2018, 35th NATIONAL RADIO SCIENCE

2018, 35th NATIONAL RADIO SCIENCE CONFERENCE (NRSC 2018), March 20 - 22, 2018 Misr International University (MIU), Cairo, Egypt

Modelling and Simulation of Narrow Band Electromagnetic Interference in Millimeter Wave Massive MIMO Systems Mohamed Shalaby 1, Mona Shokair 2, Nagy Wadie Messiha 3 1, 2, 3 Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt [email protected], [email protected], and [email protected]

ABSTRACT Electromagnetic interference (EMI) can exist among the wireless systems, especially when they are operating at the same or adjacent frequency bands. This EMI level degrades the existing systems' performance. An electromagnetic compatibility (EMC) level, among these systems, should exist to guarantee an acceptable performance. The aim of proposed work is to mathematical analyze and simulate the narrow band interference (NBI), in a millimeter wave massive multiple input multiple output, MIMO, environment. Then, the NBI impact is clarified. A novel mathematical formula for the Bit Error Rate (BER) of random data, which are carried over millimeter wave massive MIMO systems, at noisy channels, is derived and approved by the simulation results. Simulation results clarifies that the NBI degrades the proposed system performance. Furthermore, an incensement in antennas' number can improve the performance.

Keywords: Narrow Band Interference, Millimeter Wave Channel, Massive MIMO, and Bit Error Rate. I. INTRODUCTION Wireless technology is vital for several applications in life. This technology exists in mobile systems, TV systems, and much more. The existence of different wireless systems leads the spectrum to be overcrowded. These systems should be compatible to pose a satisfied performance. The EMI, which can exist due to incompatibility among wireless systems, can minimize the performance. Therefore, the EMI study, in a wireless world, becomes a necessity. The EMI phenomenon, in medical environments, is discussed in [1]. It clarified that the medical equipment may be malfunction when they are affected by intentional and non-intentional EMI from the surrounding environment. Subsequently, the authors of [2] tried to create a numerical tool for the EMI prediction in a radio system to ease the design stage. An EMI simulation tool is suggested in [3]. In more depth, an EMI automated measurement system, with a high dynamic range, is implemented in [4]. The implemented transmitter/receiver circuits should be robust to EMI radiation and susceptibility [5-6]. In [7], the NBI, affecting a wireless communication system, was statistically expressed as a noise signal. Moreover, the BER of a wireless single input single output (SISO) OFDM communication system, operating at radio frequency channels, was investigated. By the same way, the BER of a multiband OFDM system, at interfering faded channels, was analysed and then, it was simulated in [8]. Subsequently, [9] discussed the NBI and the ultra-wide band interference (UWBI) impact on the BER of a radio wireless communication system. The interest in studying the NBI, in this research, is that the narrow band systems can emit high power in narrow band width. Therefore, they can provide more disturbances in the EMI environment than other systems. The narrow band signals, in space, should be a part of the link budget equation during a radio system design. Unlike the radio frequency and microwave channels, the millimetre wave massive MIMO channels can provide a high performance. Small antennas are easily implemented. Moreover, they can present a high bandwidth. In addition, a secure communication can be provided because of low competing devices working in these bands. Furthermore, a robust and a reliable communication can occur by applying a closed loop beam forming mechanism [10-13]. In this paper, the electromagnetic NBI, in wireless millimeter wave massive MIMO systems, is explained and demonstrated in details. The NBI effect, in these systems, is mathematically analyzed and simulated. In addition, a novel mathematical formula for the BER, of a multicarrier millimeter wave massive MIMO system which is prone to the NBI, is derived and approved by simulation.

ISBN 978-1-5386-4256-6/18/$31.00 © 2018 IEEE

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2018, 35th NATIONAL RADIO SCIENCE CONFERENCE (NRSC 2018), March 20 - 22, 2018 Misr International University (MIU), Cairo, Egypt This paper is arranged as follows; Section II provides the complete mathematical derivations of the proposed system affected by NBI. In section III, the NBI is statistically modelled. In Section IV, simulation results are discussed. Finally, conclusions are outlined in Section V.

II. SYSTEM MODEL Consider a wireless mobile cellular system has NBS and NMS antennas at the base and the mobile station, respectively. Moreover, NS data streams are transferred from a base station to a mobile station (downlink transmission). The proposed system model is clarified in Figure 1. Moreover, Figure 2 (a, b) clarifies, in details, the model of a millimeter wave link.

Fig. 1: The proposed OFDM millimeter wave massive MIMO system.

(a)

(b) Fig. 2: The model of a millimeter wave link.

The transmitted signal before the antenna, S(t), can be considered as S (NS×1) vector, in such a way, that;

E[ SS H ] =

PS I NS NS

(1)

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2018, 35th NATIONAL RADIO SCIENCE CONFERENCE (NRSC 2018), March 20 - 22, 2018 Misr International University (MIU), Cairo, Egypt Where E gives the expectation, PS is the transmitted samples' power, NS is the transmitted samples' number whereas the identity matrix is given by I [14]. The transmitted signal is an OFDM one and it is represented as;

S (t ) =

EC TC

K

∑a e

j(

2π ) kt TC

0 ≤ t ≤ TC

k

k =0

(2)

where EC represents the energy for a coded data bit, TC is the duration of a symbol, K gives the subcarriers' number and ak ϵ {± 1, ± j} is the coded transmitted symbols at kth subcarrier. The mobile station signal (received one), r(t), can be given by;

r (t ) = S (t ) * h1 (t ) + U i (t ) * h2 (t ) + n(t )

(3)

where h1(t) and h2(t) are the existing millimeter wave MIMO channels. h1(t) is the channel connecting the intended base station with the intended mobile station whereas h2(t) represents the existing channel between an interfering source and the mobile station. This interfering source may be an interfering base or mobile station. The AWGN is given by n(t) whereas Ui (t) is the narrow band interfering signal that can be clarified, as a single carrier signal, as follows; 2π

i0

(4)

U i (t ) =

≤t≤T

Ei j ( Ti )t + βi bi e Ti

where Ei represents the energy per interfering bit, Ti is the interfering symbol duration, bi ϵ {± 1, ± j} is the coded interfering symbol, and βi is a phase shift. The millimeter wave MIMO channel can be represented, as in [14], as follows;

H=

N BS N MS

ρ

L

∑α a l =1

l

MS

H (θ l )a BS (ϕl )

(5)

where ρ is the average path loss between the base station and the mobile station, αl is the complex gain of the l path, and L is the available paths' number. The path amplitudes are Rayleigh distributed. The symbols θl and φl ϵ [0, 2π] are the l th paths azimuth angles of departure or arrival of the base station and the mobile station, respectively. The antenna array response vectors at the base station and the mobile station, aBS (φl) and aMS (θl), can be represented, as in [14], as; th

2π

a BS (ϕ l ) =

j 1 [1, e λ N BS

aMS (ϕl ) =

j 1 [1, e λ N MS

2π

d .sin(ϕl )

d .sin(θl )

,.........................................., e

,.........................................., e

j ( N BS −1).

j ( N MS −1).

2π

λ

2π

λ

d .sin(ϕl )

d .sin(θl )

]T

(6)

]T

(7)

where d is the antenna elements' separation distance. The received signal may be given by;

r (t ) = r1 (t ) + r2 (t ) + n(t )

(8)

where r1(t) and r2(t) represent the received data signal and the received narrow band interference one, respectively. The received data signal is represented as;

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2018, 35th NATIONAL RADIO SCIENCE CONFERENCE (NRSC 2018), March 20 - 22, 2018 Misr International University (MIU), Cairo, Egypt K

2π ) kt TC

r1 (t ) =

EC TC

r1 (t ) =

EC N BS N MS . TC ρ

j(

∑ ak e

*

N BS N MS

ρ

k =0

K

L

∑α a l

l =1

MS

H (θ l ) a BS (ϕ l )

L

∑ ∑ akα l aMS (θl )aBSH (ϕl ).e

j(

(9)

2π ) kt TC

(10)

k = 0 l =1

Then, the power of received data signal, E R.D , can be given by;

E R. D

K

E .N .N = C BS MS TC .ρ

L

∑ ∑ a .α k

k =0 l =0

2

(11)

l

The received signal, representing narrow band interference, is; 2π

r2 (t ) =

r2 (t ) =

Ei j ( Ti ) t + β i N i N MS * bi e ρi Ti Ei N i N MS . Ti ρi

Li

∑α a l =1

li MS

(θl )aiH (ϕl )

Li

∑ biα li aMS (θ l )aiH (ϕl ).e

j(

(12)

2π ) t + βi Ti

(13)

l =1

Where Ni is the antennas' number of the interfering source. The subscript i refer to the interfering source parameters. Then, the received narrow band interfering power, E N.B.I , is given by;

EN .B.I

E .N .N = i i MS Ti .ρi

Li

∑ b .α l =0

i

2

(14)

li

The total received signal, r(t), is;

r (t ) = +

EC N BS N MS . TC ρ

Ei N i N MS . Ti ρi

K

∑∑ a α a k = 0 l =1

Li

∑b α l =1

i

L

li

k

l

MS

(θ l ) a

a MS (θ l ) a (ϕ l ).e H i

j(

H BS

(ϕ l ).e

j(

2π ) kt TC

2π )t + βi Ti

0 ≤ t ≤ Ti

(15)

+ n(t ) The total signal to interference and noise ratio, SINR, per one bit can be given as follow;

EC .N BS .N MS K L 2 ak .α l ∑ ∑ TC .ρ k =0 l =0 SINR = Li Ei .N i .N MS 2 bi .α li + σ N2 ∑ Ti .ρi l =0

(16)

where σN 2 represents the noise variance (power). The BER in the binary phase shift (BPSK) keying OFDM system, at AWGN channels, can be obtained, as in [15], as follows;

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Eb ) No

1 Q( 2

BER =

(17)

where Q(x) denotes the complementary error function, Eb gives the energy of a bit, and No is the noise energy. The BER, in the suggested system applying AWGN channels, can be obtained as follows;

BER =

H 2 .Eb Eb 1 1 ) = Q( H . ) Q( 2 2 No No

(18)

III. NBI MODELLING The NBI, in this work, is statistically modeled, as a noise signal, as stated before in [7][9]. It considered that the NBI, affecting a wireless system, could be modeled as a noise signal with a distribution of Poisson. When a narrow band interferers affect a multicarrier system (wideband signal), it can affect a portion of its spectrum. The interfering power, affecting a subcarrier at fm , X, can be given by; Ω

X = ∫ P X y ( f m ) dy = 2

0

Ω

2 2Ω X y ( f m ) dy ∫ W 0

(19)

where y is the different frequency values where the interference can affect, Ω is the bandwidth of the narrow band interfering signal, and W is the bandwidth of the wideband signal [7][9]. The NBI can be represented as a random variable, as in [7][9], with a probability distribution function (pdf) value of; ∞

PZ ( z k ) = ∑ k =1

Pk 2πσ k2

−(

.e

( z −µ )2 2σ k2

)

(20)

IV. SIMULATION RESULTS The NBI impact on the suggested system is simulated. The simulation model considers a data source which is mapped to BPSK symbols. The simulation parameters are included in Table 1. There are 104 data symbols which are mapped to BPSK. Subsequently, an OFDM modulator can modulate the mapped BPSK symbols. These symbols are carried over a wireless channel which is massive MIMO one working in the millimetre wave frequency bands. The applied channel model is as in [14]. The simulated wireless channel considers a noise and a NBI source. The received modulated symbols with noise and interference are demodulated and a selection combining scheme is carried out. Subsequently, the BER is used as a metric for performance evaluation. The interfering signals, NBI, is modelled as a noise signal, with Poisson distribution, that can affect the all mapped data signals in the system. In the applied simulation, the narrow band interference is expressed as a random noise signal (real & imaginary components), with Poisson distribution, that can affect the whole OFDM subcarriers. Moreover, the applied narrow band signal (random noise signal) is assumed to affect the carried data signals as whole. This assumption is applied in order to agree with a previously published work in [7][9]. The suggested system performance, when the NBI exists, is sketched in Figure 3. The system seems to perform well when the received signals have high signal to noise ratio values. Moreover, the performance can be further improved by implementation of more transmitting or receiving antennas. A trade off, between the number of implemented antennas and the tolerated noise value at the receiver, may be existent. The BER can be reduced and then a better system performance can be obtained when the received signals have a high power or little noise. In addition, it is obvious that increasing the signal to noise ratio in interfering channels can minimize BER at certain limit however, the interference can present a performance improvement barrier as clarified in Figure 3. The BER cannot be reduced than a value of 10-3 however, the high values of received signal power.

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2018, 35th NATIONAL RADIO SCIENCE CONFERENCE (NRSC 2018), March 20 - 22, 2018 Misr International University (MIU), Cairo, Egypt In this paragraph, the deduced Eq. 18 is checked. It is compared with the simulated system performance. The analytical performance (Eq. 18) is compared with that is obtained by simulation. The performance of the proposed system is estimated at AWGN noisy channels in Figure 4. From this Figure, it can be observed that, the BER of the proposed system is improved when the signals arrive with high values of signal to noise ratio values. The analytical BER value, which is derived in Eq. (18), is compared with the simulated one. The analytical value of BER is completely equivalent to the simulated one which proves the accuracy of Eq. (18). Table 1. The simulation parameters.

Millimeter Wave Channel

OFDM Signal

Parameter Radio frequency chain at BS and MS Mobile station antennas' number Base station antennas' number Channel paths' number Carrier frequency Separating distance between base station and a user Number of iterations Number of data symbols FFT size Subcarriers' number Cyclic prefix length Mapping Combining Scheme

Value 8 2 Varied 1 28 GHz 30 m 104 104 64 52 0.25 BPSK Selection Combining

0

10

3 x 2 antennas 6 x 2 antennas 7 x 2 antennas

-1

BER

10

-2

10

-3

10

-50

-40

-30

-20

-10

0

10

20

30

Eb / No [dB] Fig. 3: The simulated BER of the proposed system.

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0

10

Analytical Simulation -1

BER

10

-2

10

-3

10

-4

10

-20

-15

-10

-5

0

5

10

15

Eb / No [dB] Fig. 4: The simulated and the theoretical proposed system's performance at AWGN channels.

V. CONCLUSION A millimeter wave massive MIMO system is simulated. The NBI effect, on the performance of this system, is mathematically analyzed and simulated. The BER, at AWGN noisy channels, is mathematically derived and plotted over the simulated one. There is a similarity between the simulated BER and the analytical one and this confirm the correctness of Eq. 18. In addition, the system performance is simulated when the NBI affects the system. It can be observed that, the system can perform well when the number of implemented antennas at the transmitter and at the receiver increases. Moreover, the NBI can degrade the system performance but its effect can be reduced when the signals emitted at the transmitter with a high power or by implementation of a lot of antennas at the transmitter and at the receiver.

REFERENCES [1] S. E. Lapinsky, A. C. Easty, ʻʻElectromagnetic Interference in Critical Care,ˮ Journal of critical care, Elsevier, Vol. 21, No. 3, pp. 267– 270, September 2006. [2] S. Loyka, ʻʻEMC/EMI Analysis in Wireless Communication Networks, ˮ International Symposium on Electromagnetic Compatibility, IEEE, Vol. 1, pp. 100-105, August 2001. [3] F. German, M. Young, and M. C. Miller, “A Multi-fidelity modeling approach for cosite interference analysis, ” International Symposium on Electromagnetic Compatibility, IEEE, pp. 195-200, August 2009. [4] M. Young, M. C. Miller, and F. German, "An Automated Measurement System for Cosite Interference Analysis, " International Symposium on Electromagnetic Compatibility, IEEE, pp. 863-868 , July 2010. [5] F. German, K. Annamalai, M. Young, and M. C. Miller, “Simulation and Data Management for Cosite Interference Prediction, ” International Symposium on Electromagnetic Compatibility, IEEE, pp. 869874, July 2010. [6] N. Neji, R. D. Lacerda, A. Azoulay, T. Letertre, and O. Outtier, ʻʻInterference Analysis for the Future Aeronautical Communication System, ˮ 20th International Symposium on Personal, Indoor, and Mobile Radio Communications, IEEE, pp. 1236-1240, September 2009.

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2018, 35th NATIONAL RADIO SCIENCE CONFERENCE (NRSC 2018), March 20 - 22, 2018 Misr International University (MIU), Cairo, Egypt [7] T. Shongwe, V. N. Papilaya, and A. J. H. Vinck, ʻʻNarrow band Interference Model for OFDM Systems for Powerline Communications, ˮ 17th International Symposium on Power Line Communications and Its Applications, IEEE, pp. 268-272, March 2013. [8] Y. Ouyang, W. P. Lin, and C. C. Liu, ʻʻPerformance Analysis of the Multiband Orthogonal Frequency Division Multiplexing Ultra Wideband Systems for Multipath Fading and Multiuser Interference Channels, ˮ Journal of Mathematical Problems in Engineering, Hindawi Publishing Corporation, Vol. 2015, ID 190809, pp. 1-9, March 2015. [9] M. Shalaby, M. Shokair, N. W. Messiha, "Evaluation of Electromagnetic Interference in Wireless Broadband Systems," published on line in wireless personal communications, Springer, on 9 May 2017. [10] I. Sulyman, A. Alwarafy, G. R. M. Cartney, T. S. Rappaport, and A. Alsanie, " Directional Radio Propagation Path Loss Models for Millimeter Wave Wireless Networks in the 28-, 60-, and 73-GHz Bands," in Transactions on Wireless Communications, IEEE, Vol. 15, No. 10, pp. 6939-6947, Oct. 2016. [11] C. Rusu, R. M. Rial, N. G. Prelcic, R. Heath, "Low Complexity Hybrid Precoding Strategies for Millimeter Wave Communication Systems," in Transactions on Wireless Communications , IEEE, pp.113, 2016. [12] P. Chandhar, D. Danev, and E. G. Larsson, "Massive MIMO as enabler for communications with drone swarms," International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, pp. 347-354, 2016. [13] C. Sun, X. Gao, S. Jin, M. Matthaiou, Z. Ding, and C. Xiao, "Beam Division Multiple Access Transmission for Massive MIMO Communications," Transactions on Communications, IEEE, Vol. 63, No. 6, pp. 2170-2184, June 2015. [14] A. Alkhateeb, O. El Ayach, G. Leus, and R. W. Heath, "Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems," Journal of Selected Topics in Signal Processing, IEEE, Vol. 8, No. 5, pp. 831-846, 2014. [15] K. Lavanya1, and M. V. S. Sairam, ʻʻImprovement of BER Performance in OFDM under various Channels with EH Code, ˮ International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 7, pp. 131-134, July 2015.

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