Multi carrier and spread spectrum systems phần 7 ppsx

4.4.3 Turbo Coding Recently, interest has focused on iterative decoding of parallel or serial concatenated codes using soft-in/soft-out SISO decoders with simple code components in an i

coding, the systematic structure of the RS code allows one to shorten the code, i.e.,remove the filled 239− K zero bytes before transmission Then, each coded packet

of lengthK+ 16 bytes will be serial bit converted

— At the end of the each packet, tailbits (e.g., 6 bits for memory 6) can be inserted forinner code trellis termination purposes

— A block consisting of [(K + 16) × 8 + 6] bits is encoded by the inner convolutional

mother binary code of rate 1/2 After convolutional coding, the puncturing operation

is applied following the used inner code rateR for the given packet This results in

a total of [(K + 16) × 8 + 6]/R bits Finally, the punctured bits are serial-to-parallel

converted and submitted to the symbol mapper

If the BER before RS decoding is guaranteed to be about 2· 10−4, then with sufficientinterleaving (e.g., 8 RS code words) for the same SNR values given in Table 4-3, a quasierror-free (i.e., BER< 10−12) transmission after RS decoding is guaranteed However, if

no interleaving is employed, depending on the inner coding rate, a loss of about 1.5–2.5

dB has to be considered to achieve a quasi error-free transmission [20]

4.4.3 Turbo Coding

Recently, interest has focused on iterative decoding of parallel or serial concatenated

codes using soft-in/soft-out (SISO) decoders with simple code components in an

inter-leaved scheme [4][28][29][30][66] These codes, after several iterations, provide Shannon performance [29][30] We will consider here two classes of codes with iterativedecoding: convolutional and block Turbo codes These codes are already adopted inseveral standards

By applying systematic recursive convolutional codes in an iterative scheme and by ducing an interleaver between the two parallel encoders, impressive results can be obtainedwith so-called convolutional Turbo codes [4] Convolutional Turbo codes are currently ofgreat interest because of their good performance at low SNRs

intro-Figure 4-38 shows the block diagram of a convolutional Turbo encoder The code ture consists of two parallel recursive systematic punctured convolutional codes A block

struc-of encoded bits consists struc-of three parts The two parity bit parts and the systematic partwhich is the same in both code bit streams and, hence, has to be transmitted only once

The code bit sequence at the output of the Turbo encoder is given by the vector b(k)

convolutional encoder

convolutional encoder

puncturing

puncturing interleaver

Figure 4-38 Convolutional Turbo encoder

Channel Coding and Decoding 163

deinterleaver

deinterleaver

interleaver

convolutional decoder

convolutional decoder

‘0’ insertion

‘0’ insertion

Figure 4-39 Convolutional Turbo decoder

In the receiver, the decoding is performed iteratively Figure 4-39 shows the blockdiagram of the convolutional Turbo decoder The component decoders are soft outputdecoders providing log-likelihood ratios (LLRs) of the decoded bits (see Section 2.1.7).The basic idea of iterative decoding is to feed forward/backward the soft decoder output

in the form of LLRs, improving the next decoding step In the initial stage, the

non-interleaved part of the coded bits b(k) is decoded Only the LLRs given by the vector l(k)

at the input of the Turbo decoder are used In the second stage, the interleaved part is

decoded In addition to the LLRs given by l(k), the decoder uses the output of the first

decoding step as a priori information about the coded bits This is possible due to the

separation of the two codes by the interleaver In the next iteration cycle, this procedure is

repeated, but now the non-interleaved part can be decoded using an a priori information

delivered from the last decoding step Hence, this decoding run has a better performancethan the first one and the decoding improves Since in each individual decoding step thedecoder combines soft information from different sources, the representation of the softinformation is crucial

It is shown in [29] and [30] that the soft value at the decoder input should be a LLR

to guarantee that after combining the soft information at the input of the decoder LLRsare again available The size of the Turbo code interleaver and the number of iterationsessentially determine the performance of the Turbo coding scheme

The performance of Turbo codes as channel codes in different carrier ple access schemes is analyzed for the following Turbo coding scheme The componentcodes of the Turbo code are recursive systematic punctured convolutional codes, each

multi-of rate 2/3, resulting in an overall Turbo code rate multi-of R = 1/2 Since performance with

Turbo codes in fading channels cannot be improved with a memory greater than 2 for

a BER of 10−3 [30], we consider a convolutional Turbo code with memory 2 in order

to minimize the computational complexity The component decoders exploit the softoutput Viterbi algorithm (SOVA) [28] The Turbo code interleaver is implemented as arandom interleaver Iterative Turbo decoding in the channel decoder uses 10 iterations.The SNR gain with Turbo codes relative to convolutional codes with R = 1/2 and

memory 6 versus the Turbo code interleaver size I TC is given in Figure 4-40 for theBER of 10−3

OFDM (OFDMA, MC-TDMA) MC-CDMA, MLSE/MLSSE MC-CDMA, MMSE equalizer

applica-Due to the large interleaver sizes required for convolutional Turbo codes, they are ofspecial interest for non-real time applications

The idea of product block or block Turbo coding is to use the well-known product codeswith block codes as components for two-dimensional coding (or three dimensions) [66].The two-dimensional code is depicted in Figure 4-41 Thek r information bits in the rowsare encoded inton r bits, by using a binary block code Cr (n r , k r ) The redundancy of the

code isr r = n r − k r andd r the minimum distance After encoding the rows, the columnsare encoded using another block code Cc (n c , k c ), where the check bits of the first code

are also encoded

The two-dimensional code has the following characteristics

— overall block sizen = n r · n c,

— number of information bits k r · k c,

— code rateR = R r · R c, where R i = k i /n i,i = c, r, and

— minimum distancedmin= d r · d c

The binary block codes employed for rows and columns could be systematic BCH(Bose–Chaudhuri–Hocquenghem) or Hamming codes [48] Furthermore, the constituent

Channel Coding and Decoding 165

Data bits

Parity bits

Parity bits

Figure 4-41 Two-dimensional product code matrix

Table 4-4 Generator polynomials of Hamming codes as block Turbo code components

codes of rows or columns can be extended with an extra parity bit to obtain extended

BCH or Hamming codes Table 4-4 gives the generator polynomials of the Hammingcodes used in block Turbo codes

The main advantage of block Turbo codes is in their application for packet sion, where the interleaver as it is used in convolutional Turbo coding is not necessary.Furthermore, as block codes, block Turbo codes are efficient for high code rates

transmis-To match packet sizes, a product code can be shortened by removing symbols In thetwo-dimensional case, either rows or columns can be removed until the appropriate size

is reached Unlike one-dimensional codes (such as Reed–Solomon codes), parity bits areremoved as part of the shortening process, helping to keep the code rate high

As with convolutional Turbo codes, the decoding of block Turbo codes is done in aniterative way [66] First, all the horizontal blocks are decoded then all the vertical receivedblocks are decoded (or vice versa) The decoding procedure is iterated several times tomaximize the decoder performance The core of the decoding process is the soft-in/soft-out (SISO) constituent code decoder High-performance iterative decoding requires theconstituent code decoders to not only determine a transmitted sequence, but also to yield

a soft decision metric (i.e., LLR) which is a measure of the likelihood or confidence ofeach bit in that sequence Since most algebraic block decoders do not operate with soft

Table 4-5 Performance of block Turbo codes in AWGN channel after three iterations

BTC constituent codes Coded packet size Code rate E b /N0 at

The decoding structure of block Turbo codes is similar to that of Figure 4-39, whereinstead of convolutional decoders, the row and column decoders are applied Note thathere the interleaving is simply a read/write mechanism of rows and columns of the codematrix The performance of block Turbo codes with three iterations for different packetsizes in an AWGN channel is given in Table 4-5

4.4.4 OFDM with Code Division Multiplexing: OFDM-CDM

OFDM-CDM is a multiplexing scheme which can better exploit diversity than tional OFDM systems Each data symbol is spread over several sub-carriers and/or severalOFDM symbols, exploiting additional frequency- and/or time-diversity [36][37] By usingorthogonal spreading codes, self-interference between data symbols can be minimized.Nevertheless, self-interference occurs in fading channels due to a loss of orthogonalitybetween the spreading codes To reduce this degradation, an efficient data detection anddecoding technique is required The principle of OFDM-CDM is shown in Figure 4-42

leaver

leaver IOFDM

deinter-symbol demapper

channel

encoder

leaver

leaver OFDM

inter-symbol mapper

spreader (HT)

detector (IHT)

CSI CSI

Signal Constellation, Mapping, Demapping, and Equalization 167

Figure 4-43 Performance of OFDM-CDM with classical convolutional codes versus OFDM with Turbo codes and interleaver size 256 and 1024, respectively

CDMA and SS-MA can be considered special cases of OFDM-CDM In CDMA, CDM is applied for user separation and in SS-MC-MA different CDM blocks ofspread symbols are assigned to different users

MC-The OFDM-CDM receiver applies single-symbol detection or more complex symbol detection techniques which correspond to single-user or multiuser detection tech-niques, respectively, in the case of MC-CDMA The reader is referred to Section 2.1.5for a description of the different detection techniques

multi-In Figure 4-43, the performance of OFDM-CDM using classical convolutional codes iscompared with the performance of OFDM using Turbo codes The BER versus the SNRfor code rate 1/2 and QPSK symbol mapping is shown Results are given for OFDM-CDM with soft IC after the 1st iteration and for OFDM using Turbo codes with interleaversizesI = 256 and I = 1024 and iterative decoding with 10 iterations As reference, the

performance of OFDM with classical convolutional codes is given It can be observedthat OFDM-CDM with soft IC and classical convolutional codes can outperform OFDMwith Turbo codes

4.5 Signal Constellation, Mapping, Demapping, and Equalization

4.5.1 Signal Constellation and Mapping

The modulation employed in multi-carrier systems is usually based on quadrature tude modulation (QAM) with 2m constellation points, where m is the number of bits

ampli-transmitted per modulated symbol, and M= 2m is the number of constellation points.The general principle of modulation schemes is illustrated in Figure 4-44, which is validfor both uplink and downlink

From encoder

(serial bits) S/P

2m Mapping

Figure 4-44 Signal mapping block diagram

Table 4-6 Bit mapping with 4-QAM

64-Table 4-6 defines the constellation for 4-QAM modulation In this table, b l , l=

0, , m− 1, denotes the modulation bit order after serial to parallel conversion.The complex modulated symbol takes the value I+ jQ from the 2mpoint constellation(see Figure 4-45) In the case of transmission of mixed constellations in the downlinkframe, i.e., adaptive modulation (from 4-QAM up to 64-QAM), a constant RMS should

be guaranteed Unlike the uplink transmission, this would provide the advantage that thedownlink interference from all base stations has a quasi-constant behavior Therefore,the output complex values are formed by multiplying the resulting I+ jQ value by anormalization factor KMOD as shown in Figure 4-44 The normalization KMOD depends

on the modulation as prescribed in Table 4-7

Symbol mapping can also be performed differentially as with D-QPSK applied in theDAB standard [14] Differential modulation avoids the necessity of estimating the carrierphase Instead, the received signal is compared to the phase of the preceding symbol [65].However, since one wrong decision results in 2 decision errors, differential modulationperforms worse than non-differential modulation with accurate knowledge of the channel

in the receiver Differential demodulation can be improved by applying a two-dimensionaldemodulation, where the correlation of the channel in time and frequency direction is takeninto account in the demodulation [27]

Signal Constellation, Mapping, Demapping, and Equalization 169

00 Q

I 10

Figure 4-45 M-QAM signal constellation

Table 4-7 Modulation dependent normalization factorKMOD

4.5.2 Equalization and Demapping

The channel estimation unit in the receiver provides for each sub-carrier n an

esti-mate of the channel transfer function H n = a n e jϕn In mobile communications, eachsub-carrier is attenuated (or amplified) by a Rayleigh or Ricean distributed variable

a n = |H n | and phase distorted by ϕ n Therefore, after FFT operation a correction ofthe amplitude and the phase of each sub-carrier is required This can be done by asimple channel inversion, i.e., multiplying each sub-carrier by 1/H n Since each sub-carrier suffers also from noise, this channel correction for small values of a n leads

to a noise amplification To counteract this effect, the SNR value γ n = 4|H n|2/σ2

n ofeach sub-carrier (where σ2

n is the noise variance at sub-carrier n) should be

consid-ered for soft metric estimation Moreover, in order to provide soft information for thechannel decoder, i.e., Viterbi decoder, the received channel corrected data (after FFT

g0

Reliability estimation Reliability estimation

To decoder

Equalization De-mapping and soft metric derivation

Figure 4-46 Channel equalization and soft metric derivation

and equalization with CSI coefficients) should be optimally converted to soft metricinformation Thus, the channel-corrected data have to be combined with the reliabilityinformation exploitary channel state information for each sub-carrier, so that each encodedbit has an associated soft metric value and a hard decision that are provided to the Viterbidecoder

As shown in Figure 4-46, after channel correction, i.e., equalization, for each mappedbit of the constellation a reliability information is provided This reliability informationcorresponds to the minimum distance from the nearest decision boundary that affects thedecision of the current bit This metric corresponds to LLR values (see Section 2.1.7) [65]after it is multiplied with the corresponding value of the SNR of each sub-carrier γ n=

4|H n|2/σ2

n Finally after quantization (typically 3–4 bits for amplitude and 1 bit for thesign), these soft values are submitted to the channel decoder

4.6 Adaptive Techniques in Multi-Carrier Transmission

As shown in Chapter 1, the radio channel suffers especially from time and frequencyselectivity Co-channel and adjacent channel interference (CCI and ACI) are furtherimpairments that are present in cellular environments due to the high frequency reuse.Each terminal station may have different channel conditions For instance, the terminalstations located near the base station receive the highest power which results in a highcarrier-to-noise and -interference power ratio C/(N + I) However, the terminal station

at the cell border has a lowerC/(N + I).

In order to exploit the channel characteristics and to use the spectrum in an cient way, several adaptive techniques can be applied, namely adaptive FEC, adap-tive modulation, and adaptive power leveling Note that the criteria for these adaptive

effi-Adaptive Techniques in Multi-Carrier Transmission 171

techniques can be based on the measured C/(N + I) or the received average power

per symbol or per sub-carrier These measured data have to be communicated to thetransmitter via a return channel, which may be seen as a disadvantage for any adap-tive techniques

In TDD systems this disadvantage can be reduced, since the channel coefficients aretypically highly correlated between successive uplink and downlink slots and, thus, arealso available at the transmitter Only if significant interference occurs at the receiver,this has to be communicated to the transmitter via a return channel

4.6.1 Nulling of Weak Sub-Carriers

The most straightforward solution for reducing the effect of noise amplification duringequalization is the technique of nulling weak sub-carriers which can be applied in anadaptive way Sub-carriers with the weakest received power are discarded at the trans-mission side However, by using strong channel coding or long spreading codes, the gainobtained by nulling weak sub-carriers is reduced

4.6.2 Adaptive Channel Coding and Modulation

Adaptive coding and modulation in conjunction with multi-carrier transmission can beapplied in several ways The most commonly used method is to adapt channel coding andmodulation during each transmit OFDM frame/burst, assigned to a given terminal sta-tion [16][17][18] The most efficient coding and modulation will be used for the terminalstation having the highest C/(N + I), where the most robust one will be applied for the

terminal station having the worstC/(N + I) (see Figure 4-47) The spectral efficiency in

a cellular environment is doubled using this adaptive technique [20]

An alternative technique that can be used in multi-carrier transmission is to apply themost efficient modulation for sub-carriers with the highest received power, where themost robust modulation is applied for sub-carriers suffering from multipath fading (seeFigure 4-48)

Furthermore, this technique can be applied in combination with power control to reduce

out of band emission, where for sub-carriers located at the channel bandwidth border

low-order modulation with low transmit power and for sub-carriers in the middle of thebandwidth higher order modulation with higher power can be used

BS

TS1

TS2

TS3

OFDM symbols up to 16-QAM mod.

OFDM symbols up to 64-QAM mod.

Figure 4-48 Adaptive channel coding and modulation per sub-carrier

4.6.3 Adaptive Power Control

Beside the adaptation of coding and modulation, the transmit power of each OFDM bol or each sub-carrier can be adjusted to counteract, for instance, the near–far problem

sym-or shadowing A combination of adaptive coding and modulation (the first approach) withpower adjustment per OFDM symbol is usually adopted [17][18]

4.7 RF Issues

A simplified OFDM transmitter front-end is illustrated in Figure 4-49 The transmittercomprises an I/Q generator with a local oscillator with carrier frequency f c, low-passfilters, a mixer, channel pass-band filters, and a power amplifier After power amplificationand filtering, the RF analog signal is submitted to the transmit antenna The receiverfront-end comprises similar components

Especially in cellular environments due to employing low gain antennas, i.e., directive antennas, high-power amplifiers are needed to guarantee a given coverage and

non-Local oscillator

Filter (low pass) D/A

Q

+

D/A

Filter (pass band)

Filter (low pass)

High power amplifier

Filter (pass band)

Figure 4-49 Simplified OFDM transmitter front end

RF Issues 173

hence reduce, for instance, infrastructure costs by installing fewer base stations tunately, high power amplifiers are non-linear devices, where the maximum efficiency isachieved at saturation point

Unfor-Furthermore, at high carrier frequencies (e.g., HIPERLAN/2 at 5 GHz) low cost

RF transmit and receive oscillators can be applied at the expense of higher phasenoise

The main objective of this section is to analyze the performance of multi-carrier andmulti-carrier CDMA transmission with a high number of sub-carriers in the presence oflow cost oscillators with phase noise and HPAs with both AM/AM and AM/PM non-

linear conversions First, a commonly accepted phase noise model is described After

analyzing its effects in multi-carrier transmission with high order modulation, measures

in the digital domain based on common phase error (CPE) correction are discussed The

effects of two classes of non-linear power amplifiers are presented, namely travelingwave tube amplifiers (TWTAs) and solid state power amplifiers (SSPAs) Two techniquesbased on pre-distortion and spreading code selection are discussed Finally, in order toestimate the required transmit RF power for a given coverage area, a link budget analysis

is carried out

4.7.1 Phase Noise

The performance of multi-carrier synchronization tracking loops depends strongly on the

RF oscillator phase noise characteristics Phase noise instabilities can be expressed andmeasured in the time and/or frequency domain

Various phase noise models exist for the analysis of phase noise effects Two oftenused phase noise models which assume instability of the phase only are described inthe following

Lorenzian Power Density Spectrum

The phase noise generated by the oscillators can be modeled by a Wiener–L`evy cess [63], i.e.,

The two-sided 3 dB bandwidth of the Lorenzian power density spectrum is given by β,

also referred to as the line-width of the oscillator

Measurement Based Power Density Spectrum

An approach used within the standardization of DVB-T is the application of the powerdensity spectrum defined by [69]

The parametersa and f1 characterize the phase lock loop (PLL) and the parameterc the

noise floor The steepness of the linear slope is given byb and the frequency f2 indicateswhere the noise floor becomes dominant A plot of the power density spectrum withtypical parameters (a = 6.5, b = 4, c = 10.5, f1= 1 kHz, and f2 = 10 kHz) is shown inFigure 4-50

This phase noise process can be modeled using two white Gaussian noise processes asshown in Figure 4-51 The first noise term is filtered by an analog filter with a transfer

RF Issues 175

function as shown in Figure 4-50, while the second term gives a phase noise floor whichdepends on the tuner technology A digital model of the phase noise process can beobtained by sampling the above analog model at frequencyf samp

Further phase noise models can be found in [2]

4.7.1.2 Effects of Phase Noise in Multi-Carrier Transmission

For reliable demodulation in OFDM systems, orthogonality of the sub-carriers is essential,which is threatened in the receiver by phase noise caused by local oscillator inaccuracies.The local oscillators are applied in receivers for converting the RF signal to a basebandsignal The effects of local oscillator inaccuracies are severe for low-cost mobile receivers.The complex envelope of an OFDM signal is given by

The effects of the common phase error and ICI are shown in Figure 4-52, where themixed time/frequency representation of the total phase error  E of the sub-carriers perOFDM symbol is shown for an OFDM system with an FFT size of 2048 The mixedtime/frequency representation of the total phase error shows in the frequency directionthe phase error over all sub-carriers within one OFDM symbol The time direction isincluded by illustrating this for 30 subsequent OFDM symbols It can be shown that eachOFDM symbol is affected by a common phase error and noise like ICI The autocorrelation

The block diagram of a common phase error correction proposed in [69] is shown inFigure 4-54.