Simu5G–An OMNeT++ Library for End-to-End Performance Evaluation of 5G Networks

Nardini, Giovanni; Sabella, Dario; Stea, Giovanni; Thakkar, Purvi; Virdis, Antonio

doi:10.1109/access.2020.3028550

Cited by 155 publications

(74 citation statements)

References 24 publications

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“…4) ILP Solver: In addition to the heuristics we explained above, we use GUROBI Solver [11] to find the optimum solution for our multi-objective maximization problem (Eqs. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Despite the NP-hard property of our problem, that method could be used for only small solution space problems.…”

Section: A Q-learning Approachmentioning

confidence: 99%

See 1 more Smart Citation

Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks

Catherine¹,

Pamuklu²,

Syed³

et al. 2022

Preprint

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Unmanned aerial vehicles (UAVs) with mounted base stations are a promising technology for monitoring smart farms. They can provide communication and computation services to extensive agricultural regions. With the assistance of a Multi-Access Edge Computing infrastructure, an aerial base station (ABS) network can provide an energy-efficient solution for smart farms that need to process deadline critical tasks fed by IoT devices deployed on the field. In this paper, we introduce a multiobjective maximization problem and a Q-Learning based method which aim to process these tasks before their deadline while considering the UAVs' hover time. We also present three heuristic baselines to evaluate the performance of our approaches. In addition, we introduce an integer linear programming (ILP) model to define the upper bound of our objective function. The results show that Q-Learning outperforms the baselines in terms of remaining energy levels and percentage of delay violations.

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Section: A Q-learning Approachmentioning

confidence: 99%

“…To simulate the UAV network in the smart farm, we used Simu5G which is a 5G network simulator library developed over Omnet++ [12]. Simu5G contains modules that model the different nodes found in a 5G and LTE network following the 3GPP standards [13].…”

Section: A Simulation Platformmentioning

confidence: 99%

Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks

Catherine¹,

Pamuklu²,

Syed³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In this section, we present the simulation environment and experiments discussion. The experiments were conducted in OMNET++ [38,39], which is widely used by the research community to simulate network technologies and standards. We ran the simulations on a laptop with a 16 M cache, 256 GB RAM, and a 4.40 GHz Intel processor.…”

Section: Performance Evaluationmentioning

confidence: 99%

An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things

Rehman

Haseeb

Lloret

et al. 2021

Sensors

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In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions.

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“…In [ 21 ], simulation of vehicle platooning using Cellular Vehicle-to-Everything (C-V2X) and D2D communication is analysed. The results show that D2D allows one to save 73% of DL frequency resources, which results in less energy consumed by the gNodeBs (gNBs).…”

Section: Introductionmentioning

confidence: 99%

“…While 5G performance evaluation using a simulation-based approach is a convenient way to evaluate performance of different services or communications, simulation based results should always be properly validated to ensure that the results obtained with it are credible and match real world deployment scenarios [ 21 ]. This requirement cannot always be met, and in certain scenarios it is even not viable.…”

Section: Introductionmentioning

confidence: 99%

5G Standalone and 4G Multi-Carrier Network-in-a-Box Using a Software Defined Radio Framework

Kiela

Jurgo

Mačaitis

et al. 2021

Sensors

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In this work, an open Radio Access Network (RAN), compatible, scalable and highly flexible Software Defined Radio (SDR)-based Remote Radio Head (RRH) framework is proposed and designed. Such framework can be used to implement flexible wideband radio solutions, which can be deployed in any region, have common radio management features, and support various channel bandwidths. Moreover, it enables easier access for researchers to nonsimulated cellular networks, reduce system development time, provide test and measurement capabilities, and support existing and emerging wireless communication technologies. The performance of the proposed SDR framework is validated by creating a Network-in-a-Box (NIB) that can operate in multiband multicarrier 4G or 5G standalone (SA) configurations, with an output power of up to 33 dBm. Measurement results show, that the 4G and 5G NIB can achieve, respectively, up to 883 Mbps and 765 Mbps downlink data transfer speeds for a 100 MHz aggregated bandwidth. However, if six carriers are used in the 4G NIB, 1062 Mbps downlink data transfer speed can be achieved. When single user equipment (UE) is used, maximum uplink data transfer speed is 65.8 Mbps and 92.6 Mbps in case of 4G and 5G, respectively. The average packet latency in case of 5G is up to 45.1% lower than 4G. CPU load by the eNodeB and gNodeB is proportional to occupied bandwidth, but under the same aggregated DL bandwidth conditions, gNodeB load on the CPU is lower. Moreover, if only 1 UE is active, under same aggregated bandwidth conditions, the EPC CPU load is up to four times lower than the 5GC.

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Simu5G–An OMNeT++ Library for End-to-End Performance Evaluation of 5G Networks

Cited by 155 publications

References 24 publications

Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks

Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks

An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things

5G Standalone and 4G Multi-Carrier Network-in-a-Box Using a Software Defined Radio Framework

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