Joint Bandwidth and Power Allocation for MIMO Two-Way Relays-Assisted Overlay Cognitive Radio Systems

Proceedings of the 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications

Yüksel

2018

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In this paper, a new and novel approach called (Unmanned Areal Vehicle) UAV-Direct is proposed, where the UAV helps in optimizing the bandwidth and power allocations of Device-to-Device (D2D) communication. In addition to that, the UAV can play the role of a relay if needed to maintain the communication links between the devices that are out of communication ranges of each other. We design a UAV-Direct protocol to manage the resource allocations to improve the system throughput. We formulate an optimization problem that maximizes the minimum device throughput while satisfying resource allocation constraints. Since the formulated optimization problem is non-convex, we propose to solve this problem in two steps. In the first step, a Taylor series successive convex approximation solution is proposed to optimize the resource allocations. Then, we propose an efficient algorithm based on a recursive shrink-and-realign process to optimize the UAV trajectory.

Section: Selected Numerical Resultsmentioning

confidence: 99%

UAV-Direct

Proceedings of the 4th ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications

Yüksel

2018

Self Cite

“…where x represents the vector containing the relaxed decision variables while the elements of the vectors c and A 0 are deduced from the objective function and constraints, respectively. In the second step, we employ the subgradient method to find the optimal Lagrangian multipliers (λ) of this linear programming problem (see [49] for more details). Hence, to obtain the solution, we can start with any initial values for the different Lagrangian multipliers and evaluate the optimal solution.…”

Section: E Relaxed Solutionmentioning

confidence: 99%

Spatial and Temporal Management of Cellular HetNets with Multiple Solar Powered Drones

IEEE Trans. on Mobile Comput.

Ghazzai

Kadri

et al. 2020

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This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic solar powered drones. A HetNet composed of a macrocell base station (BS), micro cell BSs, and drone small cell BSs are deployed to serve the networks' subscribers. The drones can land at pre-planned locations defined by the mobile operator and at the macrocell BS site where they can charge their batteries. The objective of the framework is to jointly determine the optimal trips of the drones and the MBSs that can be safely turned off in order to minimize the total energy consumption of the network. This is done while considering the cells' capacities and the minimum receiving power guaranteeing successful communications. To do so, an integer linear programming problem is formulated and optimally solved for three cases based on the knowledge level about future renewable energy statistics of the drones. A low complex relaxed solution is also developed. Its performances are shown to be close to those of the optimal solutions. However, the gap increases as the network becomes more congested. Numerical results investigate the performance of the proposed drone-based approach and show notable improvements in terms of energy saving and network capacity.Index Terms-Drone-based communications, dynamic drone small cells, energy harvesting, energy management, heterogeneous networks.

“…Cognitive radio is proposed as a novel solution to solve the spectrum deficit problems. The basic idea of cognitive radio is that unlicensed or Secondary Users (SUs) are allowed to utilize the spectrum of licensed users which are also known as primary users (PUs) in an opportunistic manner [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Multi-Band RF Energy and Spectrum Harvesting in Cognitive Radio Networks

2018 IEEE International Conference on Communications (ICC)

Neihart

Kim

et al. 2018

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This paper investigates a multi-band harvesting (EH) schemes under cognitive radio interweave framework. All secondary users are considered as EH nodes that are allowed to harvest energy from multiple bands of Radio Frequency (RF) sources. A win-win framework is proposed, where SUs can sense the spectrum to determine whether the spectrum is busy, and hence they may harvest from RF energy, or if it is idle, and hence they can use it for transmission. Only a subset of the SUs can sense in order to reduce sensing energy, and then machine learning is used to characterize areas of harvesting and spectrum usage. We formulate an optimization problem that jointly optimize number of sensing samples and sensing threshold in order to minimize the sensing time and hence maximize the amount of energy harvested. A near optimal solution is proposed using Geometric Programming (GP) to optimally solve the problem in a time-slotted period. Finally, an energy efficient approach based on multi-class Support Vector Machine (SVM) is proposed by involving only training SUs instead of all SUs. *