Optimised spreading code redistribution PAPR reduction technique for MC-CDMA systems

EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONSEur. Trans. Telecomms. 2009; 20:522–530Published online 16 June 2009 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/ett.1359

Transmission Systems

Optimised spreading code redistribution PAPR reduction technique forMC-CDMA systems

Lin Yang* and Emad Alsusa

School of Electrical and Electronic Engineering, Communication Engineering Group, University of Manchester, M60 1QD, UK

SUMMARY

Multicarrier code division multiple access (MC-CDMA) is one of the most promising techniques consideredfor future broadband mobile services. However, the high peak to average power ratio (PAPR) problemassociated with MC-CDMA systems can significantly degrade the power efficiency at the transmitter. Thispaper proposes an efficient PAPR reduction technique for the downlink of MC-CDMA systems. It regardsthe order of the CDMA spreading codes as an extra degree of freedom and exploits it to effectively reducethe PAPR of the signal. The proposed technique requires only slight modification to the MC-CDMA basestation and negligible complexity to the mobile terminals. Both lightly loaded and fully loaded systems areexamined when using the orthogonal sets of Walsh–Hadamard (WH) and Golay complementary sequences(CSs). It will be demonstrated that the proposed technique achieves significant PAPR reduction with lowsystem complexity at both transmitter and receiver. Copyright © 2009 John Wiley & Sons, Ltd.

1. INTRODUCTION

Due to its inherent robustness against multipath fadingchannels, orthogonal frequency division multiplexing(OFDM) has become a promising solution for high-speedwireless data transmission systems [1]. As a hybrid schemeof OFDM and code division multiple access (CDMA),multicarrier code division multiple access (MC-CDMA orOFDM-CDMA) inherits the advantages of both OFDM andCDMA techniques. In such systems, the CDMA part isutilised to provide multiple access ability as well as to spreadeach user’s data across the entire available frequency bandand thus exploiting the frequency diversity to reduce theimpact of frequency selective fading. The spreading code’schips are then modulated on orthogonal subcarriers andspread across the time domain via the OFDM modulationwhich provides protection against the delay spread of themultipath channel. Therefore MC-CDMA technique can

* Correspondence to: Lin Yang, Research and Innovation Centre, Alcatel-Lucent SHANGHAI BELL, 388 Ningqiao Road, Pu Dong Jinqiao 201206Shanghai, China. E-mail: [email protected]

achieve high data rate transmission with protection againstboth frequency selective fading and time dispersion channelwhile at the same time offers a spectrum efficient multipleaccess strategy [2, 3].

However MC-CDMA systems suffer from high peak toaverage power ratio (PAPR) which is one of the majordrawbacks of all multicarrier transmission schemes. A widedynamic range is required in the linear power amplifiers(PAs) at the transmitter in order to transmit a signal withlarge PAPR. If the dynamic range of PA is insufficient, thesignal could be distorted from the resulting nonlinearitywhich degrades the signal quality and leads to out ofband (OoB) radiations and hence interfere with adjacentfrequency bands.

Various PAPR reduction schemes have been proposedin literature, such as the most extensively studied multiplesignal representation (MSR) techniques [4] which includepartial transmit sequence (PTS) and selected mapping

Received 14 June 2007Revised 25 August 2008

Copyright © 2009 John Wiley & Sons, Ltd. Accepted 17 February 2009

CODE REDISTRIBUTION FOR PAPR REDUCTION 523

(SLM) [5]. The PTS technique partitions the data sequenceinto several subsets which are then respectively processedwith the inverse fast Fourier transform (IFFT) processorand jointly optimised to produce the optimal sequencewith low PAPR. The SLM technique pseudorandomlymodifies the phases of the original information symbolsin each OFDM block several times and selects the phase-modified OFDM block with the lowest PAPR factor fortransmission. However both techniques require multipleIFFT computation processes per OFDM symbol as wellas additional hardware complexity for storing randomisingsequences. Although these OFDM based schemes areapplicable to MC-CDMA system [6, 7], more efficientMC-CDMA specific algorithms have been developedby exploiting the spreading codes of the MC-CDMAsystems. For instance, Reference [8] proposed to reducethe PAPR by adaptively applying different sets oforthogonal spreading sequences. Similarly Reference [9]proposed to assign two spreading codes for each userand use two chip-matched correlators to identify whichspreading code has been used at the transmitter,and Reference [10] provided a scheme for generat-ing new spreading sequences which could offer lowPAPR.

In this paper, we propose a practical optimisedspreading codes redistribution (SCR) technique suitablefor the downlink MC-CDMA systems to achieve thebest combination of PAPR reduction ability and systemcomplexity [11]. Rather than randomising the transmittedsymbols as the SLM technique and sharing or creating newspreading codes as in Reference [8–10], we dynamicallyredistribute spreading codes between users for eachdownlink MC-CDMA symbol in order to produce a setof alternative MC-CDMA symbol representations andincrease the possibility of obtaining a low PAPR symbol.Both Walsh–Hadamard (WH) and Golay complementarysequences (CSs) are considered in the study. Moreover,it will be shown that since WH sequences can causeextremely high PAPR values in low-traffic downlink MC-CDMA system [12], we also propose a patch schemesequence optimising (SO) to aid the SCR technique inlightly loaded systems with WH codes to achieve stableperformance as in fully loaded systems. It will beshown in the results section that the proposed techniqueachieves comparable PAPR reduction ability to that ofthe SLM technique while requiring same amount ofside information (SI) as the SLM technique. Moreoversince mobile terminals only need to recognise theircorresponding spreading codes and despread the signalas in all CDMA systems, it is unnecessary to de-

randomise the received symbol as in the SLM and PTStechniques.

2. SYSTEM DESCRIPTION

A sketch map of the downlink MC-CDMA transmitterconsidered is shown in Figure 1. The system describedin the figure has K active users with the vector s(k) =[s(k)

1 , s(k)2 , . . . , s

(k)M ] denotes M data symbols of the kth

user, where k = 1, 2, . . . , K. The symbols are convertedfrom serial to parallel and then spread by the specificspreading sequence c(k) = [c(k)

1 , c(k)2 , . . . , c

(k)J ], where J

is the Spreading Factor (SF). The spread data symbolsof K users are added and then interleaved in thefrequency domain to achieve good frequency diversity.After interleaving and S/P conversion the M · J paralleldata samples are input to the IFFT of size N = M × J . Theresultant baseband transmission signal for one MC-CDMAblock 0 � t � NT can be mathematically written as

x(t) = 1√N

M∑m=1

J∑j=1

K∑k=1

s(k)m c

(k)j ei2π{M·(j−1)+(m−1)}�ft (1)

where �f = 1/NT is the subcarrier spacing, and NTdenotes the symbol period of one MC-CDMA symbol.Discrete-time representation n = 0, 1, . . . , LN − 1 of the

Figure 1. MC-CDMA downlink block diagram.

Copyright © 2009 John Wiley & Sons, Ltd. Eur. Trans. Telecomms. 2009; 20:522–530DOI: 10.1002/ett

524 L. YANG AND E. ALSUSA

OFDM signal can be expressed as

x[n] = 1√N

M∑m=1

J∑j=1

K∑k=1

s(k)m c

(k)j ei2π{M·(j−1)+(m−1)}�f nT

L (2)

where L is the oversampling factor.The MC-CDMA signal consists of the sum of several

subcarriers, which may result in a large dynamic transmittedsignal. The envelope variation of a multicarrier signal canbe estimated by the PAPR or the crest factor (CF)

CF(x[n]) =√

PAPR(x[n]) =

√√√√ max0�n�NL−1

|x[n]|2

E[x[n]2](3)

where E[·] stands for expectation function.An insufficient sampling rate for the OFDM signals

may miss some of the signal’s peak values and results inoptimistic outcomes for the PAPR. To properly approximatethe exact peaks of the continuous-time OFDM signal, L

(usually �4) times oversampling is generally used forthe discrete-time OFDM signal in many PAPR reductionschemes. However, the oversampling process increases therequired IFFT size by L times relative to the non-sampledcase and thus proportionally increases the computationalcomplexity. To maintain a good compromise betweencomplexity and accuracy, it has been suggested in Reference[13] that L = 4 can be utilised to sufficiently capture allthe peak values to provide an accurate value of PAPR.With a large number of subcarriers the large peaks ofapproximately Rayleigh distributed signal amplitude [14]happen only with a very small probability. Therefore, theabsolute PAPR is not meaningful for characterising thePAPR property. The most intuitionistic way to measurethe PAPR performance is to investigate the statisticalprobability that the PAPR of a block is larger than acertain level ζ. This is represented by the complementarycumulative distribution function (CCDF) of PAPR, arandom variable defined as

CCDF(PAPR(x[n])) = Pr(PAPR(x[n]) > ζ) (4)

3. SPREADING CODE REDISTRIBUTION ANDSEQUENCE OPTIMISATION

In this chapter the proposed technique will be systematicallypresented in three stages. After a briefly introduction ofthe WH and Golay CSs in the first section, the dynamic

spreading code redistribution (SCR) is discussed in detailwith some illustrative examples. Finally a technique for SOis proposed in order to aid the SCR technique to alleviatethe impact of WH sequences on the PAPR in lightly loadedMC-CDMA systems.

3.1. Spreading codes and PAPR

WH orthogonal spreading sequences are the rows of WHtransform matrix, which is recursively defined as follows:

HWH2N = 1√

2

[HWH

N HWHN

HWHN −HWH

N

]; HWH

2 = 1√2

[+ ++ −

]

(5)

Another set of orthogonal codes, frequently proposed forspreading, is the set of Golay CSs, obtained from Golayorthogonal matrix, which has also a recursive structure:

HCS2N = 1√

2

[HCS

N HCSN

HCSN −HCS

N

]; HCS

2 = 1√2

[+ ++ −

](6)

where HCSN is composed of HCS

N as follows: if HCSN =

[AN BN ], then HCSN = [AN −BN ].

As defined in Reference [15], the upper bound of PAPRof orthogonal sequences can be given by

PAPR �max

{|Ck [n]|2}J/2

(7)

where

Ck [n] =J∑

j=1

c(k)j ei2πjt/Ts (8)

For WH sequences, the maximum Ck[n] appearsmax{Ck[n]} = J2 when the WH sequences are onlycomposed of elements +1 [15]. Consequently, the PAPRupper bound for WH codes can be expressed as: PAPRWH �2 × J . While in the case of Golay CSs, its PAPR upperbound can be obtained by applying max{Ck[n]} = 2 × J toexpression (7), thus PAPRCS � 4. These analytical resultsclearly indicate WH sequences have a higher PAPR upperbound than CSs for J > 2.



3.2. Spreading codes redistribution (SCR)

As one of the MSR techniques but unlike SLM and PTS, theproposed SCR technique shuffles the same set of spreadingcodes between users on a symbol by symbol basis, suchthat a number of new symbol representations are generatedwith the possibility of attaining one with a low PAPRvalue.

Similar to the SLM and PTS techniques, the PAPRreduction ability of the SCR technique is mainly determinedby the number of alternative signal representations whilenot so much by how they are generated [4]. It is clear thatthe maximum number of possible symbol representationis determined by the number of spreading sequencesK: max{Ns} = K!, where Ns indicates the number ofsignal representations. However, in this paper the Ns islimited as Ns ∈ [0, J]. It will be shown in the simulationresults that J signal representations can effectively achievesufficient PAPR reduction (≈3 dB). Although increasingthe limit of Ns beyond J may achieve slightly betterPAPR performance, it costs much higher computationalcomplexity.

3.2.1. Shuffle mode. In order to produce Ns signalrepresentations, the proposed SCR technique must shufflethe spreading codes Ns times. Considering the PAPRreduction performance is not affected by the shufflingprocess but the number of signal representations, it ispreferred to use a simple shuffling mode to produce diversesignal representations at the lowest computational cost.Bear in mind that we are only interested in maximumJ different representation as stated above, we can easilyobtain the Ns shuffles by shifting the order of the spreadingsequences cyclically. In the rest of the paper, shifting modeSCRshift is assumed to be the default mode applied withSCR technique. The redistributed spreading sequences foruser i can be expressed as

c(i)SCRshift

={

c(i+Ns) when i + Ns < J

c(i+Ns−J) when i + Ns > J(9)

Consequently the SI should contain the number of shiftsso that all the users can identify their own spreading codes.The SI can be sent in the same way as in SLM/PTS, eitherwithin the data package or through dedicated channel. Sincesuch information is common to all users the associateddata loss is not going to be excessive. In fact for Q-arymodulated MC-CDMA symbols the data rate efficiency can

be expressed as

η = 1 − log2(Ns)

Nc × log2 Q(10)

where Nc is the number of subcarriers.In the rest of this section, the transmitter and receiver

models will be depicted with illustrative examples to furtherexplain the operation of SCR technique. For simplicity butwithout loss of generality, we assume K = 4; M = 2; J = 4which implies all the four users are active in the systemwith two CDMA symbols interleaved and input to the IFFTprocessor thus forming a MC-CDMA symbol with N =M × J = 8 subcarriers.

3.2.2. Transmitter Model. The flow chart of SCRtransmitter is described in Figure 2, where s

(1)1 , s(2)

1 , s(3)1 , s(4)

1indicate the first symbol from user 1 to 4 respectively andc(1), c(2), c(3), c(4) are the default spreading codes allocatedto the four users. Let us assume Golay CSs are employedin the system, from Equation (6) we can express it as

HCS4 =

c(1)

c(2)

c(3)

c(4)

= 1√

4

+ + + −+ − + ++ + − ++ − − −

The two information symbols of different users can bealso written in the form of matrix:

[s

(1)1 s

(2)1 s

(3)1 s

(4)1

s(1)2 s

(2)2 s

(3)2 s

(4)2

]

Figure 2. MC-CDMA downlink transmitter with four usersapplying spreading code reordering technique.



therefore the input to the interleaver can be defined as

[u1

u2

]=

[s

(1)1 s

(2)1 s

(3)1 s

(4)1

s(1)2 s

(2)2 s

(3)2 s

(4)2

]× 1√

4

+ + + −+ − + ++ + − ++ − − −

After S/P conversion, [u1, u2]T would be modulatedby IFFT processor. If the resulting PAPR value is belowthe predetermined threshold, then it is transmitted directlywithout performing the MSR technique. Otherwise weshift the sequence of spreading codes circularly byone which means c(2) is allocated for user 1, c(3) foruser 2 and so on. Consequently a new [u′

1, u′2]T can be

obtained as

[u′

1

u′2

]=

[s

(1)1 s

(2)1 s

(3)1 s

(4)1

s(1)2 s

(2)2 s

(3)2 s

(4)2

]× 1√

4

+ − − −+ + + −+ − + ++ + − +

It can be deduced by analogy that another two alternativeMC-CDMA symbols can be obtained before c(1) iscyclically shifted back to user 1. By applying the SCRtechnique, we hope to get an alternative MC-CDMAsymbol with a low PAPR factor. From the four MC-CDMA symbols carrying the same information, the onewith minimum PAPR will be chosen for transmissiontogether with the SI which should indicate the number ofshifting performed to the spreading codes. For instance ifthe spreading codes are shifted once, the SI needs onlyone bit, either 1 or 0. In this particular case there are fouractive users and four possibilities for the total number ofcode shifts. Therefore the SI should be log2 4 + log2 4 = 4bits.

3.2.3. Receiver model. Figure 3 shows the receiver blockdiagram corresponding to the transmitter shown in Figure 2.In order to recover the original data, the receiver mustknow how many shuffling operation were performed atthe transmitter. Let us assume the SI (′10′) indicates twocyclic shifts. Then based on this available information, thereceiver for each user can determine its correct despreadingcode for the received symbol according to Equation (9). The

Figure 3. MC-CDMA receiver with spreading code shufflingtechnique (K = J = 4, M = 2).

despreading sequences for all the users become

c(1)

c(2)

c(3)

c(4)

=

c(3)

c(4)

c(1)

c(2)

Suppose u1 and u2 are the received MC-CDMA symbolsafter the de-interleaving process, the estimates for the datasymbols of the different users can be produced by

[s

(1)1 s

(2)1 s

(3)1 s

(4)1

s(1)2 s

(2)2 s

(3)2 s

(4)2

]=

[u1

u2

]×

[c(1)T

c(2)Tc(3)T

c(4)T]

3.3. Sequence optimisation (SO)

It is shown in Figure 4 that when a 32 users MC-CDMA system is light-loaded with 16 and 8 active users,respectively, the corresponding PAPR values are 12.6 and14.8 dB. Without the use of PAPR reduction technique,very large PAPR value (more than 8 dB) appears almostwith probability of 100% for a 128 subcarriers MC-CDMAsystem when using WH spreading codes. This is becausewhen the MC-CDMA system is lightly loaded, the PAPRof the signal becomes highly dependent to the PAPR of theWH sequences. And since WH sequences inherently havevery high PAPR upper bound when J is large as discussed inSection 3.1 (Equation (7)), very likely the resultant symbolswill suffer from high PAPR.

Figure 5 shows PAPR λ0 (where Prob(λ > λ0) = 10−3)with respect to the number of active users K. It clearly



Figure 4. CCDF of 128 subcarriers MC-CDMA dowlink signalswith different number of active users spreaded by WH codes (M =4, J = 32).

Figure 5. Relationship between PAPR λ0 and active number ofusers with WH and CP sequences where Prob(λ > λ0) = 10−3.

indicates that as the number of active users decreasesin the MC-CDMA system using WH codes, the PAPRupper bound of downlink MC-CDMA signal increasesdramatically. However the PAPR performance with CSsremains fine because they have much lower PAPR upperbound (4 × M) than WH sequences (2 × M × J) whileJ � 2.

In order to reduce the unacceptable high PAPR factorcaused by WH sequences in light-loaded downlink MC-CDMA systems, a simple SO scheme is introduced belowbefore applying the SCR process.

Figure 6. PAPR λ0 of symbols with/without optimisation of 100random/CS sequences, Prob(λ > λ0) = 10−3.

The result shown in Figure 6 is obtained by transmitting1000 CDMA symbols in a 32-user-system with eightactive users. We can see that without SO, the maximumPAPR of these 1000 symbols is almost 15 dB, whichis consistent with the result shown in Figures 4 and 5.Since such high peak values are caused by the property ofWH codes, it is possible to reduce the influence of WHcodes by randomising (multiplying) the CDMA symbolwith a certain sequence as shown in Figure 7 so that theresulting PAPR value can be maintained at a reasonablelevel. In order to validate this, we transmit the 1000symbols again by multiplying them with an optimisingsequence and record the maximum PAPR. This process isrepeated 100 times with different optimising sequences toshow that there is no much performance difference withdifferent optimising sequences. It is clear that with suchoptimisation process, the maximum PAPR is effectivelyreduced to roughly 11 dB. As can be noticed in the figure,we employed both random sequences (dashed line) andCS sequences (solid line) for SO and they produce similarperformance.

Figure 7. Sequence optimisation.



Since all optimising sequences provide similar PAPRdistributed between 10 and 11.5 dB, it is unnecessary tosearch for the best optimising sequence for each datasymbol as in SLM based techniques. Instead we just simplychoose and fix any single CS to optimise the PAPR andthen reverse the process at the receiver. By doing this, thehigh PAPR of light-loaded systems can be significantlyreduced without incurring multiple IFFT processed orsearching algorithm. Figure 5 clearly shows that by applyingSO, stable PAPR performance can be obtained in bothfull-loaded and light-loaded MC-CDMA systems. It isalso feasible to search for the best optimising sequencein order to achieve slightly better PAPR performance,however this requires extra SI and computationalcomplexity.

4. NUMERICAL RESULTS AND DISCUSSIONS

In this section, we will report on several simulation results toevaluate the performance of the proposed PAPR reductiontechnique. The complementary cumulative density function(CCDF) of PAPR is used as the measure of performance.The transmitted signal is oversampled by a factor of four inthe IFFT process in order to achieve an accurate measureof the PAPR as recommended by Reference [13]. Thesimulation parameters used are summarised in Table 1.As can be seen from this table the PAPR performancefor the SCR technique is evaluated for both light-loadedand full-loaded system scenarios. All the simulations wereperformed for a downlink scenario.

The PAPR performance of a light-loaded MC-CDMAdownlink system is illustrated in Figure 8. Only eight usersare active in a 32-user-system (K = 8, J = 32) and foursymbols are transmitted each time (M = 4), therefore thenumber of subcarriers is M × J = 128. Since the systemis lightly loaded, it can be seen from both Figure 8 andprevious Figure 5 that the WH codes cause very bad PAPRperformance. For only 0.1% of the possible transmittedMC-CDMA signals without modification, the PAPR value

Table 1. Simulation parameters.

Figure 8 Figure 9 Figure 10

Modulation BPSK QPSK QAM-16Number of users 8 32 32Number of carriers 128 128/512 128/512Spreading factor 32 32 32Spreading code WH/CS WH

Figure 8. PAPR performance of a light-loaded 32 users and 128carriers MC-CDMA downlink system (K = 8, M = 4, J = 32)with our proposed technique.

is 15 dB which is highly unacceptable. However, byusing SO process, the maximum PAPR can be reducedto 11 dB. Moreover, when applying our proposed SCRtechnique, the PAPR is further reduced to 7.3 dB, which is asignificant reduction of 7.7 dB compared to the unmodifiedsignal. For CSs, no SO process is needed because thePAPR value of 0.1% of the spread signal is only 10.8 dBwhich is close to the optimised WH spread signal. WithSCR, the 0.1% PAPR value of modified signal is 8 dBwhich is a 2.8 dB reduction. Bear in mind that the SCRtechnique needs no extra randomisers, the 0.2 dB loss,whencomparing with the SLM technique is still acceptable.It will be shown later that for full-loaded systems, ourtechnique has almost the same performance as SLMtechnique.

Full-loaded MC-CDMA downlink system scenario isconsidered in Figure 9. Optimisation is not used becausethe PAPR performance of WH and CS codes are fairlygood in full-loaded system as shown in Figure 5. Figure 9clearly indicates that for 128 subcarriers MC-CDMAsystem, 0.1 per cent unmodified signals spread by WH/CScodes have a PAPR value of 10.5/10.8 dB. By shuffling32 spreading codes cyclically between the users, the PAPRis reduced to 7.3 dB for WH codes, which is a reductionof 3.2 dB. For CWSs the reduction is 3.4 dB. Similarreduction performance can be observed in the case of512 subcarriers.

In Figure 10, full-loaded MC-CDMA system withdifferent number of shuffles (Ns) is considered. The number



Figure 9. PAPR performance of full-loaded 32 users and 128/512carriers MC-CDMA downlink systems using SCR with WH andCS codes.

Figure 10. PAPR performance of full-loaded 32 users and128/512 carriers downlink MC-CDMA systems using SCR withdifferent number of cyclic shuffles Ns = 8/16/32.

of carriers has been fixed at 128 for the group of dashedlines and the number of cyclic shuffles is varied among 8,16 and 32. It is shown that 0.1% of the modified symbolshave PAPRs of 7.3, 7.5 and 7.9 dB for Ns = 8, Ns = 16and Ns = 32, respectively. Same simulation is performedto MC-CDMA downlink system with 512 carriers andshown with the group of real lines. It is obvious that theperformance can be improved by increasing the number ofcyclic shifts, but that could also introduce extra delay and

Figure 11. Data Efficiency of 32 users and 512 carriers MC-CDMA System with BPSK, QPSK and 16QAM.

complexity. Ns = 16 seems to be a good compromise whichcan be adjusted according to different system requirements.When comparing with the traditional SLM technique usingsame amount of randomising process Ns = U = 32, wecan see that the performance of both techniques are almostidentical.

The data efficiency of a 32 users (J = 32) and 512carriers (Nc = 512) MC-CDMA system with BPSK, QPSKand 16QAM modulations is shown in Figure 11 accordingto Equation (10). In this case the number of shuffles ismade constantly equal to 16 (Ns = 16). Obviously the dataefficiency of this system is less with smaller constellationmodulation techniques. 16QAM is the most data efficientmodulation scheme, while BPSK is the least data efficientone. Also it is clear from this Figure that for commonlyused QPSK modulation, the data efficiency continues to beabove 99%.

5. CONCLUSION

This paper proposed a new spreading code redistributionPAPR reduction technique for MC-CDMA system. Thistechnique utilises the order of spreading codes to minimisethe systems PAPR without the need of extra randomisersand therefore achieves much lower complexity than thetraditional SLM technique. The problem of high PAPRfactor raised by WH codes in lightly loaded systemis solved by using an optimising sequence. With theproposed technique, significant PAPR reduction can be



achieved for both light-loaded and full-loaded MC-CDMAsystems with only a slight complexity increase in the basestation but hardly any complexity increase to the mobileterminals.

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AUTHORS’ BIOGRAPHIES

Lin Yang received his B.Eng. degree with honor in electrical engineering and electronics from the Beijing Institute of Technology,China, in 2003 and the M.Sc. degree with distinction from University of Manchester Institute of Science and Technology, Manchester,U.K. in 2004. After obtaining his Ph.D. degree in electrical and electronic engineering at the University of Manchester in 2008, he isnow working as a research engineer in Alcatel-Lucent. His interests include wireless communication networks, especially, multicarriermodulation and multiple access techniques, channel estimation, coding, multiuser detection, and multiple input multiple output (MIMO)techniques.

Emad Alsusa received his PhD in Electrical & Electronic Engineering from Bath University, Bath, UK, in 2000. From June 2000 toSeptember 2003 he was with the School of Engineering and Electronics at Edinburgh University as a Postdoctoral Research fellowworking on link enhancement techniques for future wireless communication systems. He joined Manchester University in 2003 as aLecturer of Communication Engineering. He is a senior member of the IEEE. His research interests are in the area of signal processingand analysis for wireless communication networks, especially, modulation and multiple access, channel estimation, channel coding,interference mitigation, multiuser detection and MIMO techniques.

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Optimised spreading code redistribution PAPR reduction technique for MC-CDMA systems

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