Signal Processing Techniques for EDGE Wireless ...

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EUROCON 2005

Serbia & Montenegro, Belgrade, November 22-24, 2005

Signal

Processing Techniques for EDGE Wireless Modem Marko Kocic, Lidwine Martinot, and Zoran Zvonar Partitioning and importance of certain signal processing functions also changed with introduction of EDGE. While in the past emphasis was on equalization and decoding functions, performance enhancing techniques important for EDGE are placed in other data receiver modules such as RF imperfections compensation and channel estimation. Moreover, hardware and software partitioning of modem realization has also changed. Computationally intensive units, such as equalization and decoding could be mapped to hardware units, while performance-enhancing signal processing tasks are left to be highly programmable in software.

Abstract - Signal processing techniques play key role in the design of EDGE wireless modem. Performance enhancing techniques are reaching beyond traditional equalization and decoding techniques, providing final performance tuning capability to the system design. We address some of the key signal processing techniques for EDGE wireless modem, including I/Q imbalance compensation, DC offset correction and channel estimation

Keywords - EDGE, equalization, signal processing, wireless communications. I. INTRODUCTION

The role of signal processing techniques in the development of wireless modems is ever increasing with the sophistication of the modem design and the trend of communication standards to support higher data rates. Looking into history of the most successful wireless standard, GSM/GPRS, data receiver design has been critical part of the system development. Data receiver provides performance differentiation for a wireless handset through the type approval and operator acceptance process, while on the other side it determines implementation complexity and defines power consumption. In the case of evolutionary standards, such as EDGE, this is even more true: complex modulation format, different modulation/coding scenarios and possibility of link adaptation emphasize the importance of data receiver design. One can argue that EDGE receiver development follows the phases that have been established in the past: a) in the first phase of GSM development major emphasis was on the modem performance and different signal processing techniques aiming to possibly lower modem complexity, b) in the second phase, associated with volume production of wireless handsets, mature signal processing solutions were mapped onto cost-effective platforms ranging from fully programmable DSP platforms to combination of DSP and hardware accelerators, to fully ASIC solutions, and c) finally, proliferation of the integrated solutions with large computational capability on one side, and increased system requirements on the other side led to the variety of performance enhancing approaches [ 1 ]. Authors are with System Group of RF and Wireless Systems Business Unit of Analog Devices Inc., 804 Woburn Street, Wilmington, MA 01887, (marlo :o ,

1-4244-0049-X/05/$20.00 (C2005 IEEE 131

II. EDGE MODEM STRUCTURE Following the radio frequency front-end, a typical signal processing section of EDGE modem, depicted in Fig. 1, consists of a receive digital filter, digital preprocessing, synchronization and channel impulse response (CIR) estimation, followed by a prefilter, equalizer and channel decoder.

Fig. 1 . Block diagram of EDGE modem.

Digital RX filter provides the final channel selectivity and adjacent channel protection of the modem. Digital preprocessing may contain different signal processing functions depending of the overall handset terminal architecture. Typical functions include RF imperfections compensation, such as I/Q gain/phase imbalance compensation and/or DC offset correction. Advanced receiver design may also rely on adaptive filtering techniques for interference suppression. Synchronization and CIR estimation blocks acquire the initial synchronization, find the position of the training sequence in the received data buffer, and compute a CIR estimate. Low complexity, suboptimal equalizers predominantly used for EDGE require minimum phase CIR profiles for reliable performance (energy must be concentrated in the first few CIR taps). The prefilter modifies the CIR ensuring that the effective combined CIR is minimum phase and ensuring best possible equalizer performance.

If E[I(n)Q(n)] is approximately zero (in case of small IQ phase imbalance) then E I(n)Q(n) 0and

Suboptimal techniques, such as Delayed Decision Feedback Sequence Estimation (DDFSE) and Reduced State Sequence Estimation (RSSE) are typically used in EDGE [2].

n

where IB(n) and QB(n) are summed over the whole received burst (or longer):

III. I/Q PHASE/GAIN COMPENSATION

A wireless receiver RF front-end introduces a number of non-ideal effects, such as non-linearities, IQ amplitude and phase mismatch, phase noise, DC offset and others, which in general degrade the receiver performance. Some of the effects, for example, phase I/Q imbalance strongly affect the Block Error Rate (BLER) in 8PSK EGPRS channels [3]. Fig.2. gives an illustrative example of the deterioration of BLER in 8PSK EGPRS channels with increasing IQ phase imbalance. Receiver performance in terms of (BLER) is shown for the Adjacent Interference Channel (ACI) test case in MCS7 TU50 channel at 1800MHz [4] with IQ phase imbalance of 0, 3, and 6 degrees. The loss due to IQ imbalance of 6 degrees (realistic IQ phase imbalance in receivers) at the reference BLER of 10% is nearly 5dB, clearly motivating the effort to find suitable compensation in the digital domain. In RF Direct Conversion Receivers (DCR), the bandpass signal is directly down-converted to the baseband by multiplication with two sinusoidal signals, at carrier frequency, offset by 90 degrees and of equal amplitude. However, in a realistic RF receiver, the offset between the two down-conversion signals will not be exactly 90 degrees. The difference between the actual phase difference and 90 degrees is referred to as IQ phase imbalance. If the receiver pass-band signal is denoted as =

+

_z

n

Y(t)

(5)

y i., (n) 2+ O., (n) 2 yI(n)2 Q(n)2,

I(t) cos(WLOt + t') -Q(t) sin(WLot + qi)I

IB(n)B(n)

E n

=

)[I(n)2+ Q(n)2] +

sn(

2

n

sin( 0) 2

I(n)Q( n)

I

L[I( )2 +

(6)

Q(n )2]

n

Thus IQ phase imbalance at time k for small imbalances can be estimated as 6(k)

=

I IB (n)QB (n)

2 arcsin k -1

E LIn=

IB, (n) 2+ QB, (n)

n=1

2 2

n=l

(7)

IB (n)QB (n)

IB, (n) 2+ QB, (n)2

The above derivation was based on the assumption of orthogonality of I and Q signals, which is a characteristic of the 8PSK modulation, and small IQ phase imbalance. In practice the small IQ imbalance assumption is not limiting as realistic IQ phase imbalances are smaller than 20 degrees. Also, as in practice I and Q orthogonality improves with the length of the sequence, we can expect that better IQ imbalance estimates can be obtained by averaging over many bursts. The algorithm adds new data with every received burst. It is assumed that IQ phase imbalance is constant within processing interval. This assumption is valid in practice for a given frequency band (90OMHz or 1800MHz). At the same time amplitude compensation is not necessary for the typical amplitude imbalance observed in RF front-end designs.

(1)

MCS7 TU5O 1800MHo ACI

1lo

the unbalanced I and Q baseband signals are given by the following expressions: I

IB (t) = I(t) cos( V) + Q(t) sin( V) QB (t) = -I(t) sin(f) + Q(t) cos(q) .

(2)

_l 10

mz

where phase imbalance is given by Wy +0/2, (p=-0/2.

For IQ phase imbalance of 0, unbalanced ideal signals after down-conversion, and analog to digital conversion, are given by

IB(n) =I(n)cosQ92)+Q(n)sin( 2) QB(n) = I(n) sinf/ 2) + Q(n) cosQ/ 2).

1 02

L~

0

I

Next, let branches.

us

consider the

sum

10 01

15

20

Fig.2: 8PSK EDGE Receiver performance in MCS7 TU50 1800MHz ACI channel, with and without IQ phase imbalance correction.

(3)

of magnitudes of I and Q

Therefore, if in burst i the received signal is denoted by

li(n)+jQi(n), IQ phase imbalance estimate for burst i is

Is (n) + Q.,(n) = (4) + + 2 [COS2 (0/2) sin2(012)][I(n)2 Q(n)2 ]+ sin(fO)I(n)Q(n) 2

5

given by

2

i Ej

i

N-1

B,k(n)B,k(n)

k=° E, IBk (n)2 + QB,k (n)2 n=O

132

(8)

where N is the number of symbols in the burst. The algorithm was implemented as a part of an 8PSK EGPRS receiver and tested in a number of simulation scenarios. Here, we present its performance in the simulation scenario presented in Fig.2, ACI MCS7 TU50 at 1800MHz as one of the most critical cases. Performance of the receiver with IQ phase imbalance correction in the presence of a static IQ phase imbalance of 6 degrees is shown (full line with circles) in comparison with performance without IQ imbalance correction and IQ phase imbalance of 0, 3, and 6 degrees, respectively. The phase imbalance of 6 corresponds to worst case specifications for a realistic DCR. IQ imbalance correction is extremely effective bringing the performance close to the performance with no IQ imbalance while adding a very low computational burden and regains close to 5dB compared to the case with no compensation.

Residual DCO is a function of noise, channel impulse response and the training sequence. Thus, the introduction of the averaging step as the initial step makes DCO estimation independent of the magnitude of the initial DCO, implying that joint LS CIR DCO estimation should operate identically regardless of the magnitude of the static DCO. The received signal in presence of static DCO can be written as:

r1

L-1

(12)

Zhitj-i +m+z1 i=O

where m is static DCO. In matrix form, we can write the above equation as r-Th+z, where T is equal to

(13)

T

IV. DC OFFSET COMPENSATION In DCR DC Offset (DCO) estimation and compensation is essential for satisfactory EDGE receiver performance. While for GMSK modulation simple averaging of the received burst is sufficient for DCO estimation and compensation, the error introduced by this procedure is unfortunately too large for satisfactory performance with EDGE 8PSK [5]. In EDGE 8PSK modulated MCS it is necessary to resort to joint Least Squares (LS) Channel Impulse Response (CIR) DCO estimation. The first step is the removal of the mean of the digitized received signal. In presence of large DCO the original mean must be removed for the initial CIR estimation steps, such as synchronization, and CIR span estimation. While averaging removes the static DCO, there is still residual DCO that is a function of the data and channel response. If the averaging is confined to the training sequence, then the data portion of this introduced DCO is known. The drawback of limiting averaging to the training sequence only is that the noise DC term will be larger than if the averaging is conducted over the whole burst. Consider a received signal corresponding to the training sequence:

h=[ho hl .... hL_l m]T, r[rL-l rL rL+l ... r25], andZ=[ZL-1 ZL ZL+l ... Z25]'. The joint LS CIR DCO estimate is given by

(14)

h =(THT)- THr

After estimation, DCO estimate is subtracted from the received signal. MC85 HTOO Reference Sensitivity

0 -10

10I Lj 10 a]

0~~

1

L-1

rn =m+hit i=O

(9)

+Z

0

M-1

M-1

1 L-1

1

M-1

m= +=Yyj =m +-,>hi ,,tj-i + M XZj

m =-

L-1

=

n-

h

=

Yhi(tn-ii

1

(10)

M-1

1

M-1

ytj-i) + Zn -

0

Y Zj

Eb/NO

15

20

25

Unfortunately, GSM training sequences were not created specifically for joint LS CIR DCO estimation and consequently joint LS CIR DCO estimation leads to noticeable worsening of CIR estimation error variance with respect to LS CIR estimation (best case estimate when DCO is not present). Regarding performance, training sequences can be divided into two groups [6]. The first group consists of training sequences 0 and 1, which exhibit small deterioration in performance with respect to LS CIR estimation, while the second group, consisting of all the remaining training sequences (2 to 7) causes significant

After subtracting the DCO estimate from rn we get rn

10

Fig.3: 8PSK EDGE receiver performance in MCS5 HT 100 Reference Sensitivity channel with and without DC Offset estimation, and different Training Sequences.

where rn is the received signal, m static DCO, hi channel taps, ti training sequence and zn AWG noise. By averaging the signal over the training sequence we get: 1

5

133

deterioration in performance due to increase in CIR estimation error variance. Fig.3. shows performance of an EDGE equalizer in MCS5 HT100 channel with LS CIR estimation, with joint CIR DCO estimation and training sequence TSC=0 and joint LS CIR DCO estimation and TSC=5. The actual DCO is zero for all three simulations. At BLER of 10% introduction of joint LS CIR DCO estimation (TSC=0) leads to deterioration of performance by 1dB, and by another dB if TSC=5. However, in the presence of large static DCO (e.g. -3dB with respect to the desired signal), joint LS CIR DCO estimation maintains the same performance as without DCO, while performance of simpler algorithms, such as received signal averaging (full line with circles) is obviously inadequate. The remaining gap of 1 dB can be further removed by applying improved joint LS CIR estimation that can be introduced via pre-specified frequency offsets in hardware [6] or iterative LS CIR estimation with deliberately mismatched signal model (last column of matrix T in (13)) [7]. V. CHANNEL IMPULSE RESPONSE ESTIMATION

A CIR estimate is required for the computation of optimal prefilter and equalizer taps and is essential for reliable operation of the EDGE receiver. EDGE 8PSK MCS use the same training sequences as the GMSK modulated MCS. The properties of GSM training sequences can be exploited to simplify CIR estimation. Given that the autocorrelation of EDGE training sequences is equal to zero for delays between -5 and +5, crosscorrelation can be used to reliably estimate up to 6 CIR taps [8]. The main drawbacks of cross-correlation CIR estimation are its inability to secure accurate CIR estimates in long channels (HT with seven CIR taps) and relatively high CIR estimation error. MCS9 TU50 Reference Sensitivity 100

w -J

m

10 10

15

20

Eb/NO

25

30

Fig.4: 8PSK EDGE receiver performance in MCS9 TU50 RS channel with different CIR lengths. Least Squares CIR estimation overcomes both of these problems. In comparison with cross-correlation CIR estimation LS CIR estimation has the advantage of accurate CIR tap estimates for 7 and more CIR taps, unlike

134

the cross-correlation CIR estimates, and generally lower CIR estimation error, providing up to 1.6dB reduction in CIR estimation error variance in short channels (static, TU,

RA).

Regardless of the CIR estimation algorithm used, it is necessary to establish several related parameters, such as the number of CIR taps, and the relation of the CIR with respect to the position of the largest magnitude CIR tap (i.e. whether there are any significant taps before the largest magnitude tap). As GSM/EDGE operates in channel profiles of very different lengths (3 to 7-8 taps) the selection of L also has profound influence on the performance of the receiver. In short channels (static, RA, TU) inclusion of extra (zero magnitude) taps leads to deterioration in performance. Thus, it is advantageous to attempt to detect and remove these zero magnitude taps from the CIR estimate. The simplest way of doing this is to eliminate all those taps whose estimates have magnitude lower than the expected variance of the CIR tap estimation [9]. As shown in Fig.4., at reference sensitivity (BLER of 30%) suboptimal choice of L=7 taps (needed for HT channel) almost doubles the CIR estimation error with respect to L=4 in short channels and consequently deteriorates the performance by 2dB, while the inclusion of adaptive channel memory truncation brings us back to nearly optimal performance (with L=4). VI. CONCLUSIONS

The paper introduces important signal processing techniques for EDGE. Numerical examples have illustrated performance gains in typical operating scenarios, indicating the importance of signal processing techniques in wireless modem design. REFERENCES [1] D.Grant, M.Kocic, L.Martinot and Z.Zvonar, "EDGE data Receiver Design," in Proc. of the 2003 IEEE International Symposium on Circuits and Systems, Bangkok, Thailand, May 2003. [2] W.H. Gerstacker, R. Schober, "Equalization concepts for EDGE," Wireless Communications, IEEE Transactions on, Volume: 1, Jan. 2002, Page(s): 190 -199. [3] D.Molkdar, W.Featherstone and S.Lambotharan, "An overview of EGPRS: the packet data component of EDGE", Electronics and Communication Engineering Journal, February 2002, pp 21-38. [4] 3GPP TS 05.05, 3rd Generation Partnership Project; Technical Specification Group GSM/EDGE Radio Access Network; Radio transmission and reception (Release 1999) [5] B. Lindoff, "Using a direct conversion receiver in EDGE terminalsa new DC offset compensation algorithm," Personal, Indoor and Mobile Radio Communications, 2000. PIMRC 2000. The 11th IEEE International Symposium on, Volume: 2, 2000, Page(s): 959 -963 vol.2. [6] D. Hui, B. Lindoff, K. Zangi, "Enhanced dc estimation via sequence-specific frequency offset," Vehicular Technology Conference, 2002. Proceedings. VTC 2002-Fall. 2002 IEEE 56th, Volume: 1, 2002, Page(s): 161 -16. [7] M.Kocic, L.Martinot and Z.Zvonar, Method for joint DC offset correction and channel coefficient estimation in a receiver, patent

pending. [8] E. Yakhnich, "Channel estimation for EGPRS modems," Vehicular Technology Conference, 2001. VTC 2001 Spring. IEEE VTS 53rd, Page(s): 419 -422 vol.1. [9] C. Luschi, et al. "Advanced Signal-Processing Algorithms for Energy-Efficient Wireless Communications," Proceedings of the IEEE, Vol. 88, No. 10, October 2000.