Jondral, Friedrich K.

Klöck, Clemens

Körner, Christian

Renk, Tobias

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Increasing spectral efficiency by managing adjacent channel interference

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Increasing Spectral Efficiency by Managing AdjacentChannel InterferenceTobias Renk, Clemens Kloeck, Christian Koerner, and Friedrich K. JondralInstitut fu¨r NachrichtentechnikUniversita¨t Karlsruhe (TH)Email: {renk, kloeck, koerner, fj}@int.uni-karlsruhe.deABSTRACTDue to the ever–increasing demands in wireless communica-tions, spectrum seems to be a scarce resource today. Hence,new techniques that deal with the issue of spectrum man-agement have to be developed. In this paper, possibilities toreduce the guard band between consecutive channels dynam-ically are investigated, thus improving spectral efficiency atthe expense of an increasing amount of adjacent channelinterference. In a first consideration, the strategy of shift-ing carrier frequencies is investigated. A second approach isthe adaptive adjustment of roll–off factors when root raisedcosine filters are used for pulse shaping as it is the case in3G mobile communication systems. Moreover, an algorithmfor dynamic guard band adaptation is developed. A simu-lation environment is set up and the simulation results arepresented and discussed as well.1. INTRODUCTIONIn wireless communications energy from a transmitting sys-tem might spill over into consecutive frequency bands caus-ing capacity and performance degradation (see Fig. 1). Thiseffect is known as Adjacent Channel Interference (ACI). Alot of questions arise with the term ACI. What are the rea-sons for ACI? On which parameters does ACI depend? Andhow does ACI eventually affect the performance of com-munication systems? ACI occurs, e.g., if several communi-cation systems are operating within the same geographicalarea. Further reasons are transmit and receive filter inad-equacies and power amplifier non-linearities [1], [2]. Thefollowing listing shows different parameters that influencethe amount of ACI:• Spatial distance between operating systems• Antenna arrangement• Propagation environment• Required quality of transmissionGuardBandfACIChannelBandWidthFigure 1: Adjacent channel interference• Modulation scheme• Transmit power• Pulse shaping filter• Guard band between consecutive channelsA lot of previous work has been done in the research areaof ACI. Obviously, the simplest approach to reduce ACI isthe use of sharper filters. However, this increases cost andcomplexity. Further approaches deal with suppression ofACI by applying linear and decision feedback equalizers [3],using successive cancelation or joint modulation techniques[4], [5]. In [6], two digital algorithms for suppression of ACIin FM–receivers are presented. However, these approachesincrease the complexity of the receiver as well. The effects ofACI on the capacity of Frequency Division Multiple Access(FDMA) cellular systems and on the performance of differ-entially detected Gaussian Minimum Shift Keying (GMSK)signals have been investigated by S. Golestaneh et al. [7]and P. T. Mathiopoulos et al. [8], respectively. B. Vaghaniand C. Beard used linear, higher order modulation schemesin order to reduce ACI and improve spectral efficiency [9],[10].In this paper, the aim of increasing the overall spectral ef-ficiency is picked up. The ideas of either shifting carrierfrequencies or changing roll–off factors dynamically are in-vestigated. For this purpose two systems are considered,A1B1A2B2InterferenceFigure 2: Communication without a master devicewhich means one is the desired system and the other one isthe interferer.The paper is organized as follows. In Section II two typicalinterference modes are introduced. Section III describes thesystem model that was used for simulation. Moreover, defi-nitions on the channel bandwidth, ACI, carrier–to–interferenceratio, and guard band are provided. In Section IV the simu-lation environment is outlined and the results are discussed.Section V concludes the paper and summarizes the resultsof the new adaptive approaches for efficient spectrum man-agement. Finally, a short outlook on future investigations ishighlighted.2. INTERFERENCE MODESThere are two kinds of interference. The first one describesinterference between two different users of the same RadioAccess Technology (RAT), e.g., two mobiles within a Ter-restrial Trunked Radio (TETRA) network. This form ofinterference is called intra RAT interference. The secondone explains interference between two different RATs, e.g.,Universal Mobile Telecommunications System (UMTS) andGlobal System for Mobile Communications (GSM), that iswhy it is called inter RAT interference. With this back-ground information, it is possible to describe two interfer-ence scenarios, namely communication with and without amaster device. Fig. 2 and Fig. 3 illustrate these issues.In Fig. 2 both communication systems must be cognitiveradios to be able to observe environmental changes and actaccordingly to them. In Fig. 3 both communication systemsneed not to be cognitive radios, software defined radios aresufficient in this case as no environmental sensing is neces-sary.3. SYSTEM MODEL3.1 General DescriptionConsider the model shown in Fig. 4. Let xi(t) denote a M–PSK modulated signal, where the index i, i = 1, 2, . . . , N ,represents the i–th user, Gi(f) is the pulse shaping filter ofthe i–th user (gi(t) ◦−• Gi(f)), fc,i is the carrier frequency,A1B1A2B2Interference MasterDevice InterferenceFigure 3: Communication with a master deviceM-PSKM-PSKM-PSK......G ( )1fG ( )2fG ( )Nfv tN( )v t2( )v t1( )fc,1fc,2fc,Nx tN( )x t2( )y t1( )G ( )1fz t1( )n t( ){ }In,1{ }In,2{ }In,Nx t1( )Figure 4: System modeland Θi are the random phase angles, respectively.xi(t) = gi(t) · cos (2pifc,it+Θi) (1)The time variable t lies within the interval [0;TS ], whereTS is the symbol rate, and the phase angles Θi are equallydistributed on2piM(m− 1), m = 1, 2, . . . ,Mff. (2)In the simulations only two systems are considered, whichmeans that i = 1, 2, respectively. As pulse shaping filter aRoot Raised Cosine (RRC) filter is used. As a consequence,the transmitter and receiver filters are identical. The fre-quency response of a RRC filter can be expressed asGi(f) =8><>:1, |f | ≤ 1−ρi2TcoshpiT2ρi`|f | − 1−ρi2T´i, 1−ρi2T≤ |f | ≤ 1+ρi2T.0, |f | > 1+ρi2T(3)The parameter 1/T is the sampling rate of the transmis-sion system and ρi denotes the roll–off factor (0 ≤ ρi ≤ 1)of the i–th user or rather the excess bandwidth [11]. Itindicates the extension of bandwidth beyond the Nyquistbandwidth 1/2T . For example, when ρi = 0.5, the excessbandwidth is 50% of the Nyquist bandwidth. According toFig. 4, the sum of all transmitted signals is influenced by anAdditive White Gaussian Noise (AWGN) process, n(t), withthe Power Spectral Density (PSD) Φnn(f) = N0/2. Whentwo systems are considered, the overall PSD at the receiverfilter input isΦy1y1(f) = Φx1x1(f) + Φx2x2(f) +N02. (4)When N systems with one user, x1(t), and N−1 interferers,xi(t), i = 2, . . . , N , are considered the overall PSD becomesΦy1y1(f) = Φx1x1(f) +NXi=2Φxixi(f) +N02. (5)Since M-PSK signals are applied that are shifted by a car-rier frequency, fc,i, the PSD of a transmit signal can beexpressed asΦxixi(f) =12[Φvivi(f − fc,i) + Φvivi(−f − fc,i)] , (6)where Φvivi(f) is the PSD of the equivalent lowpass signalvi(t) =∞Xn=−∞In,i · gi(t− nTS), (7)and {In,i} denotes the sequence of symbols that results fromthe mapping procedure during modulation. After the RRCreceiver filter, G1(f), the overall PSD can be described asΦz1z1(f) = Φy1y1(f) · |G1(f)|2 . (8)3.2 Channel BandwidthSince a RRC filter is used for pulse shaping, it is obvious thatthe channel bandwidth of the transmission system, BC,i, iscalculated accordingly to equation (3). Assume the channelbandwidth to correspond to the frequency range in whichthe frequency response of the filter lies above 70.7% in rela-tion to full scale (3–dB range). This yields to the followingequation:BC,i =1T„4ρipiarccos14√2+ (1− ρi)«(9)As it can be seen, the channel bandwidth depends on thesampling rate of the transmission system, 1/T , and the roll–off factor, ρi. For further investigations, we assume the sam-pling rate to be fixed. However, a first parameter for adopt-ing the channel bandwidth and thus the guard band betweentwo neighboring systems can be identified, the roll–off fac-tor. As a next step, ACI must be considered more preciselyin order to find a suitable mathematical formulation for theadaptation algorithm.3.3 Adjacent Channel InterferenceA common approach to express ACI is by relating the trans-mit powers of interfering signals, xi(t), i = 2, 3, . . . , N , plusnoise to the transmit power of the desired signal, x1(t), ina given transmit channel, BC,1, that corresponds to the de-sired signal. Hence, we getACI =RBC,1“PNi=2 Φxixi(f) +N02”dfRBC,1Φx1x1(f) df. (10)fBc,1BG,1,2Bc,2fc,1fc,2Figure 5: Initial figure for the derivation of the adap-tive algorithmThis formula is true, when one desired signal and N − 1interferers are considered.As electrical engineers are used to think in decibel, thecarrier–to–interference ratio, C/IA, is calculated:CIA= 10 · log10„1ACI«(11)This is an expression that might be compared to the signal–to–noise ratio, a common term in wireless communications.Furthermore, it is designated to perform as termination cri-terion for an adaptive algorithm, i.e., if C/IA is higher than aspecific limit, the guard band between consecutive channelscan be reduced, thus improving spectral efficiency. The sameidea works the other way round. If C/IA is lower than a spe-cific limit, the guard band is widened and, consequently, thequality of service of the transmission is increased.3.4 Guard BandFor the following derivation, we assume the difference ∆fbetween two carrier frequencies to be∆f = |fc,2 − fc,1| . (12)This yields the equation (see Fig. 5)BG,1,2 = ∆f − 12(BC,1 +BC,2) . (13)If fc,2 ≥ fc,1, BG,1,2 can be expressed as follows (see (9)):BG,1,2 = fc,2 − fc,1− 2piTarccos14√2· (ρ1 + ρ2) (14)+12T(2− ρ1 − ρ2)Supposing that both systems transmit with equal power,four parameters in order to adapt the guard band dynami-cally are gained. These are:{ρi}2i=1 , {fc,i}2i=1 (15)In a first consideration, let the roll–off factors be equal andconstant:ρ1 = ρ2 = ρ (16)fffc,1fc,2f ’c,1f ’c,2Figure 6: Shifting the carrier frequenciesThus, the guard band results in the equationBG,1,2 = fc,2 − fc,1 −BC , (17)where BC is a constant value. In a second consideration,let the carrier frequencies be fixed. As a consequence, ∆f isconstant:fc,1 = const, fc,2 = const → ∆f = const (18)Herefrom, a mathematical description of the guard bandresults that depends on both ρ1 and ρ2 as well:BG,1,2 = ∆f − 2piTarccos14√2· (ρ1 + ρ2)+12T(2− ρ1 − ρ2) (19)Now, an adaptive algorithm can be formulated that is ableto control the guard band dynamically according to trans-mission environment. As termination criterion a value ofC/IAmin = 20 dB is used here.Algorithm Dynamic guard band adaptationREPEATEvaluate C/IA// guard band too small → too much interferenceIF C/IA < C/IAminCASE 1Shift carrier frequencyCASE 2Change roll–off factor// guard band too big → spectrally inefficientIF C/IA > C/IAminCASE 1Shift carrier frequencyCASE 2Change roll–off factorUNTIL termination criterion reachedfffc,1fc,2fc,1fc,2Figure 7: Changing the roll–off factorNevertheless, one point remains open: How does a receiverknow which part of the received signal belongs to the desiredsignal and which part to the interferer? A first and more ob-vious approach is to communicate with a master device, let’ssay, e.g., a base station in mobile communication systems.A second one might be the use of cognitive radios that areable to detect desired and undesired signals by blind sourceseparation1.4. SIMULATION4.1 Simulation EnvironmentIn simulations a gray–coded 4–PSK signal with an initialphase of pi/4 is used. Two signals are provided, one desiredsignal and one interferer. The channel bandwidth is calcu-lated dynamically (see equation (9)). The system behaviorwith respect to several carrier frequency spacings – whichmeans different guard band widths – and different roll–offfactors is considered. First, ACI and C/IA have been investi-gated. In addition, a bit error rate, BER, graph is providedin which the simulation results are compared to the theo-retical boundary of a 4–PSK modulated signal transmittedthrough an AWGN–channel.4.2 Simulation ResultsIn Fig. 8 the behavior of adjacent channel interference, ACI,in relation to the normalized carrier frequency spacing, ∆f/fs,can be seen, where fs denotes the sampling frequency of oursimulation system. As expected, ACI decreases if the differ-ence between the two carrier frequencies grows. Especiallyat ∆f/fs = 1 there is hardly any ACI. Fig. 9 shows theprogression of the carrier–to–interference ratio, C/IA. Ofcourse, it increases as the carrier frequency spacing grows.These graphs are calculated with respect to equation (11).In Fig. 10 C/IA is shown again, but this time in relation to1Blind Source Separation (BSS) aims at recovering singlesignals from a mixture of observed signals by exploiting theassumption of mutual statistical independence between thesignals. A famous problem that deals with BSS is the “cock-tail party problem.”0 0.5 1.0 1.5 2.0 2.500.10.20.30.40.50.60.70.80.91∆f/fsACI  Roll−Off = 0.25Roll−Off = 0.50Roll−Off = 0.75Figure 8: Adjacent channel interference at Eb/N0 =20 dB0 0.5 1.0 1.5 2.0 2.50510152025∆f/fsC/I A [dB]  Roll−Off = 0.25Roll−Off = 0.50Roll−Off = 0.75Figure 9: Carrier–to–interference ratio at Eb/N0 =20 dBthe roll–off factor which is equal for both systems in thiscase. It can be seen, that there is hardly any influenceon C/IA, if the normalized difference between the carrierfrequencies is either smaller or larger than 1.0. Hence, itmakes no sense to alter the roll–off factor in those cases.Finally, Fig. 11 represents the bit error rate of system 1when it is disturbed by system 2. It can be seen, that voicetraffic (BER ≈ 10−3) is possible if only 1 decibel of Eb/N0is invested additionally, however, with the benefit of savedoverall bandwidth.5. CONCLUSION AND FUTURE WORKNew resource allocation techniques must be developed inorder to match the growing demand for spectrum. In thispaper the influence of ACI between two systems with respectto the difference between the two carrier frequencies on theone hand and the roll–off factors on the other hand hasbeen investigated. It has been shown by simulation, that itis possible to increase spectral efficiency by either shifting0 0.25 0.5 0.75 1051015202530Roll−Off factorC/I A [dB]  ∆f/fs = 0.5∆f/fs = 1.0∆f/fs = 1.5Figure 10: Carrier–to–interference ratio versus roll–off factor ρ0 2 4 6 8 1010−610−510−410−310−210−1100Eb/N0 [dB]BER  ∆f=1.2 fs∆f=1.3 fsno interfererFigure 11: Bit error rate (BER) with the roll–offfactors ρ1 = 0.25 and ρ2 = 0.50, respectivelythe carrier frequencies closer together or altering the roll–offfactors. Nevertheless, the latter approach does not seem tobe as promising as the first. A first concept for an adaptivealgorithm has been presented that is able to control theguard band dynamically.A lot of points are still open and, hence, a lot of future workmust be done. The simulation model must be widened toseveral users. In addition, communication with or without amaster device must be taken under closer consideration. Avery interesting aspect is the creation of a kind of avalancheeffect that might arise during shifting. Of course, this canlead to disastrous impacts and must be avoided. Anotheropen question is who is actually shifting its frequency? Afair distribution among the users in a network must be guar-anteed. Furthermore, one is faced with the question of howthe shifting will be done? In discrete steps until the termi-nation criterion is reached? This might lead to the effectthat the users overlap for a short time which might decreasethe quality of service. Who will participate in the spectrumsharing process – everyone or just a few selected users? Andif only a small selection will participate, how is this selectiondone?These issues are currently under investigation and this dy-namic spectrum sharing algorithm is expected to be verypromising.AcknowledgmentThis work has been performed in the framework of the EUfunded project End-to-End Reconfigurability (E2R). Theauthors would like to acknowledge the contributions of theircolleagues from E2R consortium.6. REFERENCES[1] J. Dumont, S. Lasaulce, and JM. Chaufray. Adjacentchannel interference in WCDMA networks equippedwith multiple antennas mobile stations. In SignalProcessing for Advanced Wireless Communications(SPAWC), July 2004.[2] D. E. Thompson. Modelling adjacent channelinterference in 3G networks. In 5th European PersonalMobile Communications Conference (EMPCC 2003),Glasgow, Scotland, April 2003.[3] B. R. Petersen and D. D. Falconer. Suppression ofadjacent–channel, cochannel, and intersymbolinterference by equalizers and linear combiners. InIEEE Trans. Commun., volume 42, pages 3109–3118,December 1994.[4] H. Arslan, S. C. Gupta, G. E. Bottomley, andS. Chennakeshu. Successive cancellation of adjacentchannel signals in FDMA/TDMA digital mobileradio systems. In Proc. IEEE Veh. Technol. Conf.(VTC ’98), pages 1720–1724, Ottawa, Ontario,Canada, May 1998.[5] H. Arslan, S. C. Gupta, G. E. Bottomley, andS. Chennakeshu. Joint demodulation for thesuppression of ACI in FDMA/TDMA mobile radiosystems. In Proc. IEEE Intl. Conf. Commun. (ICC’98), pages 723–724, Atlanta, GA, June 1998.[6] A. Schlereth. Digital algorithms for suppression ofadjacent channel interference in FM–receivers. InIEEE Intl. Conf. on Acoustics, Speech, and SignalProcessing (ICASSP ’93), volume 3, pages 5–8,Minneapolis, MN, 1993.[7] S. Golestaneh, H. M. Hafez, and S. A. Mahmoud. Theeffects of adjacent channel interference on the capacityof FDMA cellular systems. In IEEE Trans. Veh.Technol., volume 43, pages 946–954, 1994.[8] P. T. Mathiopoulos, J. S. Toor, and G. K.Karagiannidis. Effects of ACI and nonlinearities onthe performance of differentially detected GMSKsignals. In IEE Proc.–Commun., volume 151, 2004.[9] B. Vaghani and C. Beard. Improving spectralefficiency and reducing adjacent channel interferenceof a wireless emergency communication system.[10] B. Vaghani and C. Beard. A scheme to measure errorrates in the presence of adjacent channel interferenceusing frequency domain analysis. In IASTEDInternational Conference on Communications,Internet, and Information Technology (CIIT 2004),St. Thomas, US Virgin Islands, November 2004.[11] J. G. Proakis. Digital Communications. McGraw–Hill,New York, 4th edition, 2001.

Increasing Spectral Efficiency by Managing Adjacent Channel Interference

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Increasing Spectral Efficiency by Managing Adjacent Channel Interference

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