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Least Mean Square Algorithm Analysis for a High Capacity Mobile Long Term Evolution Network. Leandro Vieira dos Santos Universidade Federal Fluminense Department of Telecommunication Engineering Niteroi, Rio de Janeiro Email: [email protected] Abstract-Modern communication networks use MIMO to achieve high transmission data rates. A special case of MIMO is a technic called Beamforming [1], which allows formatting a radiation pattern of an antenna in order to improve the gain at a given direction. Beamforming uses adaptive filters to steer the beams in a desired direction. T he Least-Mean Square (LMS) [1] is an algorithm that performs this function of the adaptive filter. To perform the convergence of the algorithm, simulations will be performed in a Long-Term Evolution (LTE) mobile network. Keywords: Smart antennas, beamforming, DOA, MIMO and eNode B. I. INTRODUCT ION Wireless communications are extremely important nowa- days. With the advances in the capacity of new technologies, cell phones and other devices are able to work with a high transmission data rate. Since the beginning of wireless trans- missions, the interest in multiple antenna system has improved, resulting in techniques such as Multiple Input Multiple Output (MlMO) [2] on the Long Term Evolution (LTE) [2]. While traditional communication systems explore time domain, equency domain pre-processing transmitted data and decoding received data, antenna elements on downlink or on uplink opens an extra dimension spatial with the objective of pre-coding and detection signals [3]. The space-time pro- cessing method have to achieve better performance in terms of error rates, transmission data rate, coverage and spectral efficiency. With the growth of mobile communications, it is necessary to increase coverage and capacity in order to increase the Quality of Service (QoS). Smart antennas are able to use the spectrum efficiently. A smart antenna system consist of the design of a set of antennas, signal processing algorithm, space-time processing, modelling wireless channel, coding and network performance [3]. The biggest limitation of a wireless system is co-channel interference, multipath fading and delay spread [3]. As a result, new wireless technologies are developed to work with power control technic. This technic decrease the power transmission until a lower level that allows communication [3]. In a cellular system, communication between the radio base station and the end user generates energy perceived nearby [3]. This excess energy degrades the channel in the neighborhood or makes communication impossible. In order to prevent the 978-1·4799-1397-8/13/$31.00 ©2013 IEEE Jacqueline Silva Pereira Universidade Federal Fluminense Department of Telecommunication Engineering Niteroi, Rio de Janeiro Email: [email protected]ff.br waste energy and band, it is necessary increase the spectral efficiency of the network [3]. A smart antenna system has enhanced the capability be- cause it reduces the effects of interference, giving focus to the target device, enabling wireless devices to work at a higher modulation level. Therefore, control and selection algorithms (such as Least Mean Square - LMS [1] and MUltiple Signal Classification - MUSIC [3]) are used to change the radiation characteristics of antennas, such as null, sidelobe level, main lobe level and beam width. Basically, there are two types of smart antennas: the phased and the adaptive aay [ 1], shown in figures and respectively. Figure 1. Array phased Figure 2. Adaptive array This work aims to present the simulation of a junction of an adaptive filtering algorithm and a direction of arrival algorithm for a 4th generation network LTE [3]. For this, important aspects of signal processing and filtering are explained. And so, introducing the LMS algorithm. Some important concepts are covered in antennas, to clari how an antenna aay works, which are ndamental tools for DOA (Direction of Arrival) algorithms [1]. As a DOA algorithm, will be implemented the MUSIC algorithm that enables an antenna aay to estimate the number of incident signals on the array and their directions of arrival. Introducing the LTE network, mUltiplexing technology, LTE network architecture, protocols and some features are explained. The aim of this is to explain how the Beamfoing [ 1] applies to this network. Finally, a comparison between the

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Page 1: [IEEE 2013 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) - Rio de Janeiro, RJ, Brazil (2013.08.4-2013.08.7)] 2013 SBMO/IEEE MTT-S International Microwave

Least Mean Square Algorithm Analysis for a High Capacity Mobile Long Term Evolution Network.

Leandro Vieira dos Santos Universidade Federal Fluminense

Department of Telecommunication Engineering

Niteroi, Rio de Janeiro

Email: [email protected]

Abstract-Modern communication networks use MIMO to achieve high transmission data rates. A special case of MIMO is a technic called Beamforming [1], which allows formatting a radiation pattern of an antenna in order to improve the gain at a given direction. Beamforming uses adaptive filters to steer the beams in a desired direction. T he Least-Mean Square (LMS) [1] is an algorithm that performs this function of the adaptive filter. To perform the convergence of the algorithm, simulations will be performed in a Long-Term Evolution (LTE) mobile network.

Keywords: Smart antennas, beam forming, DOA, MIMO and eNode B.

I. INTRODUCTION

Wireless communications are extremely important nowa­days. With the advances in the capacity of new technologies, cell phones and other devices are able to work with a high transmission data rate. Since the beginning of wireless trans­missions, the interest in multiple antenna system has improved, resulting in techniques such as Multiple Input Multiple Output (MlMO) [2] on the Long Term Evolution (LTE) [2].

While traditional communication systems explore time domain, frequency domain pre-processing transmitted data and decoding received data, antenna elements on downlink or on uplink opens an extra dimension spatial with the objective of pre-coding and detection signals [3]. The space-time pro­cessing method have to achieve better performance in terms of error rates, transmission data rate, coverage and spectral efficiency.

With the growth of mobile communications, it is necessary to increase coverage and capacity in order to increase the Quality of Service (QoS). Smart antennas are able to use the spectrum efficiently. A smart antenna system consist of the design of a set of antennas, signal processing algorithm, space-time processing, modelling wireless channel, coding and network performance [3].

The biggest limitation of a wireless system is co-channel interference, multipath fading and delay spread [3]. As a result, new wireless technologies are developed to work with power control technic. This technic decrease the power transmission until a lower level that allows communication [3].

In a cellular system, communication between the radio base station and the end user generates energy perceived nearby [3]. This excess energy degrades the channel in the neighborhood or makes communication impossible. In order to prevent the

978-1·4799-1397-8/13/$31.00 ©2013 IEEE

Jacqueline Silva Pereira Universidade Federal Fluminense

Department of Telecommunication Engineering

Niteroi, Rio de Janeiro

Email: [email protected]

waste energy and band, it is necessary increase the spectral efficiency of the network [3].

A smart antenna system has enhanced the capability be­cause it reduces the effects of interference, giving focus to the target device, enabling wireless devices to work at a higher modulation level. Therefore, control and selection algorithms (such as Least Mean Square - LMS [1] and MUltiple Signal Classification - MUSIC [3]) are used to change the radiation characteristics of antennas, such as null, sidelobe level, main lobe level and beam width. Basically, there are two types of smart antennas: the phased and the adaptive array [ 1], shown in figures and respectively.

Figure 1. Array phased

Figure 2. Adaptive array

This work aims to present the simulation of a junction of an adaptive filtering algorithm and a direction of arrival algorithm for a 4th generation network LTE [3]. For this, important aspects of signal processing and filtering are explained. And so, introducing the LMS algorithm.

Some important concepts are covered in antennas, to clarify how an antenna array works, which are fundamental tools for DOA (Direction of Arrival) algorithms [1]. As a DOA algorithm, will be implemented the MUSIC algorithm that enables an antenna array to estimate the number of incident signals on the array and their directions of arrival.

Introducing the LTE network, mUltiplexing technology, LTE network architecture, protocols and some features are explained. The aim of this is to explain how the Beamforming [ 1] applies to this network. Finally, a comparison between the

Page 2: [IEEE 2013 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) - Rio de Janeiro, RJ, Brazil (2013.08.4-2013.08.7)] 2013 SBMO/IEEE MTT-S International Microwave

use of a smart antenna and Multiple-Input Multiple-Output (MIMO) [3] is presented.

The motivation of this study is presenting the results of composing two algorithms and how they are working to improve quality of a network, in other words, the experience of a user in a broadband network. Using digital signals processing to produce smart antennas still is new subject, other techniques for both kind of algorithms can be used to produce even better results.

II. LMS AND MUSIC

The LMS (Least Mean-Square) algorithm is widely used in adaptive filtering system communication. This adaptive filtering algorithm uses the steepest descent gradient method to minimize the squared error of a stationary signal in the broad sense [4].

The LMS find an application in adaptive beam forming. This is spatial form of adaptive signal processing finds pratical use in several areas. In the particular type of spatial filtering, a number of independent sensors or dipoles are placed at different points in space to perceive to the received signal [4]. This set of sensors consist of antennas elements; they are responsible to receive a signal radiating from a specific direction and attenuate signals radiating from other directions of no interest. To realize this filtering, a required entry to the LMS is the desired direction. To find a desired direction, an algorithm to distinguish between mUltiple signals that have different directions of arrival is used. The algorithm chosen to produece the desired direction was MUSIC. The MUSIC spatial spectrum is computed, from which the direct of arrivals (DOAs) are estimated.

III. LTE BEAMFORMING

Some system requirements are essential for digital communications planning, such as bit rate and number of system users maximization; minimization of probability error, transmission power requirements and bandwidth [2]. However, those objectives are conflicting, as the noise is measured by a relation between signal power and noise power, known as signal-to-noise ratio.

Shannon's law [5] asserts that the maximum channel capac­ity by transmission symbol depends on bandwidth and signal to noise ratio [5]. Only a set of antenna elements provides diversity both in transmission and in reception against fading. As the number of antenna elements increase, the maximum theorical capacity increases, enabling mobile devices to work in a higher signal-to-noise ratio.

Theoretically, when using 32 antenna elements in a array of antenna elements to produce Beamforming is possible to achieve 40 Mbps but 7 dB less SINR than using MIMO with 8 antenna elements.

A. Conventional antennas In the case of simple antennas, all elements with

the same polarity radiate the same signal and it is possible to use 2xN MIMO configuration as shown figure 3. A quad port antenna, which support equal bands, can perform MIMO 4XN

configuration as shown in Figure 4. Figures 3 and 4 represents conventional antennas used in MIMO [5]. Figure 3 has cross dipoles and each composes a antenna element. Figure 4 can produce a four antenna elements due the distance between the set of crossing dipoles. Each antenna element can send information to network user. When there is no processing on the antenna elements for users, MIMO is capable of supporting up to 8 antenna elements according to release 10 of the LTE [4]. This arrangement can be formed by two four entries antennas for MIMO configuration 8xN.

x X X

X

Figure 3. Conventional simple antenna

XX XX XX XX

Figure 4. Conventional quad port antenna

As an example of conventional quad port antenna, the commscope antenna [6] HWXX - 6516DS - VTM is a quad port antenna with 65 degrees of horizontal beamwidth. This is an antenna widely used nowadays for planning a LTE network. The gain of this antenna in the frequency band 2600 MHz is 18 dBi (the gain of an antenna relative to isotropic antenna), or 16.05 dBd [6], or the irradiation intensity at a certain direction is increased about 6 times compared to an antenna isotropic or around 5.3 times relative to a half-wave dipole.

From simulations of a smart antenna with MUSIC and LMS algorithms, it will be possible to analyze the behavior of radiation intensity in the desired directions of arrival.

B. Simulation Considerations In order to simulate the Beamforming, a MAT­

LAB script was developed using LMS together with MUSIC algorithm, aiming to determine the theoretical behavior of the radiation pattern of a smart antenna that could be applied to the LTE network. A 100 samples of the input signal corresponding to a frame of 10 ms of LTE were used, considered LTE releases 8 and 9 [4]. The simulations also verify the minimum number of antenna elements and the overall gain of the smart antenna. The smart antenna specifications were as follows.

The maximum power used by an eNode B (eNB) [4] in a certain area is 24 dBm (PRAT(dBm) = 24 dBm) [4], in other words, PRAT(W) � 2,512 * 10-1 W. Considering SINR =

10 dB (Minimum ratio for using 16 QAM modulation), it is possible to calculate the Noise value = 2,512 * 10-2 W, which is somewhat 10% of the input signal. Other considerations for the simulation are that the distance between elements d �

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5,50 centimeters, which is d < �, operating frequency of LTE = 2600 MHz and sampling frequency used in LTE = 3.84

megahertz.

The model of the received signals can be expressed as a superposition of signals from all the sources and linearly added noise represented by [3]:

K

x(t) = L a(Bk)sk(t) + n(t) ( 1) k=l

When using the beamforming in the Transmission Mode 7 (TM 7) [4], for example, data and reference signals (RS) [4] are transmitted using the same antenna weighting. Because the User equipment (UE) [4] requires only the UE-specific RS (4) for demodulation of the PDSCH, the data transmission for the DE appears to have been received from only one transmit antenna, and the DE does not see the actual number of transmit antennas. Therefore, this transmission mode is also called single port antenna; 5 port [4]. Transmission appears to be made from a single "virtual" antenna whose port 5 is the antenna. In this situation, layers, that describes the mapping of symbols in antennas port for each DE, sets the number of beams that may be directed, namely the number of UE's directed for transmission, which can only be one DE, in TM 7, and up to two, in the case of TM 8. However, the Layer does not define the number of elements in an array, unlike MIMO.

The algorithm has the number of directions and angles of entry as input data. The beamforming was simulated for 4, 8, 16 and 32 different quantitative antenna elements. The first three cases were compared with the number of elements used in MIMO and the last two represent the minimum number of elements which the script returned satisfactory answers for the beamforming in terms of antenna gain. With 2 antenna elements, the results were not satisfying, since DOA algorithm was not able to show enough information. This is the reason why these results are not presented. In addition, the simulation verified the performance for the LTE release 8 simple layer, considering the direction of arrival of 45 degrees, as shown in figure 5. Figure 6 presents the direction of arrival obtained with MUSIC algorithm depending on the number of elements in the array. This information will be used as LMS reference signal.

The simulated directions of arrival of 30 degrees and 60 degrees obtained with MUSIC algorithm and the beamforming with the LMS algorithm in LTE release 9 dual layer can be seen in Figures 7 and 8 respectively.

Regarding the MUSIC algorithm, using simple layer from eight antenna elements on the MUSIC algorithm produced a high peak in amplitude helping the LMS algorithm in search of the desired signal. However, when increased the number of antenna elements from eight to sixteen, although the amplitude became the same, the pulse, that shows the desired direction, narrows. As a result, the response of LMS algorithm improves. The same happens using dual layer, but only with thirty two antenna elements the pulse is narrow enough to give a good resulting in LMS algorithm.

Using the algorithm for a simple layer radiation pattern with 16 elements resulted in a better defined that with a single

10.6 o.':----:C::, O--;:20::---;30:::----;';;40,---;:5O�--,5O��70,---;BO:::----;!9·0 Angle in degrees

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(d) Direction 45 degrees with thirty two antenna elements

Figure 5. Simulation considering simple layer

Page 4: [IEEE 2013 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) - Rio de Janeiro, RJ, Brazil (2013.08.4-2013.08.7)] 2013 SBMO/IEEE MTT-S International Microwave

10'

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Figure 7. Simulation considering dual layer

Page 5: [IEEE 2013 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC) - Rio de Janeiro, RJ, Brazil (2013.08.4-2013.08.7)] 2013 SBMO/IEEE MTT-S International Microwave

10 20 30 40 50 60 70 SO 90 Angle in degrees

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10 20 30 40 50 60 70 80 90 Angle in degrees

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60 70 80 90

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(d) DOA to 30 degrees and 60 degrees with thirty two antenna elements

Figure 8. Simulation of MUSIC algorithm considering dual layer

antenna, which is used for MIMO. Another advantage in the use of the beamforming seen in simulations is the difference in amplitude between the main lobe radiation intensity and the irradiation intensities of the lateral lobes, which are around 20 dB or 6.67 times. Only with 32 antenna elements, dual layer simulations reached a satisfactory performance. In these cases, the necessary number of elements was greater than that used for the MIMO. However, this is not a disadvantage because the antenna elements have dimensions dimensions up to 5.50 em, so it is possible to develop antennas with the same size of the panel antennas in the market,improving the experience of users in mobile networks. The MIMO has the advantage of sending information for up to 8 different users in release 10, if 8 antenna ports are used and the algorithm proposed would need to increase the number of antenna elements to deal with that amount of users.

IV. CONCLU SION

With the simulations, it was seen that the beam­forming overcomes MIMO with respect to remove interfer­ing signals and allow the user equipment work in a higher transmission data rate, in other words, the great advantage of beam forming in relation to MIMO is the possibility that a device operate with maximum a high rate longer as a result that improves the SINR. Despite the higher number of antenna elements, it was observed that even with 32 elements is possible to produce antennas with the same dimensions of a conventional panel antennas.

The cons of the beam forming are the processing time and the number of elements when used more than 16 elements, that could be seen in the execution with 16 elements dual layer, which radiation characteristics of the antenna were not as good as using simple layer. This indicates that for the eight users, future releases should increase the number of antenna elements. The cons of the beam forming technique not to far outweigh the benefits. And soon these techniques will flood the market.

ACKNOWLEDGMENT

Professors of the Department of Telecommunication En­gineering of the Universidade Federal Fluminense, who con­tributed to this work was completed. In particular, the Professor Murilo Bresciani de Carvalho, the Professor Alexandre Santos de la Vega and the Professor Edson Luiz Cataldo Ferreira.

REFERENCE S

[1] Simon Haykin. Adaptative Filter TheolY. Terceira edi�iio

[2] Sesia, Stefania; Toufik, Issam; Baker, Matthew.The UMTS Long Term Evolution - From theolY to pratice.Reino Unido. Segunda edi9iio

[3] Constantine A. Balanis e Panayiotis 1. Joannides.introduction to Smart Antennas. Arizona State University, Estados Unidos. Primeira edi9iio

[4] Eduardo Rodriguez Araque e Hector David Sanchez Paz. Analysis of MIMO Technics and Adaptive Antennas and its Pelformance in 3G-LTE Systems

[5] Bernard SklarDigital Communications. Tarzana, California. Segunda edi�iio

[6] http://www.commscope.com