Dynamic bandwidth multicode interleaved direct sequence (MCIDS), an enhanced MCIDS based Ultra-wideband (UWB) application is proposed in this paper, featuring a cognitive transmission bandwidth adaptation without any adverse effect on the data rate. By introducing a specific lowpass filtering and down-sampling into the traditional MCIDS algorithm, this system can decrease the transmission bandwidth into part of its original bandwidth but still be able to recover all the transmitted data from the reduced bandwidth. This solution can efficiently improve the coexistence ability of UWB devices in a cognitive manner without increasing hardware complexity

Dutkiewicz, E.

Huang, Xiaojing

Zhao, J.

English

Research Online

University of Wollongong Research Online Faculty of Informatics - Papers (Archive) Faculty of Engineering and Information Sciences 17-10-2007 Dynamic Bandwidth MCIDS - A Cognitive Solution for MCIDS based UWB Communications J. Zhao University of Wollongong Xiaojing Huang University of Wollongong, huang@uow.edu.au E. Dutkiewicz University of Wollongong, eryk@uow.edu.au Follow this and additional works at: https://ro.uow.edu.au/infopapers  Part of the Physical Sciences and Mathematics Commons Recommended Citation Zhao, J.; Huang, Xiaojing; and Dutkiewicz, E.: Dynamic Bandwidth MCIDS - A Cognitive Solution for MCIDS based UWB Communications 2007. https://ro.uow.edu.au/infopapers/655 Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: research-pubs@uow.edu.au Dynamic Bandwidth MCIDS - A Cognitive Solution for MCIDS based UWB Communications Abstract Dynamic bandwidth multicode interleaved direct sequence (MCIDS), an enhanced MCIDS based Ultra-wideband (UWB) application is proposed in this paper, featuring a cognitive transmission bandwidth adaptation without any adverse effect on the data rate. By introducing a specific lowpass filtering and down-sampling into the traditional MCIDS algorithm, this system can decrease the transmission bandwidth into part of its original bandwidth but still be able to recover all the transmitted data from the reduced bandwidth. This solution can efficiently improve the coexistence ability of UWB devices in a cognitive manner without increasing hardware complexity. Disciplines Physical Sciences and Mathematics Publication Details This conference paper was originally published as Zhao, J, Huang, X, Dutkiewicz, E, Dynamic Bandwidth MCIDS - A Cognitive Solution for MCIDS based UWB Communications, International Symposium on Communications and Information Technologies ISCIT 2007, 17-19 Oct, 1502-1506. This conference paper is available at Research Online: https://ro.uow.edu.au/infopapers/655 Dynamic Bandwidth MCIDS - A CognitiveSolution for MCIDS based UWB CommunicationsJie Zhao∗, Xiaojing Huang† and Eryk Dutkiewicz‡∗†‡School of Electrical, Computer and Telecommucations EngineeringUniversity of Wollongong,Wollongong NSW 2522 Australia∗E-mail: jz084@.uow.edu.au†E-mail: huang@.uow.edu.au‡E-mail: eryk@.uow.edu.auAbstract— Dynamic bandwidth multicode interleaved directsequence (MCIDS), an enhanced MCIDS based Ultra-wideband(UWB) application is proposed in this paper, featuring a cognitivetransmission bandwidth adaptation without any adverse effecton the data rate. By introducing a specific lowpass filteringand down-sampling into the traditional MCIDS algorithm, thissystem can decrease the transmission bandwidth into part of itsoriginal bandwidth but still be able to recover all the transmitteddata from the reduced bandwidth. This solution can efficientlyimprove the coexistence ability of UWB devices in a cognitivemanner without increasing hardware complexity.I. INTRODUCTIONUltra-wideband (UWB), based on the description by theFederal Communications Commission (FCC), is a wirelesstransmission technology that spreads transmitted informationover a large bandwidth. The UWB technology employs anunlicensed spectrum between 3.1-10.6 GHz allocated by theFCC. This spectrum can be shared by multiple users to achievean efficient use of radio bandwidth. There are many UWBapplications, including high data rate, low power wirelessconnectivity, and lower data rate but longer-range applications,such as ranging and imaging systems. Currently UWB hastwo main development directions, one is the direct sequence(DS) based UWB [1]. Another is the multi-band orthogonalfrequency division multiplexing (MB-OFDM) [2]. For the DSbased UWB, as it occupies a large bandwidth at the sametime, it is unavoidable to overlap with existing narrowbandtransmissions, causing interference between different systems.As a solution to the above coexistence issue, cognitiveradio (CR) is currently receiving more and more attentionworldwide. Cognitive radio was first proposed in [3], withwhich spectrum utilization can be improved significantly. Theauthors in [4] reviewed CR as an environmentally awareextension of software defined radio (SDR). Any radio with thecapability to hop around the spectrum for optimizing power,range and required data rates can satisfy the definition of CR.Cognitive radio adapts SDR’s ability of changing communi-cations protocols, and meanwhile adds another dimension -the capability to perceive the surrounding environment andlearn from its experience. The learning ability of recognizingdead zones, interference and usage patterns sets CR apartconceptually from other spectrum management techniques.Until now most research on cognitive DS based UWB hasfocused on how to generate an UWB pulse for matching theFCC spectral mask [5], [6]. Traditional MCIDS spreading[7] has already given a solution to resolve intersymbol inter-ference (ISI). However, there has been no method proposedany method to avoid interference with other devices. Thispaper extends the traditional MCIDS algorithm and proposesa solution called Dynamic Bandwidth MCIDS that enablesMCIDS spreading algorithm to receive and recover the wholeuser data from part of the received signal bandwidth. Sincethis solution is implemented in software without increasinghardware complexity of the system, it provides an opportunityfor cognitive UWB transmission in a simple and efficient way.This paper is organized as follows. In Section II, an en-hanced MCIDS based UWB system model is introduced. InSection III, discussions on the proposed system model andsimulation results are included. Conclusions are drawn inSection IV.II. ALGORITHM OF DYNAMIC BANDWIDTHMCIDSOriginal MCIDS spreads a signal for a block of N data bitsa0, a1, . . . , aN−1 with bit duration Tb, where, ai = ±1 can bedescribed mathematically as [7].N−1∑i=0P−1∑j=−P0bi,jg(t − iTc − jNTc) =N−1∑i=0aiP−1∑j=−P0ci[(j)P ]g(t − iTc − jNTc)(1)where ci[(j)P ] is the orthogonal spreading sequence withperiod P , ci(j) = 0, 1, . . . P − 1 is one period of thisperiodic sequence with duration Tc and (j)P denotes j moduloP , g(t) is defined as a unit amplitude pulse which is zerooutside the interval [0, Tc]. An example of MCIDS spread-ing is presented in Fig.1 [8], where (a) shows the spreadsignal obtained by spreading symbol +1 with its correspond-ing spreading code {+1,+1, . . . , +1}, (b) shows the spreadsignal obtained by spreading signal with its correspondingspreading code {−1,+1, . . . ,−1} and (c) is obtained in the15021-4244-0977-2/07/$25.00 c© 2007 IEEEsame way. Altogether a block interleaver then can outputdata as {a0, b0, c0, . . . , a1, b1, c1, . . . , aN−1, bN−1, cN−1 . . .}shown in (d). A length P0 cyclic prefix (CP ) is also addedin this example, which is the same as the last segment of theblock interleaved signal.Fig. 1. Example of MCIDS spreading.Now let us introduce an enhancement over the originalMCIDS spreading. This enhanced system enables signal re-covery from part of the original bandwidth of the MCIDSsystem by using a wider signal impulse. The width of theimpulse is controlled by a lowpass filter with bandwidth |f | =2−m (1/Tc), where m = 0, 1, . . . ...log2N . For each impulse,decreasing its bandwidth to 2−m also means increasing itswidth to 2m. This leads to overlapping of successive impulses.Takingm = 0, 1, 2 for example, after spectral shaping andmatched filtering, each impulse is reshaped in the time domainas illustrated in Fig. 2. Note that the limited bandwidth mustbe exactly 2−m times of the original bandwidth to facilitatefurther processing.In Fig. 2, when m = 0, at sampling points, no ISI isproduced since the impulse is zero at integer multiples of Tc.The MCIDS signals are fully passed in the frequency domain.This condition can be considered as that of the unfilteredMCIDS signal, and signal processing can be performed in thetraditional way [8]. When m = 1, however, signal bandwidthis reduced to half of the original bandwidth. Each inputimpulse has nonzero amplitudes at integer multiples of Tc.When m = 2, signal bandwidth is reduced to quarter ofthe original bandwidth. In other words, when impulse isfiltered, each output impulse sample is a contribution of severalimpulses. The signal in Fig.1 becomes the spectral shapedsignal shown in Fig. 3.Fig.4 shows the equivalent lowpass filtering model for thisFig. 2. Example fo filtered impulses, (a) m = 0, with full bandwidth, (b) m=1, impulse with half bandwidth (c) m =2, impulse with quarter bandwidthenhanced system.In order to focus on investigating the possibility of re-covering the signal though partial bandwidth, channel h(t)is assumed to be a single path slow fading channel and thereceived signal can be expressed asr (t) = x (t) ∗ h (t) + z (t) (2)where z(t) is the additive white Gaussian noise (AWGN) withdouble sided power spectral density N0. The received signal isfirst sampled with sampling period Tc, then passed though thematched filter. The filtered signal sequence can be describedas:r (n) =N−1∑i=0P−1∑j=−P0αi,j×i+2m−1∑k=i−2m+1akck [(j)P ] g [(n − ic − jN) Tc] + zn(3)where zN is the white discrete-time Gaussian noise. αi,j aretap coefficients that relate to the filter and channel response.Since slow channel fading is assumed, αi,j can be treated asconstants. Note that a−1, c−1 = aN−1cN−1 due to the cyclicprefix in front of each signal block.Despread operation in this system is the same as in the orig-inal MCIDS system [7]. Received samples are filled column-wise into a matrix. First P0 columns of the matrix are deletedin order to remove the CP . To despread, samples are originallyread out row-wise. However, in this system, samples are keptin the matrix for further processing. The remainder of theN × P matrix is illustrated is Fig.5, takingEi,j =i+2m−1∑k=i−2m(i−1)akck [(j)P ]2007 International Symposium on Communications and Information Technologies (ISCIT 2007) 1503Fig. 3. Spectral shaped signal sequenceFig. 4. block diagram of equivalent lowpass system modelBased on Equation (3), each received sample contains infor-mation of nearby 2m original samples. Therefore only N/2msamples are required to recover all transmitted data. Then, bydown-sampling the above matrix in a row-wise manner by 2m,each sampled unit in the matrix can be expressed as:r (l + 1, j + 1) = α2m,j2m(l+1)∑k=2m(l−1)−1akck [(j)P ] + Z2ml,j(4)where l = 0, 1, . . . , 2−mN − 1; j = 0, 1, . . . , P − 1.Down-sampled matrix rij with size N/2m × P is shown in Fig. 6.The dispreading code is defined asdi [n] =P−1∑j=0ci [j] (5)Fig. 5. Despread samples stored in matrixFig. 6. Down-sampled matrixFor detection of each bit ai. Each row of the matrix iscircularly correlated with di [n]. Dispread signal Uil are over-lapped components with contributions from nearby l samples.Mathematically, we haveUil =P−1∑j=0r (l + 1, j + 1) ci [(j)P ] (6)Suppose that Ui are the decision variables. To collect energyfrom all lsamples, we haveUi = Re⎡⎣2−m∑l=1Uil · α∗2m,j⎤⎦= Re2−mN∑l=1P−1∑j=0α∗2ml,jα2ml,j2m(l+1)−1∑k=2m(l−1)akck [(j)P ] ci [(j)P ]+ Re2−mN∑l=1α∗2mi,jP−1∑j=0z2mi,j(7)Because ci [(j)P ] are orthogonal sequences, they satisfy2m(l+1)−1∑k=2m(l−1)akck [(j)P ] ci [(j)P ] ={P i=k=10 otherwise(8)The decision variable Ui becomeUi = P2−mN∑l=1P−1∑j=0|α2ml,j |2 ai + Re2−mN∑l=1α∗2mi,jP−1∑j=0zmi,j2(9)Therefore, all the symbols are fully recovered from 1/2mof the original bandwidth. The calculation process of Ui using[6], [7] is shown in Fig. 7.III. DISCUSSION AND RESULTS ANALYSISIn dynamic bandwidth MCIDS based UWB system, the con-stant m determines both filtered bandwidth |f | = 2−m (1/Tc)1504 2007 International Symposium on Communications and Information Technologies (ISCIT 2007)Fig. 7. Despreading and combining structurein transmitter end and down-sampling rate 2m in receiver end.Value of m can be chosen according to applications and/orchannel environment.In the ideal conditions, a higher value of m will efficientlydecrease the wireless transfer bandwidth without risking datarate loss. Thus systems with a high value of m are verysuitable for the conditions where multiple wireless devicesshare the bandwidth. A lower transmit bandwidth improvestheir coexistence ability with other devices while retaining ahigh data rate.On the other hand, a lower value of m means a highertransfer bandwidth. This lower value leads to more frequentsampling at the receiver end. Since each date symbol is carriedout by more signal samples, collecting more samples willgather more energy and thus increase the processing gain.However, a wider bandwidth causes coexistence issues.This paper focuses on proving that it is possible to fullyrecover data under a variable transmission bandwidth in anMCIDS UWB system, with the assumptions of a slow fadingsingle path channel and interference with white Gaussiannoise. Also, the filter in this model is only a lowpass filter.Thus, we can dynamically select the signal bandwidth toprovide a solution to the coexistence issue.In our future work, a multipath fading channel and otherinterference factors will be considered to reflect more practicalscenarios. In addition, different filtering methods such ashighpass, bandpass or bandstop filters will be considered torealize more efficient bandwidth adaptation. Related signalrecovering algorithms will be also investigated.Simulations based on the algorithm and architecture of thelowpass filtering MCIDS system has been also performed.In these simulations, Tc = 4 × 10−8, Hadamard sequencesare employed as spreading codes with P = 16, the lengthof CP , P0 = 4, and a root raised cosine filter is used forlowpass filtering, with m = 0, 1, 2. Also, tap coefficients αi,jare gained by sending training sequence which is known byboth transmitter and receiver. Fig. 8 shows the system bit errorrate curves in white Gaussian noise channel.Based on the results in Fig. 8, the bit error rates underthree transmission bandwidths are very close to each other.These results prove that the enhanced MCIDS algorithm whichproposed in this paper is able to fully recover signals underdifferent bandwidth without jeopardy in performance. Notethat the 2dB losses in each curve in Fig. 8 are caused byCP. This is comfirmed by Fig. 9, which shows the systemperformance when the same parameters as those in Fig 8 areused but no CP is added.0 2 4 6 8 10 1210−810−710−610−510−410−310−210−1100Eb/N0 (dB)BER  1/2 Bandwidth1/4 BandwidthFull Band without CPFull BandwidthTheoreticalFig. 8. Performance with different transmission bandwidth0 2 4 6 8 1010−510−410−310−210−1Eb/N0 (dB)BER  1/2 Bandwidth without CP1/4 Bandwidth without CPFull Bandwidth without CPTheoreticalFig. 9. Performance without cyclic prefixIV. CONCLUSIONSThe dynamic bandwidth MCIDS based UWB system pro-posed in this paper enables full signal recovery under 1/2, 1/42007 International Symposium on Communications and Information Technologies (ISCIT 2007) 1505or less of the original transmission bandwidth. Therefore,with filtering, sampling, circular dispreading and combin-ing, this enhanced system is able to dynamically adjust itstransmission bandwidth without deceasing its data rate orincreasing hardware complexity. Unlike other cognitive UWBapproaches that try to generate specific UWB impulse underFCC spectrum mask, this paper extended tradition MCIDSalgorithm to achieve lossless bandwidth adjustment and thusprovides another efficient cognitive solution for UWB.REFERENCES[1] M. Bettayeb and S. F. A. Shah. State of the art ultra-widebandtechnology for communication systems: a review. In Proceedings of IEEEInternational Conference on Electronics, Circuits and Systems, volume 3,pages 1276–1279 Vol.3, 2003.[2] A. Batra, J. Balakrishnan, and A. Dabak. Multi-band ofdm: a newapproach for uwb. In Proceedings of the International Symposium onCircuits and Systems, volume 5, pages 365–368 Vol.5, 2004.[3] III Mitola, J. Cognitive radio for flexible mobile multimedia com-munications. In IEEE International Workshop on Mobile MultimediaCommunications, pages 3–10, 1999.[4] J. Walko. Cognitive radio. IEEE Review, 51(5):34–37, 2005.[5] X. Zhou, K. Y. Yazdandoost, H. Zhang, and I. Chlamtac. Cognospectrum:spectrum adaptation and evolution in cognitive ultra-wideband radio. InIEEE International Conference on Ultra-Wideband, pages 713–718, 2005.[6] F. Xu, W. Zhong, and Z. Zhou. Cognitive interference suppress in uwbsystem using modified hermite polynomials pulse. In Communicationsand Information Technologies, 2006., pages 1216–1220, 2006.[7] X. Huang and Y. Li. The multicode interleaved dsss system for high speedwireless digital communications. In Communications, 2001., volume 10,pages 2990–2994 vol.10, 2001.[8] X. Huang, D. Lowe, R. Gandia, and E. Dutkiewicz. An impulse ultra-wideband system capable of concurrent transmission and reception, part i:Requirements and innovations. In Communications, Circuits and SystemsProceedings, volume 2, pages 1251–1255, 2006.1506 2007 International Symposium on Communications and Information Technologies (ISCIT 2007)

Dynamic Bandwidth MCIDS - A Cognitive Solution for MCIDS based UWB Communications

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Dynamic Bandwidth MCIDS - A Cognitive Solution for MCIDS based UWB Communications

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