MIMO processing enables jammer mitigation through spatial filtering, provided that the receiver knows the spatial signature of the jammer interference. Estimating this signature is easy for barrage jammers that transmit continuously and with static signature, but difficult for more sophisticated jammers: Smart jammers may deliberately suspend transmission when the receiver tries to estimate their spatial signature, they may use time-varying beamforming to continuously change their spatial signature, or they may stay mostly silent and jam only specific instants (e.g., transmission of control signals). To deal with such smart jammers, we propose MASH, the first method that indiscriminately mitigates all types of jammers: Assume that the transmitter and receiver share a common secret. Based on this secret, the transmitter embeds (with a linear time-domain transform) its signal in a secret subspace of a higher-dimensional space. The receiver applies a reciprocal linear transform to the receive signal, which (i) raises the legitimate transmit signal from its secret subspace and (ii) provably transforms any jammer into a barrage jammer, which makes estimation and mitigation via MIMO processing straightforward. We show the efficacy of MASH for data transmission in the massive multi-user MIMO uplink.
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This content will become publicly available on May 28, 2024
Joint Jammer Mitigation and Data Detection for Smart, Distributed, and Multi-Antenna Jammers
Multi-antenna (MIMO) processing is a promising solution to the problem of jammer mitigation. Existing methods mitigate the jammer based on an estimate of its subspace (or receive statistics) acquired through a dedicated training phase. This strategy has two main drawbacks: (i) it reduces the communication rate since no data can be transmitted during the training phase and (ii) it can be evaded by smart or multi-antenna jammers that are quiet during the training phase or that dynamically change their subspace through time-varying beamforming. To address these drawbacks, we propose joint jammer mitigation and data detection (JMD), a novel paradigm for MIMO jammer mitigation. The core idea is to estimate and remove the jammer interference subspace jointly with detecting the transmit data over multiple time slots. Doing so removes the need for a dedicated rate-reducing training period while enabling the mitigation of smart and dynamic multi-antenna jammers. We instantiate our paradigm with SANDMAN, a simple and practical algorithm for multi-user MIMO uplink JMD. Extensive simulations demonstrate the efficacy of JMD, and of SANDMAN in particular, for jammer mitigation.
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- Award ID(s):
- 1717559
- NSF-PAR ID:
- 10490328
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE International Conference on Communications
- ISSN:
- 1938-1883
- Page Range / eLocation ID:
- 1364 to 1369
- Format(s):
- Medium: X
- Location:
- Rome, Italy
- Sponsoring Org:
- National Science Foundation
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Data files were used in support of the research paper titled "“Experimentation Framework for Wireless
Communication Systems under Jamming Scenarios" which has been submitted to the IET Cyber-Physical Systems: Theory & Applications journal.Authors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar
Contact: krd26@drexel.edu---------------------------------------------------------------------------------------------
Top-level directories correspond to the case studies discussed in the paper. Each includes the sub-directories: logs, parsers, rayTracingEmulation, results.
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logs: - data logs collected from devices under test
- 'defenseInfrastucture' contains console output from a WARP 802.11 reference design network. Filename structure follows '*x*dB_*y*.txt' in which *x* is the reactive jamming power level and *y* is the jaming duration in samples (100k samples = 1 ms). 'noJammer.txt' does not include the jammer and is a base-line case. 'outMedian.txt' contains the median statistics for log files collected prior to the inclusion of the calculation in the processing script.
- 'uavCommunication' contains MGEN logs at each receiver for cases using omni-directional and RALA antennas with a 10 dB constant jammer and without the jammer. Omni-directional folder contains multiple repeated experiments to provide reliable results during each calculation window. RALA directories use s*N* folders in which *N* represents each antenna state.
- 'vehicularTechnologies' contains MGEN logs at the car receiver for different scenarios. 'rxNj_5rep.drc' does not consider jammers present, 'rx33J_5rep.drc' introduces the periodic jammer, in 'rx33jSched_5rep.drc' the device under test uses time scheduling around the periodic jammer, in 'rx33JSchedRandom_5rep.drc' the same modified time schedule is used with a random jammer.--------------------------------
parsers: - scripts used to collect or process the log files used in the study
- 'defenseInfrastructure' contains the 'xputFiveNodes.py' script which is used to control and log the throughput of a 5-node WARP 802.11 reference design network. Log files are manually inspected to generate results (end of log file provides a summary).
- 'uavCommunication' contains a 'readMe.txt' file which describes the parsing of the MGEN logs using TRPR. TRPR must be installed to run the scripts and directory locations must be updated.
- 'vehicularTechnologies' contains the 'mgenParser.py' script and supporting 'bfb.json' configuration file which also require TRPR to be installed and directories to be updated.--------------------------------
rayTracingEmulation: - 'wirelessInsiteImages': images of model used in Wireless Insite
- 'channelSummary.pdf': summary of channel statistics from ray-tracing study
- 'rawScenario': scenario files resulting from code base directly from ray-tracing output based on configuration defined by '*WI.json' file
- 'processedScenario': pre-processed scenario file to be used by DYSE channel emulator based on configuration defined by '*DYSE.json' file, applies fixed attenuation measured externally by spectrum analyzer and additional transmit power per node if desired
- DYSE scenario file format: time stamp (milli seconds), receiver ID, transmitter ID, main path gain (dB), main path phase (radians), main path delay (micro seconds), Doppler shift (Hz), multipath 1 gain (dB), multipath 1 phase (radians), multipath 1 delay relative to main path delay (micro seconds), multipath 2 gain (dB), multipath 2 phase (radians), multipath 2 delay relative to main path delay (micro seconds)
- 'nodeMapping.txt': mapping of Wireless Insite transceivers to DYSE channel emulator physical connections required
- 'uavCommunication' directory additionally includes 'antennaPattern' which contains the RALA pattern data for the omni-directional mode ('omni.csv') and directional state ('90.csv')--------------------------------
results: - contains performance results used in paper based on parsing of aforementioned log files
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Data files were used in support of the research paper titled “Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning" which has been submitted to the IET Communications journal.
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All data was collected using the SDR implementation shown here: https://github.com/mainland/dragonradio/tree/iet-paper. Particularly for antenna state selection, the files developed for this paper are located in 'dragonradio/scripts/:'
- 'ModeSelect.py': class used to defined the antenna state selection algorithm
- 'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna
- 'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning
- 'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only
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Authors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar
Contact: krd26@drexel.edu---------------------------------------------------------------------------------------------
Top-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.
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classifier_training: files used for training classifiers that are integrated into SDR platform
- 'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)
- 'classTrain.py' is the main parser for training the classifiers
- 'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'
post_processing_classifier: contains logs of online classifier outputs and processing script
- 'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario
- 'classProcess.py' parses the log files and provides classification report and confusion matrix for each multi-class and binary classifiers for each observed scenario - found in 'results->classifier_performance'
post_processing_mgen: contains MGEN receiver logs and parser
- 'configs' contains JSON files to be used with parser for each experiment
- 'mgenLogs' contains MGEN receiver logs for each OTA and RTE experiment described. Within each experiment logs are separated by 'mit' for mitigation used, 'nj' for no jammer, and 'noMit' for no mitigation technique used. File names take the form *_cj_* for constant jammer, *_pj_* for periodic jammer, *_rj_* for reactive jammer, and *_nj_* for no jammer. Performance figures are found in 'results->mitigation_performance'
ray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.
- Directory contains detailed 'readme.txt' for understanding.
- Please note: the processing files and data logs present in 'validation' folder were developed by Wolfe et al. and should be cited as such, unless explicitly stated differently.
- S. Wolfe, S. Begashaw, Y. Liu and K. R. Dandekar, "Adaptive Link Optimization for 802.11 UAV Uplink Using a Reconfigurable Antenna," MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018, pp. 1-6, doi: 10.1109/MILCOM.2018.8599696.
results: contains results obtained from study
- 'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'
- 'mitigation_performance' contains figures generated by 'post_processing_mgen.'
- 'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'
tuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study
- 'dataCollect' contains a folder for each jammer considered in the study, and inside each folder there is a CSV file corresponding to a different configuration of the learning parameters of the reconfigurable antenna. The configuration selected was the one that performed the best across all these experiments and is described in the paper.
- 'data_summary.txt'this file contains the summaries from all the CSV files for convenience.
