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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:whm99@cumt.edu.cn, zhzhw@cumt.edu.cn,mashuai001@cumt.edu.cn, lishiyin@cumt.edu.cn).

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: lus@umn.edu).

H. Zhang is with the Department of Electrical and Computer En-gineering, University of California, Davis, CA 95616, USA (e-mail:hanzh@ucdavis.edu).

G. Ding is with the College of Communications Engineering, Army Engi-neering University, Nanjing 210007, China (e-mail: dr.guoru.ding@ieee.org)

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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