Routing optimization techniques for communication networks

We consider a full-duplex wireless powered communication network (WPCN) with one hybrid access point (H-AP) and a set of distributed users, where downlink wireless energy broadcasting is employed at H-AP and at the same time, uplink wireless information transmission takes place at users in a time-division multiple access manner. We extend proportional fair scheduler to this category of network when dealing with "doubly near-far problem," where users far away from H-AP achieve low throughput but suffer from both low harvested energy and high data transmission power consumption. We jointly optimize power and time allocation for each user to achieve proportional fairness while controlling the energy consumption offset for network to a low level. By using optimization techniques, the optimal transmit power and transmission time for users are obtained via proposed algorithm. Simulation results confirm the positive effect on improving the fairness metric and reducing energy consumption offset for network.

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Minimum-Throughput Maximization for Multi-UAV-Enabled Wireless-Powered Communication Networks

Sensors ◽

10.3390/s19071491 ◽

2019 ◽

Vol 19 (7) ◽

pp. 1491 ◽

Cited By ~ 6

Author(s):

Fahui Wu ◽

Dingcheng Yang ◽

Lin Xiao ◽

Laurie Cuthbert

Keyword(s):

Resource Allocation ◽

Unmanned Aerial Vehicles ◽

Communication Networks ◽

Optimization Techniques ◽

Throughput Maximization ◽

Wireless Energy Transfer ◽

Mobile Wireless ◽

Alternating Optimization ◽

Multiple Unmanned Aerial Vehicles ◽

Multi Uav

This paper considers a wireless-powered communication network (WPCN) system that uses multiple unmanned aerial vehicles (UAVs). Ground users (GUs) first harvest energy from a mobile wireless energy transfer (WET) UAV then use the energy to power their information transmission to a data gatherer (DG) UAV. We aim to maximize the minimum throughput for all GUs by jointly optimizing UAV trajectories, and the resource allocation of ET UAV and GUs. Because of the non-convexity of the formulated problem, we propose an alternating optimization algorithm, applying successive convex optimization techniques to solve the problem; the UAV trajectories and resource allocation are alternately optimized in each iteration. Numerical results show the efficiency of the proposed algorithm in different scenarios.

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QoE Optimization in a Live Cellular Network through RLC Parameter Tuning

Sensors ◽

10.3390/s21165619 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5619

Author(s):

Jessica Mendoza ◽

Isabel de-la-Bandera ◽

David Palacios ◽

Raquel Barco

Keyword(s):

Communication Networks ◽

Mobile Networks ◽

Cellular Network ◽

Parameter Tuning ◽

Optimization Techniques ◽

Radio Link ◽

Network Simulator ◽

File Transfer ◽

Streaming Service

The mobile communication networks sector has experienced a great evolution during the last few years. The emergence of new services as well as the growth in the number of subscribers have motivated the search for new ways to optimize mobile networks. In this way, the objective pursued by optimization techniques has been evolving, shifting from the traditional optimization of radio parameters to the improvement of the quality perceived by users, known as quality of experience (QoE). In mobile networks, the radio link control (RLC) layer provides a reliable link between both ends of the communication and has a great impact on the QoE. In this paper, the optimization of the QoE for users based on the adjustment of the RLC layer is proposed. For this purpose, two typical services demanded by the users of mobile networks have been selected: the real-time video streaming service and file transfer service. For a broader view of the behavior of the QoE in relation to RLC, optimization tests have been carried out in scenarios with different system bandwidths. In this way, the relationship between the QoE and the optimal configuration of RLC in different network load situations has been analyzed. A proof of concept has been carried out to show the capability of this optimization. To that end, both a cellular network simulator and a live cellular network devised for research purposes have been used.

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