waveform and space precoding for next generation downlink … · 2019. 2. 8. · tongyang xu,...

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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JIOT.2019.2896724, IEEE Internet ofThings Journal

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Waveform and Space Precoding for NextGeneration Downlink Narrowband IoT

Tongyang Xu, Member, IEEE, Christos Masouros, Senior Member, IEEE and Izzat Darwazeh, SeniorMember, IEEE

Abstract—Narrowband Internet of things (NB-IoT) was in-troduced by 3GPP in low power wide area network (LPWAN)to support low power and wide coverage applications. Since itfollows long term evolution (LTE) standard, its signal qualityis guaranteed and its deployment is straightforward via reusingexisting infrastructures. Current NB-IoT supports low data rateservices via using low order modulation formats for the purposeof power saving. However, with the increase of data rate drivenapplications, next generation NB-IoT would require data rateenhancement techniques without consuming extra battery power.In this work, a downlink framework, using a non-orthogonalsignal waveform for next generation enhanced NB-IoT (eNB-IoT),is proposed and experimentally tested in both single-antenna andmulti-antenna systems. In the single-antenna scenario, waveformprecoding is used to pre-equalize the self-created inter carrierinterference (ICI) distorted signal waveform. For the multi-antenna multi-user scenario, both waveform and antenna spaceprecoding have to be used. Measured results show that in bothsingle-antenna and multi-antenna systems, the proposed signalwaveform in eNB-IoT can increase data rate by ∼11% comparedwith NB-IoT occupying the same spectral resource in similarreceiver computational complexity.

Index Terms—Narrowband 5G, NB-IoT, eNB-IoT, precoding,multi-antenna, multi-user, MIMO, non-orthogonal, waveform,software defined radio, prototyping.

I. INTRODUCTION

NARROWBAND Internet of things (NB-IoT) [1] is acellular IoT technique standarized in 3GPP. It provides

quality guaranteed services due to its paid spectral license.Unlike other IoT techniques such as LoRa [2] and SigFox[3], NB-IoT employs multicarrier signal waveform and iscompatible with 4th generation (4G) LTE networks. Therefore,its deployment is straightforward [4] and it is becoming quiteimportant in many IoT based applications and it leads the wayfor future narrowband 5th generation (5G).

The downlink signal of NB-IoT occupies 12 sub-carrierswith total bandwidth of 180 kHz. Its uplink channel supportseither multicarrier waveform or single-tone waveform. Dueto its narrow band characteristic, NB-IoT can cover a widecommunication range [5]. NB-IoT has been standarized andits commercialized chip has been released with a reasonableprice. However, with the ever-increasing requirements and theneed to accommodate emerging applications, existing NB-IoTis approaching its limit owing to a number of factors:

T. Xu, C. Masouros and I. Darwazeh are with the Department of Electronicand Electrical Engineering, University College London (UCL), London,WC1E 7JE, UK (e-mail: [email protected], [email protected],[email protected]).

• Pressure of massive IoT data. More IoT devices are beingconnected in networks due to emerging applications.

• Shortage of spectral resources. Each NB-IoT signal oc-cupies bandwidth of 180 kHz, and this is becoming alimiting factor to NB-IoT.

• Insufficient data rate. Maximum modulation format inNB-IoT is quadrature phase-shift keying (QPSK), whichis not sufficient for data rate driven applications.

• High power consumption. To achieve a wide signalcoverage, repetitive transmission is employed but withthe challenge of extra power consumption.

Therefore, the upgrade to next generation enhanced NB-IoT (eNB-IoT) is necessary. In order to enhance existingNB-IoT, it is crucial to develop new spectrum and powerefficient communication techniques. Waveform design is anefficient solution to lead the narrowband 5G. Previous workin [6]–[9] has presented solutions for eNB-IoT using spectrallyefficient frequency division multiplexing (SEFDM) [6], Half-Sinc [7] and fast-orthogonal frequency division multiplexing(Fast-OFDM) [8], [9] non-orthogonal signal waveforms andbrings benefits against conventional orthogonal frequency di-vision multiplexing (OFDM) such as

• Improve data rate without upgrading modulation formats[6], [7].

• Double the number of connected IoT devices withoutoccupying extra spectral resources [8].

• Extend signal coverage using a power efficient waveformscheduling scheme [9].

Applications of SEFDM have been explored in differentareas, such as visible light communication (VLC) [10], [11],optical systems [12]–[14], mm-wave [15], spectral and energyefficiency [16] and capacity gains [17]. However, due to thenon-orthogonally packed sub-carriers in SEFDM, self-createdinter carrier interference (ICI) is introduced and power con-suming signal processing is required at the receiver. Therefore,it is suitable for uplink channel since the signal processing isat base stations [6]. For downlink channels, this necessitatesextra receive processing at the IoT devices which renders itimpractical. Battery power is a critical factor in NB-IoT. Assurveyed by [18], the battery life of an NB-IoT device isdesigned to last at least 10 years without battery replacement.In this case, each device has to be designed power efficiently.Therefore, the signal processing has to be as simple aspossible. Thus, in order to evolve to narrowband 5G for thedownlink channel, power efficient precoding techniques haveto be used together with waveform design.



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This work details the design and testing of the non-orthogonal SEFDM waveform in both single-antenna andmulti-antenna systems to enhance the downlink data ratefor next generation eNB-IoT. A joint two-stage waveformand space precoding architecture is proposed. The waveformprecoding is to pre-equalize the self-created ICI within thewaveform and the space precoding is to pre-cancel the multi-antenna multi-user spatial interference. This work practicallysets up a world-first real-time multi-antenna multi-user non-orthogonal waveform IoT experiment operating on a realisticover-the-air MIMO-SEFDM transmission link. Measured re-sults, on a software defined radio (SDR) testbed, verifies thatthe waveform precoding and space precoding can efficientlypre-cancel waveform interference and antenna spatial interfer-ence at the transmitter, respectively. Moreover, the collecteddata from the MIMO-SEFDM SDR testbed demonstratesthat the proposed non-orthogonal waveform for eNB-IoT canincrease data rate by ∼11% than NB-IoT while occupying thesame spectral resource and in the similar receiver computa-tional complexity.

II. WAVEFORM DESIGN AND ITS BENEFITS

There are mainly two groups of non-orthogonal waveforms.One is the bandwidth compressed multicarrier waveform in-cluding Fast-OFDM [19], SEFDM [20] and Half-Sinc [7]. Theother one is the out-of-band power suppression waveform in-cluding filterbank based multicarrier (FBMC) [21], generalizedfrequency division multiplexing (GFDM) [22] and universal-filtered multi-carrier (UFMC) [23]. The first group focuses oncompressing occupied spectral bandwidth while maintainingthe same data rate or occupying the same spectral bandwidthbut with improved data rate. The second group can suppressthe out-of-band power leakage leading to closer signal bandpacking without co-band interference. This work focuses onthe bandwidth compressed SEFDM waveform since it can beflexibly configured for different applications. In addition, workin [24] verified that the waveform shaping can also be appliedto the bandwidth compressed signals.

Sub-carrier allocation schemes for both OFDM and SEFDMare illustrated in Fig. 1. The first figure shows the typicalOFDM sub-carrier packing scheme where 12 sub-carriers areorthogonally packed with no interference. For SEFDM, twoadvantages can be achieved in either bandwidth saving ordata rate improvement. The original idea of the SEFDMsignal waveform is to pack sub-carriers closer in the frequencydomain, thus signal bandwidth is compressed as shown in Fig.1, which is “SEFDM Type-I”. Its benefit is to transmit thesame amount of data while occupying narrower bandwidth.In this case, the noise power is reduced within a narrowersignal band and the saved bandwidth could be reserved forextra IoT devices. It should be noted that this work is aimed atfuture IoT communications. Therefore, the sub-carrier spacingof SEFDM signals may be not consistent with existing 3GPPstandards. Based on the Type-I signal waveform, a data rateimproved “SEFDM Type-II” waveform is proposed to increasedata rate per sub-carrier while maintaining the same occupiedsignal bandwidth. Compared with the OFDM signal waveform,

-100 -50 0 50 100 150 200 250-0.5

0

0.5

1

-100 -50 0 50 100 150 200 250-0.5

0

0.5

1

Nor

mal

ized

Mag

nitu

de

-100 -50 0 50 100 150 200 250Frequency (Hz)

-0.5

0

0.5

1

SEFDMType-I

SEFDMType-II

OFDM

Fig. 1. Sub-carrier allocation schemes for different multicarriersignals. OFDM (12 sub-carriers, data rate is Rb). SEFDM Type-I(12 sub-carriers, bandwidth compression factor α, data rate is Rb).SEFDM Type-II (12 sub-carriers, bandwidth compression factor α,data rate is α·Rb).

the overall bandwidth is the same while the bandwidth of eachsub-carrier is wider. In this case, using the same number ofsub-carriers with a higher data rate per sub-carrier, the overallsignal data rate is improved. In this work, we will focus onthe “SEFDM Type-II” signal waveform, which can enhancethe data rate for next generation eNB-IoT applications.

A. Signal Waveform Model

In a system with N sub-carriers, the discrete SEFDM signalis mathematically represented by

Xk =1√N

N∑n=1

sn exp

(j2πnkα

N

), (1)

where Xk is the kth time sample with k = [1, ..., N ], α =∆f ·T is the bandwidth compression factor, where ∆f denotesthe sub-carrier spacing and T is the period of one SEFDMsymbol. Thus, the percentage of bandwidth saving is equal to(1 − α) × 100%. sn is a complex symbol modulated on thenth sub-carrier and 1√

Nis a scaling factor.

The matrix form of the discrete SEFDM signal is expressedas

X = FS, (2)

where S is an N -dimensional vector of transmitted complexsymbols, X is an N -dimensional vector of SEFDM timesamples and F is an N ×N sub-carrier matrix with elementsequal to e

j2πnkαN .

To simplify the signal waveform modelling, channel effectis not taken into account in the model. Therefore, the trans-mitted signal is only distorted by additive white Gaussiannoise (AWGN) Z. At the receiver, the contaminated signalis demodulated by multiplying with the conjugate sub-carriermatrix F∗ with elements equal to e

−j2πnkαN . The demodulation

is expressed as

R = F∗(X + Z) = F∗FS + F∗Z = CS + ZF∗ , (3)



3

where R is an N -dimensional vector of demodulated symbols,ZF∗ is the AWGN demodulated by the conjugate sub-carriermatrix F∗ and C is an N × N correlation matrix equals toC = F∗F whose elements are defined as

cm,n =1

N

N∑k=1

ej2πmkα

N e−j2πnkαN

=

{1 , m = n

1−ej2πα(m−n)

N(1−ej2πα(m−n)

N ), m 6= n

},

(4)

where m,n are indices of two arbitrary sub-carriers. The diag-onal elements (m = n) indicate constructive auto-correlationwhile the non-diagonal non-zero elements (m 6= n) representdestructive cross-correlation ICI.

B. Signal Generation and Demodulation

The signal can be generated directly using (1) at the expenseof high computational complexity. However, in order to makeuse of the efficient inverse fast Fourier transform (IFFT)algorithm, a proper signal generation approach has to bedesigned for SEFDM.

The basic idea is to pad zeros at the end of each inputQAM symbol vector. The length of a new QAM symbol vectorbecomes M = N/α. Therefore, it is possible to use a singleIFFT of length N/α for simplified signal generation. The zeropadding is operated as

s′

i =

{si 1≤i≤N0 N < i≤M . (5)

Therefore, the new form of the SEFDM signal is expressedas

X′

k =1√M

M∑n=1

s′

n exp

(j2πnk

M

), (6)

where n, k = [1, ...,M ]. The output symbol vector X′

of aM length is truncated with the first N samples reserved whilethe rest is discarded.

It should be noted that the signal demodulation at thereceiver is the reverse operation of the signal generation atthe transmitter. Thus, the signal demodulation can be realizedefficiently via the zero padding method as well.

C. PAPR Comparison

The dynamic range of a signal determines the dynamicrange of a component and peak-to-average power ratio (PAPR)is used to measure the variation of signal power. The cumula-tive distribution function (CDF) is used as a way to analyze theprobability of PAPR being smaller than a predefined thresholdγ. Assume a signal x(t) is obtained after the final stage ofdigital signal processing and the calculation of PAPR is givenas

PAPR =max|x(t)|2

E[|x(t)|2], (7)

where max|x(t)|2 indicates the peak power of the signal x(t),E[.] is the expectation operator and E[|x(t)|2] denotes theaverage power.

2 3 4 5 6 7 8 9

PAPR ( in dB)

0

0.2

0.4

0.6

0.8

1

Pro

babi

lity

(PA

PR

<=

)

OFDM=0.9=0.8=0.7

Fig. 2. CDF of PAPR for OFDM and SEFDM signal waveformsmodulated by 4QAM symbols.

Fig. 2 presents PAPR of four systems using the samenumber of sub-carriers. One system is the typical OFDMwith 4QAM modulation format. The other three systems arethe SEFDM with 4QAM at different α. Results indicate thatthe probability of PAPR below and equal the threshold γ ofSEFDM is better than that of OFDM. It should be notedthat the PAPR performance of SEFDM is improved with thereduction of α.

III. SYSTEM ARCHITECTURES AND PRECODING

A. System Architecture

To simplify signal processing of each IoT device, precod-ing is used to pre-cancel interference at the transmitter andtherefore the signals at each IoT device can be recovereddirectly. The ICI is known at the transmitter when the SEFDMwaveform is deterministically configured. Thus, the simplezero forcing (ZF) [25] waveform precoding method can beused at the transmitter based on the direct ICI matrix inversion.In multi-antenna scenarios, multiple antennas will transmitdifferent signals simultaneously resulting in interference toeach receiver user. The linear ZF [25] multiple input multipleoutput (MIMO) space precoding can be used for spatialantenna interference cancellation at the transmitter based onthe feedback channel state information (CSI).

System architectures for the downlink eNB-IoT in bothsingle-antenna and multi-antenna scenarios have to be re-designed with either one-stage precoding or two-stage pre-coding schemes, as illustrated in Fig. 3.

In a single-antenna single-user SEFDM eNB-IoT systemshown in Fig. 3(a), a waveform precoding module is usedbefore IFFT to pre-cancel the self-created ICI. For a typicalOFDM NB-IoT system, the waveform precoding module isremoved since there is no ICI.

In a MU-MIMO-OFDM based NB-IoT system shown inFig. 3(b), a MIMO precoding module is included. For simplic-ity, we use two antennas and two users for explanation. Eachantenna is responsible for one OFDM symbol stream. Thus,two IFFT modules are needed at the transmitter. The data fromeach antenna will interfere with each other due to the MIMO



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channel H and each user antenna will receive interfered datafrom both transmitter antennas. The MIMO precoding moduleis applied to pre-cancel the antenna spatial interference at thetransmitter based on the feedback CSI. Therefore, the userantennas will receive interference free data. The main benefitof this MIMO precoding architecture is that each receiver issimplified and can process data independently. Therefore, it issuitable for downlink IoT scenarios.

Fig. 3. Architectures of precoding single-input single-output (SISO)and precoding multi-user multiple-input multiple-output (MU-MIMO) systems for (a) one-stage precoding SISO-SEFDM basedeNB-IoT, (b) one-stage precoding MU-MIMO-OFDM based NB-IoTand (c) two-stage precoding MU-MIMO-SEFDM based eNB-IoT.

For MU-MIMO-SEFDM based eNB-IoT, as explained insection II, the resulting interference consists of both antennaspatial interference and the self-created ICI. Following thesame system architecture in Fig. 3(b), antenna spatial inter-ference can be removed but the signal internal ICI still exists.Therefore, a two-stage precoding architecture for eNB-IoT isdesigned and presented in Fig. 3(c). The system architecturepartially follows the MU-MIMO-OFDM but with a waveformprecoding module added before each IFFT. Since the wave-form characteristic is pre-defined in the correlation matrix C,therefore the waveform precoding is operated based on theknown correlation matrix without any feedback informationfrom the receiver. Thus, the two-stage precoding can efficientlyremove antenna spatial interference and SEFDM waveforminternal ICI leading to a simple receiver design for eNB-IoT.

B. Waveform Precoding

The basic principle of the waveform precoding is to pre-cancel waveform self-created ICI. This concept can be used inany waveforms when the waveform characteristic is determin-istic. In this work, we focus on SEFDM, which has predefinedself-created ICI information in the matrix C. As is shownin (2), transmitted symbols are modulated on a multicarriersignal via multiplying with the sub-carrier matrix F. For theprecoding operation, the raw symbols are pre-multiplied withthe inverse of the matrix C before modulation. Therefore, theidea is to equalize the symbols on each sub-carrier based onthe pre-defined precoding matrix Cp as the following

Cp = CH(CCH)−1. (8)

Thus, the precoded signal S is defined as

S = CpS. (9)

Then the precoded SEFDM signal is expressed as

X = FS. (10)

Following the same operation from (2) to (3), the newlydemodulated signal at the receiver is expressed as

R = F∗(X + Z)

= F∗FS + F∗Z

= CCpS + ZF∗

= S + ZF∗ ,

(11)

where S is the demodulated symbols at the receiver. The pre-coding operation is similar to the one in MIMO systems [26].However, the precoding matrix in SEFDM is deterministic andno feedback information is required.

C. MIMO Precoding

To avoid spatial interference from different antennas, we usetime orthogonal pilot symbols to measure channel conditions.Since the system is not in a massive size in this work, the basestation will send time orthogonal pilot symbols to multipleusers at the same time. Each user will receive their ownallocated pilot symbols without co-user interference. Each userindependently estimates CSI and sends it back to the basestation for precoding.

To explain the precoding operation in a simple way, weassume a two-antenna two-user scenario as illustrated in Fig.3(b)(c). The channel matrix H is therefore defined as

H =

[h11 h12h21 h22

]. (12)

The designed time orthogonal pilot symbols are sent fromtwo transmit antennas over two symbol periods. In the firsttime period, the first antenna sends symbol p1 while the secondantenna sends nothing. In the second time period, the firstantenna sends nothing while the second antenna sends symbolp2. In this case, spatial interference from two antennas can beavoided. The matrix form of the time orthogonal pilot symbolsis expressed as

P =

[p1 00 p2

]. (13)

Thus, the received symbols from two antennas at the re-ceiver are given by

[y11 y12y21 y22

]=

[h11 h12h21 h22

]×[p1 00 p2

]+

[z1z2

], (14)

where y11 and y12 are the received symbols on the first antennaat the first time period and the second time period, respectively.The two symbols y21 and y22 are received on the secondantenna. z1 and z2 are AWGN on the first and second antennas,respectively. Therefore, we can derive the channel conditioninformation in the following



5

H =

[h11 h12h21 h22

]=

[y11/p1 y12/p2y21/p1 y22/p2

]. (15)

Since the antenna spatial information H is estimated, thedata symbols X from two antennas in one time period areprecoded and expressed in matrix form as

Y = HpX

= HH(HHH)−1X,(16)

where Y = [y11, y21] indicates MIMO precoded signalsfrom two antennas and X = [x11, x21] represents precodedwaveform signals from two antennas. It should be noted thatx is one sample in one SEFDM signal X derived in (10) andx11 indicates the first sample of an SEFDM signal from thefirst antenna while x21 is the first sample of an SEFDM signalfrom the second antenna.

IV. EXPERIMENT DESIGN AND SYSTEM IMPLEMENTATION

A. Platform Setup

The system architecture and its components connectionsare shown in Fig. 4. The experiment is designed for proof-of-concept of the downlink multi-user eNB-IoT. Therefore,we are not considering massive MIMO [27], [28] but asimple system with six transmission antennas in a realisticindoor wireless environment. For simplicity we only test atwo user case. The channel is time-invariant and no obviousDoppler issues exist since each IoT device is stationary afterdeployment. We use six NI USRP-RIO 2953R SDR devicesto emulate the transmitter and user functions. Each device hastwo separate and independent RF chains, in which the fist RFchain is for signal transmission and the other one is for signalreception.

Fig. 4. System architecture and components connections. Red connec-tions indicate NI MXI-Express Gen 2 ×8 cables. Blue connectionsindicate SMA cables delivering 10 MHz reference signals. Greenconnections indicate SMA cables delivering PPS signals.

In order to synchronize six USRPs in both frequency andtime, a CDA-2990 8-channel clock distribution OctoClockmodule is used. It can split and amplify a 10 MHz referencesignal and a pulse per second (PPS) signal to support up toeight USRPs. Raw digital data is generated by LabVIEW ina control host and is sent to a cabled PCI-Express switch boxCPS-8910 via an NI MXI-Express Gen 2 ×8 Cable, whichcan support up to 3.2 GB/s. The switch box separates theserial data to six data streams, which are sent to six USRPs

in parallel. Omni-directional antennas are used in the testbedto verify the precoding feasibility. The realistic experimenttestbed operating at 2.4 GHz is presented in Fig. 5 withkey system parameters shown in Table I. It should be notedthat the SEFDM Type-II signal, shown in Fig. 1, is usedin the experiment. Therefore, to maintain the similar signalbandwidth while achieving higher data rate, the sampling ratefor eNB-IoT is higher than that of NB-IoT. Furthermore, fora fair comparison of performance we choose α=0.9 so thatthe SEFDM and OFDM signals have almost equal PAPR asshown in Fig. 2. The control host, illustrated in Fig. 6, is acomputer, which can control the USRP array such as sendingand receiving data from antennas.

Table I: System specifications

Parameter NB− IoT eNB− IoTNo. of BS antennas 6 6No. of users 2 2RF center frequency (GHz) 2.4 2.4Sampling frequency (MHz) 1.92 2.13FFT size 128 128No. of guard band sub-carriers 58 58No. of data sub-carriers 12 12No. of cyclic prefix samples 10 10Bandwidth (kHz) 180 180

Fig. 5. Base station (USRP array).

The floor plan of the control host, base station and userantennas are illustrated in Fig. 7. The floor plan shows thesimplified illustration of the experiment setup. Other unnec-essary equipments and objects are skipped in this illustration.The user antennas in this experiment are labelled with blacktriangular blocks. For proof-of-concept purposes, two userantennas (Rx antennas), shown in Fig. 8, are placed in randomlocations in a room but in line of sight of the base stationUSRP array, which has six Tx antennas. Distances are variedfrom a fraction of one meter to around half length of the roomwhich was close to 5 metres. For a worst case scenario the



6

Fig. 6. Control host.

Fig. 7. Indoor floor plan and the location of USRP base station (hasTx antennas), user IoT devices (has Rx antennas), control host andexperiment operator. The laboratory is around 9 meters long and 4meters wide.

two user antennas are placed very closely together and spacedby only 10 cm. For a realistic and better determined path lossand noise modelling for different receiver signal-to-noise ratio(SNR) values, a channel emulator (Spirent VR5) is used asdescribed in Section V-C.

B. Frame Structure

The overhead for frame timing synchronization is not in-cluded in the frame structure. The reason is that the experimentis designed for proof-of-concept and it continuously sendsand receives signals for the testing. Once the frame is syn-chronized, the following data frames will not need the frametiming sequence any more. Therefore, we remove this kind ofoverhead, which is only used once in this experiment.

In this work, the base station is assumed to have six antennasand provide services to two users. The downlink signal isgenerated in the base station with a designed frame structureand resource block structure illustrated in Fig. 9. The 10 msframe is divided into 20 time slots with each one occupies0.5 ms. The first time slot is reserved for downlink antennaspatial CSI estimation. All the other time slots ranging from 01to 19 are used for data transmission. Each time slot has sevenOFDM/SEFDM symbols as shown in the figure. In order toavoid interference between antennas, the overhead of spatialCSI estimation for each antenna is interleaved at differentsymbol locations in time. The CSI estimation overhead fromthe first antenna occupies the first OFDM symbol in oneresource block while keeping blank for the following fiveOFDM/SEFDM symbols, which are reserved for the rest fiveantennas. All the other five antennas follow the same principle

Fig. 8. User antennas (user 1 and user 2).

to avoid the overlapping interference. The last OFDM/SEFDMsymbol in one resource block from all the six antennas isreserved for the downlink pilos, which are used to compensatefor imperfect channel issues such as power normalizationand imperfect timing and phase synchronization [29]. Thedownlink pilots from all the six antennas are precoded and sentsimultaneously. It should be noted that the system is robust tooscillator variation [30] even under the self-created ICI.

In order to get accurate downlink spatial CSI estimate foreach antenna, a time-domain orthogonal sounding referencesignal (SRS) is used. Thus, antenna spatial interference canbe avoided. The SRS frame structure is shown in Fig. 9.Since the SRSs are interleaved and sent over the air, eachuser will receive staggered SRS from all of the transmitterantennas. The estimated downlink antenna spatial CSI will befedback to the transmitter for space precoding. The precodingmethod used in this work is in the time-domain where thechannel is assumed to be stable within one OFDM/SEFDMframe transmission. Since we are focusing on a system withsix transmitter antennas, therefore, each user just needs tofeedback six CSI coefficients and the overhead length isreasonable. In addition, due to the robustness to multipatheffect in OFDM/SEFDM, the compensation of this effect isoperated at the receiver via the simple one-tap equalizer [31].

It should be noted that in non-feedback systems, CSI is ob-tained based on the channel reciprocity. In addition, multipatheffect exists in a practical over-the-air transmission scenario.Therefore, precoding has to be operated on each sub-carrierin the frequency-domain with extra signal processing such asCSI interpolation. However, realistic channels are not perfectreciprocity due to the time-variant analogy components and alot of efforts have to be allocated to reciprocity calibration.Therefore, instead of using the non-feedback system architec-ture, this work employs a feedback system architecture forsimplification of the next generation eNB-IoT.

V. MEASURED RESULTS AND DISCUSSIONS

In this work, we evaluate error vector magnitude (EVM)and bit error rate (BER) for received constellation diagramstogether with data rate demonstrations. In terms of EVMcalculation, we use the reference signal normalization methoddefined as



7

Fig. 9. Frame and resource block structure for each antenna. One frame consists of 20 time slots and the first time slot is the overheadincluding SRS for downlink antenna spatial CSI estimation, which is used for space precoding at the transmitter. The locations of overheadfrom different antennas are staggered to avoid interference.

EVM =

√√√√ 1K

∑K−1i=0 ((Ii − Ii)2 + (Qi − Qi)2)1K

∑K−1i=0 ((Ii)2 + (Qi)2)

, (17)

where Ii and Qi are the ith reference in-phase and quadraturesymbols, respectively. Ii and Qi are the ith measured in-phaseand quadrature symbols, respectively. K represents the numberof symbols used in the EVM calculation.

A. Single-Antenna Single-User

First, we test the typical ‘SISO-OFDM’ based NB-IoTperformance in Fig. 10(a). Then, we test single-antenna single-user eNB-IoT performance, which is noted as ‘SISO-SEFDM’in Fig. 10(b). The SISO-SEFDM experiment is operated on thesame testbed shown in Fig. 5. It should be noted that in orderto realize a single-antenna single-user signal transmission, itis straightforward to reconfigure the testbed by blocking fivetransmitter antennas while transmitting signals on one antennaat the base station. In addition, only one user antenna ismaintained for signal reception. Therefore, the SISO systemarchitecture follows Fig. 3(a).

Three modulation formats are tested ranging from 4QAMto 16QAM. In a standard NB-IoT system, the maximumsupported modulation format is 4QAM (QPSK). For futurepotential evolutions, it is not clear whether higher ordermodulation schemes would be considered or mot. Therefore,we explore the upper limit of our proposed eNB-IoT withvarious modulation formats up to 16QAM. As is shown in Fig.10(b), two systems are tested. The column with ’WaveformPrecoding=0’ indicates no precoding is applied to SEFDMsignals. Thus, due to the self-created ICI, the receiver cannotrecover the signal properly. It is clearly seen that the constel-lation points are significantly scattered for various modulationformats. However, applying the waveform precoding in section

Fig. 10. Measured constellation diagrams with normalized EVM per-formance in single-input single-output (SISO) systems. ‘WaveformPrecoding=1’ indicates SEFDM self-created ICI precoding is enabled.

III-B, the quality of constellation is greatly improved as shownin the second column in Fig. 10(b).

It should be noted that with the waveform precoding, theSISO-SEFDM can achieve similar EVM performance as thetypical SISO-OFDM, for 4QAM and 8QAM. However, for16QAM, SISO-SEFDM is ∼5 dB worse than the SISO-OFDM.The reason for this could be the high constellation density of16QAM, which challenges the waveform precoding.



8

Fig. 11. Measured constellation diagrams with normalized EVM per-formance in multi-user multiple-input multiple-output (MU-MIMO)systems. ‘MIMO Precoding=1’ indicates the antenna spatial inter-ference precoding is enabled. ‘Waveform Precoding=1’ indicatesSEFDM self-created ICI precoding is enabled.

B. Multi-Antenna Multi-User

With multiple antennas included in the system, the spatialinterference is introduced and each received data stream isinterfered by other parallel signals. Due to the sub-carrierorthogonality in OFDM, the performance can be guaranteedwithout any waveform precoding methods. However, withoutproper antenna spatial interference cancellation, the signalcannot be easily recovered and the constellation would begreatly interfered. With the MIMO precoding, the spatialinterference can be mitigated and clean constellation pointsare obtained in Fig. 11(a).

For ‘MU-MIMO-SEFDM’ signals, double interference isintroduced and signals can not be decoded properly with onestage precoding. The first column in Fig. 11(b) illustratesclearly that with the MIMO precoding, SEFDM signals can bepartially recovered. It shows very similar constellation patternsas the SISO-SEFDM results in Fig. 10(b). However, withthe waveform precoding, the self-created ICI within SEFDMcan be removed. The constellation patterns after two stageprecoding are shown in the second column in Fig. 10(b).

It should be noted that with the two stage precoding, theMU-MIMO-SEFDM can achieve similar EVM performanceas the typical MU-MIMO-OFDM, for 4QAM and 8QAM but∼5 dB worse than the MU-MIMO-OFDM for 16QAM. Thisdiscovery is similar to the SISO systems.

C. Bit Rate Evaluation

Effective bit rate, defined as the non-error bits per second,is calculated in this section to show the benefit of using

our designed non-orthogonal signal waveform. Since we arefocusing on physical layer system design, the bit rate iscalculated only related to the signal occupied bandwidth andBER. The effective bit rate is defined as follows:

Re = (1−BER)× fs × log2O × (Nd/N), (18)

where Re is the effective bit rate, fs is sampling rate, O isconstellation cardinality, BER is the bit error rate, (1−BER)indicates the probability of non-error received bits, Nd is thenumber of data sub-carriers and N is the number of total sub-carriers.

Table II: Data rate comparisons

Parameter 4QAM 8QAM 16QAMNB-IoT (kbit/s) 360 540 720eNB-IoT (kbit/s) 400 600 800

Table II summarizes the maximum bit rate for the twosystems using different modulation schemes based on thecalculation in (18). It should be noted that the BER, in thecalculation, is set to zero. Therefore, (1−BER) is equal to ‘1’in this case. It is clearly seen that the data rate of eNB-IoT fordifferent modulation schemes is improved by approximately11% than that of NB-IoT. The data rate improvement is clearlyillustrated in Fig. 12 as well.

Modulation type300

400

500

600

700

800

Bit

rate

(kb

it/s)

eNB-IoTNB-IoT

4QAM

16QAM

8QAM

Fig. 12. Maximum bit rate comparison for both eNB-IoT and NB-IoTsignal waveforms in different modulation schemes.

Due to the limited indoor space, in order to test and compareNB-IoT and eNB-IoT at different SNR levels, a channelemulator VR5, shown in Fig. 13, is used to emulate noiseand distort signals. We assume noise mainly comes from thereceiver. Therefore, the two user antennas are connected to theVR5 via two cable extensions. By configuring the VR5, thepath loss is set for each user and AWGN with various powerlevels is added to each received signal. According to the signalcharacteristics and the VR5 resolution limitations, the receiverbandwidth is set to 180 kHz and the AWGN bandwidth is setto 1.5625 MHz.

The power of transmitted signals from the base stationantenna is -35 dBm and the receiver side signal power is set



9

Fig. 13. Channel emulator VR5 for path loss and noise emulation.

to -40 dBm. Thus, the path loss is 5 dB. Various noise poweris configured in order to obtain different SNR values. Themeasured effective bit rates are therefore shown in Fig. 14.It is clearly seen that the BER performance collected fromthe testbed is very similar between the two systems with lessthan 1 dB gap at all BER levels. Based on the BER valuesand the calculation in (18), the effective bit rate of eNB-IoT,after SNR=15 dB, reaches 400 kbit/s while NB-IoT can onlyreach 360 kbit/s. The measured results verifies the theoreticalconclusion in Fig. 12.

5 10 15 20SNR (dB)

250

300

350

400

450

Bit

rate

(kb

it/s)

eNB-IoTNB-IoT

5 10 15 20SNR (dB)

10-4

10-2

100

BE

R

BER: eNB-IoTBER: NB-IoT

Fig. 14. Measured effective bit rates for both eNB-IoT and NB-IoTsystems in 4QAM modulation formatted signals.

VI. COMPLEXITY ANALYSIS FOR IOT DEVICES

This work focuses on downlink system design. Therefore,the computational complexity is evaluated only for the receiverside IoT devices. The waveform and space precoding compu-tations are at the transmitter and will not be taken into accountas these are carried out at the base station which are typicallymore computationally resourceful. In addition, the feedbackof CSI is not considered in the complexity comparison sinceit is assumed to be the same for both OFDM and SEFDM

uplink channel signals. Therefore, the complexity comparisonfocuses only on the signal demodulation at the receiver. Sincethe signal demodulation is the reverse operation of the signalgeneration, the complexity analysis of signal demodulationwould be similar to that of signal generation.

The direct operation is based on (1) where the multiplicationof a vector and a matrix has to be computed resulting in highcomplexity. Then, the specially designed FFT method wasdescribed in section II-B where a single FFT can be used at thecost of larger FFT size. Furthermore, work in [32] proposedand implemented a pruned version of the single FFT demodu-lator. Its principle is to avoid zero operations. Thus, the systemdesign is further simplified. Table III presents numerical resultsof complex multiplication and addition operations for OFDMand SEFDM systems. It should be noted that although the threeSEFDM demodulation methods show different computationalcomplexity, however they lead to the same BER performanceand therefore the same effective bit rate over the air.

Table III: Complexity in terms of the number of complex op-erations for OFDM and SEFDM receiver signal demodulation.

Operations Multiplications Additions

OFDM− FFT N2

× log2N N × log2N

SEFDM−Direct N2 N × (N − 1)

SEFDM− FFT N2α

× log2Nα

Nα

× log2Nα

SEFDM−Pruned− FFT N2α

× log2NNα

× log2N

Fig. 15 illustrates the computational complexity of OFDMand SEFDM using different signal demodulation methods.First, it is noted that the numbers of complex additions forall the signal demodulation schemes are higher than that ofcomplex multiplications. Furthermore, it is concluded fromthe figure that the direct operation of (1) results in thehighest computational complexity for SEFDM while the singleFFT and the pruned single FFT have similar computationalcomplexity with OFDM.

System type102

103

104

105

Num

ber

of o

pera

tions

Complex AdditionComplex Multiplication

SEFDM-IFFT-Single-Pruned

SEFDM-IFFT-Single

SEFDM-Direct

OFDM-IFFT

Fig. 15. Computational complexity in terms of complex addition andcomplex multiplication operations for different algorithms.



10

VII. CONCLUSIONS

This work proposed to use a non-orthogonal multicarrierSEFDM signal waveform to enhance the data rate for nextgeneration eNB-IoT. The waveform packs sub-carriers closer.Thus, with a higher sampling frequency, the data rate isimproved while its occupied signal bandwidth is similar tothe orthogonal OFDM waveform. Waveform precoding isused to remove the self-created ICI within the non-orthogonalwaveform. In addition, space precoding has to be used aswell when multiple antennas are used. Measured EVM ofdifferent modulation schemes is first compared between dif-ferent systems. Results showed that for modulation formatssuch as 4QAM and 8QAM, eNB-IoT achieves the sameperformance as the NB-IoT in both single antenna and multipleantenna scenarios. For higher order modulation format such as16QAM, eNB-IoT performs slightly worse than NB-IoT dueto its high constellation density. Effective bit rate is calculatedbased on practical BER values. Results showed that ∼11%data rate improvement is achieved using eN-IoT comparedwith NB-IoT in different modulation schemes. Furthermore,computational complexity of each system is calculated andcompared in terms of complex multiplications and additions.Numerical results showed similar computational complexityof both eNB-IoT and NB-IoT. To summarize, the newlyproposed eNB-IoT downlink framework can achieve ∼11%data rate improvement than NB-IoT with approximately thesame receiver computational complexity.

VIII. ACKNOWLEDGEMENT

This work was funded by the Engineering and PhysicalSciences Research Council (EPSRC) “Impact AccelerationDiscovery to Use” award (Grant reference: EP/R511638/1).This work was also supported by National Instruments.

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Tongyang Xu (S’13-M’17) received the B.Eng.degree in Electronic Information Engineering fromXidian University, Xi’an, China, in 2011 and theM.Sc. degree (Distinction) in Telecommunicationsand the Ph.D. degree (Best PhD thesis Lombardiprize runner up) in Electronic and Electrical En-gineering from University College London (UCL),London, U.K., in 2012 and 2017, respectively.

He is currently a senior research associate inCommunications and Information Systems Group,Department of Electronic and Electrical Engineer-

ing, UCL. His research interests include 5G/B5G communications, waveformdesign, Internet of Things, software defined radio, real-time testbed proto-typing, machine learning, neural networks design and data analysis. He hasauthored and co-authored approximately 40 papers in the areas of wireless andoptical communications. He contributed two books entitled Signal Processingfor 5G: Algorithms and Implementations (Wiley, 2016) and Key EnablingTechnologies for 5G Mobile Communications (Springer, 2016).

Dr. Xu was a recipient of the UCL Faculty of Engineering SciencesScholarship in 2013. He was the nominated investigator of the two successful“Impact Acceleration Discovery to Use” Awards at UCL in 2016 and 2018and was the co-recipient of Smart Agriculture Entrepreneurship Grant atRothamsted Research in 2017. He received the Chinese Government Awardfor Outstanding Self-Financed Students Abroad in 2017, the Lombardi Prizerunner up for the best PhD thesis in 2018 and the National InstrumentsAcademic Research Grant Award in 2018.

Christos Masouros (M’06-SM’14) received theDiploma degree in Electrical and Computer Engi-neering from the University of Patras, Greece, in2004, and MSc by research and PhD in Electricaland Electronic Engineering from the University ofManchester, UK in 2006 and 2009 respectively. In2008 he was a research intern at Philips ResearchLabs, UK. Between 2009-2010 he was a ResearchAssociate in the University of Manchester and be-tween 2010-2012 a Research Fellow in Queen’sUniversity Belfast. In 2012 he joined University

College London as a Lecturer. He has held a Royal Academy of EngineeringResearch Fellowship between 2011-2016.

He is currently an Associate Professor in the Communications and Infor-mation Systems research group, Dept. Electrical and Electronic Engineering,University College London. His research interests lie in the field of wire-less communications and signal processing with particular focus on GreenCommunications, Large Scale Antenna Systems, Cognitive Radio, interferencemitigation techniques for MIMO and multicarrier communications. He was therecipient of the Best Paper Award in the IEEE GlobeCom 2015 conference,and has been recognised as an Exemplary Editor for the IEEE Communi-cations Letters, and as an Exemplary Reviewer for the IEEE Transactionson Communications. He is an Editor for IEEE Transactions on Communi-cations, and IEEE Transactions on Wireless Communications. He has beenan Associate Editor for IEEE Communications Letters, and a Guest Editorfor IEEE Journal on Selected Topics in Signal Processing issues ExploitingInterference towards Energy Efficient and Secure Wireless Communicationsand Hybrid Analog / Digital Signal Processing for Hardware-Efficient LargeScale Antenna Arrays. He is currently an elected member of the EURASIPSAT Committee on Signal Processing for Communications and Networking.

Izzat Darwazeh (SM’03) holds the University ofLondon Chair of Communications Engineering andleads the 70-strong Communications and Informa-tion Systems Group of the Department of Elec-tronic and Electrical Engineering, University CollegeLondon (UCL). He is also the Director of ICCS;UCL Institute of Communications and ConnectedSystems. Professor Darwazeh is a graduate of theUniversity of Jordan (Electrical Engineering-1984)and has obtained an MSc (1986) and a PhD (1991)degrees from the University of Manchester in the

UK.He has authored/coauthored more than 250 papers and book chapters in

the areas of optical and wireless communications and monolithic microwaveintegrated circuits (MMICs) and high-speed/frequency circuits. He coeditedAnalogue Optical Fibre Communications (IEE, 1995) and was a co-editor ofthe 2008 Elsevier-Newness book on Electrical Engineering. He is also the co-author of two books; the 2005 book on Linear Circuit Analysis and Modelling(Elsevier) and of the 2015 book on Microwave Active Circuit Analysisand Design (Academic Press). He currently teaches mobile and wirelesscommunications and circuit design and his current research activities are inultra high-speed microwave circuits and in wireless and optical communicationsystems. In 2002 he proposed (with Miguel Rodrigues) the Fast OFDMconcept and in 2003 the SEFDM concept and has been working on thesetopics since. Professor Darwazeh is a Chartered Engineer, Fellow of the IETand a Fellow of the Institute of Telecommunications Professionals FITP.

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