Aerospace and Electronic Systems Magazine February 2018 - 13

Xu et al.

Figure 10.

CDF of user data rates with different power allocation factor θ.

data rate (approximately 0.3 to 0.5 Mbps falloff) than those with
NOMA operation. Therefore, NOMA leads to a leap on the system performance in terms of avionics users' data rates, which can
compensate the performance loss due to the noise modulation operation. Overall, the noise modulation MuSC system can achieve
covertness, high throughput, and spectrum efficiency; making it a
promising framework for future avionics systems.

CONCLUSION
This article proposes a high-throughput, cyber security MuSC
system for avionics systems. A watermark-based data validation
technique is used to confirm the originality of transmitted data
information. During data transmission, a multiuser superposition transmission framework with noise modulation and NOMA
operations are used. The noise modulation operation modulates
the aircraft communications addressing and reporting system
(ACARS) data sequences with a reference noise signal to improve
the covertness of data transmission. NOMA is deployed to superpose various data streams intended for different users with various transmit power levels and transmit the superposed signal over
the whole frequency band. SIC is applied to recover the ACARS
information. Therefore, the MuSC system can achieve covert,
cyber-secure high system throughput within limited network resource environments. Simulation results show the advantages of
the proposed noise modulation superposition transmission and its
performance gains in terms of throughput over traditional OMAbased transmission.
Future efforts will focus on the impacts of partial-band jamming on the system performance, evaluate hardware implemenFEBRUARY 2018

tation feasibility, and conduct experiments with multiple communicating flying unmanned air vehicles. Moreover, in this article,
we consider SIC with perfect channel state information. Note that
there exist various factors that can actually cause errors during SIC,
such as deep fading, imperfect decoding, and channel estimation
errors. The imperfect channel state information will cause the error
propagation, which will introduce residual interference to affect
the sequential decoding process [29]. To investigate the impacts of
imperfect channel state information on the MuSC system, future
work includes introducing the error propagation factor, which is
inversely proportional to the signal to interference and noise ratio,
and exploits its influences on the system performance.

REFERENCES
[1]

[2]

[3]

[4]

IEEE A&E SYSTEMS MAGAZINE

Prautzsch, F., and Graves, S. Commercial SATCOM in support of protected connectivity for the warfighter and first responder. In Proceedings of the 2011 Military Communications Conference, Baltimore,
MD, 2011, 2296-2301.
Blasch, E., Busch, T., Kumar, S., and Pham, K. Trends in survivable/
secure cognitive networks. In Proceedings of the 2013 International
Conference on Computing, Networking and Communications, San Diego, CA, 2013, 825-829.
Blasch, E., Kostek, P., Pačes, P., and Kramer, K. Summary of avionics
technologies. IEEE Aerospace and Electronic Systems Magazine, Vol.
30, 9 (Sep. 2015), 6-11.
Chen, G., Chen, H., Blasch, E., Kruger, M., and Cruz, J. B., Jr. (June
2007). Information fusion and visualization of cyber-attack graph.
SPIE Newsroom. Available: http://spie.org/newsroom/0793-information-fusion-and-visualization-of-cyber-attack-graphs?SSO=1.

http://spie.org/newsroom/0793-information-fusion-and-visualization-of-cyber-attack-graphs?SSO=1 http://spie.org/newsroom/0793-information-fusion-and-visualization-of-cyber-attack-graphs?SSO=1

Table of Contents for the Digital Edition of Aerospace and Electronic Systems Magazine February 2018