Queue-aware performance optimization of heterogeneous networks

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To meet surging traffic demands, heterogeneous networks enable a more flexible, targeted and economical deployment of new infrastructure versus tower-mounted macro-only systems, which are very expensive to deploy and maintain. However, previous studies usually assumed that BSs were always busy transmitting packets to their associated users, which characterized a worst case of the performance metrics. In practice, one BS can either be busy or idle, depending on its queuing condition, in which case the performance metrics such as the packet delay should be further studied with queuing taken into account. From this approach, the power consumption of a BS in the idle state is much lower than that in the busy state, the tuning of the network design parameters would have a significant impact on the BS busy/idle status, which in turn affects the network energy efficiency. In addition, users might not be evenly distributed and may form a cluster in certain hot area. In such cases, the user association optimization in a per-tier fashion would result in a poor user experience in the overloaded areas, and a per-station association scheme is thus preferable. To address the above considerations, the thesis focuses on the optimization of both network spectrum efficiency and the network energy efficiency with practical assumptions of queuing and non-uniform user distribution. Specifically, a network queuing delay optimization problem is studied based on the assumption that incoming packets form a queue in the BS. By optimally tuning the biasing factor of each tier, the network queuing performance can be significantly improved. In addition, based on the queuing analysis, a minimization problem of the network average power consumption and a maximization problem of the network SIR coverage are formulated and solved. Simulation results demonstrate that the network average power consumption and the SIR coverage can be significantly improved by the optimal bandwidth allocation. Then, by deriving the cumulative distribution function of the traffic intensity of each tier, a minimization problem of the network average power consumption is studied by optimally tuning the activation ratio of micro BSs under the quality of service constraints. At last, a practical scenario is studied where one cell is overloaded due to the cluster of users. By maximizing the mean user utility in the area of this overloaded cell and its neighboring cells, the optimal biasing factor is obtained to significantly improve the mean user rate of the overloaded cell.
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