RFnest&amp;#x2122;: Radio frequency network emulator simulator tool

Yackoski, Justin; Azimi-Sadjadi, Babak; Namazi, Ali; Li, Jason H.; Sagduyu, Yalin E.; Levy, Renato

doi:10.1109/milcom.2011.6127587

Cited by 25 publications

(14 citation statements)

References 3 publications

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“…As illustrated in Fig. 12, our testbed platform consists of four main components: radio frequency network emulator simulator tool 7 RFnest [26], software simulator running higher-layer protocols on a PC host, configurable RF front ends (RouterStation Pro from Ubiquiti), and digital switch. We removed the radio antennas and connected the radios with RF cables over an attenuator box.…”

Section: Performance Evaluationmentioning

confidence: 99%

Deadline-Aware Scheduling With Adaptive Network Coding for Real-Time Traffic

Yang

Sagduyu

Zhang

et al. 2015

IEEE/ACM Trans. Networking

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We study deadline-aware scheduling with adaptive network coding (NC) for real-time traffic over a single-hop wireless network. To meet hard deadlines of real-time traffic, the block size for NC is adapted based on the remaining time to the deadline so as to strike a balance between maximizing the throughput and minimizing the risk that the entire block of coded packets may not be decodable by the deadline. This sequential block size adaptation problem is then cast as a finite-horizon Markov decision process. One interesting finding is that the optimal block size and its corresponding action space monotonically decrease as the deadline approaches, and that the optimal block size is bounded by the "greedy" block size. These unique structures make it possible to significantly narrow down the search space of dynamic programming, building on which we develop a monotonicity-based backward induction algorithm (MBIA) that can find the optimal block size in polynomial time. Furthermore, a joint real-time scheduling and channel learning scheme with adaptive NC is developed to adapt to channel dynamics in a mobile network environment. Then, we generalize the analysis to multiple flows with hard deadlines and long-term delivery ratio constraints. We devise a low-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotical utility optimality. The analysis and simulation results are corroborated by high-fidelity wireless emulation tests, where actual radio transmissions over emulated channels are performed to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.

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Section: Performance Evaluationmentioning

confidence: 99%

Deadline-Aware Scheduling With Adaptive Network Coding for Real-Time Traffic

Yang

Sagduyu

Zhang

et al. 2015

IEEE/ACM Trans. Networking

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“…Second, each node must have either a real wireless radio or a virtual radio via MAC and PHY layer simulation model. Building upon existing methods of creating and connecting these aspects, we have created additional components which allow an integration between the real and virtual radios [1]. CORE (Common Open Research Emulator) [2] is used to manage the scenario being tested, including creating the emulated virtual nodes via Linux network namespaces, configuring EMANE (Extendable Mobile Ad-hoc Network Emulator), allowing the user to move nodes via the GUI, and configuring the routing and other network behaviors on both the real and virtual nodes.…”

Section: Hardwarementioning

confidence: 99%

“…To allow interaction between real nodes using our emulation hardware and virtual nodes created by CORE and EMANE, we have created an EMANE transport which serves as a bridge between data sent between the EMANE radio simulation models and the Surrogate Virtual Receiver (SVR) / Surrogate Virtual Transmitter (SVT) (see [1] for more details). In EMANE, a transport is responsible for transferring packets between each node's OS stack and the corresponding simulated MAC and PHY layer.…”

Section: Hardwarementioning

confidence: 99%

“…1 www.i − a − i.com/rf nest Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored.…”

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confidence: 99%

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Mobile network performance evaluation using the radio frequency network channel emulation simulation tool(RFnest™)

Yackoski

Azimi-Sadjadi

Namazi

et al. 2013

Proceedings of the 19th Annual International Conference on Mobile Computing &Amp; Networking - MobiCom '13

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In this demonstration, we present new capabilities of Intelligent Automation Inc. (IAI) Radio Frequency Network Emulation Simulation Tool (RFnest TM ) [1] 1 . RFnest TM is a Field Programmable Gate Array (FPGA) based network channel emulator that allows all of the channels of a full network of radio nodes to be emulated in real time, with all communication nodes experiencing a realistic channel impulse response. This allows RFnest TM to be used for protocol testing, replaying field tests, and model validation. RFnest TM has a modular design with three main capabilities: 1) FPGA based emulation hardware with RF front ends that allows nodes with real radios to send their RF signal over an emulated channel without any modification to the radio, 2) modeling of time-varying channel impulse responses within the emulation hardware, with channel properties based on mobility defined with a scripted or interactive Graphic User Interface (GUI) environment, and 3) integration with network emulators and monitoring functionality that allows the user to instantiate, manage, and monitor real and virtual network nodes within the scenario. In the new version of RFnest TM we now support:• Frequency programmability: The new RFnest TM allows users to change the center frequency through software from 800 Mhz to 2.7GHz.• Increased bandwidth: RFnest TM supports a bandwidth of 60 MHz.• Higher fidelity channel modeling: Through software and hardware updates RFnest TM support wireless channels with up to 20 taps and a wide range of Doppler frequencies.• MIMO: RFnest TM now supports different combination of MIMO channel between transmitters and receivers.

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“…As illustrated in Fig. 10, our testbed platform consists of four main components: radio frequency network emulator simulator tool, RFnest TM [24] (developed and owned as a trademark by Intelligent Automation, Inc.), software simulator running higher-layer protocols on a PC host, configurable RF front-ends (RouterStation Pro from Ubiquiti), and digital switch. We removed the radio antennas and connected the radios with RF cables over an attenuator box.…”

Section: High Fidelity Wireless Testing With Hardware Implementationmentioning

confidence: 99%

Adaptive network coding for scheduling real-time traffic with hard deadlines

Yang

Sagduyu

2012

Proceedings of the Thirteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

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We study adaptive network coding (NC) for scheduling realtime traffic over a single-hop wireless network. To meet the hard deadlines of real-time traffic, it is critical to strike a balance between maximizing the throughput and minimizing the risk that the entire block of coded packets may not be decodable by the deadline. Thus motivated, we explore adaptive NC, where the block size is adapted based on the remaining time to the deadline, by casting this sequential block size adaptation problem as a finite-horizon Markov decision process. One interesting finding is that the optimal block size and its corresponding action space monotonically decrease as the deadline approaches, and the optimal block size is bounded by the "greedy" block size. These unique structures make it possible to narrow down the search space of dynamic programming, building on which we develop a monotonicity-based backward induction algorithm (MBIA) that can solve for the optimal block size in polynomial time.Since channel erasure probabilities would be time-varying in a mobile network, we further develop a joint real-time scheduling and channel learning scheme with adaptive NC that can adapt to channel dynamics. We also generalize the analysis to multiple flows with hard deadlines and longterm delivery ratio constraints, devise a low-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotic throughput-optimality. In addition to analysis and simulation results, we perform high fidelity wireless emulation tests with real radio transmissions to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.

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RFnest™: Radio frequency network emulator simulator tool

Cited by 25 publications

References 3 publications

Deadline-Aware Scheduling With Adaptive Network Coding for Real-Time Traffic

Deadline-Aware Scheduling With Adaptive Network Coding for Real-Time Traffic

Mobile network performance evaluation using the radio frequency network channel emulation simulation tool(RFnest™)

Adaptive network coding for scheduling real-time traffic with hard deadlines

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