A lightweight and accurate link abstraction model for the simulation of LTE networks in ns-3

Mezzavilla, Marco; Miozzo, Marco; Rossi, Michele; Baldo, Nicola; Zorzi, Michele

doi:10.1145/2387238.2387250

Cited by 62 publications

(42 citation statements)

References 9 publications

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“…As for the LTE technology simulations, we have utilized the LENA [40] module for ns-3. The RAN part of the LTE network constitutes a single eNB node (located at the lower left corner of the simulation area, coordinates (0, 0)) representing the only LTE cell.…”

Section: Simulation Environmentmentioning

confidence: 99%

LTE and IEEE 802.11p for vehicular networking: a performance evaluation

Mir

Filali

2014

J Wireless Com Network

218

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Various wireless communication systems exist, which enable a wide range of applications and use cases in the vehicular environment. These applications can be grouped into three types, namely, road safety, traffic efficiency, and infotainment, each with its own set of functional and performance requirements. In pursuance of assisting drivers to travel safely and comfortably, several of these requirements have to be met simultaneously. While the coexistence of multiple radio access technologies brings immense opportunities towards meeting most of the vehicular networking application requirements, it is equally important and challenging to identify the strength and weaknesses of each technology and understand which technology is more suitable for the given networking scenario. In this paper, we evaluate two of the most viable communication standards, Institute of Electrical and Electronics Engineers (IEEE) 802.11p and long-term evolution (LTE) by 3rd Generation Partnership Project for vehicular networking. A detailed performance evaluation study of the standards is given for a variety of parameter settings such as beacon transmission frequency, vehicle density, and vehicle average speed. Both standards are compared in terms of delay, reliability, scalability, and mobility support in the context of various application requirements. Furthermore, through extensive simulation-based study, we validated the effectiveness of both standards to handle different application requirements and share insight for further research directions. The results indicate that IEEE 802.11p offers acceptable performance for sparse network topologies with limited mobility support. On the other hand, LTE meets most of the application requirements in terms of reliability, scalability, and mobility support; however, it is challenging to obtain stringent delay requirements in the presence of higher cellular network traffic load.

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Section: Simulation Environmentmentioning

confidence: 99%

LTE and IEEE 802.11p for vehicular networking: a performance evaluation

Mir

Filali

2014

J Wireless Com Network

218

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“…We use this value at the MAC layer of the base station for AMC mapping, i.e., to determine the size of the transport block (TB) to be transmitted to the UE. A typical AMC behavior is to select a TB size that yields a BLER between 0 and 10 % [29]; the TB size for each given modulation and coding scheme and number of RBs are given by the LTE specification in [30].…”

Section: Lte Kpi Prediction Enginementioning

confidence: 99%

“…To simulate the scenarios, we use the ns-3-based LTE-EPC network simulator (LENA) [35] which features an almost complete implementation of the LTE protocol stack, from layer two above, together with an accurate simulation model for the LTE physical layer [29]. The use of such detailed simulator provides a performance evaluation which is reasonably close to that of an experimental LTE platform.…”

Section: Evaluation Setupmentioning

confidence: 99%

Machine learning-based dynamic frequency and bandwidth allocation in self-organized LTE dense small cell deployments

Bojović

Meshkova

Baldo

et al. 2016

J Wireless Com Network

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Self-organizing networks (SONs) are expected to minimize operational and capital expenditure of the operators while improving the end users' quality of experience. To achieve these goals, the SON solutions are expected to learn from the environment and to be able to dynamically adapt to it. In this work, we propose a learning-based approach for self-optimization in SON deployments. In the proposed approach, the learning capability has the central role to perform the estimation of key performance indicators (KPIs) which are then exploited for the selection of the optimal network configuration. We apply this approach to the use case of dynamic frequency and bandwidth assignments (DFBA) in long-term evolution (LTE) residential small cell network deployments. For the implementation of the learning capability and the estimation of KPIs, we select and investigate various machine learning and statistical regression techniques. We provide a comprehensive analysis and comparison of these techniques evaluating the different factors that can influence the accuracy of the KPI predictions and consequently the performance of the network. Finally, we evaluate the performance of learning-based DFBA solution and compare it with the legacy approach and against an optimal exhaustive search for best configuration. The results show that the learning-based DFBA achieves on average a performance improvement of 33 % over approaches that are based on analytical models, reaching 95 % of the optimal network performance while leveraging just a small number of network measurements.

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“…Regarding the type of SC, we consider medium scale factor "metro cells", as the Alcatel-Lucent 9764 Metro Cell Outdoor, featuring a maximum transmission power of 38 dBm. The (time-varying) BS capacity (in terms of number of resource blocks allocated to the users) is defined based on [9]. This includes the simulation of the wireless channels and the selection of the modulation and coding scheme (MCS) for each user, based on the particular channel conditions and on the (dynamically computed) system interference.…”

Section: System Modelmentioning

confidence: 99%

Distributed Q-learning for energy harvesting Heterogeneous Networks

Miozzo

Giupponi

Rossi

et al. 2015

2015 IEEE International Conference on Communication Workshop (ICCW)

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Abstract-We consider a two-tier urban Heterogeneous Network where small cells powered with renewable energy are deployed in order to provide capacity extension and to offload macro base stations. We use reinforcement learning techniques to concoct an algorithm that autonomously learns energy inflow and traffic demand patterns. This algorithm is based on a decentralized multi-agent Q-learning technique that, by interacting with the environment, obtains optimal policies aimed at improving the system performance in terms of drop rate, throughput and energy efficiency. Simulation results show that our solution effectively adapts to changing environmental conditions and meets most of our performance objectives. At the end of the paper we identify areas for improvement.

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A lightweight and accurate link abstraction model for the simulation of LTE networks in ns-3

Cited by 62 publications

References 9 publications

LTE and IEEE 802.11p for vehicular networking: a performance evaluation

LTE and IEEE 802.11p for vehicular networking: a performance evaluation

Machine learning-based dynamic frequency and bandwidth allocation in self-organized LTE dense small cell deployments

Distributed Q-learning for energy harvesting Heterogeneous Networks

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