Parallel concatenated convolutional coding for an LMDS system with multimedia services

206 IEEE TRANSACTIONS ON BROADCASTING, VOL. 46, NO. 3, SEPTEMBER 2000

Parallel Concatenated Convolutional Coding for anLMDS System with Multimedia Services

Jin Young Kim

Abstract—In this paper, performance of LMDS (local multipointdistribution service) system with parallel concatenated convolu-tional coding (PCCC) is analyzed and simulated. The channel ofa LMDS system is modeled as a Rician fading channel with strongRician factor because it is generally accepted that there always ex-ists a line-of-sight (LOS) path between base station and subscriberunits. The performance is evaluated in terms of codeword errorprobability. From the simulation results, it is confirmed that theperformance is significantly improved by employing the PCCC.The results in the paper can be applied to radio modem design ofa LMDS system.

Index Terms—Parallel concatenated convolutional code, LMDS,multimedia service.

I. INTRODUCTION

I N recent years, telecommunications industries are activelyinvolved in the research and development of wireless com-

munications and internet related technologies. The wirelesstelephony and internet access for web surfing, e-mailing, ande-commerce are getting mature services while broadbandinteractive multimedia services are now in the early stage andbecoming attractive research topics [1]. The wireless multi-media technologies are now being developed in three directions[2]: 1) fixed wireless networks, 2) mobile cellular networks,and 3) local area networks. In the fixed wireless networks, twomajor systems under discussion are WLL (wireless local loop)and LMDS (local multipoint distribution service) [3]. The fixedwireless networks are attractive in that deployment cost andtime can be significantly saved compared to the constructionof wired infrastructure [4], [5].

The LMDS system was recommended in 1997 by FCC inthe 28 GHz band with a bandwidth of 1.3 GHz for point-to-multipoint services. The LMDS is intending to provide ter-restrial interactive multimedia services to residences andbusinesses. There are many challenging issues in realizing theLMDS system associated with its deployment at 28 GHz suchas rainfall attenuation, shadowing, and multipath fading effects,etc. To cope with these impairments, possible counter measuressuch as directional antenna or powerful coding scheme can betaken into account [6].

From Shannon theory, it is well known that the performanceof a coded system can be improved by increasing code lengthor constraint length. However, the decoding complexity of

Manuscript received November 9, 2000.The author is with IMT-2000 Development Group, Central Research and De-

velopment Center, SK Telecom, Korea.Publisher Item Identifier S 0018-9316(00)10697-3.

maximum likelihood (ML) decoding algorithms increasesexponentially as the block length increases. Thus, so far, therehave been many efforts to achieve an effective coding schemethat is practically realizable. In the coding community, the mostimportant advance in this decade is a discovery ofparallelconcatenated convolutional code(PCCC) or also calledturbocode in 1993 by Berrouet al. [7]. The PCCC has shown toreach close to Shannon limit with reasonable complexity, andbeen evaluated in many systems such as optical communicationsystems, wireless CDMA (code division multiple access) sys-tems, an OFDM (orthogonal frequency division multiplexing)systems, etc. [8]–[10].

The PCCC cleverly integrates code concatenation in apseudorandom manner. Randomness of the code is providedby an interleaver which does not impose decoding complexity.This fact comes from iterative decoding strategy where theconstituent codes are alternatively decoded, andextrinsicinformation (a part of soft output provided bya posterioriprobability algorithm) is passed to the next decoding stage.Therefore, the PCCC decoding can be understood as an instanceof Pearl’s ‘Belief Propagation’ algorithm which is well knownin artificial intelligence community. Since its appearance,much attention has been paid on structural properties of thePCCC, interleaver design, decoding algorithm, implementationcomplexity, comparison with other coding schemes, and itsapplications to many kinds of existing systems. Therefore,now it would be meaningful to investigate the possibility ofapplication of the PCCC to the LMDS system which is a kindof fixed wireless system.

The bandwidth, power, noise constraints of the LMDS systemmake forward error correction (FEC) a powerful method forenhancing the overall system performance. The conventionalFEC schemes include the convolutional code with Viterbi de-coding, Reed-Solomon code, and their concatenated code. Sofar, there has not been any approach to improve performance ofthe LMDS system through the PCCC. As has been evidenced inmany channel situations, the PCCC is expected to achieve moreimproved performance with reasonable complexity compared tothe conventional FEC schemes. This is a main motivation of thispaper.

In this paper, performance of LMDS (local multipoint dis-tribution service) system with parallel concatenated convolu-tional coding is analyzed and simulated. The channel of a LMDSsystem is modeled as a Rician fading channel with strong Ri-cian factor because it is presumed that there always exists aline-of-sight (LOS) path between base station and subscriberunits. The performance is evaluated in terms of codeword error

0018–9316/00$10.00 © 2000 IEEE

KIM: PARALLEL CONCATENATED CONVOLUTIONAL CODING FOR AN LMDS SYSTEM WITH MULTIMEDIA SERVICES 207

Fig. 1. Configuration of LMDS system.

probability. The performance improvement with the PCCC isconfirmed with varying the number of iterations in the decodingprocess, interleaver size for the various decoding algorithms.

The rest of the paper is organized as follows: In Section II,configuration of LMDS system and the PCCC are described.In Section III, upper bound on codeword error probability fora PCCC coded LMDS system is derived for a Rician fadingchannel. Simulation examples are presented in Section IV, andconclusions are drawn in Section V.

II. SYSTEM MODEL

A. LMDS System Description

The basic objective of a fixed wireless network is to providetwo-way (interactive), high-speed data communications (mul-timedia services) to residences and small businesses withina few kilometers surrounding base station. On the subscriberunit, this wireless access system can support voice, data,and video including internet. On the base station (orhubstation), the wireless access system may be connected to PSTN(public switched telecommunication network) or data network.Therefore, the LMDS system can be understood as a means ofdelivering broadband wireless services (voice, video, or data)to subscriber units with higher throughput relative to wiredtelephone modems. This system attracts much attention due tothe possibility of saving installation costs compared to cable orwireline modem access media.

The LMDS system provides millimeter-wave (typically, 30to 300 GHz) broadband access service operating above 20 GHz[11], [12]. In the United States, FCC allocated the band of 28–31 GHz for LMDS system while 20–40 GHz band is allocatedin Singapore. The frequencies at 2.6 GHz are also allocatedto MMDS for broadband data and entertainment communica-tion services. The millimeter wave typically forms pencil-likebeams, thus the LOS links are required to ensure the desiredsignal quality. Since the millimeter wave attenuates very rapidlyand is very sensitive to the surrounding propagation obstructions(such as buildings and trees) and weather conditions (fading dueto rainfall), the channel characterization is one of the critical fac-tors in the design of the LMDS system. These days, the channelmeasurements of LMDS system are undergoing in many areas[6].

In the LMDS system, the interference sources primarily comefrom AWGN, atmospheric propagation loss, rain fade, multi-path fading effects, and shadowing. The effect of rain fade isaccommodated by design of link margin. An LMDS networkis based on cellular structure, and typically consists of severaloverlapping cells of 2 to 6 km diameter each. The deploymentcost of LDMS system heavily depends on the cell size becausethe smaller cell size will require more base stations for a givenarea. As shown in Fig. 1, each cell typically consists of a hubstation with an omnidirectional antenna or sector antenna anddispersed fixed subscriber units (residence or business) with di-rectional antenna. The antenna of the hub is mounted on a talltower or rooftop of a tall building to establish LOS links withsubscriber unit antenna.

B. Parallel Concatenated Convolutional Coding

In Fig. 2(a) and (b), the block diagrams of PCCC encoderand decoder are shown. The PCCC encoder consists of the par-allel concatenation of two identical recursive systematic con-volutional code with rate 1/2 and an interleaver. This encoderstructure shown in Fig. 2(a) is calledparallel concatenationbe-cause the two constituent encoders operate on the same set ofinput bits, rather than one encoding the output of the other [13].The interleaver is used to permute the input bits such that thetwo encoders are operating on the same set of input bits, but dif-ferent input sequences. The input of the encoder 1,, producesoutput pair, while the input of the second encoder,,produces output pair, . The input bits are grouped intofinite-length sequence whose length is equal to the size of in-terleaver. The overall code rate is 1/3 because both encoders aresystematic and operate on the same set of input bits. By usingpuncturing mechanism, the code rate can be adjusted.

The PCCC decoder shown in Fig. 2(b) consists of twoconstituent decoders, interleavers, and deinterleaver. Theinterleavers are identical to the interleaver in the PCCC encoderand are used to reorder the sequences so that each decoder isproperly synchronized. For the PCCC decoding, state-space ofthe PCCC is too large to perform optimal decoding. It is wellknown that MAP (maximuma posteriori) decoding algorithmperforms remarkably well and may converge to the optimaldecoding solution [14], [15]. The MAP decoder computesaposteriori probability conditioned on the received sequence.


Fig. 2. Structure of PCCC. (a) Encoder. (b) Decoder.

The iterative decoder makes use ofa posteriori probability(APP) in the form of a log-likelihood ratio given by

(1a)

(1b)

whereis received sequence,is extrinsic information from the th decoder ( = 0or 1),is a priori log-likelihood ratio of the systematic bit,andis log-likelihood ratio of thea posterioriprobabilitiesof the systematic bit.

The iteration process continues until a desired performance isachieved at which point a final decision is made by comparingthe final log-likelihood ratio to the threshold 0. The extrinsic in-formation is a reliability measure of each component decoder’sestimate of the transmitted sequence based on the correspondingreceived component parity sequence, and is essentially indepen-dent of the received systematic sequence. Since each compo-nent decoder uses the received systematic sequence directly, theextrinsic information allows the decoders to share informationwithout significant error propagation.

III. PERFORMANCEANALYSIS

In the performance analysis, the followings are assumed: 1)perfect phase tracking of phase perturbation process, 2) perfect

channel state information is available at the receiver, and3) channel is very slowly varying due to fixed subscriber units.

A. Signal Modeling

BPSK (binary phase shift keying) is used for data modulation.The transmitted signal of coded waveform is given by

Re (2)

where is carrier frequency and is complex envelope oftransmitted signal. The complex envelope can be represented by

(3)

whereis energy per channel symbol,is coded symbol duration,is binary sequence of encoder output, andis normalized complex envelope with unit energy on(0, ).

The transmitted signal goes through the LMDS channel thatcan be characterized by a Rician fading distribution. From themultiplicative property of fading process, the transmitted signalbecomes distorted by amplitude and phase of fading signal [16].Then, the received signal is given by

Re (4)

whereis fading amplitude,is time-varying phase of fading signals with uniformdistribution over , and


is the AWGN component with two-sided power spec-tral density .

The probability distribution function (p.d.f.) of fading amplitudewith Rician distribution is given by [17]

(5)

where and are direct (specular) and diffuse components,respectively, and is the modified Bessel function ofthe first kind and zeroth order. The diffuse components aretypically modeled as individual quadrature components withGaussian distribution of zero mean and variance[18]. TheRician factor is defined as the power ratio of specular anddiffuse components and given by

(6)

The received signal energy per channel symbol is the sumof specular and diffuse components. In fixed wireless networkssuch as the LMDS system, the subscriber unit is fixed, thus itis reasonable to assume that the fading amplitude is constantwithin a codeword. The received signal can also be representedby

Re (7)

where is specular component and is random dif-fuse component.

B. PCCC Coded Performance

Assuming all zero sequence is transmitted, we consider atrellis path that merges again at the all zero path. For deriva-tion of codeword error probability, we apply union bound usingthe linear property of the PCCC code. The PCCC is known asa linear block code although it consists of concatenation of twoparallel convolutional codes. The codeword error probability ofa linear code with information bits in an AWGN channel isgiven by

(8)

where is the pairwise error probability between referencecodeword and codeword. The reference codeword is typicallychosen to be the all-zero codeword. For BPSK and soft-decisiondecoding, the pairwise error probability between the all-zerocodeword and codeword is given by

(9)

where= is SNR per bit,is code rate, andis Hamming weight of codeword .

To find the Hamming weight of each of the codewords, weshould determine the weight distribution of the code. So, thecodeword error probability becomes

(10)

where is weight distribution (the number of codewords inthe code space with weight ). It is well known that thefirst nonzero element of weight distribution occurs atwhere is the minimum distance of the code. For a linearblock code, the minimum distance can be found from paritycheck matrix, and is typically to be the smallest number ofcolumns in parity check matrix that sums to zero. The minimumdistance is very useful in finding bounds on the performance ofthe PCCC.

As the block length of the code becomes large, the entireweight distribution gets very complicated to calculate, espe-cially for the case of high SNR. We can approximate the code-word error probability as

(11)

Since the PCCC is a kind of linear systematic code, we canfind the first terms of the weight distribution from infor-mation sequences with weight less than and Hammingweights of the resulting codeword. From the systematic prop-erty, the weight of each codeword is equal or larger than that ofinformation sequence because the input sequence directly be-comes a part of output sequence. The instantaneous receivedpower in a Rician fading channel has the noncentral-p.d.f.given by

(12)

To derive the average performance, the uniform interleaver isemployed. It outputs distinctive permutations with equalprobability for a given input block of length and weight[19]. The codeword error probability in a slow Rician fadingchannel is obtained by averaging theover the Rician fadingstatistics. From (11), the codeword error probability in the slowRician fading channel is given by

(13)

C. Decoding of PCCC

The PCCC employs a procedure of iterative decoding of con-catenated codes with exchange of soft information. In the firstpaper of the PCCC by Berrou in 1993, the BCJR decoding al-gorithm (which is MAP decoding) has been employed [20].The conventional Viterbi algorithm produces hard decision out-puts (0 or 1) for each estimated bit. However, the BCJR algo-rithm outputs continuous value that weights the confidence orlog-likelihood of each estimated bit. The BCJR algorithm triesto minimize bit error probability by estimating the APP of in-dividual bits of codeword while the Viterbi algorithm attemptsto minimize codeword error probability by finding a maximumlikelihood (ML) of the transmitted codeword.

The complexity of the BCJR algorithm is significantly higherthan that of the Viterbi algorithm. Therefore, the efforts to findmodified decoding algorithms have been sought while guaran-teeing reasonable complexity and comparable performance with


the BCJR algorithm. One approach is to simplify the compu-tation of APP based on modifications of the Viterbi algorithmsuch as list-decoding Viterbi algorithm and soft-output Viterbialgorithm (SOVA) [15]. These algorithms are suboptimal in thatthey do not provide the required APP. An another approach isto simplify the BCJR algorithm. In the sliding window BCJR(SW-BCJR), the decoder operates on a fixed memory span andthe APP outputs are forced with a given delay. This algorithmsubstantially reduces both the memory storage requirement anddelay, and has an ability to implement a continuous stream MAPdecoder.

1) BCJR Algorithm: Let stand for the informationsequence, for channel input symbols, for the receivedsymbols, for the time index, and for the number of states.And, let be the encoder states at thetime . The and are one of the precursors andsuccessors of defined by the transition from (or

) to . Define

(14)

(15)

(16)

(17)

whereis thea posterioritransition probability from state

at time ,is the joint probability of channel input symboland channel output ,is the probability of the state of the encoder at timeconditioned on the past and current received signals,andis the probability of the state of the encoder at time

conditioned on the future received signals.Then, the APP is computed by

(18)

where is calculated from the knowledge of thea priori prob-abilities of the channel input symbolsand of the transitionprobabilities of the channel . The and canbe obtained from the forward and backward recursion equationsgiven by

(19)

(20)

where , , and are constants used for normalization. Theand are initialized as:

,otherwise

(21)

,otherwise

(22)

or

(23)

depending on whether the decoder is terminated in a known stateor not. The BCJR algorithm can be formulated through the fol-lowing steps:

Step 1) Initialize and .Step 2) Upon receiving a sequence of, the decoder com-

putes and stores and for all , , and.

Step 3) After receiving the entire sequence, the decoderbackward recursively computes and uses themwith the stored ’s and ’s for computation of theAPP’s.

2) SW-BCJR Algorithm:From the above steps, it is easilyfound that the BCJR algorithm requires the entire sequence tobe received to start the decoding process. To avoid the delayof waiting times in reception of the entire block, and to reducethe memory size required to store all the’s and ’s for theblock, a more flexible and simplified BCJR algorithm calledSW-BCJR algorithmhas been proposed. This algorithm oper-ates on a fixed delay span. Since the window size is relativelysmall compared to the block size, it can significantly reduce thememory requirement and delay. This algorithm can be formu-lated through the following steps:

Step 1) Initialize .Step 2) Do forward recursion .Step 3) Initialize backward recursion :

, for all .Step 4) Do backward recursion from time

to time .Step 5) A posterioritransition probabilities at time is

computed as

(24)

Step 6) The APP at time is computed as

(25)

D. Services of LMDS System

The LMDS system basically operates in a point-to-multipointconfiguration as shown in Fig. 1. The LMDS system can provideshared downstream (hub to subscribers) data rates of 10 Mbpsand shared upstream (subscribers to hub) data rates of 2 Mbps ormore. These capabilities are competitive with and complimen-tary to the wired access technology such as ADSL (asymmetricdigital subscriber line) or data-over-cable TV Coax services.The fixed wireless systems can be deployed in the areas wherewired access is not available, or as a competitive or complimen-tary service offering in other areas. The LMDS system can beeither for use at home or for use at a small home-office businesssuch as SOHO (small office home office). This system can alsobe combined with other broadband entertainment services.

The residential/business service typically includes voice(telephone POTS) in addition to the broadband IP (internetprotocol) data service. The base station with sector antennahas sectored radio coverage with typically a minimum of foursectors. Each sector provides service for a region of neighbor-hood. As shown in Fig. 1, the LMDS system supports two-way


Fig. 3. Codeword error probability for a varying number of iterations.

radio communication services between base station and manysubscriber terminals, and the base station can interface tosome data networks or PSTN network. The subscriber unit isequipped with a directional antenna that is aligned with the basestation. These antennas are used to assure the necessary radiolink gain and to exclude interference. The radio propagationwill be ideally line of sight with minimum obstruction by onlya few trees, and the alignment of subscriber antenna toward thebase station with the best (unobstructed) sight line is to be setat the installation stage. The subscriber radio unit can interfaceto its computing equipment using a standard data interface such10Base-T and IP. Due to the increasing demand of internetaccess traffic, the fixed wireless network should be designed toeasily accommodate IP packets and services.

To reduce the subscriber terminal and base station costs, thesystem design should be based on industrial standards suchas MCNS DOCSIS which has been developed for coax cableapplications to deliver multimedia services (video, ATM cell,Ethernet packet, etc.). The system design based on industrialstandard ensures compatibility with commercial interfacesand services and promotes common hardware componentsincluding channel decoders, interfaces, and protocol proces-sors. The MCNS DOCIS protocol standard provides a flexibleand efficient means for packing uplink data and signaling,and for controlling the TDMA (time division multiple access)multiplexing. The MCNS standard can be recommended asa protocol for use in mm-wave radio environment and foradditional services including IP datagrams and low-rate codedspeech.

The radio access equipment of the LMDS system can be cat-egorized as follows: 1) network interface, 2) radio modem, and3) sectored antenna at the base station, and 1) directional an-tenna, 2) radio modem, and 3) customer interface unit at eachsubscriber location. The common equipment at the base stationand the subscriber unit is a radio modem that supports error-correcting capability. With the current technology in mm-waveband, the implementation of low cost subscriber unit requiressub-watt RF amplifier and phase recovery loop with minimalrequirements.

IV. SIMULATION RESULTS

Some simulation results are presented in this section. For thesimulation examples, we consider the wide range of Rician fac-tors. The high Rician factor indicates that the high-gain direc-tional antenna is employed in the subscriber unit so that theLOS path is always guaranteed. A parallel concatenated convo-lutional code with overall code rate 1/3 and generator polyno-mial (37,23) of octal form consists of two recursive systematicconstituent codes: a convolutional code with 16 states and rate1/2 and a convolutional code with 16 states and rate 1. Thesekinds of constituent codes have been known to be best con-stituent codes for given code rate and states [22].

It can be noted that the channel model employed in this paperis sufficiently general in that by varying Rician factor , wecan get other fading amplitude distributions. Whenis equalto zero, becomes Rayleigh distribution while be-comes AWGN channel when approaches infinity.

The simulation process is based on Monte-Carlo method[23], [24], and done in the following order: 1) generation oftransmitted signal, 2) generation of random scatterers aroundthe transmitter, 3) addition of channel response and AWGN,and 4) computation of codeword error probability. In the Ricianchannel simulation, the number of multipath is assumed to bethree, and exponential multipath intensity profile is employed.

In Fig. 3, codeword error probability vs. SNR is shown for adifferent number of iterations. Simulation examples are shownfor interleaver length = 1000, Rician factor = 10 dB, andthe BCJR algorithm. It is confirmed that the BER performanceis significantly improved by the PCCC, and the performanceis improved by degrees as the number of iteration increases. Itcan be noted that if the number of iterations exceeds a certainnumber (in this case, 12), the more iterations offer only marginalcoding gain because the soft information is not available anylonger after sufficient number of iterations.

In Fig. 4, codeword error probability vs. SNR is shown fora different interleaver length of the PCCC encoder. Simulationexamples are shown for the 8 iterations in the decoding process,


Fig. 4. Codeword error probability for a varying interleaver length.

Fig. 5. Codeword error probability for a varying Rician factor.

= 10 dB, and the BCJR algorithm. The codeword perfor-mance is greatly enhanced as the interleaver length increases.However, there is a trade-off relationship in interleaver lengthand codeword error performance. It is generally known that theperformance of the PCCC using iterative decoding algorithms isstrongly dependent on the type and depth of interleaver. This isdue to the fact that the interleaver structure affects the distanceproperty of the resulting PCCC.

In Fig. 5, codeword error probability vs. SNR is shown for adifferent Rician factor. Simulation examples are shown for the8 iterations, interleaver size = 1000, and the BCJR algorithm.As the Rician factor increases (that is, the channel environmentapproaches AWGN channel), the codeword error performanceis gradually enhanced. Since the LDMS system is assumed tobe operating in the LOS channel conditions, the practical rangeof interest for the Rician factor will be greater than the Ricianfactor = 10 dB.

In Fig. 6, codeword error probability vs. SNR is shown fordifferent decoding algorithms. Simulation examples are shownfor the 8 iterations = 2, = 1000, and = 10 dB. The BCJR

algorithm shows better performance compared to the SW-BCJRand the SOVA algorithms. But, the performance difference is notsignificant. Therefore, it can be concluded that the suboptimalalgorithms such as SOVA is deemed to be more promising thanthe optimal BCJR algorithm for the practical implementation ofchannel decoder.

In Fig. 7, codeword error probability is compared betweenthe PCCC and the convolutional code (CC). Simulation exam-ples for the turbo code are shown for the 8 iterations, interleaversize = 1000, and the BCJR algorithm. The Viterbi decodingis used in the decoding of convolutional code with constraintlength 7 and code rate 1/3 [25]. The turbo code achieves lowercodeword error probability than the convolutional code. In thiscomparative figure, the PCCC can obtain the coding gain of afew dB over the CC for a wide range of SNR. It is expectedthat if we increase the interleaver size of the PCCC encoder andthe number of iterations, we can get more coding gains fromthe PCCC. In the design of the PCCC encoder and decoder, theinterleaver size and the number of iterations should be deter-mined according to the system requirements such as decoding


Fig. 6. Codeword error probability for different decoding algorithms.

Fig. 7. Comparison of codeword error probability between convolutional code (CC) and PCCC.

delay and complexity. Therefore, it is confirmed that the PCCCis more powerful coding scheme than the CC.

For practical applications of the PCCC to various kinds ofsystems, decoder complexity has been a hot issue. As a simpleexample, the decoder complexity is approximately proportionalto and for the PCCC and convolu-tional code, respectively, where is the number of iterations,

is a factor determined by decoding algorithm,is memorysize of constituent code, and is code rate. The PCCC withSOVA (soft output Viterbi algorithm) ( = 2), 16 iterations,code rate 1/3, and memory size 2 has roughly equivalent com-plexity to the convolutional code with code rate 1/3 and memorysize 8.

V. CONCLUSION

The codeword error probability of the LMDS system withparallel concatenated convolutional coding was analyzed andsimulated in a Rician fading channel with high Rician factor.For the PCCC decoding, the various decoding algorithms

were considered. It is confirmed through the simulations thatturbo coding offers considerable coding gain with reasonableencoding/decoding complexity. Also, it is demonstrated thatthe codeword error performance is substantially improved byincreasing the interleaver length and the number of iterationsused in the decoding process. Therefore, we can conclude thatthe use of the PCCC can decrease the required transmissionpower of the LMDS system through coding gain. We haveconsidered BPSK as a data modulation scheme for simplicity ofperformance analysis. However, in order to enhance spectrumefficiency, more bandwidth efficient modulation schemes suchas QAM or OFDM modulations can be employed. The exten-sions to those modulation schemes are very straightforwardfrom the analysis of this paper. The results in the paper can beapplied to the modem design of a LMDS system.

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Jin Young Kim received the B.Sc., M.Sc., and Ph.D.degrees from the School of Electrical Engineering,Seoul National University, Seoul, Korea, in 1991,1993, and 1998, respectively. From 1995 to 1997,he has conducted various areas of research onwireless communication systems and networkswith special attention on CDMA systems at theWireless Communications Laboratory, Institute ofNew Media and Communications (INMC) of SeoulNational University. He was a researcher at theInter-university Semiconductor Research Center

(ISRC) of Seoul National University in 1998. He was Postdoctoral ResearchFellow at the Information Sciences and Systems Research Group, Departmentof Electrical Engineering, Princeton University, NJ, USA from Aug. 1998 toFeb. 2000. He is now with Central Research and Development Center, SKTelecom, Korea. He has been selected as a recipient of an overseas postdoctoralresearch fellowship program from KOSEF (Korea Science and EngineeringFoundation) in 1998.

His research interests include spread-spectrum communications withemphasis on PN acquisition and tracking for applications to mobile andsatellite communication systems, broadcasting technology, adaptive antennaarray, packet radio network, optical communication, synchronization, channelcoding, multimedia transmission technique, wireless internet, wireless ATM,detection and estimation, and information theory. Dr. Kim is now a member ofIEEE, IEE, IEICE, IEEK, and KICS.

Parallel concatenated convolutional coding for an LMDS system with multimedia services

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