Aerospace and Electronic Systems Magazine February 2018 - 9

Xu et al.

Figure 4.

Receiver design.

to decode the signal and recover the desired data for each receiver
sequentially.
Particularly, the SIC process is implemented at the user receiver. The optimal decoding order in the SIC process is in the order
2
h
of increasing k , k = 1,2, · · ·, N. Upon receiving the superposed
N 0, k
signal, the user first detects the strongest signal by regarding other
weaker signals as interference. Then, it subtracts the decoded signal from the received aggregated signal. By repeating the same
procedure, it can retrieve its own signal. On the basis of this order, each user is able to decode and remove the signals intended
for other users whose decoding order in the SIC process becomes
available. For example, at receiver user k, it first decodes the signal
p1 hk

2

p2 hk

2

pk hk

2

>
> >
, i.e.,
N 0, k
N 0, k
N 0, k
signal of User 1 has the strongest received signal strength. Then,
user k reconstructs the signal and subtracts it from the received aggregated signal. Next, it will decode the signal of User 2 and so on.
Ultimately, user k will decode its own signal sk, while regarding the
remaining signals of k + 1 to N as interference. For User 1, it does not
perform SIC because it comes first in the decoding order, whereas
user N is the last one to decode. On the basis of Shannon's equation
[26], the total aggregated throughput of N users is expressed in (2).

intended for User 1 because

N

Rsum =  Rk ,NOMA = R1, NOMA + R2, NOMA +  RN , NOMA
k =1

=



p1 h1
N
k =2

2
2

p1 h1 + N 0,1

+



p2 h2
N
k =3

2
2

p2 h2 + N 0, 2

++

pN hN

2

(2)

N 0, N

Next, the decoded signal will further go through the watermark
decoder to check whether the adversary may have manipulated the
data and disrupted the watermarks during data transmission.
On the basis of the findings in [18], [19], the noise-like data
transmission increases the difficulty for linearly polarized receivers
to identify, decode, or extract useful information from it. Hence, the
system is immune to interference and jamming caused by linearly
polarized signal transmissions, as these signals are rejected during
the receiver correlation process. Dispersive effects caused by the
transmission medium are minimized as both polarization channels
are identically affected because they operate over the same frequency band. For the sake of avionics security, without knowledge
of modulation schemes and the power allocation ratio, any third
FEBRUARY 2018

party that intercepts the composite signal cannot decode it successfully. Thus, the MuSC intelligently chooses modulation schemes
and power ratios for different users to increase the difficulty of
being intercepted. Moreover, future generations of protected commercial, civil, and military avionics systems need to connect and
communicate with multiple users within (network) resource-limited
environments. Therefore, NOMA-aided superposition transmission
provides a promising alternative to form a multiuser communication establishing a secure and spectrally efficient approach.

SIMULATION AND NUMERICAL ANALYSIS
In this section, simulations are performed to exploit the potentials
of the proposed high-throughput, cyber-secure MuSC system in
an avionics scenario. Both the watermark-based data validation
scheme and the noise-modulated superposition transmission mechanism are evaluated and demonstrated to achieve high spectrum
efficiency and security.
In this simulation, an avionics scenario focuses on the downlink transmission: sending the automatic dependent surveillance
broadcast (ADS-B) navigation position information and air traffic control communication messages. The uplink case can be analyzed by following a similar approach. For the sake of simplicity,
we take an avionics system as an example, which consists of one
transmitter and two users (N = 2). It is assumed that the channel
2
2
h
h
gain of User 1 is smaller than that of User 2, i.e., 1 < 2 . The
N 0,1 N 0,2
transmitter superposes the signals with a power allocation factor
θ ∈ [0,1], i.e., p1 = θP and p2 = (1 − θ)P. To guarantee the QoS of
the avionics platform (e.g., User 1) experiencing a poorer channel
quality, the MuSC system sets θ ≥ 0.5 so that the p1 > p2, which is
illustrated in Figure 5.
To better evaluate the effectiveness of the watermark-based
data attestation scheme, false data injection is used to simulate
how malicious manipulation attacks on the avionics communication system are launched and detected. In this section, a threatmonitoring sensor receives watermarked data from the senders
and transfers the data. When the sensor is compromised, it can
selectively replace the data with probability ρ ∈ [0,1], which is
defined as the severity of attack. Without loss of generality, let the
n
transmitted data from user k be  i = 0 Wi , k + Mo[ PN i , k , Qk ( Akωi , k )],
where i denotes the index of threat detection sensors. Using the

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