Wireless Personal Communications

, Volume 100, Issue 4, pp 1661–1676 | Cite as

Design of Simulation System for LTE-U Using 5 GHz Band in MATLAB

  • Muhammad Tariq
  • M. R. Anjum
  • Muhammad Amjad


Unused spectrum is being a limited commodity for the telecom industry. However, up to 500 MHz of unlicensed spectrum in 5 GHz band is in use for several applications and services, particularly in WiFi around the world. Unlicensed spectrum has foreseen to play a vital role in 5G. It is already under discussion at different forums that how the unlicensed band advantage can promote 4G Long Term Evolution (LTE) networks. Long Term Evolution Unlicensed (LTE-U) is an extension of existing LTE mobile networks, and allow LTE technology to use unlicensed bands as an adjunct to the licensed band. It will not only deliver a better user experience to end users but also boost capacity for network operators. In this paper, a Simulink based LTE PDSCH (Physical Downlink Shared Channel) model is introduced which uses the 5 GHz band according to the specifications prescribed by LTE-U Forum and 3GPP (Third Generation Partnership Project). It demonstrates multi-codeword transmission within a small cell from an eNodeB to a User Equipment (UE). LTE enabling technologies like OFDMA, closed-loop spatial multiplexing, turbo channel coding and link adaptations are used to design this model. Minimum mean square error (MMSE) technique as a linear detection method is used for spatially multiplexed Multiple Input Multiple Output (MIMO). Bit error rate is analyzed for QPSK, 16-QAM, and 64-QAM and compared for hard and soft decisions MMSE based MIMO detection. Fairly improved throughputs are obtained for multi-antenna configurations considering the low mobility scenarios and low correlation settings between links.


LTE-U Unlicensed spectrum 5 GHz band Turbo coding Closed-loop spatial multiplexing MIMO 

1 Introduction

Extending LTE to the unlicensed band at 5 GHz is a tempting vision to deliver carrier-grade wireless service. Until today, WiFi has become the most popular choice for radio access in the 5 GHz band. The work currently underway with LTE-U will offer real-world experience and help to improve the next versions of the 3GPP standards. By leveraging the LTE performance characteristics to LTE-U, we shall receive the advantages over WiFi in terms of high data rates, capacity gains, excellent coverage, low latency, better link performance and mobility management. These benefits and 500 MHz of spectrum available in the 5 GHz band make LTE-U more promising and more preferable than all former radio access technologies [1, 2, 3].

The use of WiFi devices is very common around the globe. It is essential for newly deploying technologies to use such techniques which enable them to co-exist with WiFi ecosystem without interfering it. It is obvious that same spectrum may be occupied by different LTE-U operators. This may result in the form of excessive RF interference with already existing co-channel WiFi nodes. To overcome this problem, it is necessary to select a best operating channel for LTE-U while interference with nearby WiFi and other network is minimum. Moreover, 3GPP has proved through extensive testing that if an LTE-U node will cause less interference than adding a new WiFi node. There are scenarios where WiFi and LTE-U can easily transmit using the same channel. For example, if energy detection threshold of LTE-U becomes − 62 dBm over 20 MHz then WiFi devices will initiate back off to LTE-U transmission [4, 5].

There have been recent studies on the impact of inter-system interference when LTE uses the unlicensed band in the vicinity of WLAN, and no co-existence was quantified analytically [6, 7, 8]. The 5 GHz spectrum is sub-divided into three different bands governed by Unlicensed Information Infrastructure (U-NII) of Federal Communication Commission (FCC) [9] rules summarized as in Table 1.
Table 1

Band definition of LTE-U in 5 GHz spectrum


LTE band

Spectrum (MHz)




Band 252





Band 253/254




Band 255




U-NII-2 is reserved for future use

EARFCN stands for EUTRA Absolute Radio Frequency Channel Number

Unlicensed spectrum is a wide spectrum. The prevailing deployment of LTE has only a 100 kHz channel raster. There will be vast search space in the unlicensed spectrum for eNodeB or User Equipment (UE) that may result in inevitable processing delays. However, LTE-U operations have been bounded to the following carrier frequencies of U-NII-1 and U-NII-3 bands to overcome this challenge [4].
  • U-NII-1

    • {f-0.2, f-0.1, f, f + 0.1, f + 0.2|f = 5160, 5180, 5200, 5220, 5240} MHz

  • U-NII-3

    • {f-0.2, f-0.1, f, f + 0.1, f + 0.2|f = 5745, 5765, 5785, 5805, 5825} MHz

This set of the carrier selected frequencies significantly reduces the search space for eNodeB or UE and helps to improve the performance by reducing latency. There is no WiFi channel at 5160 MHz. The first 20 MHz WiFi channel starts at 5180 MHz. Therefore, the first 5 allowed EARFCNs (255242–255246) for band 252 do not correspond to the 20 MHz WiFi channel. There is no EARFCN corresponding to the lower edge (5725–5735 MHz) and the upper edge (5835–5850 MHz) of U-NII-3 due to the lack of 20 MHz channel availability.

The exploding demand for mobile broadband requires more spectrum either licensed or unlicensed. LTE-U may provide supplementary downlink in unlicensed band adjunct to the licensed band. The consumer may experience improved performance due to data offloading on extra supplemental downlink other than licensed anchored downlink. There may be several channels available for transmission in the 5 GHz band if constrained to small cell deployment. Figure 1 illustrates that how licensed spectrum is used by the primary cell to provide a robust connection for control signaling, mobility and user data, and unlicensed spectrum used by a small cell to carry the best effort user data with a variable speed boost. The aim of this research is to design and simulate the LTE supplemental downlink (SDL) in an unlicensed band along with all LTE physical layer procedures to observe its unpredictable performance. Organization of this paper is based on four sections. Section II elaborates the detailed structure and working concepts of a simulation model and perform transmission from an eNodeB to a UE using the unlicensed band. Simulation results are discussed in section III. Section IV concludes the paper.
Fig. 1

Working principle of LTE-U in small cell infrastructure

2 Simulator Structure

The LTE-U downlink transmission comprises of Orthogonal Frequency Division Multiple Access (OFDMA) and Multiple Input Multiple Output (MIMO) with closed loop spatial multiplexing. In OFDMA selective channel is divided into many fading sub-channels [10]. This technique enables the system to function in the channel of the 20 MHz bandwidth available in an unlicensed band.

Figure 2 illustrates the LTE-U transmitter block diagram. Design Framework based on specifications of LTE-U Forum and 3GPP Release-10. MATLAB/Simulink (Version: Release: R2015b) is used to design the model. This model demonstrates a downlink transmission between an eNodeB and a UE using precoded spatially multiplexed MIMO. Overall processing is divided into Transport Block processing and PDSCH processing focused incorporating Frequency Division Duplex (FDD) mode as illustrated by Fig. 2. No control channels are considered. System parameters and configurations are given in Table 2.
Fig. 2

Functional blocks of LTE-U transmitter within downlink transmission chain

Table 2

LTE-U downlink design parameters



Channel bandwidth

20 MHz with 100 resource blocks

No. of layers

2, 4

No. of codewords


Cyclic prefix

‘Normal’ i.e. 7 OFDM symbols per slot

Modulation type

‘QPSK’, ‘16QAM’, ‘64QAM’

Layer mapping mode

Multi-antenna, spatial multiplexing

Code rate


Channel coding

Turbo coding with 8 decoding iterations

OFDM points

1024, 2048

OFDM symbols/sub-frame


Duplexing mode


Antenna configurations

2x2, 4x4

Codebook index

[0:3] for 2 antennas; [0:15] for 4 antennas

Figure 3 illustrates the LTE FDD radio frame graphically. In the time domain, payload data bits transmitted to the UE in the form of a radio frame. An LTE radio frame is comprised of 10 sub-frames of 1.0 ms and each sub-frame is made up of two-time slots having an equal duration of time (i.e. 0.5 ms). Each time slot includes 7 OFDM symbols for normal cyclic prefix duration. At the lower part of the Fig. 3, 1 OFDM symbol is zoomed out in two dimensions as time and frequency domains. It is important to mention that 1 resource block equals to one-time slot carrying 12 sub-carriers of 180 kHz and sub-carrier space is kept 15 kHz. The working of each block, shown in Fig. 2, is explained in the sub-sections with reference to the relevant specifications given in [11, 12, 13, 14, 15, 16].
Fig. 3

LTE FDD radio frame structure

The eNodeB is the only LTE network element which is used as radio access element. An eNodeB may handle one or several cells (sectors). The eNodeB communicates UE through Uu interface. The eNodeB is responsible for Radio Resources management during communications with UEs, providing Radio Barrier Control, Radio Admission Control, measurement collections and evaluation, MME selection at the attached UE, transmission of broadcast information, user data routing to SAE gateway, transmission of paging messages coming from MME, dynamic resource allocation (scheduling) depending upon the QoS and channel conditions (Fig. 4).
Fig. 4

Top view of Simulink LTE-U model

2.1 Transport Block Processing

Transport block acts as an interface between the MAC and the PHY layers. The main downlink transport channel type in LTE is the Downlink Shared Channel (DL-SCH). Two codewords of equal size are generated in the form of transport blocks as per transmission time interval (TTI) and then sent for Cyclic Redundancy Check (CRC) attachment (Fig. 5).
Fig. 5

Transport block processing

A CRC is used for error detection in transport blocks. A cyclic generator polynomial \(g_{CRC24A}\) is used by CRC Generators to produce 24 parity bits which are then padded at the end of transport block. Any transport block that goes beyond the supported size is segmented into smaller blocks and processed successively. 24-bit CRC attachment allows for early detection of correctly decoded codewords at the receiver which results in term of early termination of the iterative decoding for that transport block.

Channel coding refers to the science of correcting errors caused by the physical medium when data is either transmitted through noisy channels or is stored on storage devices. Channel coding is an integral part of almost all modern day communication and storage systems. DL-SCH used turbo coding as the channel coder. It is also known as a parallel-concatenated convolutional coder. It is composed of two recursive convolutional coders and a Quadratic Permutation Polynomial (QPP) interleaver. Turbo encoder gives two different versions of the same data as illustrated in Fig. 6.
Fig. 6

Turbo encoder [13]

After channel coding, the exact set of transmitted bits that can fit within a subframe is extracted by the Rate Matching block. Rate matching also handles the requested coding rates. At the end of DL-SCH processing, codewords are concatenated together and sent for PDSCH processing.

2.2 Physical Downlink Shared Channel (PDSCH) Processing

Two coded transport blocks (codewords) are transmitting at the same time on PDSCH contingent with precoding scheme. Upon receiving the codewords from DL-SCH, scrambling, modulation, layer mapping, precoding and resource element mapping are performed on codewords subsequently. PDSCH may be imagined as a set of time–frequency resources. It is just like the mapping of transport blocks to a corresponding physical channel. PDSCH is used as a main physical channel in a unicast transmission.

The codewords are bit-wise multiplied with an orthogonal sequence and a UE-specific scrambling sequence to generate \(b^{\sim\left( q \right)} \left( 0 \right) \ldots b^{\sim\left( q \right)} \left( {M_{bit}^{q} - 1} \right)\) series of symbols for codewords. The variable \(M_{bit}^{q}\) are the quantity of bits in codeword q. The scrambling sequence relies on the physical layer cell identity to serve the purpose of inter-cell rejection, and generated by using length-31 Gold sequence generator. It is, then, initialized at the start of each sub-frame as:
$$c_{init} = n_{RNTI} \times 2^{14} + q \times 2^{13} + \left[ {\frac{{n_{s} }}{2}} \right] \times 2^{9} + N_{ID}^{cell}$$
where \(n_{RNTI}\) is called slot number within radio network temporary identifier associated with the PDSCH transmission, \(N_{ID}^{cell}\) is the cell ID, \(n_{s}\) is the slot number within radio frame and q is the codeword index, q = {0,1}.

Scrambled codewords are, then, converted to complex modulated symbols using downlink data modulation. The supported modulation schemes are QPSK, 16QAM or 64QAM corresponding to 2, 4, and 6 bits per symbol respectively. Depending on the channel conditions, the choice of a modulation scheme allows maximizing the data transmission. Modulation type can be selected from Model Settings block.

The modulated complex symbols, d(0) (0), d(0) (1), d(0) (2)… are mapped to two or four layers depending on the antenna configuration and spatial multiplexing. A full rank transmission is supposed in this model, hence either two layers or four layers are accommodated for 2x2 or 4x4 antenna configuration. Antenna configuration may be selected from Model Settings block.

For 2x2 Antenna Configuration—Even symbols and odd symbols are mapped to layer 0 and layer 1 as described in Fig. 7.
Fig. 7

Two layers symbol mapping

For 4x4 Antenna Configuration—Mapping of input symbols to the layers will be sequential as shown in Fig. 8.
Fig. 8

Four layers symbol mapping

The layered modulated symbols are pre-coded using the codebook index. Only two entries are allowed for 2x2-antenna configuration whereas 16 entries from the Householder matrix may be used for 4x4-antenna configuration (Fig. 9).
Fig. 9

Physical downlink shared channel processing

By enabling Precoding Matrix Indication (PMI) feedback and adjusting Codebook index parameters on the Model Settings, allow us to select the codebook-based feedback from UE to eNodeB. To transmit complex-valued modulated symbols on the antenna ports, precoding is performed. There are three types of precoding i.e. single antenna port transmission, transmit diversity and spatial multiplexing. Spatial Multiplexing can be divided into two schemes: Open-loop and Closed-loop. This model is designed for closed-loop spatial multiplexing.

The pre-coded symbols are mapped to the resource blocks of the resource grid. The unlicensed spectrum is divided into channels of 20 MHz. There are 12 sub-carriers for mapping on each resource block with sub-carrier spacing of 15 kHz equals to 180 kHz of spectrum. Therefore, 20 MHz bandwidth is required to map 100 resource blocks. Cell-Specific Reference (CSR) signals are used for channel estimation at the receiver side depending on the antenna configuration.

The final step is multi-carrier transmission based on OFDM transmission scheme. OFDM block is used to organize modulated symbols of each layer as time–frequency resource grid and then produces time-domain OFDM symbols by applying Inverse Fast Fourier Transform (IFFT) for transmitting.

2.3 MIMO Channel Model

LTE networks are expected to provide high data rates with high spectral efficiency and best service to cell edge users by means of multiple antenna techniques. This delivers additional robustness to the radio link. In multiple antenna techniques, fragmented data in the form of multiple parallel streams transmit through multiple antennas whereas each stream contains a different portion of data i.e. each stream represents different information. The MIMO fading profiles are implemented by the MIMO Fading Channel block as depicted in Fig. 10. The closed-loop spatial multiplexing has been applied to achieve high data rates. It is applicable for low mobility scenarios only and the higher mobility profiles have been excluded. It uses low correlation setting between the multiple links.
Fig. 10

MIMO channel model

Figure 11 describes the working of 4x4 MIMO. All four transmitted signals arrive at four receiving antennas. It is possible to exploit the signals arriving from multiple reflections if receiving antennas are uncorrelated, and transmitted signals can be reconstructed from all receiving signals.
Fig. 11

4x4 MIMO processing

Mathematically, transmitting and receiving signals can relate as:
$${\mathbf{y}} = {\mathbf{Hx}} + {\mathbf{z}}$$
where x = [x 1 , x 2 , x 3 , x 4 ]T and y = [y 1 , y 2 , y 3 , y 4 ]T represent spatially-multiplexed data and corresponding receiving signals respectively. Let H denotes the channel matrix where each h mn value represents the channel gains between mth transmitting and nth receiving antenna. Let z = [z 1 , z 2 , z 3 , z 4 ]T denotes the Additive White Gaussian Noise (AWGN) with a variance of \(\sigma_{z}^{2}\) at corresponding receiving antenna.
Linear detection tries to nullify or minimize the effect of receiving signals except the desired one. For this purpose, precoded spatial multiplexing is employed by inverting the effect of the channel using a weight matrix W as:
$${\tilde{\mathbf{x}}} = \left[ {\tilde{x}_{1} \tilde{x}_{2} \ldots \tilde{x}_{{N_{T} }} } \right]^{T} = {\mathbf{Wy}}$$
where W is the precoding vector which is inversely multiplied with the codewords at precoding stage at the transmitter side and is selected by the codebook index indicated by receiver feedback.

2.4 User Equipment (UE) Processing

All functional blocks of the receiver (UE) are shown in Fig. 12. Receiver processing includes demodulators, MIMO receiver, layer de-mapper, de-scramblers and channel decoders.
Fig. 12

Receiving blocks of user equipment (UE)

OFDM demodulator converts the signals from frequency domain to time domain and performs resource element de-mapping. MIMO Receiver block is the channel estimation block that estimates the channel according to CSR, and linear interpolation of data elements is performed over the sub-carriers. MIMO receiver acts as a linear MMSE signal detector at the receiver side. It helps to mitigate the interference of multi-antenna transmissions. MMSE criteria are employed by the codebook selection to calculate the codebook index per sub-frame [17]. On enabling precoding matrix indication, UE fed back the index to eNodeB for use at the next time step. On the other hand, user-specified codebook index is used. To facilitate downstream turbo decoding, soft-decision de-modulation has employed per codeword. Hard and soft decision-based PDSCH decoding bits are received for codeword-1 and codeword-2 respectively.

3 Simulator Results and Performance

The simulator is user-friendly and highly configurable. Channel bandwidth is fixed at 20 MHz of unlicensed band. It can adopt 2x2 and 4x4 antenna configuration along with various modulations. The channel model employed is EPA-LTE [18] MIMO fading block and AWGN block. In AWGN block, bit energy per signal noise (Eb/No) allowed to be adjusted so that effect of bit error rates against signal power can be observed. Model computes the error rates of PDSCH bits per codeword (PDSCH BER) and throughput in Megabits per second (Mbps) per codeword to validate the results.

3.1 Bit Error Rate Results

This model is designed for two codewords and multi-antenna configurations incorporating spatially-multiplexed MIMO with different digital modulations. BER performance is analyzed for different combinations of these and against different values of SNR. Figure 13 shows the plots of PDSCH BER and SNR values for QPSK. From the plots, it is observed that BER performance of 2x2-antenna configuration is better than that of 4x4-antenna configuration. We also observed that BER tends to zero in case of 2x2 configuration whereas it tends to be constant after 25 dB in 4x4 configuration.
Fig. 13

BER versus Eb/No (dB) for QPSK

Figure 14 illustrates the PDSCH BER and SNR values for 16-QAM. The performance of the model degrades than that of QPSK in terms of increased signal power is required to lower BER. On comparing hard versus soft decision in 4x4 configuration, the BER is remained close for both codewords.
Fig. 14

BER versus Eb/No (dB) for 16-QAM

Figure 15 exhibits the behavior of PDSCH BER against different values of SNR for 64-QAM. From the plots, it can be perceived that BER converges to 0.001 after 30 dB in 2x2 antenna configuration whereas becomes constant near to 0.1 after 30 dB in 4x4 antenna configuration. The BER is remained very close for both codewords.
Fig. 15

BER versus Eb/No (dB) for 64-QAM

Collectively, it is concluded that 2x2 antenna configurations may result in lower BER as compared to 4x4 antenna configurations. It is also observed that BER performance of soft decision based linear MMSE detector is higher than that of hard decision. It can be analyzed that higher order modulations increase the bit error. For 4x4 MIMO configuration, we can observe a stronger degradation in BER performance, which could be the effect of multiple antenna transmission.

3.2 Comparison of Codewords for Different Modulations

Figure 16 describe the codewords performance of the proposed model for three different modulation schemes as illustrated. BER of codeword-1 is computed using linear MMSE detector based on hard decision whereas BER of codeword-2 is computed using linear MMSE detector based on soft decision scheme.
Fig. 16

Performance comparison of codewords

It has realized that performance codeword-2 in terms of BER is better than that of codeword-1. It is also observed that EVM and MER values changes in decreasing fashion if SNR values are increased and changes in increasing fashion if higher bit modulation is selected.

3.3 Throughput Analysis

The maximum peak data rate depends upon the channel bandwidth, cyclic prefix and modulation used. If ideal conditions are assumed and high SINR allows to used 64-QAM modulation, it means each subcarrier carries 6-bits. For 20 MHz channel using 64-QAM and normal cyclic prefix, the data rate can be calculated as:
$$\begin{aligned} {\text{Data Rate}} & = {\text{RBs}} \times {\text{Subcarriers per Slot}} \times {\text{Modulating Bits}} \div {\text{LTE Symbol Duration}} \\ {\text{Data Rate}} & = 100 \times 12 \times 6 \div 71.4\,\upmu{\text{s}} = 100.8\,{\text{Mbps}} \\ \end{aligned}$$
Figure 17 is a graphical representation which illustrates the effect of 2x2 and 4x4 antenna configurations. It is observed that higher throughputs can be achieved by increasing the number of antennas at transmitter and receiver. Note that for all modulations, throughput increases significantly especially for 64QAM with 4x4 antenna configuration results as 149.776 Mbps (3.8 times data rate of 2x2 configuration) per codeword. However, BER performance is degraded.
Fig. 17

MIMO throughput per codeword

Table 3 differentiate the performance of proposed model and WiFi 802.11ac [19] in terms of throughput which indicates that LTE-U has comparatively higher throughput than WiFi 802.11ac using 20 MHz.
Table 3

Comparison of throughputs of LTE-U and WiFi 802.11ac for different coding rates


Code rate

Throughput of proposed model @ 20 MHz (Mbps)

Throughput of WiFi 802.11ac @ 20 MHz (Mbps)





























4 Conclusions

Spectrum scarcity is a critical issue of wireless communication networks nowadays. Demand of bandwidth has significantly increased due to new applications and services, especially cloud computing. Unlicensed spectrum has predicted to have a key role in the upcoming generation of mobile networks and the telecom industry readily seems to embrace the concept of LTE-U. In this paper, we have designed a simulation model for LTE-U comprises of all LTE enabling technologies operating in 5 GHz band. Through multi-codeword transmission, we computed the maximum throughput of 300 Mbps for 64-QAM using 4x4-antenna configuration. Throughput of the model is also compared WiFi 802.11ac standard throughputs for different code rates. BER is analyzed for different LTE modulation schemes. Hard and soft decisions for MMSE linear detection is employed as spatially multiplexed MIMO on codewords and compare their performance in terms of BER. The model gives the insight that there is a tradeoff between BER and throughput which depends upon channel conditions.

It helps us to understand that how existing LTE technology can be leveraged to use unlicensed band. Performance of LTE-U system is highlighted through the model. It is clear with an increase in antenna number and higher order modulations collectively result as higher throughputs.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronic EngineeringIslamia University of BahawalpurBahawalpurPakistan

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