signal interference

IEEE Communications Magazine • October 2013162 0163-6804/13/$25.00 © 2013 IEEE

INTRODUCTION

Transmitter-based signal processing techniquesare becoming increasingly popular in recentyears, in response to a growing demand for highdata rate multimedia communications combinedwith persisting requirements for simple, cost-effective and computationally-efficient mobiledevices. Illustrative of the above, is the fact thatprecoding schemes are gradually being intro-duced in modern communication standards, withthe most prominent example being the 3GPPlong term evolution (LTE) [1] amongst others. It

has been demonstrated that precoding tech-niques can facilitate achieving the ever-growingperformance targets of communication systems,while at the same time shifting the signal pro-cessing computational effort from the mobileunits to the base stations of cellular communica-tion networks during downlink transmission.

Particular effort has been placed on acquiringand using the communication channel’s stateinformation (CSI) to counteract its effects ontransmission. It has been shown that in bothtime- and frequency- division duplex modes theCSI can be made known to the transmitter (asituation termed as CSIT). The a priori knowl-edge of interference is therefore not an uncom-mon situation and it is in fact readily available atdownlink transmission, when CSIT combinedwith the knowledge of all data symbols intendedfor transmission can be used to explicitly predictthe resulting interference between the symbols.Costa in [2] has shown by information theoreticanalysis that in the cases where CSIT is avail-able, known interference does not affect thecapacity of the broadcast channel, which is there-fore equivalent to the respective noise-onlychannel. In [2] it is also stressed that the opti-mum strategy to achieve this capacity would beto invest power not in cancelling interferencebut rather in coding along interference. Never-theless, the majority of existing precoding imple-mentations attempt to eliminate, cancel orpre-subtract interference. Indeed, a number ofimportant contributions exist that make use ofthe channel knowledge to mitigate or manageinterference. Only recently however, there hasbeen a rising interest in making use of the inter-ference power to enhance the useful signal. Inaccordance with this and from a viewpoint ofimproving the reliability of transmission andenhancing the error rate performance, this arti-

ABSTRACT

Interference in wireless communications istraditionally treated as a cause of performancedegradation. Whilst from a statistical viewpointthis is entirely justified, this article discusses sce-narios where the interfering signal enhances thedesired signal’s useful power on an instanta-neous basis and provides an unexplored sourceof additional signal power. The potential of har-vesting this energy, which naturally exists in thecommunication system, is shown to be readilyavailable in downlink systems where interferencecan be predicted. The main concept discussed isthat instead of using knowledge of the interfer-ence to cancel, eliminate or avoid it, it is poten-tially more fruitful to use this knowledge tomanipulate and make use of the interferencetowards the system’s advantage. A significantsource of useful signal power, which with con-ventional transmission techniques is left unex-ploited, can be used to improve the radiosystems’ performance and achieve reliablepower-efficient communications where perfor-mance benefits are yielded without the need toincrease the average transmitted power.

ACCEPTED FROM OPEN CALL

Christos Masouros, University College London

Tharmalingam Ratnarajah, University of Edinburgh

Mathini Sellathurai, Heriot-Watt University Edinburgh

Constantinos B. Papadias, Athens Information Technology

Anil K. Shukla, QinetiQ

Known Interference in the Cellular Downlink: A PerformanceLimiting Factor or a Source of Green Signal Power?

MASOUROS_LAYOUT_Layout 9/27/13 12:28 PM 62

IEEE Communications Magazine • October 2013 163

cle raises two major questions: “Is all interfer-ence always harmful and is removing it alwaysoptimal from an error rate performance perspec-tive?” “Are there performance benefits to begained from judiciously exploiting the interfer-ence power?” From a statistical perspective theanswers to the above seem obvious. Interferenceimposes a “random,” noise-like perturbation tothe transmitted information and introduces avariance to the received signal which on averagehinders detection and deteriorates the resultingperformance. This perception is the topic of thepresent article, which is based on an instanta-neous, as opposed to statistical, view of interfer-ence in the generic communication system. Weattempt to show that interference can contributeto the detection of the useful signal and thisphenomenon can be utilised in the CSIT-assisteddownlink transmission and other known-interfer-ence scenarios to improve performance withoutraising the transmitted power. In modern sys-tems where transmitted power restrictions are,for a variety of reasons, becoming more andmore central in the overall designs and perfor-mance, the use of signal power from interferencewhich is inherent in the communication systemprovides an important source of additional greenuseful power for reliable signal detection andtherefore an exciting topic for further research.

In the following we first discuss the separa-tion of interference to constructive and destruc-tive components for phase shift keying (PSK)modulation. The validity and extensions of thisconcept are investigated for quadrature ampli-tude modulation (QAM) that also appears incurrent communication standards such as theabovementioned LTE and WiMax. We thenquestion the optimality of the conventionalerror rate minimization based on uniform meansquare error (MSE) constraints. A number ofinitial directions towards influencing andexploiting interference are overviewed, based onexisting work on linear and non-linear precod-ing and potential applications of the genericconcept on more advanced transmissionschemes are discussed. Extensions of the aboveconcepts to other transmission scenarios such ascooperative communications and cognitive radioare also investigated. In this context, this articlefinally proposes some open research problemswith respect to the interference exploitationconcept.

INTERFERENCE ANALYSIS: IS ALL INTERFERENCE HARMFUL?

This section presents a qualitative analysis ofinstantaneous interference and explores the pos-sibility of treating part of interference as con-structive, as a step towards the design ofinnovative transmission schemes. As a start, afundamental example of two users with orthogo-nal (non-interfering) and non-orthogonal (inter-fering) channels h1 and h2 is geometricallyrepresented in Fig. 1. One could think of this asa multiple input single output (MISO) channelwith two transmit antennas and one receiveantenna. To focus the study on the interferencebetween the two transmissions, noise is assumedto be zero in this example. In all three subfig-ures, the axes depict the directions of the chan-nels and x1, x2 Œ {–1, 1} (specifically x1 = –1, x2= 1 in this example) represent binary-PSK(BPSK) modulated symbols transmitted fromeach of the transmit paths. The bold-lined arrowin each subfigure represents the received signal rand the purple arrows denote its projection toeach of the channel axes which represents thematch filtered symbols d1, d2 at the receiverbefore the decision stage. The orthogonal case(denoted by the fact that the angle between thetwo channels is right) is shown in Fig. 1a whereit can be seen that the received signal r (theaddition of both transmitted signals modulatedby the respective channels) has a projection oneach of channel axes that is identical to the cor-responding transmitted symbol. In this case, dueto the orthogonality of the two transmissions,the received symbols are unaffected by eachother. In the case of Fig. 1b, in the absence oforthogonality, the two transmitted symbols addup destructively to form a smaller received signalcompared to Fig. 1a. Consequently the projec-tion of the received signal on the channel axesyields reduced symbol energy and the detectionis destructively affected by interference. In thethird case of Fig. 1c however, the addition of theusers’ transmitted data yields a received signalwhich has higher amplitude compared to thedestructive case but more importantly even high-er than the one for the orthogonal case of Fig.1a. As a result the detected symbols representedby the projections of the received signal on thechannel axes have higher amplitudes which in apractical scenario and in the presence of noise

Figure 1. Geometrical representations of interference scenarios: a) orthogonal (no interference), b) destruc-tive, c) constructive.

c)b)a)

1

1

1

-1-1-1

-1 -1-1

h2 axish2 axis

h2 axis

h1 axish1 axis

x2⋅h2x2⋅h2

x2⋅h2

x1⋅h1x1⋅h1

x1⋅h1

r=x1⋅h1+x2⋅h2r=x1⋅h1+x2⋅h2

r=x1⋅h1+x2⋅h2d2

d2

d2

d1d1d1

1

h1 axis

1

1

“Is all interference

always harmful and

is removing it always

optimal from an

error rate perfor-

mance perspective?”

“Are there

performance benefits

to be gained from

judiciously exploiting

the interference

power?”

MASOUROS_LAYOUT_Layout 9/27/13 12:28 PM Page 163

IEEE Communications Magazine • October 2013164

translate to higher signal to noise ratios (SNRs).Note that in all three cases the amplitude of thetransmitted symbols x1, x2 (and hence the trans-mitted power) is the same. Moreover, note thatin the case where different combinations of sym-bols x1, x2 are transmitted, the configurations ofFig. 1b, c may result in constructive and destruc-tive interference respectively. In other words, achannel configuration that yields constructiveinterference for a specific symbol combinationmay result in destructive interference for othercombinations and vice versa. It should be high-lighted that while the above example refers to atwo-user transmission scenario for illustrationpurposes, the interference can alternatively bethought of as the addition of different multipathcomponents h1 and h2 at the receiver, in a multi-path fading scenario. In this case the interfer-ence described reflects the inter-symbolinterference between subsequent data symbolsx1, x2. Furthermore, the example shown can beextended to model inter-cell interference in amulti-cell environment and other generic inter-ference limited transmissions. It is clear from theabove that the characterization of interferenceand its separation into constructive-destructivedepends not only on the correlation of the trans-mission paths but also on the instantaneous sym-bol values.

To illustrate the usefulness of the aboveobservations for PSK modulation, Fig. 2 showsexamples of possible received constellationpoints for different PSK modulations. Theseare represented by randomly positioned dots inthe PSK constellations, centered around thenominal PSK constellation points. The red dotsdenote received signals corrupted by destructiveinterference while the green dots representreceived symbols resulting from constructiveinterference. To specifically characterize inter-ference, the generic criterion is: constructive isthe interference that yields received signals thathave increased distances from the decisionthresholds of the PSK constellation comparedto the nominal constellation points. For theBPSK modulation of Fig. 2a the desired user’ssignal x ∈ {–1, +1}, the decision threshold isthe imaginary axis, so interference from a spe-cific symbol is constructive when it has thesame sign as the desired data. For quadrature-PSK (QPSK) modulation, since there are two

decision thresholds (the real and imaginaryaxes) the above criterion has to be applied sep-arately to the real and imaginary part of thereceived signal. Again, the received symbolsthat satisfy this requirement are shown in greencolor in Fig. 2b. Analytical characterisation cri-teria of the interference for B-, Q- and higherorder PSK modulation are derived in [3]. Earlywork carried out on simple precoding tech-niques that will be discussed in the following,indicates that there are significant benefits tobe derived by the above observations. Theimportant feature is that these benefits aredrawn not by increasing the transmitted powerof the useful signal, but rather by the reuse ofinterference energy that already exists in thecommunication system; a source of green signalenergy that with conventional interference can-cellation techniques is left unexploited.

UNIFORM MSE MINIMIZATION:CHALLENGING ITS OPTIMALITY

Traditionally, the precoding techniques orientedtowards error-rate performance optimization (asopposed to capacity achieving methods) aim tominimize the received MSE given by

e = E{Ôr – xÔ2} (1)

where x is the information symbol (or vector ofsymbols), r is the received symbol (or vector ofsymbols), ÔyÔ denotes the amplitude and E{y}denotes the expectation of the random variabley. It has been proven that MSE minimizationstrategies achieve average SNR optimality [4]and consequently maximize performance. Letus explore the optimality of this uniform MSEconstraint in Eq. 1 by means of the exampleshown in Fig. 3a. Here the contours of a set oftwo MSE constraints corresponding to Eq. 1achieving different error values e1, e2 are shown,denoted by the areas enclosed by the black-coloured circles. Without loss of generality, theexample focuses on the 1 + i constellationpoint of QPSK modulation. The fact that theMSE constraint is uniform is seen by the factthat the contours are of circular shape whichresults in a constant error bound e1, e2 for eachof the cases, in all directions around the con-

Figure 2. PSK constellations and constructive (green) — destructive (red) interference sectors.

Im

8PSK

Re

c)

Im

1+i-1+i

1-i-1-i

QPSK

Re

b)

-1 1

BPSK

a)

It is clear that the

characterization of

interference and its

separation into

constructive-destruc-

tive depends not

only on the

correlation of the

transmission paths

but also on the

instantaneous

symbol values.



stellation point. Based on the above discussionand specifically on the QPSK case of Fig. 2b itcan be seen that in terms of error-rate perfor-mance the uniform MSE contours are equiva-lent to the non-uniform contours denoted bythe red-coloured lines for the worst case inter-ference that has a destructive direction (i.e. anorientation towards the decision thresholds ofthe constellation). This is evidenced by the factthat the minimum distances to the decisionthresholds (real and imaginary axes) are equalfor both types for contours for both sets oferror constraints e1, e2. However, the non-uni-form contours allow for the received symbols tomove towards the direction opposite to thedecision thresholds. Note that in this case,while the cost function in Eq. 1 is violated, theresulting symbols have a higher averagereceived SNR which results in an improved tol-erance to noise and consequently yields animproved average probability of correct detec-tion. Moreover, note that the constraints denot-ed by the non-uniform contours are relaxedcompared to the ones resulting from the tradi-tional MSE minimization approach. This is evi-denced by the fact that the red shaded sectorspans a larger area compared to the black shad-ed area for conventional MSE. It furtherimplies that the resulting optimization is moreefficient which, based on the specific transmit-ter technique used, could translate to bettertransmit power minimization or allow for fur-ther performance improvement. In other wordsthe non-uniform contours benefit the communi-cation system in two ways: First, by allowing thereceived signals to fall in the constructive inter-ference part of the constellation as shown inFig. 2b the received signal benefits from theenergy of constructive interference. Secondly,by relaxing the optimization constraint a moreefficient optimization is achieved, which typical-ly allows for further optimization of the trans-mit power.

It should be reminded that the application ofthe above concept requires the knowledge of theinterfering data symbols to determine the con-structive interference sectors and the resultingerror-constraining contours. Initial work first

proposing the idea of relaxing the MSE opti-mization appears in [5] where the authors pro-pose an adaptive, data-dependent MSEminimization for use at the base stations of wire-less cellular networks. It is demonstrated thatthe relaxed-MSE optimization achieves perfor-mance equivalent to the conventional MSE min-imization for lower values of transmitted power.This comes at the cost of increased transmitcomplexity due to the need of adapting the data-dependent optimization criterion on a symbol-by-symbol basis. While computationallyexpensive, the technique sets an upper bound onthe tradeoff between transmit power and achiev-able performance that can be yielded by allowingfor constructive interference in the MMSE sense.A number of initial computationally more effi-cient techniques have also been developed inprevious work, which will be briefly overviewedin the following.

CONSTRUCTIVE INTERFERENCE INQAM CONSTELLATIONS

It was shown in the previous section that thereare benefits to be gained from utilizing interfer-ence in PSK-based communication systems.Notably, low order PSK appears in numerousscenarios in many communication standards [1].Indeed BPSK and QPSK are favored in highinterference scenarios where the achievablerates are limited due to the ill-conditionednature of the channel or the density of the com-munication access points. Evidently, the morethe interference, the more the gain from utiliz-ing it as opposed to eliminating it. In a highlycorrelated or a densely populated multi-accesschannel conventional schemes would employlow order PSK modulation and invest most oftheir power in canceling the existing interfer-ence, so it is in these scenarios where it isexpected to gain the most from exploiting inter-ference.

For the completeness of the discussion how-ever, we must not omit situations where highertransmission rates are achievable, in which casehigher order QAM modulation would be used

Figure 3. Conventional MSE constraint and worst-case-SE-equivalent contour.

ε2

Conventional MSE constraintImIm

b)a)

Re

Re

Worst-case-SE equivalent contour

1+j

ε1

In a highly correlated

or a densely populat-

ed multi-access

channel conventional

schemes would

employ low order

PSK modulation and

invest most of their

power in canceling

the existing interfer-

ence, so it is in these

scenarios where it is

expected to gain the

most from exploiting

interference.



according to the communication standards. It istherefore reasonable to raise the following ques-tions: “Can the above concept be applied toQAM constellations?,” “How much benefit canbe extracted from interference energy in thesecases?.” To answer these issues let us observethe 16-QAM constellation, shown in Fig. 3b,along with the MSE-equivalent contours thataccommodate constructive interference. It canbe seen that for the inner constellation points,since they are bounded by decision thresholds inall directions around them, the concept of con-structive interference does not hold. Interferencethat shifts the received inner constellation pointaway from one decision threshold pushes it clos-er to another decision threshold. In this case theconventional MSE minimization is indeed opti-mal and the error bounds are circular as shownin the figure. However, for the outer constella-tion points there is still some space for construc-tive interference. Indeed for the points at thecorners of the 16-QAM constellation the condi-tions are identical to the ones for the QPSKconstellation points. Therefore, as shown in Fig.3b the SNR optimization contours are similar tothe ones discussed above. Moreover, for theouter constellation points in-between the cornerpoints again there exists a margin of constructiveinterference as shown in Fig. 3b, which couldallow for a relaxation of the conventional MSEconstraints. These remarks indicate that, whilethe advantages of interference exploitation aremore pronounced in systems using PSK modula-tion, there are still benefits to be gained inQAM-based systems. While the benefits for PSKconstellations have been studied in previouswork on linear precoding techniques, it is yet tobe explored how these qualitative observationsquantify in performance gain for the QAM con-stellations.

OVERVIEW OF APPLICATIONS,EXISTING WORK AND EXTENSIONS

The discussion so far and specifically the exam-ple as set in Fig. 1 use the interference betweendifferent spatial streams in a MISO system toillustrate the main concept treated in this article.The above, however, can be applied to any sys-tem where there exists a correlation betweensimultaneous transmissions and the resultinginterference can be predicted. Clearly, in termsof multiple access techniques, it is straightfor-ward to extend this approach to multiple inputmultiple output (MIMO) systems but also tocode division multiple access (CDMA) schemesfor the interference resulting from the correla-tions of different users’ codes. Moreover, thiscan be applied to orthogonal frequency divisionmultiple access in multiple spatial layers(MIMO-OFDMA) and so on. In terms ofresource allocation techniques, the aboveapproach can be used for developing alternativetechniques that allocate the available resourceswith the aim of enhancing constructive andavoiding destructive interference. This sectionpresents an overview of some initial work oninterference exploitation and its potential exten-sions.

INTERFERENCE EXPLOITATION INLOW COMPLEXITY PRECODING SCHEMES

Early work such as the one in [3, 6] has lookedat adapting simple precoding techniques toaccommodate for constructive interference inboth CDMA and MIMO systems. The scenarioconsidered there is that of downlink transmis-sion where a single base station transmits tomultiple mobile users. Here the main idea is toretain the correlation between the transmittedsymbols when it yields constructive interferenceand eliminate the correlation when it results indestructive interference by means of zero forcing(ZF) precoding. A further step towards transmit-ting along the interference is shown in [7] forMIMO systems. Instead of observing and char-acterizing the interference and zero-forcing itaccordingly, the precoder actively influences theinterference by means of rotational precoding toyield constructive interference constantly. In thiscase the useful signal benefits from all interfer-ing signals’ energy at every symbol period. Ageneric block diagram of the low-complexity pre-coding adaptations for a MIMO downlink isshown in Fig. 4. The essential additional compo-nents involve the symbol-by-symbol characteriza-tion of interference and the judicious precodingblock. As an indicative measure of the potentialbenefits of exploiting interference Fig. 5a com-pares the symbol error rate performance of thetechniques in [3, 7] with conventional zero-forc-ing (ZF) and regularized ZF precoding (termedchannel inversion and regularized channel inver-sion in [8]) for a 4 × 4 MIMO system with QPSKand 8PSK modulation. It is evident that bothadaptive techniques benefit from constructiveinterference while the rotational precoding of [7]shows further improvement by actively influenc-ing interference, which persists for higher ordermodulation. The reader is referred to the rele-vant articles for further details.

INTERFERENCE EXPLOITATION INDIRTY PAPER CODING SCHEMES

The capacity achieving alternative to low-com-plexity linear precoding is dirty paper coding(DPC). Relevant work such as the one in [9]investigates the interference channel expressed by

Y = X + S + N (2)

where X is the transmitted coded signal, Y is thereceived signal, S is the interference known non-causally at the transmitter and N is the Gaussiannoise. In [9] and relevant DPC work the centralidea is to apply a strategy that pre-subtracts theinterference S in the form of X = t ([u – S]modL)where u denotes the useful symbol, [.]modL rep-resents the modulo operation with base L andt(.) denotes a generic coding strategy. The receiv-er then applies a corresponding strategy Y =[Y]modL to achieve an effective noise-only chan-nel Y = [u + N]modL where N is the noise com-ponent at the receiver after decoding,independent of the useful symbol u. In this pro-cess since interference is not judiciously charac-terized, the transmitter unavoidably subtracts theinterference that instantaneously contributes to

While the benefits

for PSK constellations

have been studied in

previous work on

linear precoding

techniques, it is yet

to be explored how

these qualitative

observations quantify

in performance gain

for the QAM

constellations.



the useful signal’s energy. In line with the con-cept in the current article, [10] uses an encodingstrategy where the amplitude and phase of theuseful signal is optimized, within the constructiveconstellation sectors as shown in Fig. 2 andunder an SNR threshold g, such that the result-ing interference is better aligned to the symbolsof interest. In this way the power required tosubtract the interference (and therefore thetransmitted power) is minimized, leading to amore power-efficient transmission. The benefitsof the above strategy are shown by the compari-son in Fig. 5b for a 4 × 4 MIMO system, wherethe transmit powers of the techniques in [9, 10]based on the well known Tomlinson-Harashimaprecoder (THP), are compared for differentthreshold values g. The figure illustrates the per-centage of transmit power used with respect toTHP. The results show that with the strategy in[10] the transmitted power can almost be halvedfor the same performance as the THP of [9], justby judiciously precoding along the interference.

INTERFERENCE OPTIMIZATION BYRESOURCE ALLOCATION AND

RELEVANT MAC LAYER TECHNIQUES

The gains obtained by the above adaptationscould possibly be augmented by employingspecifically tailored resource allocation tech-niques. This of course covers a vast area of con-ventional techniques that aim to optimize theallocation of resources such as spatial streams inMIMO systems with transmit diversity, subcarri-ers in OFDMA, codes in CDMA, space-frequen-cy combined resource blocks in LTE systems,and so on. To enhance the performance of theinterference exploitation schemes the goal ofresource allocation would be, instead of allocat-ing resources that inherently experience mini-mum interference, to optimize the interferencebetween the resources according to specific per-

formance optimization criteria. It is these crite-ria that need to be reformed to accommodatefor constructive interference, either by directlytargeting at optimizing interference or in a con-figuration that jointly optimizes interferencealong with one of the conventional criteria (forexample transmission rates). There is currentlylittle work in this area and evidently this offersan interesting open problem for future work.

Complementary to the above the potential ofexploiting interference stimulates the adaptationof higher layer techniques such as medium accesscontrol (MAC) and cross-layer techniques. Userselection, admittance and scheduling in the con-ventional downlink transmission are some of theobvious candidates. A possible selection tech-nique would choose a user that maximizes con-structive interference. Again the possibility ofoptimizing the above in conjunction with achiev-able transmission rates maximization can lead tonovel and potentially fruitful cross-layer tech-niques. Finally, systems with adaptive modula-tion capabilities, currently supported bycommunication standards, could be a topic ofgreat importance especially as the benefits of theconcepts discussed above are dependent on thetype and order of modulation used.

EXTENSIONS TO COOPERATIVEMULTICELL COMMUNICATIONS

Let us now explore the adaptation of theabove ideas in more advanced system scenarios,envisaged for the collaboration between commu-nication networks in future generations of wire-less communications. Figure 6 shows theconventional single-cell broadcast channel dis-cussed up to this point (Fig. 6a) along with themost prominent candidates for future networks,cooperative communications [11] (Fig. 6b, c) andcognitive radio [12] (Fig. 6d, e).

Figure 4. A generic linear precoding block diagram for the exploitation of interference. Three distinct opera-tions can be observed: interference estimation, interference characterisation and judicious precoding.

......

......

x

Modulation01001101

Re

Im-1+i 1+i

-1-i 1-i

Judiciousprecoding

Interferencecharacterization

Re

BS transmitter

Im

Interferenceestimation

H

Detectionx^2

n2

......

Demodulation01001101

Detection

MU receivers

x^K

nK


Detectionx^1

n1


Systems with

adaptive modulation

capabilities, currently

supported by

communication

standards, could be

a topic of great

importance especially

as the benefits of

the concepts

discussed above are

dependent on the

type and order of

modulation used.



COOPERATIVE DOWNLINK TRANSMISSION

Cooperative transmission techniques have beenproposed for the enhancement of current cellu-lar networks. The two main configurations inves-tigated so far are shown in Fig. 6b,c. Fig. 6bshows the base station collaborative cellularsetup. Typically it is expected that a cluster ofbase stations belonging to the same operatorexchange information through a backhaul net-work to mutually enhance the performance ofthe cell-border users by eliminating inter-cellinterference. This backhaul link is typicallyassumed to employ a high speed connection [13]which facilitates the exchange of data betweenthe collaborating base stations, that is essentialfor identifying and utilizing constructive interfer-ence. Network MIMO [14] is an emergingparadigm of cell cooperation with data exchange.

Evidently, the option of allowing part of interfer-ence that instantaneously enhances the cell-bor-der users’ received power would have a positiveimpact on the overall network performance andthroughput.

The alternative cooperative transmission sce-nario of Fig. 6c involves an inactive mobile unitserving as a relay for the transmission of aremote user. In this case the relay is used toimprove the spatial diversity of the channel byacting as an auxiliary antenna to create a virtualMIMO system. This can have a significantimpact on the performance of cell-border usersby providing a low attenuation signal path wheretypically the direct channel is ill-conditioned.The relay techniques existing in the literatureare separated into two main categories: amplifyand forward and detect and forward. It is in thelatter case, where the relay detects the symbols

Figure 5. Performance and power efficiency results: a) Uncoded symbol error rate for channel inversion, selective channel inversion, reg-ularized inversion, selective regularized inversion, correlation rotation precoding in the single cell broadcast channel [7]; b) transmitpower with respect to Tomlinson-Harasima Precoding (THP), for THP and Interference Optimized THP [10], Transmit signal tonoise ratio required for an uncoded symbol error rate of 10–2 vs. number of secondary users for channel inversion and correlation rota-tion in the; c) CR broadcast network [15] and ;d) cognitive relay assisted network [16].

Number of secondary users Number of secondary users

Transmit signal to noise ratio (txSNR) (dB) Transmit signal to noise ratio (txSNR) (dB)

4 tx antennas, 4 users with 1rx antenna each

(a)

-5-10

10-1

Sym

bol e

rror

rat

e

10-2

10-3

100

0 5 10 15 20 25 30

4 tx antennas, 4 users with 1rx antenna each

(b)

5

Tran

smit

pow

er w

ith

resp

ect

to T

HP

70%

50%

60%

80%

90%

100%

110%

35 403025201510

6 primary tx antennas, 6 PUs - precoding at the cognitive relay

(d)

2

Tran

smit

sig

nal t

o no

ise

rati

o fo

r un

code

d SE

R=10

-2

28

29

30

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9 10876543

6 primary tx antennas, 6 PUs - precoding at the secondary BS

(c)

2

Tran

smit

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nal t

o no

ise

rati

o fo

r un

code

d SE

R=10

-2

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9 10876543

Channel inversionCorrelation rotation

Channel inversion - primaryCorrelation rotation - primaryChannel inversion - secondaryCorrelation rotation - secondary

Channel inversionSelective channel inversionRegularized channel inversionSelective regularized channel inversionCorrelation rotation

Tomlinson-Harashima precodingIO-THP, γ=20dBIO-THP, γ=15dBIO-THP, γ=10dBIO-THP, γ=txSNR

8PSK

QPSK



transmitted by the original source, that thecausal knowledge of the transmitted signal canbe used to predict and exploit interference asseen at the receiver.

COGNITIVE RADIO DOWNLINK CHANNELSCognitive Radio (CR) [12] is an emerging tech-nology that seeks to overcome spectrum scarcityintroduced by the traditional approach of allo-cating different frequency bands to different ser-vices. CRs promise a more efficient, flexible anddynamic spectrum access that can be achievedthrough the utilization of techniques that enablesensing, prompt measurement, disseminationand adaptation to the real-time conditions of thenetwork environment. The benefit of circum-venting the scarcity of spectrum, however, comeswith the trade-off of introducing a new source ofinterference. Contradictory to cooperative sys-tems, the coexistence of transmissions here couldinvolve separate and heterogeneous communica-tion networks. Typical examples involve thecoexistence of local WiFi networks with mobilenetwork providers, home UHF femtocells withTV broadcasters ect. Two main configurations ofthe relevant CR scenarios are shown in Fig. 6d,e. The main difference to cooperative communi-cation is that here it is only the system thatopportunistically accesses the resources (typically

unlicensed users) of the legacy system (thelicensed user of the spectrum) which has to beaware of the interference while it is a require-ment that the primary system is not overwhelm-ingly affected by this opportunistic transmission.In the case of Fig. 6d the unlicensed base stationis aware of the transmission of the primary basestation and precodes its signal in order to mini-mize its effect on the primary transmission. Thetypical paradigms studied in the literature are:• Overlay, where a degree of cooperation

between primary and secondary transmit-ters is possible in a master-slave manner,given that secondary transmitter assists theprimary communication by partially trans-mitting the primary message.

• Underlay, where the secondary system com-municates independently, subject to itsinterference to the primary falling under anacceptable threshold.

• Interweave, where the secondary opportunis-tically utilizes resources that are unused bythe primary system in order to achieve com-pletely orthogonal transmission.Clearly, potentially fruitful results could

emerge by incorporating the ideas describedabove in the overlay and underlay scenarios. Arealization of the above concept in CR is shownin [15] where a cognitive transmitter adaptively

Figure 6. Current and future applications of interference exploitation in cooperative communications and cognitive radio: a) broadcasttransmission; b) cooperative broadcast transmission; c) relay assisted; d) cognitive base station transmission; e) cognitive relay assistedtransmission.

d) e)

c)b)a)

PMU

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precodes its transmission to allow constructiveinterference to the primary user. The cognitiveuser in this case transmits its own message whileactively assisting the transmission of the primaryuser. At the same time it can use some of thetransmit power or degrees of freedom, conven-tionally utilized for suppressing its interferenceto the licensed user, to improve its own trans-mission. An indicative performance result isshown in Fig. 5c for a CR channel with a sec-ondary cell with varying numbers of secondaryusers (SUs) affecting 6 primary users (PUs) outof the total number of users in the primary cell.The required transmit SNR for an uncoded sym-bol error rate of 10–2 for both primary and cog-nitive links is compared for the conventional CIapproach and the correlation rotation forincreasing numbers of secondary users. For agiven noise power the transmit SNR require-ment directly translates to a transmit power bud-get. The results show that significant transmitpower savings can be obtained by exploiting theintercell interference, benefiting from the greensignal power provided by the opportunistic cog-nitive users. Hence, the existence of the cogni-tive transmission within the licensed spectrum isin this case facilitated by allowing the primarytransmission to benefit from the constructiveinter-link interference.Such a potential couldpossibly make the cognitive radio concept moreattractive to the network operators who are cur-rently hesitant to allow for opportunistic accessof their resources from unlicensed users.

An extension of the above scenario is shownin Fig. 6e, where a cognitive relay is present thatensures that both primary and secondary systemsachieve the required performance [16]. In thesescenarios it may be the case that none of thebase stations are aware of each other’s transmis-sion. The awareness of the interference at therelay is used conventionally to cancel interfer-ence by orthogonalizing the directions of trans-missions by the base stations. Clearly, there aretransmit power savings to be gained by designingan adaptive transmission from the relay thatfacilitates the exploitation of constructive inter-ference between the coexisting links. An indica-tive result for this scenario is shown in Fig. 5d,illustrating the transmit SNR requirement for theprimary link as discussed above. Again it can beobserved that considerable power savings can beobtained by exploiting the inter-cell interferenceby means of simple and practical precoding.

All the application areas discussed above con-sist of open research topics, as the existing workoverviewed above has only introduced the poten-tial of exploiting interference by means of simpleadaptive techniques for initial observations. Theencouraging results so far and the current lackof studies in the more advanced systems promisea prolific field for further research.

CONCLUSIONThe performance of modern cellular communi-cation systems is currently dominated by theconstraints on the transmitted power of the basestations. It is anticipated that, with the growth ofthe population of wireless devices, the limita-tions imposed by the restrictions of transmission

power will become more severe in the imminentfuture. This article has discussed the potential ofmaking use of a source of green signal powerthat is currently being largely ignored by conven-tional transmission techniques in existing com-munication systems. The judicious utilization ofconstructive interference could provide a meansof circumventing the bottleneck in current wire-less broadcast channels imposed by transmitpower restrictions. While initial work in thisdirection has already provided an early proof-of-concept, open research problems have beenidentified in the areas of advanced single cellprecoding and resource allocation, as well ascooperative multicell communications. The pos-sible applications of the overall concept offer abroad field for exciting research for the upcom-ing years.

ACKNOWLEDGMENTThis work was supported by the Royal Academyof Engineering, UK, and the Seventh Frame-work Programme for Research of the EuropeanCommission under grant number HIATUS-265578.

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While initial work in

this direction has

already provided an

early proof-of-con-

cept, open research

problems have been

identified in the

areas of advanced

single cell precoding

and resource

allocation, as well as

cooperative multicell

communications.
