Self-Organising Load Balancing for OFDMA Cellular Networks

In this thesis, self-organising load balancing is investigated to deal with the uneven load distribution in OFDMA based cellular networks. In single-hop cellular networks, a self- organising cluster-based cooperative load balancing (CCLB) scheme is proposed to overcome the ‘virtual partner’ and the ‘aggravating load’ problems confronted in the conventional mobility load balancing schemes. Theoretical analysis and simulation results show that the proposed scheme can effectively reduce the call blocking probability, the handover failure rate, and the hot-spot cell’s load. The proposed CCLB scheme consists of two stages: partner cell selection and traffic shifting. In the partner cell selection stage, a user-vote assisted clustering algorithm is proposed, which jointly considers the users’ channel condition and the surrounding cells’ load. This algorithm can select appropriate neighbouring cells as partners to construct the load balancing cluster, and deal with the ‘virtual partner’ problem. In the traffic shifting stage, a relative load response model (RLRM) is designed. RLRM coordinates multiple hot-spot cells’ shifting traffic towards their public partner, thus mitigating the ‘aggravating load’ problem of the public partner. Moreover, a traffic offloading optimisation algorithm is proposed to balance the hot-spot cell’s load within the load balancing cluster and to minimise its partners’ average call blocking probability. The CCLB scheme is modified to apply in multi-hop cellular networks with relays deployed. Both fixed relay and mobile user relay scenarios are considered. For fixed relay cellular networks, a relay-level user shifting algorithm is proposed. This algorithm jointly considers users’ channel condition and spectrum usage of fixed relay, in order to reduce the handover failure rate and deal with the ‘aggravating load’ problem of fixed relay. In the mobile user relay scenario, the user relaying assisted traffic shifting algorithm is proposed to improve the link quality of shifted edge users, which brings about an increase in the achievable rate of shifted edge users and decrease in the handover failure rate

Context-aware Self-Optimization in Small-Cell Networks

Most mobile communications take place at indoor environments, especially in commercial and corporate scenarios. These places normally present coverage and capacity issues due to the poor signal quality, which degrade the end-user Quality of Experience (QoE). In these cases, mobile operators are offering small cells to overcome the indoor issues, being femtocells the main deployed base stations. Femtocell networks provide significant benefits to mobile operators and their clients. However, the massive integration and the particularities of femtocells, make the maintenance of these infrastructures a challenge for engineers. In this sense, Self-Organizing Networks (SON) techniques play an important role. These techniques are a key feature to intelligently automate network operation, administration and management procedures. SON mechanisms are based on the analysis of the mobile network alarms, counters and indicators. In parallel, electronics, sensors and software applications evolve rapidly and are everywhere. Thanks to this, valuable context information can be gathered, which properly managed can improve SON techniques performance. Within possible context data, one of the most active topics is the indoor positioning due to the immediate interest on indoor location-based services (LBS). At indoor commercial and corporate environments, user densities and traffic vary in spatial and temporal domain. These situations lead to degrade cellular network performance, being temporary traffic fluctuations and focused congestions one of the most common issues. Load balancing techniques, which have been identified as a use case in self-optimization paradigm for Long Term Evolution (LTE), can alleviate these congestion problems. This use case has been widely studied in macrocellular networks and outdoor scenarios. However, the particularities of femtocells, the characteristics of indoor scenarios and the influence of users’ mobility pattern justify the development of new solutions. The goal of this PhD thesis is to design and develop novel and automatic solutions for temporary traffic fluctuations and focused network congestion issues in commercial and corporate femtocell environments. For that purpose, the implementation of an efficient management architecture to integrate context data into the mobile network and SON mechanisms is required. Afterwards, an accurate indoor positioning system is developed, as a possible inexpensive solution for context-aware SON. Finally, advanced self-optimization methods to shift users from overloaded cells to other cells with spare resources are designed. These methods tune femtocell configuration parameters based on network information, such as ratio of active users, and context information, such as users’ position. All these methods are evaluated in both a dynamic LTE system-level simulator and in a field-trial