High Spectral Efficiency Secure Communications with Non-Orthogonal

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High Spectral Efficiency Secure Communications with Non-Orthogonal Physical and Multiple Access Layers Min Jia, Senior Member, IEEE, Dongbo Li, Student Member, IEEE, Zhisheng Yin, Student Member, IEEE, Qing Guo, Member, IEEE, Xuemai Gu, Member, IEEE. Abstract—Internet of Things (IoT) as an essential integrated part of the future wireless communication system provides ubiquitous connectivity and information exchange to enable a range of applications and services, which has triggered spectrum resource pressure, multiple access, bandwidth efficiency and security issues. Focusing on these issues, a high spectral efficiency secure access (HSESA) scheme based on dual non-orthogonal is proposed firstly in this paper. The scheme which can be recognized as a dual non-orthogonal scheme is designed by the non-orthogonal multiplexing and non-orthogonal multiple access. Particularly, HSESA scheme is equipped with secure multiplexing by using security matrix to improve physical layer security. Moreover, spectral efficiency analysis is given and the throughput of HSESA has been derived. Moreover, iterative detection (ID) and maximum likelihood (ML) are respectively combined with message passing algorithm (MPA) as detection schemes, and their respective performance advantages are analyzed. Simulation results show that the detection scheme using ID combined with MPA has lower complexity, while ML combined with MPA has better bit error rate (BER) performance, and the spectral efficiency is also enhanced by the proposed HSESA. 1 Index Terms—IoT, HSESA, spectral efficiency, security, non-orthogonal multiple access, non-orthogonal multiplexing.

I. INTRODUCTION As a crucial integrated part of future wireless communications systems, internet of things (IoT) provides ubiquitous connectivity and information exchange to achieve a range of services and applications such as smart cities, global satellite communications and security emergency system [1]-[4]. However, IoT has also triggered a large pressure of spectrum resource, bandwidth efficiency and security concerns, due to massive data access and the heterogeneous network environment [5]-[7]. Consequently, these aspects of IoT has aroused widespread concern recently. Orthogonal multiple access (OMA) schemes such as Orthogonal Frequency Division Multiple Access (OFDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), and Frequency Division Multiple Access (FDMA) assign different users to orthogonal resources Manuscript is received May, 2018. The authors are with the Communication Research Center, School of Electronics and Information Engineering, Harbin Institute of Technology.(e-mail: [email protected]) Digital Object Identifier Copyright (c) 2012 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to [email protected].

or subcarriers in either the time, frequency, or code domain in order to mitigate inter carrier interference (ICI). OMA schemes are ideal choices for achieving system-level throughput performance through single-user detection in packet-domain services. However, the spectral efficiency is alarming when the scarce bandwidth resources is solely occupied by users with poor channel conditions. OMA schemes can hardly meet the coming requirements such as data rate, spectrum resource and network spectral efficiency for IoT [8]. Non-orthogonal multiple access (NOMA) has been proposed in the third generation partnership project long-term evolution (3GPP-LTE) and is envisioned to be an essential enabling technology of IoT. The main feature of NOMA is that multiple users are served at the same time/ frequency/ code, which yields significant spectral efficiency gain over traditional OMA. It is generally divided into two categories, namely the power domain multiplexing and code domain multiplexing such as low-density spreading (LDS) [9], sparse code multiple access (SCMA) [10]-[12], multiuser shared access (MUSA) [13], and so on. LDS and SCMA both enjoy the low complexity reception technique but SCMA provides additional coding gain over LDS by introducing multi-dimensional complex codebooks or constellations. Some other multiple access schemes such as lattice partition multiple access (LPMA) [14], pattern division multiple access (PDMA) [15], bit division multiplexing (BDM) [16]. In NOMA schemes, each orthogonal resource block serves more than one user, which ensures that users with different channel conditions can utilize the same bandwidth resources concurrently, thus achieving a good balance between user fairness and system spectral efficiency [17]-[20]. Non-orthogonal data transmission schemes in both time and frequency domains have been proposed to improve bandwidth efficiency relative to the conventional orthogonal frequency division multiplexing (OFDM). Spectrally efficient frequency division multiplexing (SEFDM) and faster-than-Nyquist signaling (FTN) have been the two important research directions exploring how to transmit at rates exceeding the orthogonality of Nyquist limit for interference free transmission for a given channel, which provides advantage significant bandwidth saving for the same symbol rate[21]-[26]. In this paper, high spectral efficiency secure access (HSESA) is proposed based on OFDMA and has more advanced properties: i) binary time domain data are mapped to multidimensional complex domain sparse codewords with security by non-orthogonal security mapping, ii) the overload

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factor of HSESA can be changed by designing the codebooks so that the system can withstand multiple user accesses, iii) non-orthogonal overlapped subcarriers are used for non-orthogonal multiplexing to enhance bandwidth utilization by reducing the spacing between the sub-carriers in frequency, iv) by changing the bandwidth compression factor, HSESA can be flexibly switched between orthogonality and non-orthogonality during the multiplexing process, v) HSESA jams the reception of the eavesdropper by using security matrix to improve physical layer security, vi) iterative detection (ID) combined with message passing algorithm (MPA) detection and maximum likelihood (ML) combined with MPA detection are applied in HSESA to mitigate the serious ICI produced by multiplexing overlapped carriers and detect the sparsity of multidimensional codewords. The rest of this paper is arranged as follows. Section II gives the orthogonal system model and the proposed system model of HSESA. Section III describes the detection scheme including non-orthogonal signal demultiplexing and multiple users detection. The spectral efficiency analysis and the complexity analysis are given in Section IV, after which the simulation results are presented and discussed. Finally, Section V concludes this paper. II. SYSTEM MODEL A. OFDMA System Model In OFDMA uplink system, NOFDM orthogonal subcarriers are divided into QOFDM subchannels. Each subchannel is composed of P  NOFDM / QOFDM subcarriers. Assume that there are J OFDM ( J OFDM  QOFDM ) users in OFDMA system and one subchannel or multiple subchannels are allocated to a user. The block diagram of OFDMA is shown in Fig.1. Mapper

S/P

IFFT

···

PSK&QAM Modulation

···

User Layer 1

P/S H



··· Mapper

S/P

IFFT

P/S

Noise ω



···

PSK&QAM Modulation

···

User Layer j



HSESA is shown in Fig.2. 1) Non-orthogonal Security Mapping The encoder is defined as a map from m  log 2 | M | bits to a N -dimensional complex codebook with size M . We define

b j  b j ,0 , b j ,1 ,..., b j ,m-1 

T

T

and x j   x j ,0 , x j ,1 ,..., x j , N 1  are respectively represent the input binary bits and multi-dimensional codewords of user j . The inherent features of HSESA system limit the number of subcarriers that each user can access, which means that user layers need spread its signal in a sparse way. Thus, the function can be formulated as follows. d j  p j (b j ) (1) where

d j  d j ,0 , d j ,1 ,..., d j , L 1 

T

is

a

L

-dimensional

constellation point corresponding to b j sent by user j and p j is a constellation function of user j . Then, the L -dimensional constellation point d j is mapped to a N -dimensional codeword x j through a mapping matrix V , which can be realized as follows.

x j  Vd j

(2) where V is a N  L matrix which is constituted by inserting N  L all-zero rows into a L -dimensional identity matrix. We define N  L to ensure the sparsity of codewords. The



codewords of all the users form x j ,1  j  J



are then

mapped into MPSK/MQAM constellations. The secure mapping is illustrated in Fig.3 according to the phase of the codeword.  2 2   j , 0  angle  x j , n    2 2 2   2 2   j ,  angle  x j , n      2 2 2 x j ,n  1 j  J ,0  n  N 1 2 2 3    2  j 2 ,   angle  x j , n   2  2 3  2 j ,  angle  x j , n   2   2 2 2

(3)

T

Demapper

P/S

FFT

···

Demodulation

···

···

User Layer 1

S/P

User Layer j

Fig.1. Block diagram of OFDMA system model

Quadrature amplitude modulation (QAM) or phase shift keying (PSK) is used to modulate symbol. Mapping P symbols of each user to a subset of NOFDM subcarriers by interleaving subcarrier mapping. Then the Inverse Fast Fourier Transform (IFFT) of size N is performed over the mapped signals. The received signal after passing through the channel performs Fast Fourier Transform (FFT). Binary bits of each user layer can be then estimated by demapper and demodulation. B. HSESA System Model In this paper, HSESA is proposed based on the OFDMA system model for the uplink. Assume that J users share N non-orthogonal subcarriers and J  N . The block diagram of

The codeword x j is converted to S j   S j ,0 , S j ,1 ,..., S j , N 1  , which can be formulated as follows. S j  φ j x j ,1  j  J (4) T

where φ j   j ,0 ,  j ,1 ,...,  j , N 1  is a N -dimensional column T

T

vector. S j   S j ,0 , S j ,1 ,..., S j , N 1  and x j   x j ,0 , x j ,1 ,..., x j , N 1  are both N -dimensional column vectors. x j , n is the n th row element of x j , which is converted to a MPSK/MQAM symbol S j , n through multiplying a mapping parameter  j , n . is Hadamard product, which means the product of the corresponding elements of two matrices. Security matrix A is a J  N matrix which is used for security mapping. Each transmission symbol of the security matrix A is unique and the receiver needs to know the corresponding security matrix A to demodulate the message.

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Non-orthogonal Security Mapping

Encoder

S1,0 S1,1

S1

Security Matrix

S/P

K-N Ignore

SJ

sJ ,0 sJ ,1

S/P

K-point IDFT

S J , N 1

P0

R0

r0

y1

P1

R1

r1

y N 1

Detector

K-point DFT

RN 1

PN 1

K-N Ignore

User Layer J

S/P

rN 1 ···

Security Demapping

···

P/S

···

··· bˆ J

y

Decoder

Non-orthogonal Demultiplexing

···

User Layer 2

Noise ω

r

y0

···

bˆ 2

User Layer 1

P/S

K-N Ignore

Non-orthogonal Security Demapping bˆ 1



sJ

sJ , N 1

···

K-N Zeros

s



Security Matrix

S J ,0 S J ,1

h

s1



xJ

P/S

···

Spreader

s1, N 1

Non-orthogonal Multiplexing

···

dJ

Encoder

K-point IDFT

···

Non-orthogonal Security Mapping

bJ

s1,0 s1,1

S1, N 1 K-N Zeros

··· User Layer J

Spreader

x1

···

User Layer 1

d1

···

b1

Non-orthogonal Multiplexing

K-N Zeros

Fig2. Block diagram of HSESA system model T

A can be written as A   A1 ,..., A j ,..., A J  and the j th row and the n th column element is defined as Aj , n  x j , n / S j , n , j  1,..., J ; n  0,..., N  1

(5)

where F 1 is the normalized K -point IDFT matrix. FN-1 is the first N rows and N columns of F 1 . The k th row and the n th column element of F 1 is defined as follows. 1

Fk, n1 

Im

N

e

j 2

kn K

, 0  n  N  1, 0  k  N  1

(7)

The n th sample of the transmitted signal vector of the j th user can be represented as Re

s j ,n 

Fig3. Security Mapping

2) Non-orthogonal Multiplexing S j is transmitted over N shared subcarriers. In the Non-orthogonal multiplexing process, K -point Inverse Discrete Fourier Transform (IDFT) operator is used for generating HSESA time signal. K -N zeros are first filled following S j . The output of the K -point IDFT block is truncated with only the first N samples retained while the remaining samples are discarded. The generated baseband HSESA sample signal vector can be



represented as s  s1 , s2 ,..., s J



T

, and the sample signal vector

of user j can be represented as s j   s j ,0 , s j ,1 ,..., s j , N 1 

s j  F 1  STj

T

T

0T K  N 1   FN1  S j

(6)

1

N 1

S e N n 0

j 2

kn  N

n

, 0  n  N  1, 0  k  N  1 (8)

where   N K denotes the bandwidth compressing factor. Assuming that the sampling frequency is chosen as Fs  N f , where f is the subcarrier spacing for HSESA, then there will be N samples in one HSESA signal period T , and f   T . Specially,   1 corresponding to f  1 T results in an orthogonal scheme. The time domain signal s is modulated and then transmitted through wireless channels. The time domain sample signal r received from the Gaussian channel in the HSESA receiver can be represented as J

r  h j s j  ω

(9)

j1

where h j  diag h j ,0 , h j ,1 ,

, h j , N 1 

T

is the channel state

information of j th user. ω denotes the Gaussian White Noise. The time domain sampled signal achieves K -point Discrete Fourier Transform (DFT) transform by inserting K -N zero following the tail of time domain sampled signal

r   r0 , r1 ,..., rN 1  . Similarly, the corresponding first N T

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4 samples of the output of DFT block are extracted for further demultiplexing. Hence, received signal in frequency domain can be written as R   R0 , R1 ,..., RN 1  . T

T

R  F  rT

0T K  N 1   FN r

(10)

where F is the K -point DFT matrix, FN is the first N rows and N columns of F . The k th row and the n th column element of F is defined as follows. Fk , n 

1

e

N

- j 2

kn K

, 0  n  N  1, 0  k  N  1

(11)

The frequency domain signal R is converted to T P   P0 , P1 ,..., PN 1  by detector. Then P is converted to

y   y0 , y1 ,..., yN 1  by security demapping, which can be

B. Message Passing Algorithm Message Passing Algorithm is utilized to reduce the computation complexity with the help of Factor Graph. It is worth noting that the messages are bidirectional [9] [10]. In this paper, we assume I v j  rn and I rn v j to be the message passing from the j th user layer to the nth subcarrier and the message passing from the nth subcarrier to the j th user layer. The message will be updating iteratively, in the q th iteration, the updating progress can be formulated by I rqn v j (x j )   M n ( X)  ( I vq1 rn (x )) (15)  n / j XX[n ] x j x j

I

q v j  rn

(x j )  normalize(

T

realized as follows.

yP

Aj

(12)

T

where A j   Aj 0 , Aj1 ,..., AjN 1  is a N -dimensional vector. P is sent to the decoder of HSESA system. III. DETECTION SCHEME A. Non-orthogonal Detection Additional demodulators are required to eliminate interpolation distortion due to loss of orthogonality between subcarriers in HSESA system. This system lists two detection methods: ID and ML. The detection methods are separately used to mitigate the serious ICI produced by multiplexing overlapped carriers and compared the BER and complexity. 1) Iterative Detection ID algorithm is a compensation approach against the distortion to remove the corresponding ICI. The main idea of ID is that a signal P is distorted by distortion matrix G and can be expressed as P  GP . The distortion signal P can used to recover P . The algorithm of iteration calculation for HSESA can be represented as follows. Pn   P0   I  G  Pn 1 (13) where  is a convergence factor, Pn is a vector of recovered symbols after n iterations and Pn 1 is a vector after n-1 iterations. I is a identity matrix. If the power of distortion is less than the power of the signal which can be represented as P  GP  P , the desired signal can be recovered after an 2 2 infinite number of iterations by ID method. 2) Maximum Likelihood Detection ML algorithm searches the entire set of transmitted signal , and determines the transmit signal Sˆ ML that is Sˆ  conducive to minimizing the Euclidian distance from the receive vector R , i.e[27].

Sˆ ML  arg min R  Sˆ ˆ S

2

(14)

where denotes the constellation space. Finally, the ML ˆ demodulated symbol vector S ML is seen as the estimation of the transmitted symbols from secure matrix, which is used to secure demapping and further demultiplexing for multiusers.

 (I

 ε j / n

（x )))

q r v j

(16)

[η ] where X n  {Cn ,1  Cn ,2 ..  Cn ,dc } and C1  C 2  ...  C J  means

the Cartesian product. normalize stands for normalization. v j represents the variable node of user j and rn represents the function node of subcarrier n . X  {x1 , x2 ,..., x J } is the codewords of J user layers. X is one combination of X[ η ] with x j  x j . Besides, n

M n ( X)  exp(

1

2

( yn   x j , n ))

(17)

jn

Once the iteration number reaches the maximum number or the codewords of user layers have been converged into a specific level, the algorithm will stop and estimate the most reliable codewords of each user layer. IV. NUMERICAL ANALYSIS AND SIMULATION A. Spectral Efficiency Analysis Assuming that the signal bandwidth is chosen as B  N f , where f is the subcarrier spacing for HSESA, then there will be N samples in one HSESA signal period T , and f   T . In order to make fair comparisons between different systems, all considered systems shall use the same channel bandwidth B . To achieve this, the number of subcarriers of HSESA shall be set as follows. N  NOFDM  (18) where NOFDM is the number of OFDM subcarriers in the bandwidth under consideration. According to [28] and the codebook, the covariance matrix N s of the codeword can be considered as follows. 2 Ns  diag ( E (s1,1 )

where

sj

E(s1,2 N )

E(s 2j , n )

E(sJ2,1 )

E( sJ2, N )) (19)

2 denotes a 1 N column vector and E ( s j , n )

represents the average power which is allocated to the codeword of the j th user in the n th dimension. HSESA throughput can be derived by referring to [29]. The mutual information can be represented as follows. I (s; r | h)  log(( e) N det( N 0 I N  hN s h* ))  N log( eN 0 ) 1  log det(I N  hN s h* ) N0

(20)

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5 where N 0 is the average power of noise and the conjugate transpose of h is denoted as h* . Then, the covariance expression of Eq. (19) is substituted into Eq. (20), 1 hN s h* ) I (s; r | h)  log det(I N  N0

where  j , n is defined to show the connection between users

  2 2 (21)   h j ,n E (s j ,n )  j 1    log 1+   N0 n 1     The frequency domain received signal vector of the n th sub-channel can be represented as follows.

The average power of the j th user can be defined as p j and

J

N

 J  J Rn  F   h j  s j  ω    FNn h j FN1S j  FNn ω (22)  j 1  j 1 Further dividing the expression can be expressed as follows.

and subcarriers, i.e.,  j , n = 0 means user j is not connected to subcarrier n , otherwise,  j , n = 1 . Average power

allocated to the codeword of the j th user in the n th dimension.

p1   p j  pJ  p .

F h F j 1

n N

j

J

h

1 N

J

(29)

N

C  f  log 1+ n  n 1

     N f log 1+   N 0  12 N  

N 1

1 J N 1 1 J N 1 N 1 j 2 S j , n    e  N j 1 i  0 N j 1 i  0 m  0

i m  n

1 J N 1 N 1 j 2   S j , n    e N j 1 i  0 m  0 j 1 J

i m  n K

N

N 1

1

S j ,m 

 wi e

N 1

we

- j 2

ni K

i

i 0

i 0

- j 2

ni K

i 0

we

N

mn

N 1

1

S j ,m 

K

mn

- j 2

(24)

ni K

i

  1 N0  E    N

N 1

we i 0

dc  p / dv J

N 1

 p j 1 m  0 mn

j ,m



1 e 1 e

j 2  m  n  j 2  m  n  N

    2     

(30)

Therefore, the spectral efficiency of HSESA system is         dc  p / dv SE  log 1+ (31) 2  j 2  m  n    J N 1  1 1 e  N 0  2  p j , m   j 2  m  n  N j 1 m  0   mn 1 e N   B. Complexity Analysis

From (24), the second term represents the ICI induced by the employment of non-orthogonal subcarriers and the third term is noise term. The average power of noise can be calculated as follows. - j 2

ni 2 K

i

2   n  N  

N 1

 e

- j 2

ni 2 K

(25)

i 0

The average power of ICI can be calculated as follows.  imn  1 J N 1 N 1 j 2 K PI  E    e S j,m  N j 1 i  0 mm  0n 

2

  1  2  N 

J

N 1

 p j 1 m  0 mn

j,m



1  e j 2  m  n  1 e

j 2  m  n 

2

(26)

N

(27)

n 1

where  n represents the signal-interference-noise-ratio (SINR) of the n th sub-channel.  n can be represented as follows. J

 h2j ,n E (s 2j ,n ) j 1

N 0  PI

J



h j 1

 j ,n p j ,n

2 j ,n

N 0  PI

each iteration,  I  G   Pn 1 performs N 2 CM and N 2  N

 I  G   Pn1 .

N

C  B  log 1+ n 

ID combined with MPA detection and ML combined with MPA detection are applied in HSESA, and they have different detection principles. The complexity of these two methods is evaluated by number of complex additions (CA) and complex multiplications (CM). For ID combined with MPA detection, the matrix G is first obtained which need N 3 CM and N 3  N 2 CA.  P0 and G performs N 2  N CM.  I  G  will add N 2 CA. In CA, and N CA is performed by the add operator of  P0 and

The throughput can be defined as follows.

n 

 j ,n p j ,n =dc  p j /dv

2 j ,n

The throughput of HSESA system is

S j  FNn ω

(23) Taking into account the characteristics of the Gaussian channel, received signal vector in frequency domain of nth sub-channel can be simplified as follows. Rn 

j 1

1 N

 i ,i N 1 j 2 i  m  n   1 N 1 - j 2 ni K S j ,m   wi e K  h j   e    N  0 j 1 i  0  m0 i    N 1 j 2 i  m  n  1 J N 1 1 J N 1 1 K   hij,i  S j , n    hij,i   e S j ,m    N j 1 i  0 N j 1 i  0  N m0 mn   

d v is defined as the number of

subcarriers connected to one user layer ， and a variable d c defined as the number of user layers connected to one subcarrier. Then

n N

J

p j , n is

(28)

Therefore, the computation complexity of ID

 

3 2 detection can be calculated as N  N   v  1  N CM and

N  N 3

2

  v CA, where v

denotes the number of iterations.

The computation complexity of MPA detection can be d calculated as ((dc  1)M c dv J  (dv  1)Mdc N )v CM and

M dc dv Jv CA. For ML combined with MPA detection, Sˆ is a fixed vector, and R is a vector with N 4 possibilities. When only one

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6 possibility is considered, R  Sˆ is provided with N CA. 2

R  Sˆ

performs N CA and N  1 CM. Therefore, the

compression factor approaches 1, both ML combined with MPA detection and ID combined with MPA detection show good performance.

computation complexity of ID detection can be calculated as N 5 CM and 2N 5  N 4 CA. The comparison of the complexity of ID combined with MPA and ML combined with MPA are summarized as TABLE I.

-1

10

-2

10

BER

TABLE I COMPARISON OF THE COMPLEXITY OF ID COMBINED WITH MPA AND ML COMBINED WITH MPA

0

10

-3

10

ID combined with MPA

N +N

CM

3

2

   v  1 +N

+((d c  1) M dc d v J N + N 3

2

  v+M

dc

ID combined with MPA  =4/5 ID combined with MPA  =4/6 ML combined with MPA  =1

-4

N  ((dc  1) M dv J dc

5

10

ML combined with MPA  =4/5 ML combined with MPA  =4/6

(dv  1) Mdc N )v

0

2

4

6

dv Jv

2 N  N +M dv Jv 5

4

dc

C. Simulation Results In this section, the comparison of ID combined with MPA detection and ML combined with MPA detection in BER performance is given in AWGN channel. Further, the analysis of CM and CA computation numbers for ID combined with MPA detection and ML combined with MPA detection are given. Finally, the spectral efficiency performance of HSESA system is given compared with OFDMA combined with security. The configurations of HSESA and OFDMA are set as the following TABLE II. TABLE II CONFIGURATIONS OF HSESA

10

x 10 3.5

ID combined with MPA ML combined with MPA

3 2.5 2 1.5 1 0.5 0 1

0 0.9

Implication

Value

Subcarrier bandwidth of OFDMA

x1 x2 x3 x4

The number of OFDMA subcarriers The number of OFDMA user layers

p

The transmit power per user

K

The number of FFT or IFFT point



Bandwidth compression factor

N

The number of HSESA subcarriers

N / N/K NOFDMA /  OV  N

J

The number of HSESA user layers

OV

Overload factor of HSESA

J/N

f

Subcarrier bandwidth of HSESA

  fOFDM

dv dc

12

The complexity evaluated by the computation numbers of CM and CA are shown in Fig.5 and Fig.6 respectively by using the analyzed results in TABLE I.

50

0.8

The number of subcarriers connected to one user layer The number of user layers connected to one subcarrier

100

0.6 0.5

150

DFT size



Fig.5. Comparison of HSESA system CM computation numbers for ID combined with MPA and ML combined with MPA

In Fig.5, three elements of CM computation number, the different bandwidth factors and the DFT sizes constitute a three-dimensional figure. Compared to ML combined with MPA detection, ID combined with MPA detection has demonstrated significant reduction in complexity and is almost negligible. Similarly, three elements of CA computation number, the different bandwidth factors and the DFT sizes constitute a three-dimensional figure and we can get almost exactly the same results as in Fig.6. Therefore, it can be concluded that ID combined with MPA detection has much lower complexity than ML combined with MPA detection.

x5 dv  J / N

Fig.4 shows the comparison of ID combined with MPA detection and ML combined with MPA detection in BER performance. The number of HSESA subcarriers is 4 and the number of HSESA user layers is 6. The codebook of different user layers is obtained by referring to [11]. Six curve groups are respectively denoted the BER performance for ID combined with MPA and ML combined with MPA detections for a range of bandwidth compression factors   4 6, 4 5,1 . ML combined with MPA detection has better BER performance than ID combined with MPA detection. As the bandwidth

10

x 10 7

complexity of complex additions

fOFDM NOFDMA J OFDMA

10

Fig.4. Comparison of HSESA system BER performance for ID combined with MPA and ML combined with MPA

0.7

Symbol

8

Eb/N0 (dB)

+(d v  1) Md c N )v

CA

ID combined with MPA  =1

ML combined with MPA

complexity of complex multiplications

Symbol

ID combined with MPA ML combined with MPA

6 5 4 3 2 1 0 1

0 50

0.9 0.8

100

0.7 0.6 0.5

150

DFT size



Fig.6. Comparison of HSESA system CA computation numbers for ID combined with MPA and ML combined with MPA

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7 The comparison of uplink spectral efficiency performance of HSESA for different overload factors and OFDMA combined security is shown in Fig. 7 and Fig. 8 for a wide range of SNR

8 OFDMA combined with security

   N  . It should be noted that for the 2

0

fairness of comparison, the security mapping in HSESA is also applied in OFDMA. The subcarrier bandwidth of OFDMA is 15kHz . The transmit power per user is 1dBm . The number of OFDMA subcarriers is 4. The number of subcarriers connected to one user layer is 2. The gain of HSESA is obvious and it grows as the SNR increases. The overload factor (OV) is an important indicator of the system and is limited by the codebook. The system considers OV as 150% and 250% by referring to [11] and [30] respectively. It can be seen clearly that HSESA shows a better spectral efficiency performance than OFDMA for the same bandwidth. This situation is due to ICI after subcarrier spacing is compressed. The spectral efficiency of HSESA is demonstrated at different bandwidth compression factors   1, 4 5, 4 6 in the case of each OV. It can be seen from the figure that the smaller bandwidth compression factor of HSESA has better spectral efficiency performance. It can be seen from Fig. 9 that the bigger OV of HSESA with   4 / 5 has better spectral efficiency performance. 10 OFDMA combined with security HSESA  =1 OV=150%

9

Spectral Efficiency（ bit/s/Hz）

HSESA  =4/5 OV=150% HSESA  =4/6 OV=150%

8

HSESA  =4/7 OV=150% 7 6 5 4 3 2 1

0

2

4

6

8 SNR(dB)

10

12

14

16

Spectral Efficiency（ bit/s/Hz）

and SNR=10log10 E S j ,n

7

OFDMA combined with security HSESA  =1 OV=250% HSESA  =4/5 OV=250%

Spectral Efficiency（ bit/s/Hz）

5

4

3

1 0

2

4

6

8 SNR(dB)

10

12

14

16

Fig.9. 4/5 compression factor HSESA vs OFDMA combined with security spectral efficiency performance.

V. CONCLUSION A high spectral efficiency secure access based on dual non-orthogonal scheme has been proposed to deal with three significant issues which are data rate, multi-user access and private security in IoT network. HSESA is the firstly proposed by its dual non-orthogonal characteristics in multi-user access and data multiplexing, where non-orthogonal subcarrier resource is arranged by non-orthogonal sparse codes. Particularly, a secure matrix is combined into security mapping, providing security for indiscriminate connections. More importantly, the performances of spectral efficiency and reliability have been given for analysis. The analyzed and simulated results show that the dual non-orthogonal characteristics in HSESA can coexist and it brings enhanced performance greatly. Especially, the advantages of higher spectral efficiency are achieved with the HSESA scheme when it compared with orthogonal schemes. According to the advantage of ML combined with MPA detection scheme in BER performance and the advantage of ID combined with MPA detection scheme in terms of complexity, HSESA can be applied to different practical scenarios. ACKNOWLEDGEMENT

HSESA  =4/6 OV=250% HSESA  =4/7 OV=250%

8

REFERENCES [1]

7 6 5

[2]

4 3 2 1

6

This work was supported by National Natural Science Foundations of China (No. 61671183, 61771163 and 91438205).

11

9

HSESA  =4/5 OV=250%

2

Fig.7. 150% overloaded HSESA vs OFDMA combined with security spectral efficiency performance.

10

HSESA  =4/5 OV=150%

[3] 0

2

4

6

8 SNR(dB)

10

12

14

16

Fig.8. 250% overloaded HSESA vs OFDMA combined with security spectral efficiency performance.

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Min Jia received her M.Sc degree in information and communication engineering from Harbin Institute of Technology (HIT) in 2006, and her Ph.D. degree from SungKyungKwan University of Korea and HIT in 2010. She is currently an associate professor and Ph.D supervisor at the Communication Research Center and School of Electronics and Information Engineering, HIT. Her research interests focus on advanced mobile communication technology and non-orthogonal transmission scheme for 5G, cognitive radio, digital signal processing, machine learning and broadband satellite communications. Dongbo Li received his M.Sc degree in Signal and Information Processing from Harbin University of Science and Technology of China in 2015. From 2015 to 2017, he joined Space star technology co., LTD as a system designer, where he mainly worked in the field of satellite applications and smart cities. He is currently pursuing the Ph.D. degree in information and communication engineering, Harbin Institute of Technology (HIT). His research interests focus on multiple access techniques and satellite communications. Zhisheng Yin received his M.Sc degree in communication engineering from Civil Aviation University of China, in 2016. He is now a Ph.D candidate student in Communication Research Center and School of Electronics and Information Engineering, HIT. His research interests include non-orthogonal waveform design techniques and aviation mobile communications system.

Qing Guo received his M.Sc. and Ph.D. from Beijing University of Posts and Telecommunications and HIT in 1985 and 1998, respectively. He is currently a professor and president at the School of Electronics and Information Engineering, HIT. His research interests focus on satellite communications and broadband multimedia communication techniques.

Xuemai Gu received his M.Sc. and Ph.D. from the Department of Information and Communication Engineering, HIT in 1985 and 1991, respectively. He is currently a professor and president of the Graduate School of HIT. His research interests focus on integrated and hybrid satellite and terrestrial communications and broadband multimedia communication technique.

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