Resource Management and 3D Deployment in Power-Efficient Multi-UAV Networks

In this paper, the scenario in which multiple unmanned aerial vehicles (UAVs) provide service to ground users is considered. Under the condition of satisfying the minimum rate per user and system load balance, the user association, bandwidth allocation and three dimensional (3D) deployment of multi-UAV networks are optimized jointly to minimize the total downlink transmit power of UAVs. Since the problem is hard to solve directly, it is decomposed into three sub-problems, and then the problem is solved by alternating iteration algorithm. First, when the UAV’s location is determined, a modified K-means algorithm is used to obtain balanced user clustering. Then, when the user association and UAV’s 3D deployment are determined, the convex optimization method is used to obtain the optimal bandwidth allocation. Finally, when the user association and optimal bandwidth allocation are determined, a modified differential evolution algorithm is proposed to optimize the 3D location of the UAVs. Simulation results show that the proposed algorithm can effectively reduce the transmit power of UAVs compared with the existing algorithms under the conditions of satisfying the minimum rate of ground users and system load balance.

Association Control Based Load Balancing in Wireless Cellular Networks Using Preamble Sequences

<p>The efficient allocation and use of radio resources is crucial for achieving the maximum possible throughput and capacity in wireless networks. The conventional strongest signal-based user association in cellular networks generally considers only the strength of the signal while selecting a BS, and ignores the level of congestion or load at it. As a consequence, some BSs tend to suffer from heavy load, while their adjacent BSs may carry only light load. This load imbalance severely hampers the network from fully utilizing the network capacity and providing fair services to users. In this thesis, we investigate the applicability of the preamble code sequence, which is mainly used for cell identification, as an implicit information indicator for load balancing in cellular networks. By exploiting the high auto-correlation and low cross-correlation property among preamble sequences, we propose distributed load balancing schemes that implicitly obtain information about the load status of BSs, for intelligent association control. This enables the new users to be attached to BSs with relatively low load in the long term, alleviating the problem of non-uniform user distribution and load imbalance across the network. Extensive simulations are performed with various user densities considering throughput fair and resource fair, as the resource allocation policies in each cell. It is observed that significant improvement in minimum throughput and fair user distribution is achieved by employing our proposed schemes, and preamble sequences can be effectively used as a leverage for better cell-site selection from the viewpoint of fairness provisioning. The load of the entire system is also observed to be balanced, which consequently enhances the capacity of the network, as evidenced by the simulation results.</p>

Association Control Based Load Balancing in Wireless Cellular Networks Using Preamble Sequences

<p>The efficient allocation and use of radio resources is crucial for achieving the maximum possible throughput and capacity in wireless networks. The conventional strongest signal-based user association in cellular networks generally considers only the strength of the signal while selecting a BS, and ignores the level of congestion or load at it. As a consequence, some BSs tend to suffer from heavy load, while their adjacent BSs may carry only light load. This load imbalance severely hampers the network from fully utilizing the network capacity and providing fair services to users. In this thesis, we investigate the applicability of the preamble code sequence, which is mainly used for cell identification, as an implicit information indicator for load balancing in cellular networks. By exploiting the high auto-correlation and low cross-correlation property among preamble sequences, we propose distributed load balancing schemes that implicitly obtain information about the load status of BSs, for intelligent association control. This enables the new users to be attached to BSs with relatively low load in the long term, alleviating the problem of non-uniform user distribution and load imbalance across the network. Extensive simulations are performed with various user densities considering throughput fair and resource fair, as the resource allocation policies in each cell. It is observed that significant improvement in minimum throughput and fair user distribution is achieved by employing our proposed schemes, and preamble sequences can be effectively used as a leverage for better cell-site selection from the viewpoint of fairness provisioning. The load of the entire system is also observed to be balanced, which consequently enhances the capacity of the network, as evidenced by the simulation results.</p>

Offline Joint Network and Computational Resource Allocation for Energy-Efficient 5G and beyond Networks

In order to cope with the ever-increasing traffic demands and stringent latency constraints, next generation, i.e., sixth generation (6G) networks, are expected to leverage Network Function Virtualization (NFV) as an enabler for enhanced network flexibility. In such a setup, in addition to the traditional problems of user association and traffic routing, Virtual Network Function (VNF) placement needs to be jointly considered. To that end, in this paper, we focus on the joint network and computational resource allocation, targeting low network power consumption while satisfying the Service Function Chain (SFC), throughput, and delay requirements. Unlike the State-of-the-Art (SoA), we also take into account the Access Network (AN), while formulating the problem as a general Mixed Integer Linear Program (MILP). Due to the high complexity of the proposed optimal solution, we also propose a low-complexity energy-efficient resource allocation algorithm, which was shown to significantly outperform the SoA, by achieving up to 78% of the optimal energy efficiency with up to 742 times lower complexity. Finally, we describe an Orchestration Framework for the automated orchestration of vertical-driven services in Network Slices and describe how it encompasses the proposed algorithm towards optimized provisioning of heterogeneous computation and network resources across multiple network segments.