Network slicing is emerging as a promising method to provide sought after versatility and flexibility to cope with ever-increasing demands. To realize such potential advantages and to meet the challenging requirements of various network slices in an on-demand fashion, we need to develop an agile and distributed mechanism for resource provisioning to different network slices in a heterogeneous multi-resource multi-domain mobile network environment. We formulate inter-domain resource provisioning to network slices in such an environment as an optimization problem which maximizes social welfare among network slice tenants (so that maximizing tenants’ satisfaction), while minimizing operational expenditures for infrastructure service providers at the same time. To solve the envisioned problem, we implement an iterative auction game among network slice tenants, on one hand, and a plurality of price-taking subnet service providers, on the other hand. We show that the proposed solution method results in a distributed privacy-saving mechanism which converges to theoptimal solutionof the described optimization problem. In addition to providing analytical results to characterize the performance of the proposed mechanism, we also employ numerical evaluations to validate the results, demonstrate convergence of the presented algorithm, and show the enhanced performance of the proposed approach (in terms of resource utilization, fairness and operational costs) against the existing solutions.

Agile networking can reduce over-engineering, costs, and energy waste. Toward that end, it is vital to exploit all degrees of freedom of wireless networks efficiently, so that the service quality is not sacrificed. In order to reap the benefits of flexible networking, we propose a spatial network configuration (SNC) scheme, which can result in efficient networking; both from the perspective of network capacity and profitability. First, the SNC utilizes the drone-base-stations (drone-BSs) to configure access points. Drone-BSs are shifting paradigms of heterogeneous wireless networks by providing radically flexible deployment opportunities. On the other hand, their limited endurance and potential high cost increase the importance of utilizing drone-BSs efficiently. Therefore, second, user mobility is exploited via user-in-the-loop (UIL), which aims at influencing users' mobility by offering incentives. The proposed uncoordinated SNC is a computationally efficient method, yet, it may be insufficient to exploit the synergy between the drone-BSs and UIL. Hence, we propose a joint SNC, which increases the performance gain along with the computational cost. Finally, the semi-joint SNC combines the benefits of the joint SNC with computational efficiency. The numerical results show that the semi-joint SNC is two orders of magnitude faster than the joint SNC, and a profit of more than 15% can be obtained compared to conventional systems.

Drone base stations (DBSs) can enhance network coverage and area capacity by moving supply towards demand when required. This degree of freedom could be especially useful for future applications with extreme demands, such as ultra reliable and low latency communications (uRLLC). However, deployment of DBSs can face several challenges. One issue is finding the 3D placement of such BSs to satisfy dynamic requirements of the system. Second, the availability of reliable wireless backhaul links and the related resource allocation are principal issues that should be considered. Finally, association of the users with BSs becomes an involved problem due to mobility of DBSs. In this paper, we consider a macro-BS (MBS) and several DBSs that rely on the wireless links to the MBS for backhauling. Considering regular and uRLLC users, we propose an algorithm to find efficient 3D locations of DBSs in addition to the user-BS associations and wireless backhaul bandwidth allocations to maximize the sum logarithmic rate of the users. To this end, a decomposition method is employed to first find the user-BS association and bandwidth allocations. Then DBS locations are updated using a heuristic particle swarm optimization algorithm. Simulation results show the effectiveness of the proposed method and provide useful insights on the effects of traffic distributions and antenna beamwidth.

Agility and resilience requirements of future cellular networks may not be fully satisfied by terrestrial base stations in cases of unexpected or temporary events. A promising solution is assisting the cellular network via low-altitude unmanned aerial vehicles equipped with base stations, i.e., drone-cells. Although drone-cells provide a quick deployment opportunity as aerial base stations, efficient placement becomes one of the key issues. In addition to mobility of the drone-cells in the vertical dimension as well as the horizontal dimension, the differences between the air-to-ground and terrestrial channels cause the placement of the drone-cells to diverge from placement of terrestrial base stations. In this paper, we first highlight the properties of the drone-cell placement problem, and formulate it as a 3-D placement problem with the objective of maximizing the revenue of the network. After some mathematical manipulations, we formulate an equivalent quadratically-constrained mixed integer non-linear optimization problem and propose a computationally efficient numerical solution for this problem. We verify our analytical derivations with numerical simulations and enrich them with discussions which could serve as guidelines for researchers, mobile network operators, and policy makers.