIM for the allocated subcarriers. Moreover, SSCIM methods have better spectrum efficiency than two others. However, the results are penalised by less macrocell performance. Additionally, the SSCIM's cell capacity is less than two others because of less allocated subchannels. Furthermore, a sub-optimal spectrum and power allocation (sOSPA) method is also proposed to maximise sum rate in a simple HetNet model. SOSPA combines some techniques, such as local search and penalty function, to solve the nonlinear and nonconvex optimisation problem. sOSPA achieves the near optimum by finding out equilibrium of equal power allocation in each subchannel of the mutual interfering networks and sets either less or no power for violated subchannels. In the high-interference environment, with the proper SINR threshold, sOSPA achieves higher rate than the other methods. Additionally, sOSPA achieves the near optimum by considering both channel gain and inter-cell interference with a high rate of convergence.