Advanced Self-interference Cancellation and Multiantenna Techniques for Full-Duplex Radios
Dani Korpi, Sathya Venkatasubramanian, Taneli Riihonen, Lauri Anttila, Strasdosky Otewa, Clemens Icheln, Katsuyuki Haneda, Sergei Tretyakov, Mikko Valkama, Risto Wichman
AAdvanced Self-interference Cancellation andMultiantenna Techniques for Full-Duplex Radios (Invited Paper)
Dani Korpi ∗ , Sathya Venkatasubramanian † , Taneli Riihonen † , Lauri Anttila ∗ , Strasdosky Otewa † ,Clemens Icheln † , Katsuyuki Haneda † , Sergei Tretyakov † , Mikko Valkama ∗ , and Risto Wichman †∗ Department of Electronics and Communications Engineering, Tampere University of Technology, Finlande-mail: dani.korpi@tut.fi † Aalto University School of Electrical Engineering, Finlande-mail: sathya.venkatasubramanian@aalto.fi
Abstract —In an in-band full-duplex system, radios transmitand receive simultaneously in the same frequency band at thesame time, providing a radical improvement in spectral efficiencyover a half-duplex system. However, in order to design sucha system, it is necessary to mitigate the self-interference dueto simultaneous transmission and reception, which seriouslylimits the maximum transmit power of the full-duplex device.Especially, large differences in power levels in the receiver front-end sets stringent requirements for the linearity of the transceiverelectronics. We present an advanced architecture for a compactfull-duplex multiantenna transceiver combining antenna designwith analog and digital cancellation, including both linear andnonlinear signal processing.
I. I
NTRODUCTION
Full-duplex communications, where a transceiver transmitsand receives signals on the same frequency and time slot, hasgained a lot of interest in the recent years, e.g., [1]–[7]. Thesesystems provide radical improvement in spectral efficiencyover half-duplex systems, especially in relaying applications,where the frequencies can be reused. This leads to efficientutilization of the available spectrum. The main bottleneckin designing such in-band full duplex systems is the self-interference (SI) at the receiver caused by its own transmission.In order to mitigate the SI, cancellation must be done at theantenna, radio frequency (RF), and digital domains.Although previous work has been done to cancel the SI, theachieved cancellation levels need to be improved for practicalimplementations and robust solutions are necessary. In thisregard, we propose different techniques to mitigate the SIsignal in the antenna and digital domains. Since it is expectedthat relays are the first candidates for such full-duplex systems,in Section II we suggest isolation improvement techniques inthe antenna domain for a compact relay using loops for field
The research work leading to these results was funded by the Academyof Finland (under the projects suppression. Section III discusses the nonlinearities occurringin the receiver chain of a full-duplex transceiver and proposesan algorithm to estimate and cancel a nonlinearly distorted SIsignal. Finally, the conclusions are drawn in Section IV.II. A
NTENNA I SOLATION I MPROVEMENT FOR A C OMPACT R ELAY
In this section, we consider the scenario of a compact relaywith back-to-back antennas operating at the same frequencyand time slot, as depicted in Fig. 1. The full-duplex relayoperates at the same frequency for the the source-to-relay, andrelay-to-destination downlinks [8]. The uplink and downlinkfrequencies or timeslots are different. These compact relayscan be used in outdoor-to-indoor scenarios or installed onlamp-posts as hotspots to improve the dead-spot coverage andprovide higher capacity to the end user.Based on the earlier work in [9] on measurements of theloop-back interference channel, the following specificationswere considered for designing the compact relay: • the relay dimension is comparable to that of a wirelessaccess point, and • the relay antennas operate at 2.6 GHz with a minimumbandwidth of 100 MHz.We consider two dual-polarized patch antennas placed back-to-back as shown in Fig. 2. The antenna on one ground planeis used for the source-to-relay link and the antenna on thesecond ground plane is used for the relay-to-destination link. Fig. 1: In-band full-duplex relay. a r X i v : . [ c s . OH ] J a n ig. 2: Design of the compact relay. The antennas on one side of the ground plane are tilted by ◦ to improve the isolation. As the separation distance betweenthe transmit (TX) and receive (RX) antennas is electricallysmall, i.e., . λ at 2.6 GHz, we need to analyze the nearfields created by the antennas to calculate the isolation betweenthem. The isolation level obtained is the SI cancellationachieved between the TX and RX antennas.In order to improve the isolation for this antenna, differentmethods can be used, including the use of wavetraps [10],band-gap structures [11], slots on the ground plane [12] andRF absorbers. In this paper, we investigate the use of loopsto cancel the magnetic field and hence improve the isolationbetween the TX and RX antennas. When investigating theelectromagnetic fields around the antenna using HFSS sim-ulations, which uses the finite element method [13], it wasobserved that the near-field impedance, which is the ratioof the transverse electric and magnetic fields, was less than π Ω . This indicates that the magnetic fields are dominantin the region between the ground planes. Hence, cancellationof the magnetic fields can provide an improvement in theisolation.To this end, loops were designed on the ground plane onone side of the relay as shown in Fig. 3. The concept of usingloops stems from Faraday’s law of electromagnetic inductionand Lenz’s law [14]. The induced currents in the loops createmagnetic fields, which oppose the change in the flux producingit, thereby partially cancelling the local reactive magneticfields produced by the antenna. Figs. 4(a) and 4(b) show theisolation of the relay antenna without the field cancellationloops and with the loops on the ground plane, respectively.It can be observed that, at the operating frequency of 2.6GHz, we have an improvement of 6 dB in the worst caseisolation shown by the black circles, indicating a minimumisolation of 55 dB between the transmit and receive antennas.The isolation can be further improved by combining differenttechniques mentioned above for practical deployment of full-duplex relays. Fig. 3: Top view of antenna structure with loops for field cancellation.(a) Isolation without loops.(b) Isolation with loops for field cancellation.Fig. 4: The isolations between the antenna ports with and without loops.
III. C
ANCELLATION OF
RX-
INDUCED N ONLINEAR D ISTORTION
In addition to antenna attenuation and possible analog SIcancellation, usually additional SI attenuation is required inthe digital domain. This is due to the fact that the power of theSI is usually not attenuated sufficiently in the analog domainand thus, without further SI suppression in the digital domain,the decrease in the receiver chain signal-to-interference-plus-noise ratio (SINR) would be intolerable. Traditionally, theI suppression in the digital domain, or digital cancellation,has been performed using linear processing methods. Thismeans that the SI coupling channel, including the effects oftransmit chain, actual coupling channel between the antennas,and receiver chain, is estimated with linear channel estimationmethods [2]–[4], [15]. However, the power of the own transmitsignal entering the receiver chain may be in the order of 60–80 dB higher than the power of the weak received signal ofinterest. Thus, unless assuming highly linear receiver compo-nents, the power the total signal entering the receiver chainmay be high enough to drive the components out of theirlinear operating region. This means that the total signal willbe distorted nonlinearly in the receiver chain, and it is notpossible to regenerate and suppress the SI signal using a linearSI channel estimate.There have recently been studies on nonlinear SI cancella-tion, but none of them concentrate on RX-induced nonlineardistortion [5], [16], [17]. In this paper, it will be shown thatnonlinear distortion produced in the receiver chain can indeedlimit the performance of a full-duplex transceiver, and thisprovides strong motivation to develop an algorithm capableof attenuating also a nonlinearly distorted SI signal. Thus, wedescribe an algorithm capable of estimating and modeling non-linear distortion, which will increase the amount of achieveddigital cancellation in a typical full-duplex transceiver.
A. MIMO Full-duplex Transceiver Model
To allow for a more general scenario, in this paper aMIMO direct-conversion full-duplex transceiver is assumed.Thus, there are several transmitters and receivers operatingsimultaneously in the considered transceiver. This means thatthe total SI signal coupling to a single receiver antennaconsists of the sum of all the transmitted signals. An idealbaseband model of this type of a MIMO full-duplex transceiveris presented in, e.g., [6]. For the different calculations andmodeling, a 2x2 MIMO is considered in this paper, meaningthat the full-duplex transceiver has two transmit antennas, tworeceiver antennas, and four RF chains. A block diagram ofthis transceiver is shown in Fig. 5.The considered MIMO full-duplex transceiver model hastwo active SI cancellation stages, in addition to passive antennaattenuation. At the input of each receiver chain, RF cancella-tion is performed, where the transmitted signals are subtractedfrom the received signal. After the actual receiver chain,further SI cancellation is performed in the digital domain.This is done by estimating the channel of the SI signal andthen regenerating it based on the channel estimate. The novelnonlinear SI cancellation algorithm proposed in this paperis in essence increasing the precision of the regenerated SIsignal, and thus increasing the amount of achievable digitalSI cancellation when operating with practical nonlinear RFcomponents.To model a realistic scenario, it is assumed that the receiverchains of the considered MIMO full-duplex transceiver havetypical low to medium-cost components. This means that thelinearity of the amplifiers and IQ mixer is not as high as for the
TABLE I: System level and general parameters of the 2x2 MIMO full-duplextransceiver.
Parameter
ValueSNR requirement 10 dBBandwidth 12.5 MHzReceiver noise figure 4.1 dBSensitivity -88.9 dBmReceived signal power -83.9 dBmAntenna separation 40 dBRF cancellation 20 dBADC bits 12PAPR 10 dBPA gain 27 dBTABLE II: Parameters for the relevant components of the receiver chains.
Component Gain (dB) IIP2 (dBm) IIP3 (dBm) NF (dB)
LNA (RX) 25 43 -15 4.1Mixer (RX) 6 42 15 4VGA (RX) 0-69 43 10 4 more expensive state of the art components. The consideredparameter values are listed in Tables I and II. Since theobjective of this analysis is to determine the effect of thereceiver chain nonlinearities, it is assumed that the PAs ofthe transmit chains are completely linear. This allows for adetailed analysis of only the RX-induced nonlinear distortion.
B. RX-induced Nonlinearities in a MIMO Full-DuplexTransceiver
To obtain some insight into the effect of receiverchain induced nonlinear distortion occuring in a full-duplextransceiver, simplified system calculations are performed. Thisreveals the approximate power levels of the different signalcomponents after linear digital cancellation for one receiverchain. In this analysis, the power levels are calculated withrespect to transmit power of a single transmit antenna underthe assumption that all the transmit chains use the sametransmit power. A detailed derivation of all the necessaryequations for a SISO scenario can be found in [18]. In thisanalysis, it is assumed that digital cancellation attenuates thelinear SI component slightly below the thermal noise floor.This is a reasonable assumption in this context, as the purposeof this simplified example is to illustrate the relative powerlevel of the receiver chain induced nonlinear distortion.The different power levels of the signal components for onereceiver chain, corresponding to the chosen parameters, areshown in Fig. 6. It can be observed that the 2nd- and 3rd-order RX-chain induced nonlinearities are the most significantdistortion components with transmit powers above 10 dBm.Furthermore, it is evident that the strength of the nonlineardistortion is sufficient to significantly degrade the SINR withhigh transmit powers. Thus, the capability to model andattenuate also a nonlinearly distorted SI signal will provideperformance gain for a full-duplex transceiver, especially withhigher transmit powers. ransmit RF chains
TX2 RX1
BPF LNA IQ Mixer LPF VGA x c oup li ng c hanne l ADC
Receive RF chains
Amplitude& phase matching LO LPFIQ MixerVGAPA DAC
Attenuator- 30 dB
Digital cancellationRF can- cellation
SI regeneration
To detector
TX1
LPFIQ MixerVGAPA DAC
Attenuator- 30 dB
Transmit bit stream 1Transmit bit stream 2
RX2
BPF LNA IQ Mixer LPF VGA ADC
Amplitude& phase matching
Digital cancellationRF can- cellation
SI regeneration
To detector y RF,1 (t)y
RF,2 (t) y (t)y (t)x (t)x (t) x (n)x (n) Fig. 5: A block diagram of the assumed 2x2 MIMO full-duplex transceiver. P o w e r o f d i ff e r en t s i gna l c o m ponen t s ( d B m ) Antenna separation: 40 dB, RF cancellation: 20 dB 2nd order nonlinearity3rd order nonlinearitySelf−interferenceSignal of interestQuantization noiseThermal noise
Fig. 6: An example plot of the power levels of the different signal componentsat receiver chain detector input with respect to transmit power.
C. Nonlinear SI Cancellation Algorithm and Parameter Esti-mation
The nonlinear distortion occuring in the receiver chain ofa full-duplex transceiver can be approximated with polyno-mials. There have been previous studies on modeling thenonidealities of receiver chains [19], and thus the modelsfor the nonlinearities are not derived in this paper. Instead,a model presented in [19] is used, with the addition of a2nd-order nonlinear term, produced by the baseband compo- nents and the IQ mixer. The baseband equivalent model forthe low-noise amplifier is now y RF,i ( t ) = k LNA x i,in ( t ) + α i | x i,in ( t ) | x i,in ( t ) , where x i,in ( t ) is the input signal of the i th receiver chain. The combined baseband equivalent modelfor the IQ mixer and variable gain amplifier can be expressedas y i ( t ) = k BB y RF,i ( t )+ β i | y RF,i ( t ) | + γ i (cid:2) y ∗ RF,i ( t ) (cid:3) , where () ∗ denotes the complex conjugate. During a training periodwhen there is no actual signal of interest present, the overallsignal model at the digital baseband of one receiver chain isas follows: y i ( n ) = a i, x i,in ( n ) + a i, | x i,in ( n ) | + a i, | x i,in ( n ) | x i,in ( n ) + a i, (cid:2) x ∗ i,in ( n ) (cid:3) + z i ( n ) , (1)where x i,in ( n ) is now the total SI signal at the input of the i th receiver chain, a i,k are the combined coefficients of thedifferent linear and nonlinear terms, and z i ( n ) represents theother noise sources. Here, only terms up to 3rd order areconsidered, as it was observed that the higher-order terms areinsignificant with the chosen parameters. Based on (1), it ispossible to calculate the coefficients of the different termsby stacking the sampled signals to vectors of observationperiod N .However, for that, the input signal of the receiver chainmust first be determined. This can be done by obtaining anestimate of the linear SI coupling channel with a low transmitpower. In that case, the receiver chain can be assumed to becompletely linear, and linear channel estimation provides anaccurate result. The signal model during a training period,ssuming a low transmit power, can be written as follows: y i ( n ) = N tx (cid:88) j =1 h ij ( n ) (cid:63) x j ( n ) + z i ( n ) , (2)where N tx is the number of transmit antennas and h ij ( n ) is theeffective channel experienced by the j th transmit signal x j ( n ) when propagating to the i th receiver, including the effects oflinear RF cancellation and the receiver chain. Because of theMIMO context, the total SI signal is a sum of all the transmitsignals, each of which experiences a slightly different couplingchannel. With vector notations, and using again an observationperiod of N samples, (2) can be rewritten as follows: y i = N tx (cid:88) j =1 X j h ij + z i = (cid:2) X X · · · X N tx (cid:3) h i h i ... h iN tx + z i = X tot h i,tot + z i , (3)where h ij = (cid:2) h ij (0) h ij (1) · · · h ij ( M − (cid:3) T , h i,tot = (cid:2) h Ti h Ti · · · h TiN tx (cid:3) T , X tot = (cid:2) X X · · · X N tx (cid:3) ,and X j is a covariance windowed convolution matrix of theform X j = x j ( M − x j ( M − · · · x j (0) x j ( M ) x j ( M − · · · x j (1) ... ... . . . ... x j ( N − x j ( N − · · · x j ( N − M ) .Here, M denotes the length of the channel estimate. This samesignal model is also presented in [6] with matrix notations.Now, the SI channel responses for each receiver can becalculated by, e.g., linear least squares as follows: ˆh i,tot = ( X Htot X tot ) − X Htot y i , (4)where y i = (cid:2) y i ( M − y i ( M ) · · · y i ( N − (cid:3) T , and () H denotes the Hermitian transpose. Since low transmit poweris used during this training period, the effect of the receiverchain can be approximated by merely a scalar multiplication.Thus, ˆh i,tot includes in fact estimates of the channels expe-rienced by the transmit signals before the i th receiver chain,scaled by the amplification of the receiver chain in question.Assuming that the channels change sufficiently slowly, thesechannel estimates can be used to form an estimate of the re-ceiver chain input signal during another training period, wherethe coefficients of the nonlinear terms are then calculated.To determine the coefficients of the nonlinear terms, thesignal model presented in (1) must first be written with vector TABLE III: Additional parameters for waveform simulations.
Parameter Value
Constellation 16-QAMNumber of subcarriers 64Number of data subcarriers 48Guard interval 25 % of symbol lengthSample length 15.625 nsSymbol length 4 µ sOversampling factor 4 notations as follows: y i = X nl,i a i + z i , (5)where X nl,i = (cid:104) x i,in | x i,in | | x i,in | x i,in (cid:2) x ∗ i,in (cid:3) (cid:105) , a i = (cid:2) a i, a i, a i, a i, (cid:3) T , x i,in = (cid:2) x i,in ( M − x i,in ( M ) · · · x i,in ( N − (cid:3) T ,and all the transformations in the matrix X nl,i are performedelement-wise to the column vector x i,in . As this signal modelis linear in parameters, the coefficients a i can be calculatedwith linear least squares as follows. ˆa i = ( X Hnl,i X nl,i ) − X Hnl,i y i . (6)Since this estimation procedure is performed during a trainingperiod with an arbitrary transmit power, the signal y i mightalso be nonlinearly distorted, unlike previously.Now, using the linear channel estimates ˆh i,tot and theknown transmit signals x j ( n ) to form an estimate of thereceiver chain input signal x i,in , the estimates for the coef-ficients of the nonlinear terms a i can be calculated with (6).With these estimates, it is possible to reconstruct the SI signalwhen using the same transmit power that was used duringthe estimation of the coefficients. Thus, even a nonlinearlydistorted SI signal can be suppressed in the digital domain byknowing only the transmitted symbols, as long as the overallchannel remains approximately constant. D. Waveform Simulations
To evaluate the performance of the proposed algorithm,waveform simulations are performed. The same transceivermodel is used as presented in Fig. 5, with the parametersshown in Tables I and II. Furthermore, in the simulationsan OFDM signal is assumed, with the parameters presentedin Table III. Basically, these parameters correspond to aWLAN system. In addition, the two transmit data streamsare independent. The lengths of the channel estimate ( M )and observation period ( N ) are chosen to be and ,respectively. In the simulations, the transmit power is variedwith 2.5 dB intervals, and 20 realizations are calculated foreach transmit power. The SINR for each transmit power iscalculated as the average value of these realizations.The simulated SINRs corresponding to one receiver chain,with respect to the transmit power of a single antenna, are S I NR ( d B ) Antenna separation: 40 dB, RF cancellation: 20 dB,M = 5, N = 10000 Nonlinear estimationLinear estimationNonlinear estimation with linear RX chain
Fig. 7: The simulated SINRs corresponding to one receiver chain. presented in Fig. 7. The SINR is shown for three scenarios:using only linear digital SI cancellation for a nonlinear receiverchain, and using the proposed nonlinear digital SI cancellationalgorithm for nonlinear and linear receiver chains. There aretwo significant observations that can be made from the figure.Firstly, it can be observed that the proposed nonlinear SI can-cellation algorithm increases the achievable SINR significantlywith higher transmit powers, when compared to using onlylinear digital cancellation. Secondly, even with a completelylinear receiver chain, the proposed algorithm is not able toachieve the ideal SINR of 15 dB, which corresponds to asituation where the SI signal is cancelled completely. Thereason for this lies in the estimation procedure of the linearSI channel, which is done with a lower transmit power toensure the linearity of the receiver chain. The lower transmitpower means that the power of the SI signal is also lowerwith respect to the noise floor, and this results in a morenoisy SI channel estimate than could be achieved with ahigher SI power. Thus, the estimate of the receiver chaininput signal is also noisy, which decreases the achievableSINR with higher transmit powers, where very precise channelestimates are required to suppress the SI signal below the noisefloor. Anyway, the proposed nonlinear digital SI cancellationalgorithm still achieved higher SINRs than when using linearprocessing methods, indicating that it is indeed beneficial tomodel also the nonlinearities occuring in the receiver chain.IV. C
ONCLUSION
We have presented methods to mitigate the self-interferencein the antenna and digital domains. Antenna isolation level of55 dB was achieved in a compact relay using loops for fieldsuppression. In the digital domain, an algorithm capable ofcancelling a nonlinearly distorted self-interference signal wasproposed, assuming a nonlinear receiver chain. Although thesetechniques provide solutions to attenuate self-interference,further work is necessary to design cancellation techniquesfor practical implementations of such systems. R
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