Multi-Antenna LTE detection for DynamicSpectrum Access: A Proof of Concept

Nicola MICHAILOW1, David DEPIERRE2 and Gerhard FETTWEIS3

1,3Vodafone Chair Mobile Communications SystemsTechnische Universitat Dresden

01069 Dresden, Germany

2THALES Communications and SecurityBoulevard de Valmy 16092700 Colombes, France

Email: 1nicola.michailow@ifn.et.tu-dresden.de, 2david.depierre@thalesgroup.com, 3fettweis@ifn.et.tu-dresden.de

Abstract—Detection of occupied frequency bands is the foun-dation for applications of dynamic spectrum access (DSA). Inorder to convince network operators that DSA is feasible incellular frequencies, it has to be shown that a reliable detectionof their primary signals is possible. In this paper, we present theresults of experimental validation of an algorithm and hardware,which can detect the presence of a Long Term Evolution (LTE)signal. In contrast to the classical mono antenna approach, anarray of antennas is used, which allows to enhance the detectioncapabilities, particularly when besides the useful signal there isalso interference.

Index Terms—cognitive radio testbeds, spectrum sensing, fea-ture detection, LTE

I. Motivation

Cognitive radio (CR) is a research area that has grownsignificant popularity since it was introduced by Mitola [2].While CR is not only about wireless communications but alsoincludes aspects from other areas like artificial intelligence andmachine learning, it is often used to address what is actuallydynamic spectrum access (DSA). DSA can be classified intoseveral categories of spectrum use: An exclusive use model,an open sharing model and a hierarchical access model [3].Reliable spectrum sensing is the foundation for coexistence ofwireless communications systems in the latter two of thosecategories. There are three main methods for this process[4]. In matched filter detection, coeherent demodulation isperformed. Thus the structure of the signal to-be-sensed needsto be known. This method maximizes the signal-to-noiseratio (SNR). Energy detection is a simplified, non-coherentmethod that is typically based on the fast Fourier transformalgorithm. It is however susceptible to varying noise levelsand interference. Finally, the cyclostationary feature detectionis a sophisticated approach that relies on periodicity in thetransmit signal, e.g. cyclic prefixes or frame structures.Further, the sensing process can be enhanced by using an

antenna array [1]. Multi-antenna reception and antenna pro-cessing can be of great benefit in sensing. It allows to improvethe detection performance by several means: The detection ismore robust to fast fading thanks to spatial diversity. If thelevel of the received signal is very low at one given antennadue to a deep fade, the signal level on the neighbor antennamight not be subject to such fading. With a distance of onewavelength apart, the phases of a propagation channel canbe considered independent, provided that the angle spread issufficiently high at the receiver. Additionally, signals fromdifferent spatial directions can be separated. This allows todetect a given source even with very low signal-to-interferenceratio (SIR). When the antenna array receives several sourcesemitted from different locations, the antenna processing can re-combine the signals in order to detect one given source, whilethe other are considered as interference which is spatiallyrejected. The presented techniques must not be mistaken withdistributed sensing. Here, the sensors are co-located roughlyone wavelength apart. This kind of setup is typically referredto as single input multiple output (SIMO), i.e. a system withone antenna at the emitter and several antennas at the receiver.

The rest of this paper is organized as follows. In section 2multi-antenna sensing in cellular networks is motivated withtwo use cases. In section 3, the mathematical backgroundof the detection algorithm is presented. Section 4 contains adescription of the hardware platform, the experiment setupas well as the results. Section 5 presents how this proof ofconcept is related to cognitive radio experimentation in theCREW project. In section 6, conclusions are drawn.

II. Use Cases for LTE Detection

In this work, a hardware platform for the detection of LongTerm Evolution (LTE) downlink signals is presented, whichoperates based on matched filter multi-antenna detection usingthe primary and secondary synchronization sequences (PSS

Fig. 1. Metrology oriented use case: Interference analysis based on smartantenna processing

and SSS) as a reference. This approach is particularly relevantfor two use cases:

The platform can be employed as a metrology tool fornetwork planning, as it allows downlink interference analysisof LTE cellular networks. The scenario is depicted in Fig.1. The process consists of creating a list of the receivedsignals on an LTE channel as well as obtaining their physicalcharacteristics and identifying the base stations which aretransmitting these signals. Interference analysis is based onthe smart antennas approach employing an array of antennas.When this antenna array receives several sources emitted bydifferent base stations, the signals are recombined in order todetect and demodulate each received source, while the otherreceived signals are considered as interference that can berejected when it comes from different directions. Since thematched filter approach is used for detection, the signal canbe demodulated and system parameters like the physical layercell identity, cyclic prefix length, duplex mode, base stationsignal level, Ec/I0, as well as time and frequency channelimpulse response can be determined. This functionality canbe used by a network operator, either to optimize the networkas co-channel interference due to frequency reuse can neverbe completely avoided in cellular systems, or to complain tothe regulator about interference systems occupying adjacentfrequency bands.

LTE detection with spatial interference rejection capabilitiescan also be useful for sensing in CR environments. Considerthe scenario depicted in Fig. 2: A mobile opportunistic receiver(Rx) uses a white space in a certain frequency band that isavailable at a particular geographic location (a). The systemhas to check periodically for the presence of an incumbentuser that might be utilizing that band. When the mobileopportunistic Rx moves to a location where the white space isno longer available, it receives the signal from the incumbentLTE transmitter (Tx). While the opportunistic system is notaware of the presence of the incumbent Tx, it is still com-municating and for the opportunistic Rx, the signal comingfrom the incumbent Tx is considered as interference (b). Whenthe opportunistic Rx senses the spectrum, the signal comingfrom the incumbent LTE Tx is now the signal of interestand the signal from the opportunistic Tx the interference,as it is jamming the sensing process (c). Typically, duringthis process silent periods are allocated in the opportunistic

Fig. 2. Cognitive radio oriented use case: Primary user detection withinterference rejection

system waveform, in order to perform sensing without beingjammed by its own system. But sensing can also be performedsimultaneously with the data transfer, if the opportunistic Rxhas interference rejection capabilities, i.e. by using an antenna-array and antenna processing algorithms.

III. Theoretical Background

Let

x[n] =

x1[n]...

xM[n]

(1)

be a vector that contains snapshots of the received signalof an array with M antennas, where xm[n] denotes the timesignal received at time n on antenna m. Presence or absenceof the synchronization signal at time n can be formulated asa composite hypothesis-testing problem with the two optionsHypothesis H0 ”Absence of the synchronization signal” andHypothesis H1 ”Presence of the synchronization signal”. Un-der H0, the discrete time signal x can be written as

x[n + k] = n[n + k], (2)

while under H1, it is denoted by

x[n + k] = hd[k] +∑p,0

hpd[k − p] + n[n + k]. (3)

Here, n[n] represent the contribution of background noise,h +

∑p,0 hpδ[n − p] is the unknown transfer function of the

discrete time propagation channels between the transmitterand the M receive antennas and d[k], k = 0, . . . ,N − 1 is thesynchronization sequence of length N.

A. Optimal Spatial Detector

The the joint probability distribution of the sequence{x[n + k], k = 0, . . . ,N − 1} depends on several unknown pa-rameters. It is thus not possible to derive optimum detectionprocedures in the most general case. In order to motivatethe use of sub-optimum algorithms, we first address the case

CP

CP 1C

P2

C

P3

C

P4

C

P5

C

P6

C

P7

1 CP 2 CP 3 CP 4 CP 5 CP 6

LTE slot : 0.5 ms

3840 samples (for 5MHz bandwidth)

Normal CP

Extended CP

40 samples 36 samples 512 samples

548 samples552 samples

128 samples 512 samples

640 samples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

LTE frame: 10 ms

20 slots

76800 samples (5MHz mode)

slot

SSSPSS

Fig. 3. LTE frame structure and synchronization sequences in FDD mode

where the noise n[n] is temporally white and the channeltransfer function is reduced to h, which implies a single pathpropagation channel between each active base station and thereceiver. In that case, the received signal under H1 can bewritten as

x[n + k] = hd[k] + n[n + k]. (4)

With these assumptions, it is possible to derive the maximumlikelihood ratio test as

c(n) =

(det (R0)det (R1)

)N

. . .

exp

−∑N−1k=0 (x[n + k] − hd[k]) R−1

1 (x[n + k] − hd[k])

−∑N−1

k=0 x[n + k]R−10 xH[n + k]

, (5)

where R0 and R1 are the unknown covariance matrices of thenoise under H0 and H1 respectively.

Together with the vector h they have to be estimated undereach hypothesis in the maximum likelihood (ML) sense. UnderH0, R0 can be estimated as R0 which is given by

R0 = Rxx[n], (6)

while under H1 the estimates for h and R1 are

h =1||d||2

rxd[n] (7)

and

R1 = Rxx[n] −1||d||2

rxd[n]rHxd[n]. (8)

Therein, ||d||2 =∑N−1

k=0 |d[k]|2, rxd[n] =∑N−1

k=0 x[n + k]d∗[k] andRxx[n] =

∑N−1k=0 x[n + k]xH[n + k].

By using (6), (7) and (8) in (5), the ML ratio is obtained as

c(n) =rxd[n]HR−1

xx [n]rxd[n]||d||2

. (9)

B. Use of Periodicity

To improve the performance of the test, the periodicityof the synchronization sequences can be exploited. For thatpurpose, the hypothesis-testing problem has to be modified totake into account for the number of observed frames Nfr. Witha frame duration Tfr, H0 can be rewritten as

x[n + mTfr] = n[n + mTfr] (10)

and H1 as

x[n + mTfr] = hd[k] + n[n + mTfr], (11)

where m = 0, . . . ,Nfr − 1. The ML test then consists ofcomparing the quantity

c(n) =1

Nfr

Nfr−1∑m=0

−ln (1 − c(n + mTfr)) (12)

to a threshold. If all c(n + mTfr) are small compared to 1, theexpression can be reduced to

c(n) =1

Nfr

Nfr−1∑m=0

c(n + mTfr). (13)

Thus, c(n) can be seen as an instantaneous criterion, whilec(n) constitutes an averaged criterion.

C. Application to LTE

The downlink parameters of LTE are summarized in TableI for the 5 MHz and 20 MHz mode. The symbols are grouped

Parameter Mode 1 Mode 2

channel bandwdith 5 MHz 20 MHz

FFT size 512 2048

subcarrier bandwidth 15 kHz 15 kHz

occupied subcarriers 300 1200

occupied bandwidth 4.515 MHz 18.015 MHz

sampling bandwidth 7.68 MHz 30.72 MHz

TABLE IRelevant LTE downlink parameters

in slots with a duration of 0.5 ms. One frame consists of 20slots, which corresponds to 10 ms. The number of symbolsin a slot depends on the length of the cyclic prefix (CP): Innormal CP mode, one slot contains 7 symbols and the lengthof the first symbol’s CP is longer in order to keep the slotduration constant. In extended CP one slot contains 6 symbols.An overview of the frame structure can be found in Fig.3. Synchronization is achieved in LTE with synchronizationsequences that occupy the 62 central subcarriers [5]. PSS andSSS further carry the physical layer identity of the cell, thecyclic prefix mode and also inform the user equipment (UE)whether the cell uses frequency division duplex (FDD) or timedivision duplex. The structure of PSS and SSS is also shownin Fig. 3. Both synchronization signals are transmitted twiceper frame. For the PSS, the same sequence is transmitted eachtime, while for the SSS, a different sequence appears in slot 1and 11. Together, they indicate the physical layer cell identity.The detection of the PSS is achieved by computing thecriterion according to (13) at each time position over halfa frame. Three criteria must be computed with, one foreach different PSS corresponding to a possible cell identitywithin the group. The three criteria are then compared toa threshold. Each position for which one of the criteria isgreater than a given threshold is considered as a possibledetection of a primary synchronization sequence transmittedby a base station. The detection threshold is select for a

multi-channel receiver

SM

AS

MA

SM

AS

MA

SM

AS

MA

SM

AS

MA

RF signal 1

RF signal 2

RF signal 3

RF signal 4

multi-channel acquisition boardS

MB

SM

BS

MB

SM

B

PC

MC

IA

FI signal 1

FI signal 2

FI signal 3

FI signal 4

control computer

USBUSB

frequency and gain commandP

CM

CIA

digitizedsamples

RS232

GPScoordinates

hard disk

LTE signal processing

LTE GUI

Fig. 4. Block diagram of multi-antenna LTE sensing platform

chosen probability of detection (POD) according to a desiredprobability of false alarm (PFA).The detection of the SSS is done with the same algorithmfor the positions, where a potential PSS has been detected.For each of these time positions, 168 different sequences haveto be tested to obtain the identity of the group. Each SSS istested at 8 different timing positions: 2 for the possible CPmodes, 2 for the possible duplex modes and 2 for the possibleposition in the frame. Here, no threshold is applied and fromall calculations, the combination yielding the highest criterionis chosen.

IV. Experimental validation of the LTE sensing platform

The sensing platform consists of three units as shown inFig. 4 and Fig. 6. Filtering and gain control are appliedto the signal in the multi-channel receiver unit, the multi-channel acquisition board is used to convert the signals todigital domain and a control computer handles processing andevaluation of the digital samples.

The multi-antenna LTE sensing platform is validated withlab tests by measuring sensitivity and co-channel interferencerejection with real LTE eNBs. A hardware array simulatorconsisting of splitters, coupling modules and a set of cablesof particular lengths is employed to virtually create a multi-antenna, mono-path propagation channel with two directionsof arrival.

LTE eNB #1

hardware array simulator

multi-antenna LTE sensing platform

coupling module

variable attenuator

LTE eNB #2

Fig. 5. Experimentation setup with hardware array simulator providing afixed angle of arrival

Fig. 6. Multi-antenna LTE sensing hardware

Measurements of the interference rejection are performedwith the setup depicted in Fig. 5. The first input of thehardware array simulator is connected to the LTE evolvedNodeB (eNB) producing the useful signal, while the signalof an interfering eNB is provided on the second channel.The interfering base station operates in 20 MHz mode withfixed transmit power, while the to-be-detected base stationtransmits a 5 MHz signal and the received signal strength isgradually lowered with a variable attenuator. The detectionalgorithm uses 8 frames for averaging the detection criterion,the detection threshold is chosen for a PFA for 1% and thetarget POD is set to 80%.The results of the experiments are in Table II. First of all, it

1 antenna 4 antennas multi-antenna gain

sensitivitylevel of 1steNB

-92 dBm -124 dBm 32 dB

rejectioncapacity of2nd eNB

11 dB 43 dB 32 dB

TABLE IIInterference rejection test results

can be seen that the useful base station signal can be detectedwith a SIR 32dB lower when using 4 antennas compare tothe 1 antenna, thanks to the interference rejection of the plat-form. When comparing this number to the simulation resultsdepicted in Fig. 7, actually a multi-antenna gain of 26 dB is tobe expected for the chosen combination of POD and PFA. The6 dB difference between simulation and experimental resultscan be explained with the fact that the base station hardwareis configured such that it emits the PSS and SSS at a 6 dBhigher level than the remaining resources. This becomes alsoevident from the spectrum snapshot in Fig. 8.

V. Cognitive Radio ExperimentationMethodologyThe experiment that lead to the proof of concept in this

work has been conducted in framework of cognitive radioexperimentation methodology developed in the CREW project[6]. Particularly, it is part of the usage scenario ”CognitiveRadio in Cellular Networks” [7]. CREW is a federation ofseveral testbeds among European partners and provides themeans to perform CR experiments by enabling access tofacilities and equipment on the one hand and supportingexperimenters with a framework and guidelines for successfulexperiments on the other hand.

Among the functionality provided by the CREW federation[8], three particular things have been of benefit for this ex-periment. Firstly, CREW provides several modes of operationfor the individual testbeds. For instance, before conducting themulti-antenna sensing experiment, reference signal files havebeen recorded in the LTE testbed and tested with the sensingdevice before joining both in the same physical location.During the experiment, the setup and the equipment have beencarefully monitored and recorded, as part of the benchmarkingmethodology which is being suggested in order to enablecomparable and reproducible results. Finally, the parametersof the experiment, as well as the results have been put into acommon data format that allows to sharing among individualinstitutions.

-50 -40 -30 -20 -10 0 100

0.2

0.4

0.6

0.8

1

SIR [dB]

det

ecti

on

pro

bab

ility

1 antenna

2 antennas

3 antennas

4 antennas

26 dB

Fig. 7. Simulated probability of detection vs. SIR for an LTE signal withPFA = 0.01, Nfr = 8 in a mono path channel without mobility and in presenceof an interfering signal

channel BW

PSS/SSS

sampling BW

leve

l (d

Bm

)

frequency (MHz)

Fig. 8. Spectrum view of the measured signal

Thus, besides verifying the algorithm and giving a proof ofconcept for the multi-antenna sensing algorithm and hardwarein a setup with real signals, this experiment is also an examplehow the CREW facilities and methodologies can support CRresearch.

More information about this experiment can be found onthe project website [6].

VI. Conclusions

This work presents the outcome of the experimentalvalidation of a multi-antenna LTE sensing algorithm anddevice, which is relevant for cognitive radio use cases likeinter-cell interference analysis or spectrum sensing withinterference rejection capabilities. It demonstrates, how adetection criterion that is derived from a multi-antennasignal can be applied to the signal emitted from an LTEbase station in order to decide if a certain base stationsignal is present or not. It shows that the practicallyachieved 32dB gain from multi-antenna processing matchessimulation results. Lastly, the benefits of conducting thisproof of concept in the CREW testbed federation are outlined.

Acknowledgements

This work has been performed in the framework of theICT projects ICT-248454 QOSMOS and ICT-258301 CREW,which are partly funded by the European Union.

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performance of UMTS-FDD downlink Multi-channel demodulators withtheir extension to space tiem transmit diversity”, IST Summit 2003

[2] 1. Mitola, J.; ”Cognitive Radio: An Integrated Agent Architecture forSoftware Defined Radio”, Royal Institute of Technology, May 2000

[3] Qing Zhao; Sadler, B.M.; , ”A Survey of Dynamic Spectrum Access,”Signal Processing Magazine, IEEE , vol.24, no.3, pp.79-89, May 2007

[4] Cabric, D.; Mishra, S.M.; Brodersen, R.W.; , ”Implementation issues inspectrum sensing for cognitive radios,” Signals, Systems and Computers,2004. Conference Record of the Thirty-Eighth Asilomar Conference on, vol.1, no., pp. 772- 776 Vol.1, 7-10 Nov. 2004

[5] 3GPP TS 36.211 ”Physical Channels and Modulation”[6] www.crew-project.eu[7] CREW D2.1 ”Definition of Internal Usage Scenarios” available

at http://www.crew-project.eu/sites/default/files/deliverables/CREW D2.1 TUD R PU 2011-01-31 final PRC.pdf

[8] CREW D3.1 ”Basic Operational Platform” available athttp://www.crew-project.eu/sites/default/files/CREW D3.1 TCFR P 2011-09-30 final.pdf