Signal Detection in Satellite-Ground IoT Link Based on ...

Research ArticleSignal Detection in Satellite-Ground IoT Link Based on BlindNeural Network

Qing-yang Guan 1,2 and Wu Shuang 1

1College of Engineering, Xi’an International University, Xi’an 710077, China2College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China

Correspondence should be addressed to Qing-yang Guan; [email protected]

Received 19 February 2021; Revised 8 April 2021; Accepted 21 April 2021; Published 7 May 2021

Academic Editor: Xin Liu

Copyright © 2021 Qing-yang Guan and Wu Shuang. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original workis properly cited.

At present, there are many problems in satellite-ground IoT link signal detection. Due to the complex characteristics of the satellite-ground IoT link, including Doppler and multipath effect, especially in scenarios related to military fields, it is difficult to usetraditional method and traditional cooperative communication methods for link signal detection. Therefore, this paper proposesan efficient detection of satellite-ground IoT link based on the blind neural network (BNN). The BNN includes two networkstructures, the data feature network and the error update network. Through multiple iterations of the error update network, theweight of BNN for blind detection is optimized and the optimal elimination solution is obtained. Through establishing asatellite-to-ground link model simulation of the low-orbit satellite, the proposed BNN algorithm can obtain better bit error ratecharacteristics.

1. Introduction

In recent years, researchers have proposed a wide range ofapplications for signal detection, including delay detection[1], power spectrum detection [2], and periodic spectrumdetection [3]. For signal detection, most of the current detec-tion algorithms should obtain related prior detection-infor-mation, which should be obtained by setting the signal pilotinformation. Many signal processing algorithms requireprior information, such as the encoding of the transmittedand received signal information, channel estimation infor-mation, and setting pilot information of sending and receiv-ing signals, and other prior information.

For many satellite-to-ground link scenarios, especiallymilitary application-related scenarios, it is difficult to usethe pilot signal agreed by the transceiver to obtain the priorinformation of detection. Therefore, many scenarios needto use blind detection.

Urkowitz has first proposed the concept of blind algo-rithm in literature [4], especially in different fields, includingwireless communication signal processing and voice signal

processing. Based on this basis, related literatures have pro-posed energy-based blind signal detection algorithms forblind information processing [5], blind signal detection algo-rithms based on least squares algorithm detection [6], andblind signal detection based on least square [7]. Literature[7, 8] proposed a blind detection algorithm for general signaltransmission systems. Since the detection algorithm in theliterature is based on ideal conditions, the hypothetical con-ditions are not suitable for many communication conditions.Literature [9] proposed the blind detection algorithm forspeech signals and gave a theoretical closed-loop derivation.However, it is not applicable in real scenarios. Literature[10, 11] proposed a high-complexity blind signal detectionalgorithm, but due to the high complexity, it is not suitablefor processor-limited scenarios.

For blind signal processing, researchers [12–14] combinedsparse coding for signal detection. In recent years, relatedscholars have conducted in-depth research on blind signaldetection in communication systems. Literature [15–18] givesblind signal detection for MIMO channel systems. In particu-lar, the literature [15] gives the blind signal detection of

HindawiWireless Communications and Mobile ComputingVolume 2021, Article ID 5547989, 10 pageshttps://doi.org/10.1155/2021/5547989

https://orcid.org/0000-0002-2332-1275

https://orcid.org/0000-0001-9305-2173

https://creativecommons.org/licenses/by/4.0/

https://creativecommons.org/licenses/by/4.0/

https://doi.org/10.1155/2021/5547989

massive MIMO systems under general conditions. Literature[16] gives a signal detection scheme by analyzing the sparsitycharacteristics of MIMO channels. Literature [17] is used toblindly detect the signal by establishing the channel errormatrix and the spatial characteristics of the subprojection.For MIMO systems, the literature [19–22] proposed blind sig-nal detection with high-order features, which effectivelyimproved the blind signal detection performance of MIMOsystems. Literature [23] proposed a blind algorithm based onCMA, which uses constant modulus features to detect MIMOsystems.

Due to the development of neural networks, speed of sig-nal processing has been increased by neurons. Neural net-works with specific functions are formed through linkingneurons. For example, the functional neural network pro-posed in the literature [24–27] uses the reverse parametertuning model of BP network. Through the inverse parameteradjustment training model, deep network is established. Onthe basis of these BP networks, the literature [28–31] uses aforward network and a reverse network to form a two-waytraining model. Each layer of neural network includes aninput layer and a training layer, through the establishmentof a specific network layer.

Regarding the satellite IoT network, related scholarsmainly conduct research on system resource allocation [32–34], without involving the relevant physical layer technology.At present, there are many problems in satellite-ground IoTlink signal detection. For the complex characteristics of thesatellite-ground link, including Doppler and multipath effect,especially in scenarios related to military fields, it is difficultto use traditional cooperative communication methods forlink signal detection. Therefore, the paper proposes an effi-cient detection for satellite-ground links based on blind deepneural network (BNN).

2. System Model

Due to the high-speed movement of the satellite to theground and the long transmission delay of the satellite-ground IoT link, the link produces a large Doppler shift. Atthe same time, due to the multipath transmission effect ofthe satellite-ground IoT link, the signal received by the termi-nal induces greater interference. The satellite-to-ground linksignal transmission process is shown in Figure 1.

xðnÞ is defined as the transmitted signal, ξ is defined asthe carrier frequency offset, and hðn, lÞ is defined as thesatellite-to-ground link channel response. Signal received bythe ground station is yðnÞ, which is as follows:

y nð Þ = 〠L−1

l=0x nð Þ ∗ h n, lð Þ: ð1Þ

ξ is introduced as the carrier interference for Dopplershift. A signal received by ground user is defined as yðnÞ,which is expressed as follows:

y nð Þ = 〠L−1

l=0x nð Þ ∗ h n, lð Þ½ � ⋅ exp j2πnξ

N

� �, ð2Þ

where yðnÞ is after demodulation by N-DFT at the receivingground user. YðkÞ in the frequency domain can be expressedas follows:

Y kð Þ = X kð ÞH kð ÞC 0ð Þ + 〠N−1

l=0l≠k

X lð ÞH lð ÞC l − kð Þ, ð3Þ

where HðkÞ is the frequency domain expressing for channel.The first part is the noncarrier interference part, and the sec-ond part is the interference.

C l − kð Þ = sin π l + ξ − kð Þð ÞN sin π/Nð Þ l + ξ − kð Þð Þ ⋅ exp jπ

N − 1N

� �l + ξ − kð Þ

� �,

ð4Þ

where ξ = 0.By calculating Equation (4), we could obtain Cð0Þ = 1.

When ξ = 0, the interference coefficient between subchannelsis 1. There is no interference between the subchannels trans-mitted by the satellite-to-ground link. When ξ ≠ 0, the inter-ference between subchannels is superimposed on thetransmission signal XðlÞ. And, as the interference increases,superimposed multiplicative interference ∑N−1

l=0l≠k

HðlÞCðl − kÞ

occurs. Equation (4) also gives the energy leakage betweenthe carriers. As the carrier frequency deviation increases,the energy interference leakage increases.

3. Blind Neural Network Algorithm

3.1. Architecture for BNN. Figure 2 gives out the architectureof BNN. The proposed BNN includes two parts. The first partis the signal feature acquisition network, and the second partis the error network. The goal is to obtain the optimal net-work weight through the error network.

qrðkÞ and qiðkÞ are defined as the real and imaginaryparts, respectively, and as the input for the data network partof BNN. XðkÞ is considered as the frequency domain signal,which is expressed as follows:

X kð Þ = qr kð Þ + jqi kð Þ: ð5Þ

ϒ r is defined as the characteristic real part of BNN datanetwork, and ϒ i is defined as the characteristic imaginarypart of BNN data network, which can be expressed as follows:

ϒ r kð Þ = E qr kð Þj j4� �E qr kð Þj j2� � ,

ϒ i kð Þ = E qi kð Þj j4� �E qi kð Þj j2� � :

ð6Þ

2 Wireless Communications and Mobile Computing

ebnn,r and ebnn,i are defined as the real part and imaginarypart, which are also expressed as, respectively, follows:

ebnn,r kð Þ = E qr kð Þj j2 − ϒ rð Þ2� �� ,

ebnn,i kð Þ = E qi kð Þj j2 − ϒ ið Þ2� �� :

ð7Þ

Combining the cost function of the error network of theBNN, respectively, we could obtain the following:

ebnn,input kð Þ = E qr kð Þj j2 − ϒ rð Þ2� �� + E qi kð Þj j2 − ϒ ið Þ2� ��

:

ð8Þ

Error feature ebnn,inputðkÞ is used as input information toenter the error update network of BNN, as shown inFigure 3. Figure 3 gives out the architecture of BNN algo-rithm, which includes two parts. The first part is the signalfeature acquisition network, and the second part is the errorupdate network.

W is the weight of the error network, ebnn,outputðkÞ is theoutput of the BNN blind deep learning error weight network,and ebnn,inputðkÞ is the input of the BNN blind deep learningerror weight network. ebnn,optðkÞ is defined as the optimalstate of the error weight network of the optimal BNN blinddepth network; it can obtain the best signal detection perfor-mance through the weight network. It is because ebnn,optðkÞcan obtain the best interference cancellation characteristicsthrough nonlinear fitting of the weight of the activation func-tion. Optimal ebnn,optðkÞ can be achieved by obtaining theoptimal w defined as the initialized weight network. In this

case, iterative elimination performance can be obtained.The input error part is the weight part, which can beexpressed as follows:

ebnn,input kð Þ = eBNN,r kð Þ + eBNN,i kð Þ: ð9Þ

BNNerrorðkÞ is the error of ebnn,output and ebnn,input, whichcan be expressed as follows:

BNNerror kð Þ = ebnn,output kð Þ − ebnn,input kð Þ,

J =min E ebnn,output − ebnn,input�� 2n o

:ð10Þ

Therefore, the optimization goal of the algorithm is tominimize J ; according to ebnn,outputðkÞ at the first-order Tay-lor expansion of ebnn,inputðkÞ, we can obtain the following:

ebnn,output ≈ ebnn,input +Η wout −wopt� �

, ð11Þ

where

Η ≈∂ebnn,output∂wout

,⋯,∂ebnn,output∂wout

: ð12Þ

Equation (12) has been introduced, and we could get the

3G/4Gnetwork

SatelliteIoT

network

Networkmanagement

center

Networkbusiness

Gateway

Serviceprovider

Spacesegment

Usersegment

Vehicleterminal

IoT UplinkIoT downlink

Mobileterminal

Figure 1: Satellite-ground IoT link signal transmission.

Input satellitesignal network

Errornetwork

Featuresnetwork

Terminalsignal

Weight update

Figure 2: Model information processing based on BNN.

3Wireless Communications and Mobile Computing

following:

min E ebnn,output − e i+1ð Þbnn,opt

�� 2� ��

≈min E eioutput + f wopt −w ið Þoutput

�− e i+1ð Þ

output − f w i+1ð Þoutput −wi

output

�� 2� ��

:

ð13Þ

In order to reduce the objective cost function, a linearsearch method is used to obtainwout to approximate the opti-mization wopt. From the search direction λi, we can obtainthe weights of the f th iteration to approximate wi

out the opti-mization weights wi

out. Therefore, the optimization weightsare the weight approximation process, which can beexpressed as follows:

w i+1ð Þout =wi

out + λi ⋅ BNNie: ð14Þ

By approaching the λi direction, we can obtain the fol-lowing:

λi = arg minλiE eibnn,output − e i+1ð Þbnn,output

�� 2� �

= arg minλiE wioutput −w i+1ð Þ

output �∗

Ηi� �∗Ηi wi

output −w i+1ð Þoutput

�h i:

ð15Þ

Equation (14) can be obtained by expanding the follow-ing formula.

Substituting Γ = ðwopt −wioutÞ, we can get the following:

E wopt −w i+1ð Þout

�∗Ηi� �∗

Ηi w −w i+1ð Þout

�h i

≈ E I − λiΗi �

ki �∗

Ηi� �∗Ηi I − λiΗi

�ki

h i:

ð16Þ

In order to get the λi direction, we can get the following:

λi = Ηi� �∗Ηi + σ2 Ii Ii

� �H �−1 −1

Ηi� �∗: ð17Þ

Equation (17) can obtain the direction of the weightapproximation wi

out. For the ith iteration, we can obtain thefollowing:

Γi Γi� �H = ηI, ð18Þ

where η is considered as a constant.Equation (17) can be simplified into the following:

λi = Ηi� �∗Ηi + σ2 ηIð Þ−1

h i−1Ηi� �∗

: ð19Þ

Equation (19) can get the optimal direction.

3.2. Processing for BNN. Figure 4 shows the blind detectiontarget feature of the system when the weight is optimal. Theweight update of the blind neural network converges to theoptimal value, and the blind detection objective function isas follows:

minx

λ wbnnk k1 + Y −wbnn,optX�� 2

2: ð20Þ

The objective function is to obtain accurate signal inter-ference cancellation. Therefore, the error of the optimalweight detection can be obtained. Therefore, the optimal sig-nal detection can be obtained by the following:

minx

λ wbnnk k1,w + Y −wbnn,optXt

�� 22, ð21Þ

Network learning for signal Network learning for error

d(k)

d(k)

d(k)

W

ebnn, output ebnn, output ebnn, output

ebnn, input

ebnn, input

ebnn, input

woptwoptwopt

w w w

𝛶

𝛶tk-1

𝛶tk-1

𝛶tk-1

𝛶tk-1

𝛶

𝛶ebnn, opt

ebnn, optebnn, opt

ebnn, opt

Figure 3: Signal flowing for BNN.


where

wk k1,w = 〠K

i=1wi: ð22Þ

The first part of Equation (22) is the nonzero regulariza-tion part, and the second part is the error elimination part.

More specifically, the objective function feature is con-structed by constructing the rank operation of the minimizedmatrix and the feature transfer matrix.

4. Discussion

In order to further optimize the solution of the objectivefunction, we consider k⋅k1 as an alternative matrix and min-imize the closed-loop solution of blind deep neural networksestablished wBNN by iterative methods. Therefore, when thecurrent data x will become more effective, we use an updatediteration method to solve the current objective function. Theimproved iteration method is as follows:

minx

λ wk k1,w + αt X − X�� 2

2 + Y −wbnn,optX�� 2

2: ð23Þ

The established objective iteration function can be

Blind signal detectionWeight update network

Signaloutput

Figure 4: Blind signal detection after w weight convergence.

2

1.5

1.5 2

1

1

0.5

0.5

0

0

–0.5

–0.5

–1

–1

–1.5

–1.5–2

–2

Figure 5: QPSK signal constellation with ζmax = 0:1.

2

1.5

1.5 2

1

1

0.5

0.5

0

0

–0.5

–0.5

–1

–1

–1.5

–1.5–2

–2

Figure 6: QPSK signal constellation with LS algorithm.

2

1.5

1.5 2

1

1

0.5

0.5

0

0

–0.5

–0.5

–1

–1

–1.5

–1.5–2

–2

Figure 7: QPSK signal constellation with CMA algorithm.

2

1.5

1.5 2

1

1

0.5

0.5

0

0

–0.5

–0.5

–1

–1

–1.5

–1.5–2

–2

Figure 8: QPSK signal constellation with BNN algorithm.


expressed as follows:

L X, z, ttð Þ = αt X − X�� 2

2 + Y −wbnn,optX�� 2

2 + t wbnn,opt − wbnn,opt�� 2

2:

ð24Þ

The iterative method of weighted x, w, and u can be

expressed as follows:

X k+1ð Þt = arg min

xαt X − Xt

�� 22 + yt −wtXk k22 + β w −w kð Þ

t + p kð Þt

�� 22

� �,

w k+1ð Þbnn,opt = arg min

ww − wbnn,opt

�� 22 + β w k+1ð Þ

bnn,opt −w kð Þbnn,opt + p kð Þ

�� 22

� �,

p k+1ð Þt = p k+1ð Þ

t + X k+1ð Þt −w k+1ð Þ

t :

ð25Þ

100

10–1

10–2

10–3

10–4

10–5

10–60 5 10

Average SNR

Aver

age B

ER

15 20

Ideal conditionsNo detectionLS algorithm

CMA algorithn [23]BNN algorithm

Figure 9: BER performance of QPSK signal under different algorithms (with frequency offset ξmax = 0:2).

100

10–1

10–2

10–3

10–4

10–50 5 10

Average SNR

Aver

age B

ER

15 20





The ability of BNN could be obtained with the constella-tion simulation. Defining SNR = 15 dB, the frequency offsetsare defined within the range [-ξmax, ξmax]; ξmax is the maxi-mum frequency offsets. Figure 5 gives the signal constellation

within ξmax = 0:1. Figure 6 gives the signal constellation withthe LS algorithm. Figure 7 gives the signal constellation withthe CMA algorithm. Figure 8 gives the signal constellationwith the BNN algorithm. As seen from figures, the CMA

100

10–1

10–2

10–3

10–4

10–5

10–60 5 10

Average SNR

Aver

age B

ER

15 20




Table 1: Channel model parameter in urban environment.

Tap Distribution function Parameter Parameter distribution Numerical value/dB Time delay/ns

1LOS: Rician Rice factor K 5.3

0nLOS: Rayleigh Power 2σ2l -12.1

2 Rayleigh Power 2σ2l -17.0 60



Table 2: Channel model parameter in suburban environment.






Table 3: Channel model parameter in rural environment.






scheme could not detect completely for variable frequencyoffset. The BNN can effectively eliminate interference.

It is because the BNN needs to adjust to different channelmodel characteristics through parameter fitting, so as tocomplete the acquisition of feature weights. The essence isto seek the optimal weights. The LS algorithm and theCMA algorithm only adopt one single-layer signal transfor-mation, which mainly adopts hard decision method andintroduces relatively large errors. This is due to the hard deci-sion characteristics of the single-layer transform, and there isno need to obtain extreme values in the signal plane space.Because the BNN algorithm can obtain the best in the signalspace and weight matrix, thereby it improved the perfor-mance of the algorithm.

5. Experimental and Analysis

In order to research the BNN, the simulation model of a LEOwith a height of 1500 km is established. And the time for asingle low-orbit satellite to pass the top is 10 minutes. Weuse the 3-path model with direct main path components.The main path is accord to the Rician distribution. The max-imum extended delay is 250ns. The maximum working ele-vation angle is 35°. Define the uplink transmission bit ratebe 40Mbit/s.

Setting IoT link that the cyclic prefix length is greaterthan the maximum delay, it can be known from the delayextension that the guard interval should be 1μs, and the sym-bol period can be 1μs × 5 + 1 μs = 6 μs. Therefore, each sym-bol needs to transmit ð40Mbit/sÞ/ð1/6 μsÞ = 240 bits. At thistime, the relative carrier frequency deviation factor can becalculated as 0.4. With QPSK modulation, each subcarriercan transmit 2 bits, and 240/2 = 120 subcarriers are required.8 zero-padded subcarriers can facilitate the implementationof 128-point FFT/IFFT.

In order to facilitate the analysis, defining the number ofusers in the uplink access system is 4. The subcarrier map-ping method is IFDMA, and the signal mapping method isQPSK. The simulation uses block pilots for informationtransmission. Take the cyclic prefix length greater than themaximum extended delay of each path.

5.1. BER Analysis. ξmax is defined as the maximum frequencydeviation range allowed by the carrier. Figure 9 andFigures 10 and 11 show different bit error rate curves for allusers, including the case of no detection, ideal condition, LSalgorithm, the CMA criterion-based equalization algorithmproposed in [23], and the proposed BNN frequency offsetinterference elimination algorithm. Figure 9 shows the differ-ent bit error rate curves under the current condition ξmax =0:2. Figure 10 shows the different bit error rate curves undercurrent condition ξmax = 0:35. Figure 11 shows the bit errorrate curve current conditions ξmax = 0:4. The conditions ofFigure 9 and Figures 10 and 11 all assume that each subchan-nel is independent. It can be obtained that the bit error ratecurve without algorithm is larger. Due to interference, withincreasing of the SNR, the BER performance does not havea trend of improvement. The CMA algorithm can eliminatesome ICI interference. Compared with the blind algorithmbased on CMA, the BNN algorithm improves the frequencyoffset elimination performance in the frequency domain.

It can be seen from the simulation figures that for the iter-ative BNN algorithm, this is because the weight w can con-verge to a position close to the optimal value, which canobtain better bit error rate performance for user signals andgreater interference cancellation performance. The BER curveof the four users of uplink access is different within frequencyoffset ranges. As the frequency offset range increases, the BERperformance of the user signal increases. When the weight isclose to the optimum, the system signal BER is optimized.

0 20 40

Number of symbols

60 80 100 120

1

0.9

0.8

0.7

0.6

0.5

0.4M

SE0.3

0.2

0.1

0

Suburban enviromentRural enviroment

Urban enviroment

Figure 12: Analysis for convergence of different iteration numbers based on BNN.


It is because the BNN needs to be adapted for differentchannel model characteristics through parameter fitting, soas to complete the acquisition of feature weights. The essenceis to seek the optimal gradient direction to obtain the optimalweights. Under the condition of low signal-to-noise ratio,especially within 3 dB, the LS algorithm and the CMA algo-rithm have better characteristics. This is due to the hard deci-sion characteristic of the single-layer transform, and there isno need to obtain the optimal value. Because the parametersof the BNN are difficult to adjust, it is difficult to obtain theoptimal weight under the low signal-to-noise ratio. Interfer-ence under low signal-to-noise ratio conditions makes it dif-ficult to obtain better extreme values in the signal space.Under the condition of high signal-to-noise ratio, the BNNalgorithm can obtain the best in the signal space and obtainthe best weight matrix, thereby improving the performance.

5.2. Convergence Analysis. We establish different satellite-to-ground link channel models and conduct statistical analysisof the BNN algorithm to prove the effectiveness and reliabil-ity in different satellite-to-ground link scenarios.

It is characterized by the convergence analysis of thealgorithm on different satellite-to-ground links, so as toobtain the high-quality performance.

Convergence is embodied in the BNN parameter adjust-ment time under the complex environment of differentsatellite-ground links, especially simulation modeling underthree different link scenarios, urban environment given inTable 1, suburban environment given in Table 2, and ruralenvironment given in Table 3. This also shows the fittingtime of the blind neural network to environmental parame-ters in different environments.

The specific scenarios are as follows:The BNN needs to be adaptive for different channel

model characteristics through parameter fitting given inFigure 12. So as to complete the acquisition of featureweights, the essence is to seek the optimal gradient directionto obtain the optimal weights. Due to the complexity of theenvironment, the tuning parameter of the algorithm isincreased, especially in search for the weight of the optimalgradient direction. It can be obtained from Figure 12; thechannel model of the complex environment increases theoptimal weight, and the gradient iteration is improvement.

5.3. Algorithm Complexity and Efficiency. In core modulebased on BNN algorithm, which is the iterative convergenceof the optimal gradient, the core iteration module has thecomplexity of Oð2nÞ. The current convergence is for curveanalysis and simulation of computer CPU configuration withIntel i7 4 cores. The processor has a clocked frequency of3.8GHz and 64G memory, and the convergence runningtime of the core algorithm is 50ms. The algorithm also hasreal-time performance.

6. Conclusion

This paper proposes an efficient detection of satellite-to-ground links based on blind deep neural networks (BNN).The BNN includes two network structures, the data feature

network and the error update network. Through obtainingthe optimal weight, the weight of BNN for blind detectionis optimized and the optimal elimination solution is obtainedwith iterations error updating network. In order to obtainsimulation performance, we establish the satellite-groundIoT link model; we could obtain better bit error ratecharacteristics.

Data Availability

The data used to support the findings of this work are avail-able from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Qing-yang Guan and Wu Shuang contributed equally to thiswork.

Acknowledgments

This work was supported by the Regional Innovation Capa-bility Guidance Project of Science and Technology Depart-ment of Shaanxi Province (Grant No. 2021QFY01-08), theNational Natural Science Foundation of China (No.61501306), the Scientific Research Initiation Funds for theDoctoral Program of Xi’an International University (GrantNo. XAIU2019002 and XAIU2018070102), the General Pro-ject of Science and Technology Department of Shaanxi Prov-ince (Grant No. 2020JM-638), and the Natural ScienceFoundation of Liaoning Province of China (No.2015020026).

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