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Open Access Publications from the University of California

Video Transmission in Tactical Cognitive Radio Networks Under Disruptive Attacks

  • Author(s): Soysa, Madushanka Dinesh
  • Advisor(s): Cosman, Pamela C
  • Milstein, Laurence B
  • et al.

In this dissertation, I examine the performance of a cognitive radio (CR) system in a hostile environment where an intelligent adversary tries to disrupt communications with a Gaussian noise signal. I analyze a cluster-based network of secondary users (SUs). The adversary can limit access for SUs by either transmitting a spoofing signal in the sensing interval, or a desynchronizing signal in the code acquisition interval. By jamming the network during the transmission interval, the adversary can reduce the rate of successful transmission.

In the first part (Chapters 2 and 3), I investigate the optimal strategy for spoofing and jamming to minimize the SU throughput in a generic communication system. I investigate the system performance under attack over slow and fast Rayleigh fading channels. I present how the adversary can optimally allocate power across subcarriers during sensing and transmission intervals with knowledge of the system, using a simple optimization approach.

I determine a worst-case optimal-energy allocation for spoofing and jamming, which gives a lower bound to the overall information throughput of SUs under attack. I then extend the analysis to optimal spoofing power allocation for a CR network operating in Nakagami-m fading. The optimized adversary reduces the throughput by a factor of 4 to 5, relative to an adversary who divides power equally across all bands, around 25 dB jamming-to-signal-power ratio (JSR), under slow fading. Under fast fading, the optimized adversary can disrupt the communication at a JSR 10 dB lower than an unoptimized adversary.

In the second part (Chapters 4 and 5), I consider the disruptive attacks on a video-transmitting CR network. I investigate the optimal strategy for spoofing, desynchronizing and jamming a cluster based CR network with a Gaussian noise signal. I generalize the optimization approach from Chapter 1 to show how the adversary can optimally allocate its energy across subcarriers during sensing, code acquisition and transmission intervals.

I determine a worst-case optimal-energy allocation for spoofing, desynchronizing and jamming, which gives an upper bound to the received video distortion of SUs.

I also propose cross-layer resource allocation algorithms and evaluate their performance under disruptive attacks. The optimized adversary can reduce the received video peak-signal-to-noise-ratio up to 5 dB lower than an equal-power adversary, at low JSR.

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