deep learning for signal demodulation in physical layer
TRANSCRIPT
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Deep Learning for Signal Demodulation in Physical
Layer Wireless Communications: Prototype
Platform, Open Dataset, and AnalyticsHongmei Wang, Zhenzhen Wu, Shuai Ma, Songtao Lu, Han Zhang, Guoru Ding, and Shiyin Li.
Abstract—In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and establish the first opendataset of real modulated signals for wireless communication sys-tems. Specifically, we propose a flexible communication prototypeplatform for measuring real modulation dataset. Then, based onthe measured dataset, two DL-based demodulators, called deepbelief network (DBN)-support vector machine (SVM) demodu-lator and adaptive boosting (AdaBoost) based demodulator, areproposed. The proposed DBN-SVM based demodulator exploitsthe advantages of both DBN and SVM, i.e., the advantage of DBNas a feature extractor and SVM as a feature classifier. In DBN-SVM based demodulator, the received signals are normalizedbefore being fed to the DBN network. Furthermore, an AdaBoostbased demodulator is developed, which employs the k-NearestNeighbor (KNN) as a weak classifier to form a strong combinedclassifier. Finally, experimental results indicate that the proposedDBN-SVM based demodulator and AdaBoost based demodulatorare superior to the single classification method using DBN, SVM,and maximum likelihood (MLD) based demodulator.
Index Terms—Machine learning, DBN-SVM based demodula-tor, AdaBoost based demodulator.
I. INTRODUCTION
Conventional wireless communication systems are gener-
ally designed in accordance with the rigorous mathematical
theories and accurate system models [1]. However, because of
increasing wireless service requirements, such as the use of
smartphones, virtual reality, and internet of things (IoT), it is
Manuscript received January 22, 2019. Corresponding author: Shuai Ma.)H. Wang, Z. Wu, S. Ma, and S. Li are with Information and
Control Engineering, China University of Mining and Technology,Xuzhou, China 221116 (Email:[email protected], [email protected],[email protected], [email protected]).
S. Ma is also with the State Key Laboratory of Integrated ServicesNetworks, Xidian University, Xi’an 710071, China.
S. Lu is with the Department of Electrical and Computer Engineering, Uni-versity of Minnesota, Minneapolis, MN 55455, USA (e-mail: [email protected]).
H. Zhang is with the Department of Electrical and Computer En-gineering, University of California, Davis, CA 95616, USA (e-mail:[email protected]).
G. Ding is with the College of Communications Engineering, Army Engi-neering University, Nanjing 210007, China (e-mail: [email protected])
The work of S. Ma and S. Li were supported by the FundamentalResearch Funds for the Central Universities under Grant 2017QNA32,by theNational Natural Science Foundation of China under Grant 61701501, Grant61771474; by the Natural Science Foundation of Jiangsu Province underGrant BK20170287; by China Postdoctoral Science Foundation under Grant2016M600452; by the State Key Laboratory of Integrated Services Networks(Xidian University) under grant ISN19-07; and by the Key Laboratory ofCognitive Radio and Information Processing , Ministry of Education (GuilinUniversity of Electronic Technology) under grant CRKL180204, by KeyLaboratory of Ocean Observation-Imaging Testbed of Zhejiang Province. Thework of H. Wang was supported by the National Natural Science Foundationof China under Grant 61601464.
challenging to characterize future complex wireless commu-
nication networks accurately by using tractable mathematical
models or system models [2]. Recently, deep learning (DL)
[3], as an effective method to handle complex problems, has
attracted increasing attention from both academia and industry.
DL has been applied in image recognition [4], [5], computer
vision [6], natural language processing [7] and spectrum pre-
diction [8], etc. In addition, some literatures have focused on
using DL to optimize performance of wireless communication
systems [9]–[11]. In [9], an unsupervised learning-based fast
beamforming method is proposed to maximize the weighted
sum rate under the total power constraint. In [10], a deep
recurrent neural network based algorithm is proposed to tackle
energy efficient resource allocation problem for heterogeneous
IoT. In [11], a three dimensional message-passing algorithm
based on deep learning scheme is proposed to minimize the
weighted sum of the secondary interference power for cogni-
tive radio networks. Recent works [12], [13] have interpreted
an end-to-end wireless communication system as an auto-
encoder. This is promising for applications of DL to wireless
communications.
Demodulation is one of the fundamental modules for wire-
less communications systems for high-speed transmission with
a low bit error rate. Theoretically, optimum demodulators of
conventional wireless communication systems are designed
for additive white Gaussian noise (AWGN) channels [1].
Moreover, both channel state information (CSI) and channel
noise distribution are usually required. Most previous studies
[14]–[19] have assumed that each receiver can accurately
estimate the fading coefficients. However, practical wireless
communication channels may suffer from multi-path fading,
impulse noise, spurious or continuous jamming, and numerous
other complex impairments, which deteriorate demodulation
performance significantly. Because of the limited length of the
training sequence, the estimate CSI will have limited accuracy
[20]. Especially, for fast-fading scenarios, it is difficult to
accurately estimate CSI because the fading coefficients change
rapidly during the data transmission period. Designing opti-
mum demodulators for different channel models is challenging
because the channel model may not be known at the receiver
end.
Given the above issues, DL-based model-free demodulators
have attracted a considerable amount of attention, where the
requirements for a priori knowledge can be widely relaxed or
even removed [21]. Because the information of the modulated
signals is represented by the amplitude and phase, feature
2
extraction is of critical importance for signal demodulation.
DL-based demodulators have been investigated in conven-
tional radio frequency (RF) systems. In [22], a deep con-
volutional network demodulator (DCND) is proposed to de-
modulate mixed modulated signals, which can further reduce
the bit error rate compared with the coherent demodulation
method. In [23], the authors show that the proposed demod-
ulator based on deep belief network (DBN) is feasible for
an AWGN channel with a certain channel impulse response
and a Rayleigh non-frequency-selective flat fading channel. In
[24], a DL-based detector is proposed for signal demodulation
in short-range multi-channels without a signal equalizer. In
[25], the authors show that deep convolutional neural networks
(DCNN) for frequency-shift keying (FSK) demodulation can
substantially reduce error bit probabilities over an AWGN
Rayleigh-fading channel. To the best of our knowledge, most
of existing DL-based demodulation schemes are based on
simulated data rather than real measured data.
This paper presents a data-driven framework for DL-
based demodulators. Specifically, two data-driven demodula-
tion methods based on DBN-support vector machine (SVM)
and adaptive boosting (AdaBoost) [26] are developed for end-
to-end wireless communication systems. These methods learn
and extract features from the received modulation signals with-
out any prior knowledge of the channel model. Moreover, the
performance of the two data-driven demodulators are evaluated
on different modulation schemes through real measured data.
The main contributions of this paper are as follows:
• A flexible end-to-end wireless communication prototype
platform is developed for application in real physical
environments, which can generate real signals. The
prototype is used to establish measured modulation
datasets from real communication systems in actual
physical environments in eight modulation schemes,
i.e., binary phase shift keying (BPSK) and multiple
quadrature amplitude modulation (M -QAM) modulation,
where M = 2φ and φ = {2, 3, 4, 5, 6, 7, 8}. The
received SNR of the eight modulated signals are
measured from 3 dB to 25 dB. An open online
real modulated dataset is established, available at
https://pan.baidu.com/s/1biDooH6E81Toxa2u4D3p2g or
https://drive.google.com/open?id=1jXO9OMZOyVMOYv
QSn3WVmlfQoQbonKuo , where the transmission
distance of the eight modulated signals is measured in
an indoor environment. To the best of our knowledge,
this is the first open dataset of real modulated signals
for wireless communication systems.
• Then, based on the measured data, two DL-based de-
modulators are proposed, namely, DBN-SVM based de-
modulator and AdaBoost based demodulator. The pro-
posed DBN-SVM based demodulator, which has a novel
demodulation architecture, exploits the advantages of
both DBN and SVM, i.e., the advantage of DBN as
a feature extractor and SVM as a feature classifier. To
accelerate the convergence rate, the received signals are
first normalized before being fed to the DBN network so
that the features of the received signals can be extracted,
the SVM is utilized to classify these features.
• An AdaBoost based demodulator, which utilizes multiple
KNNs as a weak classifier to form a strong combined
classifier, is developed. The proposed AdaBoost based
demodulator increases the weights for the error demod-
ulated symbols and decreases the corresponding weights
for correctly demodulated symbols during the iterations.
• Finally, the demodulation performance of the two pro-
posed data-driven demodulators are investigated. Specif-
ically, the demodulation accuracies of the two DL-based
demodulators decrease over the respective transmission
and modulation orders for a fixed transmission distance.
The experimental results also show that the demodulation
accuracy of the DBN-SVM based demodulator is higher
than those of DBN-based and SVM-based demodulators.
Moreover, the demodulation accuracy of the AdaBoost
based demodulator is higher than that of the DBN-
SVM based demodulator at the lower SNR regions, and
the accuracies of the two demodulators are similar at
high SNRs. For the high SNR scenario, a high-order
modulation is generally preferred.
The remainder of this paper is organized as follows. Sec-
tion II describes the system model. Section III explores the
structures of the DBN-SVM and AdaBoost, including detailed
descriptions of the data stream and how to make classification
decisions. In section IV, the data analysis results are provided
and analyzed. Finally, the conclusions from the study are
drawn in Section V.
Notations: Boldfaced lowercase and uppercase letters rep-
resent vectors and matrices, respectively. The transpose of a
matrix is denoted as (·)T. L∆= {1, 2, ..., L}, L1
∆= {1, ..., L1},
Mk∆= {1, 2, ...,Mk}, Nk
∆= {1, 2, ..., Nk}, D
∆= {1, ..., D},
Q∆= {0, 1, . . . , M}, and M
∆= {0, 1, . . . ,M − 1}.
II. SYSTEM MODEL
An end-to-end wireless communication system1 is consid-
ered, which includes a single antenna transmitter and a single
antenna receiver, as illustrated in Fig. 1. By adopting the BPSK
or M -QAM digital modulation schemes, the transmitted signal
x (t) is given as
x (t) = Vm cos (2πfct+ θm) , m = 1, ...,M, 1 ≤ t ≤ T,
(1)
where Vm, θm and T denote the amplitude, phase, and period
of the signal x (t), respectively; fc is the carrier frequency.
101001
x
y
100101
Modulation Transmitter Wireless channel
ReceiverDL-based demodulation
s
�s
Fig. 1: End-to-end wireless system model
Let g (t) denote the multipath channel between the trans-
mitter and the receiver, which may suffer nonlinear distortion,
1The term end-to-end wireless system model implies that signal features arelearned from a single deep neural network, without the complex multi-stageexpert machine learning processing [12], [13], [27]–[29].
3
interference, and frequency selective fading. At the receiver,
the received signal y (t) is given by
y (t) = g (t)x (t) + nr (t), (2)
where nr (t) denotes the received noise.
Then, the received analog signal y (t) is converted to
the digital signal via the vector signal analyzer. Let y∆=
[y1, y2, ..., yNL]T
denote the total sampled digital signal vec-
tor, where yn = y(
n−1N
T)
is the nth sample, N is the number
of samples of one period, and L denotes the number of signal
periods.
Before the demodulation process, the received signal y is
normalized to [0, 1], which can accelerate the DL network
processing speed [30]. Senerally, the normalized data y∆=
[y1, y2, ..., yNL]T
is given by
yi =yi − ymin
ymax − ymin, 1 ≤ i ≤ NL, (3)
where ymin = min1≤i≤NL
yi, and ymax = max1≤i≤NL
yi.
Because the information of the BPSK and M -QAM are
represented by amplitudes and phases, DL is used to extract
information features from the received signals. Specifically,
with the sampled signal vector y, two DL-based demodulators
are proposed: DBN-SVM based demodulator and AdaBoost
based demodulator. The DL-based demodulators consist of
two phases: training phase and testing phase. During the
training phase, the parameters of the DL-based demodulators
are optimized with the training dataset. Then, in the testing
phase, the demodulators demodulate the received signal and
recover the transmitted information.
Let zl denote the label signal of the lth period, where
l ∈ L and Φ is the label set, i.e., Φ = {z1, z2, . . . , zL},which is determined by the modulation scheme. Let T1 ={(y1, z1) , (y2, z2) , . . . , (yL1
, zL1)} denote the labeled train-
ing signal set, where yl =[
y1+(l−1)N , y2+(l−1)N , ..., ylN]T
denotes the normalized signal of the lth period, and L1 denotes
the total number of training signal periods (L1 < L).
III. DBN-SVM BASED DEMODULATOR
As an unsupervised features extraction method, the DBN
can efficiently extract high-level and hierarchical features from
the measured signal, while the SVM minimizes the structure
risk and shows good learning and generalization performance
with a small amount of samples. Inspired by those advantages
of the two approaches, a combination of DBN and SVM for
demodulation is proposed. The DBN-SVM demodulator is
shown in Fig. 2, the DBN is used as a feature generator and
the SVM is used as a classifier.
1) DBN: The proposed DBN includes three stacked re-
stricted Boltzmann machines (RBM) [31], i.e., RBM1, RBM2,
and RBM3, as shown in Fig. 2. Specifically, RBMk is an
undirected, bipartite graphical model, and it composes a vis-
ible layer vk = [vk,1, vk,2, ..., vk,Mk]T and a hidden layer
hk = [hk,1, hk,2, ..., hk,Nk]T , where vk,α and hk,β are the
αth neuron of vk and the βth neuron of hk, respectively,
α ∈ Mk, β ∈ Nk, k ∈ {1, 2, 3}. The visible layer vk
and hidden layer hk are fully connected via a symmetric
DBN
OVO-SVM
1SVM
å å å
0u 1u 1Mu -
1,0t
0Adder 1Adder 1AdderM -
�z
, 1M Mt
-,1Mt
SVMM
1h
2h
3hHidden layer
Hidden layer
Hidden layer
Visible layer
BP
1RBM
2RBM
3RBM
1v
0SVM
0,0t 0,1t 1, 1Mt -
( )2v
( )3v
Y
�ly
{ }arg maxm
m
uÎ
{g m
Fig. 2: Structure of DBN-SVM based demodulator
undirected weighted matrix Wk = [wk,1,wk,2, . . . ,wk,Nk]T ,
where wk,β = [w(1)k,β , w
(2)k,β , . . . , w
(Mk)k,β ]T is a weight vector
between vk and hk,β . For the three RBM, there is no intralayer
connections between either the visible layer or the hidden
layer.
For RBMk , the energy E (vk,hk) is defined by combining
the configuration of both vk and hk as follows
E (vk,hk) = −hTkWkvk − aTk vk − bT
k hk, (4)
where ak = [ak,1, ak,2, ..., ak,MK]T is an offset vector of vk,
and bk = [bk,1, bk,2, ..., bk,NK]T
is an offset vector of hk.
Based on E (vk,hk), the probability of vk is given by
p (vk) =1
Zk
∑
hk
eE(vk,hk), (5)
where Zk =∑
vk,hk
eE(vk,hk) is the normalization factor.
During the training phrase, the goal of the RBMk is to
maximize the log-likelihood function as follows
maxWk,ak,bk
∑
vk
log p (vk). (6)
To solve equation (6), the gradient descent method is used
to iteratively calculate the variables Wk, ak , and bk, where
the corresponding partial derivative with respect to Wk, ak,
4
and bk can be written as
∂ log p (vk)
∂w(α)k,β
= vk,αp (hk,β = 1|vk)
−∑
vk
p (vk)p (hk,β = 1|vk) vk,α, α ∈Mk, β ∈ Nk, (7a)
∂ log p (vk)
∂ak,α= vk,α −
∑
vk
p (vk)vk,α, α ∈ Mk, (7b)
∂ log p (vk)
∂bk,β= p (hk,β = 1|vk)
−∑
vk
p (vk)p (hk,β = 1|vk) , β ∈ Nk. (7c)
According to [32], the conditional probability
p (hk,β = 1|vk) and p (vk,α = 1 |hk ) are respectively
given by
p (hk,β = 1|vk) = sigmoid(
bk,β + vkTwk,β
)
, (8a)
p (vk,α = 1 |hk ) = sigmoid
ak,α +
Nk∑
β=1
hk,βw(α)k,β
, (8b)
where sigmoid (x)∆= 1
1+e−x , α ∈ Mk, β ∈ Nk, hk,β , and
vk,α ∈ [0, 1].Then, the variables Wk, ak, and bk are updated by the
following equations [33]
w(α)k+1,β ← w
(α)k,β + η
∂ log p (vk)
∂w(α)k,β
, α ∈Mk, β ∈ Nk, (9a)
ak+1,α ← ak,α + η∂ log p (vk)
∂ak,α, α ∈Mk, (9b)
bk+1,β ← bk,β + η∂ log p (vk)
∂bk,β, β ∈ Nk, (9c)
where η > 0 is the learning rate.
By employing the gradient descent method, RBM1 is trained
first, where v1 = yl and l ∈ L1. Then, let v2 = h1, and RBM2
is trained. Similarly, after training RBM2, let v3 = h2, and
RBM3 is trained. Moreover, when RBM3 is trained, the pa-
rameters of DBN can be obtained, i.e., {Wk, ak,bk}k∈{1,2,3}.
Then, the parameters {Wk, ak,bk}k∈{1,2,3} are further fine-
tuned by the supervised back propagation (BP) algorithm [34].
After DBN is trained, it outputs the extracted feature yl1 =h3, where l1 ∈ L1. Let Y = [y1, y2, . . . , yL1
]T
denote the
output feature set.
2) OVO-SVM: With the extracted feature set Y, the
one-versus-one (OVO)-SVM is adopted for further classi-
fication, which achieves multiclassification by solving the
two-classification subproblems [35], [36]. As shown in Fig.
2, OVO-SVM exploits M nonlinear two-class SVMs, i.e.,
SVM0,...,SVMM , to classify M categories for M -QAM mod-
ulation, where M∆= M(M−1)
2 − 1.
To map pedestrian features to a high dimensional space, a
Gaussian kernel is introduced, which can be expressed as
Gq (yl1 , yl2) = exp
(
−‖yl1 − yl2‖
2
2σ2q
)
, (10)
where σq > 0 is the bandwidth of the Gaussian kernel and
q ∈ Q.
According to the nonlinear SVM theory [37], the nonlinear
two-class SVMq problem can be formulated as
mincq
1
2
L1∑
l1=1
L1∑
l2=1
cq,l1cq,l2zl1zl2Gq (yl1 , yl2)−L1∑
l1=1
cq,l1
(11a)
s.t.
L1∑
l1=1
cq,l1zl1 = 0, (11b)
0 ≤ cq,l1 ≤ K, l1 ∈ L1, (11c)
where cq = [cq,1, cq,2, . . . , cq,L1]T
and q ∈ Q.
By solving linear programming (11), the optimal solution
c∗q =[
c∗q,1, c∗q,2, . . . , c
∗q,L1
]Tis obtained. Then, the nonlinear
two-class SVMq decision function fq (yl1), with l1 ∈ L1, q ∈Q, is given as
fq (yl1) = γ
(
L1∑
i=1
c∗q,izi exp
(
−‖yi − yl1‖
2
2σ2q
)
+ b∗q
)
,
(12)
where b∗q∆= zl1 −
L1∑
i=1
c∗q,l1zi exp
(
−‖yi−yl1‖
2
2σ2q
)
is a biased
variable [38], and
γ (x)∆=
{
1, if x ≥ 00, if x < 0
. (13)
Let τq,m and τq,m denote the output of the SVMq , and
τq,m and τq,m are the inputs of the Adderm and the Adderm,
respectively, where τq,m, τq,m ∈ {0, 1}, τq,m + τq,m = 1, m,
m ∈M, and m 6= m. Then, for Adderm, the number of votes
is updated by um = um+ τq,m, where the initial value of um
is 0, and m ∈ M.
Then, with the number of votes {um}M−1m=0 , the output label
z is obtained as follows
z = argmaxm∈M
{um} . (14)
Finally, z is mapped to the demodulation result s.
After the entire network is trained, the parameters Wk ,
ak, and bk of the DBN, and cq , b∗q, σq of the OVO-SVM are
optimized, where k ∈ {1, 2, 3} and q ∈ Q. Then, the test
signal T2 = {(yL1+1, zL1+1) , (yL1+2, zL1+2) , . . . , (yL, zL)}is converted to the feature vector y, where L2 is the number
of test signal periods. The details of the DBN-SVM based
demodulator are listed in Algorithm 1.
IV. ADABOOST BASED DEMODULATOR
AdaBoost is a general method used to improve machine
learning algorithms [39], which integrates multiple indepen-
dent weakly classifiers into a stronger classifier. In this section,
we exploit the k-Nearest Neighbor (KNN) classifiers as the
weak classifier for constructing the AdaBoost.
As shown in Fig. 3, the proposed AdaBoost consists D
KNN classifiers. The labeled training signal set is denoted by
T = {(y1, z1) , (y2, z2) , . . . , (yL1, zL1
)}.
5
Algorithm 1: The DBN-SVM based demodulator
1. Given the labeled training signal yl, l ∈ L1;2. Initialize v1, h1, W1, a1 and b1;3. For k = 1, . . . , 3 do4. Train kth RBM according to formula (4)− (6);5. Update Wk,ak and bk are according to formula (7) − (9);6. End for;7. Get the extracted feature vector set Y, and classified by
OVO-SVM;8. Update Adderm , m ∈ M;9. Output label z:
z = argmaxm∈M
{um}.
( )1�f y
( )2�f y ( )�F y
KNN D
KNN 1
KNN 2
( )�Df y
AdaBoost
1w
2w
Dw
Fig. 3: Structure of AdaBoost based demodulator
Let wd = [wd (1) , wd (2) , . . . , wd (L1)]T
denote the weight
vector of dth KNN, where 0 ≤ wd (l) ≤ 1, l ∈ L1, andL1∑
l=1
wd (l) = 1, d ∈ D. For the 1st KNN, w1 (l) = 1L1
,
l ∈ L1. Based on the weight vector wd, the dth KNN re-
samples the training set T and generates a new training set
Td = {(yd1, zd1
) , (yd2, zd2
) , . . . , (ydL1, zdL1
)}, dl ∈ L1.
Then, a vector in Td is searched with the minimum distance
from yl, i.e.,
l∗ = argmini∈L1
‖ydi− yl‖2, l ∈ L1, d ∈ D. (15)
Because the label of yd∗
lis zd∗
l, fd (yl) = zd∗
l, which
implies that the classification result of dth KNN for yl is zd∗
l.
Let χd denote the weight sum of misclassified samples of
dth KNN as follows
χd =
L1∑
l=1
wd (l) I (fd (yl) , zl), d ∈ D, (16)
where I (x, y) is the indicator function, i.e.,
I (x, y) =
{
1, if x 6= y
0, if x = y.(17)
Then, for (d + 1)th KNN, weight w(d+1) =[
w(d+1) (1) , . . . , w(d+1) (L1)]T
is updated as
wd+1 (l) =wd (l) exp (−αdI (fd (yl) , zl))
Qd
,
l ∈ L1, d ∈ D, (18)
where αd = 12 ln
(
1−χd
χd
)
, and Qd =L1∑
l=1
wd (l) exp (−αdI (fd (yl) , zl)) is the normalization
Algorithm 2: The KNN based AdaBoost demodulator
1. Given the labeled training signal set T ;2. Initialize signal weight w1 (l) =
1L1
;
3. For d = 1, . . . ,D do4. Train dth KNN according to weights wd;5. Get weak classifier fd (yl) ∈ M with error rate
χd =L1∑
l=1wd (l) I (fd (yl) , zl) d ∈ D;
6. Update:
wd+1 (l) =wd(l) exp(−αdI(fd(yl),zl))
Qd, l ∈ L1
where αd = 12ln
(
1−χd
χd
)
, and
Qd =L1∑
l=1wd (l) exp (−αdI (fd (yl) , zl));
7. End for;8. Output the final decision classifier:
F (yl) = zl = argmaxz∈Φ
D∑
d=1αd (1− I (fd (yl) , zl)).
factor. If yl is classified correctly, i.e., I (fd (yl) , zl) = 0,
wd+1 (l) =wd(l)Qd
. Otherwise, wd+1 (l) =wd(l) exp(−αd)
Qd.
After training D KNNs, AdaBoost classifies yl as follows
F (yl) = zl = argmaxzl∈Φ
D∑
d=1
αd (1− I (fd (yl) , zl)), (19)
where αd is the coefficient of (1− I (fd (yl) , zl)) and
I (fd (yl) , zl) can be regarded as the voting value, i.e., if
I (fd (yl) , zl) = 0, fd (y) classifies signal yl into class zl, oth-
erwise, yl does not belong to class zl. The class with the max-
imum sum of weighted voting value, αd (1− I (fd (yl) , zl)),for all classifiers, is identified as the classification result zl of
the Adaboost classifier, and then zl is mapped to demodulation
result sl. The details of the KNN-based AdaBoost demodulator
are listed in Algorithm 2.
V. EXPERIMENTAL RESULTS AND DISCUSSIONS
In this section, the performance of the proposed DBN-SVM
based demodulator and AdaBoost based demodulator is inves-
tigated. Also the performance of the the DBN based, SVM
based, and maximum likelihood (MLD) based demodulation
methods are presented for comparison.
A. The end-to-end wireless communication system prototype
As shown in Fig. 4, an end-to-end wireless communication
system prototype is first established to collect the dataset,
which consists of a source, a RF vector signal generator, a
transmitter antenna, a receiver antenna, and a vector signal
analyzer. The parameters of the devices of the proposed end-
to-end wireless communication system prototype are listed in
Table I.
TABLE I: Experimental equipment and parameters
Experiment setup Type and parameters
EXG RF vector signal generator Keysight N5172B
MXA vector signal analyzer Keysight N9020B
Antenna Gain 24 dBi
6
Fig. 4: End-to-end wireless communication system prototype
The volume environment is a 15×5×3(
m3)
office, where
15, 5, and 3 denote the length, width, and height, respectively.
Note that the distance between the transmitter and the receiver
is approximately 10 meters. The power of the background
noise is 78 dBm.
The carrier frequency fc and the sampling rate fs are
2.4 GHz and 100 MHz/s, respectively. For each M -QAM
modulation scheme, the number of sample points N has four
cases, i.e., N = 10, 20, 40, and 80.
To reduce the generalization error, the collected data set
contains 10000 transmit signal periods, in which 8000 periods
are used for training and 2000 periods are used for testing.
B. Experimental Results
DBN-SVM based and AdaBoost based demodulators are
trained on these training sets. The DBN-SVM based de-
modulator training ends after 110 epochs, after which the
training loss almost does not decline, and the AdaBoost based
demodulator training ends when the iteration error is less
than 10−3. In the experiment, signal sets with different SNRs,
ranging from 3 to 25 dB, are chosen as the validation sets;
the DBN based, SVM based, and MLD based demodulation
methods are used for comparison.
In Fig. 5 and Fig. 6, the demodulation performance versus
SNR of the proposed demodulator and the three baseline
schemes are compared by the demodulation of 4-QAM and 16-
QAM, respectively. The demodulation accuracy of the models
increases as SNR increases. In particular, Fig. 5 indicates
that the demodulation accuracy of all methods are close to
100% when SNR ≥ 15dB, and the proposed AdaBoost based
demodulator is significantly superior to the other models
when SNR ≤ 13dB. Besides, the proposed DBN-SVM based
demodulator has better performance than the DBN-based and
SVM-based demodulation methods. In Fig. 6, compared with
Fig. 5, we focus on the same performance index at 16-QAM.
It shows the designed AdaBoost based demodulator is close
to 100% when SNR ≥ 15dB. However, other methods cannot
approach 100% as SNR increases. Furthermore, among these
demodulation methods, the AdaBoost based demodulator ob-
viously outperform the other four methods. It can be observed
that the demodulation accuracy achieved by DBN-SVM based
demodulator exceeds ones by the DBN-based, SVM-based
demodulation methods. Although the overall trend of MLD
classification accuracy increases as SNR increases, it has a
obvious fluctuation. The reason is that the practical wireless
channels include complicaful interferences, but the robustness
of MLD is poor.
3 5 7 9 11 13 15 17 19 21 23 25
SNR(dB)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Dem
od
ula
tio
n a
ccu
racy
AdaBoost
DBN-SVM
DBN
SVM
MLD
Fig. 5: Demodulation accuracy comparison of different de-
modulators with N = 40 and 4-QAM
In Fig. 7, the accuracy performance for different sampling
points at 16-QAM is simulated. It can be observed that the
demodulation accuracy increases with the number of sample
points. Furthermore, the demodulation accuracy can approxi-
mately achieve 100% with N = 40 or N = 80 when SNR
≥ 15dB. However, with an increase in the number of sample
points, the computational complexity also increases.
Fig. 8 shows the demodulation accuracy achieved by the
AdaBoost based demodulator versus the number of training
3 5 7 9 11 13 15 17 19 21 23 25
SNR(dB)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Dem
od
ula
tio
n a
ccu
racy
AdaBoost
DBN-SVM
DBN
SVM
MLD
Fig. 6: Demodulation accuracy comparison of different de-
modulators with N = 40 and 16-QAM
7
3 5 7 9 11 13 15 17 19 21 23 25
SNR(dB)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1D
emodula
tion a
ccura
cy
Fig. 7: Demodulation accuracy of AdaBoost versus SNR with
16-QAM
1000 3000 5000 7000 9000
Number of training signal periods
0.8
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
1
Dem
od
ula
tio
n a
ccu
racy
16QAM, SNR=12dB
32QAM, SNR=12dB
Fig. 8: Demodulation performance versus different number of
training signal periods with 16-QAM and 32-QAM
signal periods, where the number of sampling points is 40and SNR = 12dB. The result shows that the demodulation
accuracy initially increases with an increase in the number of
training signal periods, and then, it reaches saturation when the
number of training signal periods is 5000. It can be observed
that, compared with 32-QAM, 16-QAM can achieve higher
accuracy. Meanwhile, 16-QAM can provide stable perfor-
mance with relatively fewer training signal periods. Different
modulation models have different requirements with different
number of training signals periods. In general, higher orders
require longer training signals periods.
Fig. 9 presents the demodulation accuracies of BPSK and
M -QAM modulation schemes. In this experiment, the Ad-
aBoost based demodulation algorithm was employed, where
the number of sampling points is N = 40. The demodulation
accuracy for all modulation schemes increases with SNR.
3 5 7 9 11 13 15 17 19 21 23 25
SNR(dB)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Dem
odula
tion a
ccura
cy
BPSK
4-QAM
8-QAM
16-QAM
32-QAM
64-QAM
128-QAM
256-QAM
Fig. 9: Demodulation accuracy comparison of different mod-
ulation mode versus SNR with AdaBoost based demodulator
Meanwhile, the accuracy achieved by the BPSK-modulation
scheme is better than the other seven schemes for the same
SNR. Furthermore, Fig. 9 also indicates that the demodulation
accuracy reduces with an increase of the modulation order.
In Fig. 10, the same modulation schemes, demodulation
algorithm, and sampling points are used as in Fig. 9, where the
effective capacity of different modulation methods versus SNR
are reported. The effective capacity by BPSK, 4-QAM, and 8-
QAM almost remain unchanged with an increase in SNR. It
is found that the modulation order has a considerable positive
impact on the performance of the transmission capacity. The
performance gap between the low order and the high order
modulation is clearer when SNR ≤ 15dB. However, the
demodulation accuracy of high order modulation is low, so
there is a trade-off between the demodulation accuracy and
the effective capacity.
VI. CONCLUSION
In this paper, a flexible end-to-end wireless communi-
cations prototype platform was proposed for real physical
environments. Then, the first open measured modulation data
dataset with eight modulation schemes, i.e., BPSK, 4-QAM,
8-QAM, 16-QAM, 32-QAM, 64-QAM, 128-QAM, and 256-
QAM, were established and accessed online. Furthermore, two
DL-based demodulators, i.e., DBN-SVM based demodulator
and AdaBoost based demodulator, were proposed. Based on
the real dataset, the demodulation performance of the pro-
posed demodulators were tested. Finally, experimental results
indicated that the proposed demodulators outperform the DBN
based, SVM based, and MLD based demodulators at various
scenarios.
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