A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

Distributed resource allocation for inter cell interference mitigation in irregular geometry multicell networks

Extensive increase in mobile broadband applications and proliferation of smart phones and gadgets require higher data rates of wireless cellular networks. However, limited frequency spectrum has led to aggressive frequency reuse to improve network capacity at the expense of increased Inter Cell Interference (ICI). Fractional Frequency Reuse (FFR) has been acknowledged as an effective ICI mitigation scheme but in irregular geometric multicellular network, ICI mitigation poses a very challenging issue. The thesis developed a decentralized ICI mitigation scheme to improve both spectral and energy efficiency in irregular geometric multicellular networks. ICI mitigation was realized through Distributed Resource Allocation (DRA) deployed at the cell level and region level of an irregular geometric cell. The irregular geometric cell consists of a minimum of four regions comprising three sectors and a central region. DRA at the cell level is defined as Multi Sector DRA (MSDRA), and at the region level is defined as Distributed Channel Selection and Power Allocation (DCSPA). MSDRA allocates discrete power to every region in a cell based on Game Theory and Regret Learning Process with correlated equilibrium as the optimum decision level. The DCSPA allocates power to every channel in a region based on non-coalesce liquid droplet phenomena by selecting optimum channels in a region and reserving appropriate power for the selected channels. The performance was evaluated through simulation in terms of data rate, spectral efficiency and energy efficiency. The results showed that MSDRA significantly improved cell data rate by 58.64% and 37.92% in comparision to Generalized FFR and Fractional Frequency Reuse-3 (FFR-3) schemes, respectively. The performance of MSDRA at the cell level showed that its spectral and energy efficiency improved 32% and 22%, respectively in comparison to FFR-3. When the number of sectors increased from three to four, data rate was improved by 30.26% and for three to six sectors, it was improved by 56.32%. The DCSPA further improved data rate by 41.07% when compared with Geometric Water Filling, and 86.46% in comparison to Asynchronous Iterative Water Filling. The DCSPA enhanced data rate achieved in MSDRA by 15.6%. Overall, DRA has shown to have significant improvement in data rate by 53.6%, and spectral efficiency by 38.10% as compared to FFR-3. As a conclusion, the DRA scheme is a potential candidate for Long Term Evaluation – Advanced, Fifth Generation networks and can be deployed in future heterogeneous irregular geometric multicellular Orthogonal Frequency Division Multiple Access networks

Energy-efficient non-orthogonal multiple access for wireless communication system

Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed

Optimization and learning algorithms for orthogonal frequency division multiplexing-based dynamic spectrum access.

Ph.DDOCTOR OF PHILOSOPH

Hierarchical Resource Allocation Framework for Hyper-Dense Small Cell Networks

This paper considers joint power control and subchannel allocation for co-tier interference mitigation in extremely dense small cell networks, which is formulated as a combinatorial optimization problem. Since it is intractable to obtain the globally optimum assignment policy for existing techniques due to the huge computation and communication overheads in ultra-dense scenario, in this paper, we propose a hierarchical resource allocation framework to achieve a desirable solution. Speci cally, the solution is obtained by dividing the original optimization problem into four stages in partially distributed manner. First, we propose a divide-and-conquer strategy by invoking clustering technique to decompose the dense network into smaller disjoint clusters. Then, within each cluster, one of the small cell access points is elected as a cluster head to carry out intra-cluster subchannel allocation with a low-complexity algorithm. To tackle the issue of inter-cluster interference, we further develop a distributed learning-base coordination mechanism. Moreover, a local power adjustment scheme is also presented to improve the system performance. Numerical results verify the ef ciency of the proposed hierarchical scheme, and demonstrate that our solution outperforms the state-of-the-art methods, especially for hyper-dense networks