Wireless Pers Commun DOI 10.1007/s11277-017-4294-0
Evaluation of Electromagnetic Interference in Wireless Broadband Systems Mohamed Shalaby1 • Waleed Saad1 • Mona Shokair1 Nagy Wadie Messiha1
•
Springer Science+Business Media New York 2017
Abstract Nowadays, the electromagnetic compatibility in radio frequency communication systems attracts a great attention as the wireless spectrum becomes overcrowded. A lot of wireless systems operate in the same bands or in the adjacent frequency ones. This operation results in an electromagnetic interference (EMI) among these systems. This EMI can degrade the performance of the wireless systems. Moreover, critical medical equipment, which is near to an electromagnetic radiation field, may be malfunction. A lot of studies handled the EMI phenomenon in radio systems. These studies range from the definition and the causes of the phenomenon to the development of suitable simulation tools for evaluating the EMI value. This paper handles the effect of the EMI existence on the performance of a broadband communication system in a complex radio environment. Moreover, this performance is clarified in a form of bit error rate. In addition, the complex radio environment is modeled as a summation of noise, narrow band interference (NBI), and ultra wide band interference (UWBI). The complete analysis model of a broadband radio system, which is interfered by a NBI and an UWBI, is carried out. Furthermore, the total interfering power is derived in a novel closed form formula. Finally, the performance of the system is simulated. Keywords EMI Broadband Systems BER NBI UWBI
& Mohamed Shalaby
[email protected] Waleed Saad
[email protected] Mona Shokair
[email protected] Nagy Wadie Messiha
[email protected] 1
Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
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1 Introduction The wireless systems appear in life in a lot of applications. In the recent age, the wireless technology starts to extend in daily applications. This technology is the backbone of the cellular systems, the radio and TV systems, the mobile systems, the navigation systems, and much more. The variation of the wireless systems and the limitation of the radio spectrum lead the radio spectrum to be overcrowded. The wireless systems may operate in adjacent frequency channels. Moreover, some wireless applications apply the concept of frequency reuse in order to efficiently use the radio frequency spectrum. The operation of wireless systems in the same bands leads to an interference among them. This interference is called the electromagnetic interference (EMI) or the radio frequency interference. Firstly, the EMI can be defined as a disturbance that affects the electrical circuits due to their electromagnetic conduction or electromagnetic radiation emitted from an external source. By the same way, the electromagnetic environment represents the variation of the signals’ power with time over different frequencies. The main source of the interference, in a radio system, is the nature. In fact, this interference can result from the nature and the man-made noise sources. The electromagnetic spectrum can extend from 0 Hz to 300 GHz. The EMI sources may be natural, intentional, or non-intentional. The intentional interference is a result of the existence of radar systems which can emit high power. On the other side, the non-intentional interference is the interference comes from the systems which are not initially used for communication. These systems include; power lines, monitors, and medical equipment [1, 2]. The EMI can have a bad effect on the performance of a radio system. It can lead an equipment to be malfunction. Moreover, it can let a radio system have a high BER value. In addition, it can destroy the system itself. The immunity of a device or a system to work and perform well in the existence of an EMI is called the electromagnetic compatibility (EMC). A system is an electromagnetic compatible one when it has the ability to operate in a radio environment without emission of interference to this environment and without affecting by the existing interference in this environment. The EMI can generally result from the narrow band pulses and the ultra wide band pulses. In general, the immunity against the EMI, in a radio system, can be increased by applying spread spectrum techniques, shielding the system or the device, and using the EMI filters [1, 2]. The effect of the EMI on the existing electronic systems and the radio ones should have a great attention. Therefore, the authors of Ref. [3] explained the importance of studying the EMI problem in life. They concentrated on the effect of EMI on medical equipment in hospitals. This EMI may exist due to intentional and non-intentional equipment. Intentional equipment may be mobiles, bluetooth, wireless networks, and much more. On the other hand, the non-intentional equipment may be computers, monitors, or any other electric and electronic ones. This interference may cause the medical equipment to be malfunction especially, when this equipment is near to a radiation source. The authors clarified that the EMI danger on medical equipment can be minimized if the medical staff, in a hospital, is aware about their equipment specifications. The immunity and the susceptance of medical equipment should be exactly studied. The EMI and the EMC analyses, in a wireless network, were carried out by using a computationally efficient approach [4]. The analyses depended on a modified behavioral level numerical simulation. In fact, they were carried out in order to be applicable in the frequency planning stage of wireless networks. There were three factors which can affect the EMI value in a wireless network. These factors are; spurious radiation of transmitters,
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spurious response of receivers, and non-linear behavior of receivers. Moreover, the response of the filters and the amplifiers can have a role in the EMI prediction in a wireless network. By the same way, a simulation tool for the EMI prediction in multiple transmitters and receivers environment was developed in Ref. [5]. This tool, which was called EMIT, can simulate and predict the value of the EMI margin for cosite interference cases. In the EMIT simulator, there are a radio profile model and a circuit level model. These models allow users to enter their data, which they have, and run the program to calculate the EMI threats and margin based on the system sensitivity. The cosite interference analyses were extended in Ref. [6] in which an automated measurement system was proposed. It analyzed the EMI in the existence of a transmitter and a receiver. The spurious emission of the transmitter can cause an EMI for the surrounding electromagnetic environment. Moreover, the receiver sensitivity and the frequency response can also greatly affect the EMI value. In addition, the antenna to antenna coupling can have a contribution on the EMI value. The automated system was based on a vector network analyzer. Moreover, it could model the transmitter and the receiver with a dynamic range value of 40 dB more than a spectrum analyzer. When it is easy to have data about the transmitter, the receiver, the radio channel, and the existing communication circuits, a prediction model can be built by using the tool in Ref. [7]. The EMI can have a high value especially in directive and high emitted power systems such as radar systems and L-band digital aeronautical communication system (LDACS). The EMI analyses, in these systems, were handled in Ref. [8]. It explained the compatibility of LDACS 1 and LDACS 2 systems with the other legacy ones in the space. Moreover, a three dimensional spherical system was applied in the free space. Furthermore, the main causes of EMI on a certain LDACS receiver were determined and they were; the distance between the interfering systems and the receiver, the receiver bandwidth, and the frequency offsets. The intentional, non-intentional, and nature EMI, which affect a radio communication system, were explained in details in Ref. [9]. The authors presented a measurement set up able to characterize and measure the EMI in a radio system. They used low cost devices such as personal computers, access points, and local area network links. It was clarified that the EMI signal can change the frequency of received intended signal. Moreover, the EMI can have a very bad effect when the power to frequency ratio of the interfering signal is equivalent to that of the intended received signal. Furthermore, the EMI value can rotate the constellation of the intended received digital signal. The fore-mentioned set up can characterize both of the intended received signal and the interfering one with its capability of presenting them on an oscilloscope. The electromagnetic field distribution inside a closed environment was explained in Ref. [10]. By using a hybrid simulation technique, the radio channel characteristics were demonstrated. This was carried out through analyses of the transmission antenna and the receiving one in the existence of near and far objects. The need for such a channel model is a necessity in order to determine the acceptable level of transmission power which can control the EMI affecting the neighboring systems with an acceptable BER value for the intended radio system. The authors used a signal generator, a power splitter, and a spectrum analyzer in order to build a measurement set up able to assure their mathematical wireless channel model. The intentional EMI, in a radio system, had a great attention in the researches. It is an important type due to its great effect. This effect resulted from the high emitted power of the interferer source. This interference type can cause a BER value of 10%. The matched filter can provide a good performance in this interference type since it can provide a high
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correlation with the intended signal. In Ref. [11], the authors presented their measurements in order to check the receiver sensitivity to weak signals. They also used the power spectral density (PSD) in order to check the purity of the received detected filtered signals. The power lines can emit non-negligible levels of the EMFs in space which can affect the existing devices and the radio systems. Moreover, they can act as an antenna in such a way that they can detect the surrounding EMFs and destroy the carried data on them. Therefore, these lines should be protected and checked against the EMI radiation and susceptibility. The power line quality should be assured before usage [12]. By the same way, the electromagnetic susceptance of the three commonly used differential high speed wire line interfaces was tested and compared. These interfaces include; low voltage differential signaling, current mode logic, and voltage mode logic. The performance of these interfaces could be affected by the surrounding EMI [13]. Regard the wireless cellular systems; the intentional EMI can affect the cellular system base stations and handsets. The terrestrial trunked radio (TETRA) is a digital radio standard that can provide services to the mobile nodes. The performance of these systems, in the existence of the EMI, was clarified in Ref. [14]. It can be observed that the EMI can degrade the performance of a cellular system. Moreover, it also can lower the performance of a wireless human body communication system [15]. The previous paragraphs explained that the EMI can result from the NBI pulses and the UWB ones. Therefore, the UWB channels and the UWB interferers will be explained, in details, in this paper. In fact, the existing UWB systems can provide a sufficient level of the EMI in the free space. Firstly, the UWB signal can be defined as the signal which has a bandwidth value more than 0.25 of the center frequency. The UWB systems can have a bad effect on the neighboring radio systems and devices only as they are short range systems and low emitted power ones. The UWB pulses have very short duration and they are emitted at a high rate. Therefore, they can be called impulse radio or carrierless radio. They can have applications in special radar systems and the short range communication ones [16]. In this paper, the EMI analyses, in radio systems, will be explained with a different point of view. This paper will handle the analyses of an OFDM victim receiver in interfering radio channels. The BER is the metric used for evaluating the system performance. The interference sources, in the proposed work, are; a narrow band source and an ultra wide band one. In fact, the estimation of the BER in an OFDM in existence of narrow band interference was clarified in Ref. [17] and it is not a novel approach. However, this paper demonstrates the application of the all band interference in an OFDM system beside the narrow band interference and this is not clarified until now. This is the motivation of the proposed work. The analyses of the proposed system model, in this paper, are carried out by using the mathematical model which was explained before in Ref. [18]. The previous model could analyze the OFDM system in multiuser interfering channel. However, this paper provides the analyses of an OFDM broadband radio system which is dominated by the NBI and the UWBI. By applying the two different interference types, the performance of a broadband victim receiver can be evaluated under cosite and complex interfering circumstances. The novelty of this paper is the evaluation of the EMI effect on the performance of a digital broadband radio system in terms of BER. Moreover, the EMI is evaluated by applying a complex radio environment which has two different types of interference. This environment handles the all band interference, in such a way, that the victim receiver can face the worst case of interfering channels. Finally, a closed form formula for calculation of the total interfering power, in a digital wireless broadband system, is derived.
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This paper is organized as follows; Sect. 2 provides the mathematical analysis of the proposed system. In Sect. 3, the NBI model is explained in details. In addition, Sect. 4 handles the mathematical model of the UWB channel. Subsequently, Sect. 5 discusses the simulation results. Finally, conclusions are given in Sect. 6.
2 System Model The proposed system model, which is shown in Figs. 1 and 2, represents a radio broadband communication system which is affected by two different types of interference in the radio channel. As shown in the previous section, the EMI can exist because of the transmitter, the radio channel, and the receiver. With the aid of Ref. [5], the EMI margin level, in a wireless communication system, can be clarified in Eq. (1). This equation looks like that was stated in Ref. [5], however this paper considers the interference term, I, of the radio channel in the link budget equation. M ¼ Ptx LT þ ðGTmax þ Ge ð/Þ þ ATAÞ LJ ðr; /Þ I TRLoss ðdBÞ Srx ð f Þ
ð1Þ
where M is the EMI margin level in the system, Ptx is the transmitted power, LT is the cables and the overall transmitter losses, GTmax is the maximum antenna gain, Ge(-u) is the antenna gain in a certain phase (direction), ATA is the antenna to antenna coupling, LJ(r, u) is the path loss in the propagation medium, TRloss(dB) is the summation of losses in the receiver, Srx(f) is the receiver sensitivity, and I is the interference level, of the radio channel, which will be analyzed and modeled throughout this paper. In this work, the interference can exist in two different types which are; I ¼ IUWB þ INBI
ð2Þ
where IUWB is the interference comes from ultra wide band systems whereas INBI is the interference comes from narrow band systems. This work aims to create an analysis model for the EMI in a wireless communication system especially the broadband radio system. It is assumed that the transmitted signal is an OFDM signal whereas the interference can be modeled as a summation of a NBI signal and an UWB signal. Assume that the transmitted signal is;
Fig. 1 The proposed OFDM radio system model
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Fig. 2 The detailed channel model of the proposed system
SðtÞ ¼
rffiffiffiffiffiffi K 1 EC X T
ak ejð T Þkt 2p
0tT
ð3Þ
k¼0
where Ec is the energy per coded bit, T is the symbol duration, K is the total number of the OFDM subcarriers, and ak 2 {±1, ±j} is the coded data transmitted at the kth subcarrier. The interference signal can be modeled as; rffiffiffiffiffi K1 rffiffiffiffiffi Ei X Ei jð2pT Þktþ/i jð2p kt Þ aim e T þ ¼ UiUWB ðtÞ þ UiNBI ðtÞ 0 t T ð4Þ Ui ð t Þ ¼ bi e T m¼o T where Ei is the energy per interference bit, aim is the transmitted coded data of the ith UWB interference user at the mth subcarrier, bi is the transmitted coded data of the NBI signal, and aim, bi 2 {±1, ±j}. The received signal can be expressed as; r ðtÞ ¼ sðtÞ hðtÞ þ
IUWB X1
niUWB ðtÞ þ
INBI 1 X
i¼0
r ðt Þ ¼
K 1 X k¼0
Sk ðtÞ þ
IUWB X1 i¼0
UiUWB ðtÞ hiUWB ðtÞ þ
niNBI ðtÞ þ W ðtÞ
ð5Þ
i¼0 INBI 1 X
UiNBI ðtÞ hiNBI ðtÞ þ W ðtÞ
ð6Þ
i¼0
where i is the index of an interferer, Sk(t) is the received signal at the kth subcarrier, W(t) is the white Gaussian noise whose double sided power spectral density is Nw watt per Hertz, h(t) is the channel impulse response between the intended OFDM transmitter and the OFDM victim receiver, hiUWB(t) is the channel impulse response between the UWB interferers and the OFDM victim receiver, and hiNBI(t) is the channel impulse response between the NBI interferers and the OFDM victim receiver. Assume that all existing channels are linear ones;
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H ðxÞ ¼ aðxÞejhðxÞ
ð7Þ
HiUWB ðxÞ ¼ aiUWB ðxÞejhiUWB ðxÞ
ð8Þ
Evaluation of Electromagnetic Interference in Wireless…
HiNBI ðxÞ ¼ aiNBI ðxÞejhiNBI ðxÞ
ð9Þ
The frequency response at each kth subcarrier, Hk, can be expressed as; 2p 2p 2p k ¼a k ejhðð T ÞkÞ ¼ ak ejhk Hk ¼ H T T
ð10Þ
where Hk is the frequency response at frequency 2pk T . Therefore, the received signal can be expressed as; rffiffiffiffiffiffi rffiffiffiffiffiffi 2p EC EC jð2p kt Þ T Sk ðtÞ ¼ ð11Þ ¼ Hk ak e ak ak ejðð T Þktþhk Þ 0 t T T T The channel frequency response at the mth subcarrier between the ith interferer and the OFDM receiver can be expressed as; 2p 2p 2p m ¼ ai m ejhi ð T mÞ ¼ aim ejhim Him ¼ Hi ð12Þ T T The received signal caused by the ith UWB interferer can be expressed as; rffiffiffiffiffi K 1 IUWB X1 2p Ei X 2p m aim ejð T Þmt UiUWB ðtÞ hiUWB ðtÞ ¼ HiUWB T T m¼0 i¼0 rffiffiffiffiffi K 1 X 2p Ei ¼ aim aim ejðð T Þmtþhim Þ 0 t T T m¼0
ð13Þ
The received signal caused by the ith NBI interference can be expressed as; rffiffiffiffiffi K1 INBI 1 X 2p Ei X 2p m bi ejð T Þmt UiNBI ðtÞ hiNBI ðtÞ ¼ HiNBI T T m¼0 i¼0 rffiffiffiffiffi K 1 X 2p Ei ¼ aim bi ejðð T Þmtþhim Þ 0 t T T m¼0
ð14Þ
The received signal, after OFDM demodulation and phase compensation, can be written in the following form; 1 Yk ¼ pffiffiffiffi T
ZT
rðtÞejð T Þktþhk dt ¼ 2p
pffiffiffiffiffiffi EC ak ak þ IUWB þ INBI þ W
ð15Þ
0
where the first term is the desired demodulated signal, IUWB is the ultrawide band interference term, INBI is the narrow band interference term, and W is the white Gaussian noise. The total interfering power, P(I), at the victim receiver can have a closed form formula as follows; K 1 X Ei ð16Þ PðI Þ ¼ PðINBI þ IUWB Þ ¼ jaim j2 jaim j2 þjbi j2 T m¼0
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3 NBI Model A well defined model of the NBI interferer, which was explained in Ref. [17], is applied in this paper. The NBI model is described, explained, and applied in this work. This model considers the interference which comes from independent sources. The probability of a certain number of k interferers, Pk, on a system spectrum can have Poisson distribution as follows; Pk ¼
gk g e k!
ð17Þ
where g is the average number of occurrence of certain events, and k is the number of interferers. In addition, the average of bandwidth occupied by NBI, k, can be obtained by applying the following relation; k¼
gX BW
ð18Þ
where BW is the OFDM system bandwidth, and X is the average bandwidth of interference occurrence. The interfering power on one subcarrier, due to the NBI, in the OFDM victim receiver can be obtained by evaluation of the power spectral density (PSD) at this certain subcarrier. Moreover, the NBI interference can affect more than one subcarrier when the bandwidth of the interfering signal is more than one subcarrier bandwidth. The interfering power which affects a subcarrier at fm is denoted by X and it can be calculated by; X¼
ZX
2 2X PXy ðfm Þ dy ¼ BW
0
ZX
Xy ðfm Þ2 dy
ð19Þ
0
where y is the different frequency values (spectrum positions) at which the interference can affect. Therefore, the power contribution due to the k interferers is; r2k ¼ k X
ð20Þ
r2k
where is the effective NBI power for given k interferers. Therefore, the total average effective NBI power in the system is; r2 ¼
1 X
r2k Pk ¼ X
k¼1
1 X
kPk ¼ Xg
ð21Þ
k¼1
The NBI, due to k interferers, can be represented by a random variable with Gaussian (pdf) and it can have a value of z with mean l and variance r2k . Therefore, the probability distribution function (pdf) can be described as follows; 1 X
Pk pffiffiffiffiffiffiffiffiffiffi e PZ ðzjkÞ ¼ 2pr2k k¼1
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ðzlÞ2 2r2 k
ð22Þ
Evaluation of Electromagnetic Interference in Wireless…
4 UWB Channel Model The UWB channel model is based on the IEEE 802.15.3a standard model [19]. This model considers that the UWB channel can have an impulse response as follows; hi ðtÞ ¼ Xi
L1 X K1 X
aik;l d t Tli sik;l
ð23Þ
l¼0 k¼0
where aik;l represents the multipath gain coefficients, Tli is the delay of the lth cluster, sik;l is the delay of the kth multipath component relative to the lth cluster arrival time (Tli ). Xi represents the log normal shadowing and i is the ith realization. The multipath components are defined in clusters and rays. The cluster arrival has Poisson distribution with a rate K. Inside each cluster, the ray arrival has also Poisson distribution with a rate kx (ray arrival rate), and kx K. The distribution of cluster and rate arrival time can be expressed as; pðTl jTl1 Þ ¼ KeðKðTl Tl1 ÞÞ
l[0
p sk;l sðk1Þl ¼ kx eðkx ðsk;l sðk1Þl ÞÞ
k[0
ð24Þ ð25Þ
The fading distribution in the UWB channels has a log normal distribution. Moreover, the shadowing has also a lognormal distribution. The shadowing term can be described by; ð26Þ 20 Log10 ðXi Þ / Normal 0; r2x
5 Simulation Results In this section, the proposed system is simulated. The simulation considers an OFDM system in an environment where narrow band interferers exist beside to the ultra wide band ones. The performance of the OFDM system, which has the two types of interferes, is expressed as the bit error rate (BER) versus the energy of the transmitted bit per noise level. Table 1 presents the simulation parameters in details. Figure 3 shows the performance of an OFDM radio system applying the AWGN channel. From this figure, it can be observed that, the performance of the OFDM radio system can be degraded when the noise level increases. Moreover, the BER of the proposed OFDM radio system can be decreased and then the performance of the system is increased when the received signal have a high value of signal to noise ratio. The effect of the NBI on the OFDM radio system is studied. Figure 4 displays the performance of an OFDM radio system when this system is affected by the NBI and the AWGN. It can be observed that, the NBI can degrade the performance of an OFDM radio system. In addition, the performance of an OFDM radio system can be improved, in NBI radio channels, by increasing the signal to noise ratio levels. This improvement can be achieved by transmission of data signals with high levels of power. In Fig. 5, the performance of an OFDM radio system, affected by the NBI, the UWBI, and the noise signals, is clarified. It can be noticed that, the UWBI can extremely degrade
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M. Shalaby et al. Table 1 The simulation parameters
Parameter
Value
UWB channel Cluster arrival rate (GHz)
0.067
Ray arrival rate (GHz)
2.1
Cluster decay factor
14
Ray decay factor
7.9
Standard deviation of log-normal variable for cluster fading
3.4
Standard deviation of log-normal variable for ray fading
3.4
Standard deviation of log-normal shadowing of entire impulse response
3
Number of random channel realization
1
Number of paths
103
OFDM signal
10
BER
10
10
10
10
Number of symbols
104
FFT length
64
Number of data subcarriers
52
Length of cyclic prefix
0.25
-1
AWGN Analytical AWGN Simulation
-2
-3
-4
-5
0
2
4
6
8
10
12
Eb / No [dB]
Fig. 3 The performance of the proposed OFDM radio system applying the AWGN channel
the performance of an OFDM radio system. Moreover, the performance of an OFDM radio system is very poor for radio channels which are dominated by the UWBI. The performance of the OFDM radio system is investigated applying different types of interference in
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10
AWGN+NBI -2
BER
10
-3
10
-4
10
-5
10
0
5
10
15
20
25
Eb / No [dB] Fig. 4 The performance of the proposed OFDM radio system applying the NBI and the AWGN 10
0
BER
AWGN+NBI+UWBI 10
-1
10
-2
10
-3
0
5
10
15
20
25
Eb / No [dB]
Fig. 5 The performance of the proposed OFDM radio system applying the NBI, the UWBI, and the AWGN
Fig. 6. The performance, applying UWBI channels, is the lowest one. The reason for this low performance is that the UWBI can cause degradation in all the subcarriers of the OFDM signal.
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0
10
AWGN+NBI AWGN+NBI+UWBI -1
BER
10
-2
10
-3
10
0
5
10
15
20
25
Eb / No [dB]
Fig. 6 The performance of the proposed OFDM radio system applying the two different interference types
6 Conclusions In this paper, a broadband communication system, applying the OFDM technique, is exposed to severe interference in the radio channel. The interference is a collection of NBI interferers and UWB interferers. Moreover, the UWBI affects the OFDM system in all subcarriers in such a way that it can represent the all band interference. The performance of the OFDM system is estimated applying AWGN, NBI, and UWBI. It is noticed that, the performance, applying the NBI, is worse than the corresponding one applying AWGN only. Moreover, the performance, applying UWBI, is the worst case of interference. In addition, this paper provided the complete analyses of an OFDM radio system in which there are two different types of interference. Furthermore, a closed form formula for evaluation of the interfering power in a digital wireless broadband communication system, in a complex interfering environment, is derived.
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M. Shalaby et al. Mohamed Shalaby was born in Menoufia, Egypt, in 1986. He received the B.Sc. degree in electronics, and electrical communications engineering from Menoufia University, Menoufia, Egypt, in 2008. His research interests include wireless communications, broadband technologies, mobile communications, and next generation networks.
Waleed Saad has received his B.Sc. (Hons), M.Sc. and Ph.D. degrees from the Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt, in 2004, 2008 and 2013, respectively. He joined the teaching staff of the Department of Electronics and Electrical Communications of the same faculty since 2014. In 2005 and 2008, he worked as a demonstrator and assistant lecturer in the same faculty, respectively. He is a co-author of many papers in national and international conference proceedings and journals. His research areas of interest include mobile communication systems, computer networks, cognitive radio networks, D2D communication, OFDM systems, interference cancellation, resource allocations, PAPR reduction, physical and MAC layers design, and implementation of digital communication systems using FPGA.
Mona Shokair received B.Sc., and M.Sc. degrees in electronics engineering from Menoufia University, Menoufia, Egypt, in 1993, and 1997, respectively. She received the Ph.D. degree from Kyushu University, Japan, in 2005. She received VTS chapter IEEE award from Japan, in 2003. She received the Associated Professor degree in 2011. Recently, she is a Professor at Menoufia University. Her research interests include AAA, CDMA system, WIMAX system, OFDM system, Cognitive Radio Networks, M2 M networks and wireless sensor Networks. She published about 67 papers until 2015.
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Evaluation of Electromagnetic Interference in Wireless… Nagy Wadie Messiha received the B.S. in Electrical Engineering Telecommunication Department, Ein Shams University, Cairo, Egypt, June 1965, and M.S. in telecommunication engineering, Helwan University, Cairo, Egypt, 1973, and the german (Dipl. Ing.) and (Dr. Ing.) from University of Stuttgart, in 1978 and 1981 respectively. From 1981 to 1987. Currently, he is a professor at the Department of Communication Engineering, Menoufia University, Menouf, Egypt. His research interested is traffic modeling and performance enhancement in communication and computer networks, cognitive networks, and network security.
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