A Practical Space-Code Correlator Receiver for DSP · PDF fileA Practical Space-Code Correlator Receiver for DSP Based Software Radio Implementation in CDMA2000 ... systems based on

Wireless Pers Commun (2009) 49:245–261DOI 10.1007/s11277-008-9570-6

A Practical Space-Code Correlator Receiver for DSPBased Software Radio Implementation in CDMA2000

Kerem Kucuk · Adnan Kavak · Mustafa Karakoc ·Halil Yigit · Caner Ozdemir

Published online: 22 August 2008© Springer Science+Business Media, LLC. 2008

Abstract Development of practical algorithms for beamforming in 3G CDMA systemsand their software radio implementations are still a challenging task, which will facilitateupgrading of traditional base stations into smart antenna capable 3G base stations. In thispaper, we propose a practical space-code correlator (SCC) receiver structure for its soft-ware radio implementation a DSP. SCC’s advantage comes from the fact that it doesn’trequire any training sequence or learning parameter as in other algorithms (LMS or CM).DSP implementations of the SCC are performed using Texas Instruments C67xx familyplatforms. In the simulations, reverse link base band signal format of CDMA2000 is usedand the effects of different array topologies (uniform linear array-ULA or uniform circulararray-UCA) are considered. The implementation results regarding beamforming accuracy,weight vector computation time (execution time), search resolution effect on DOA estima-tion accuracy, DSP resource utilization, and received SINR are presented. The results showthat DSP based SCC beamformer can estimate weight vectors within less than 10 ms withDOA search resolution of 2◦ especially when C6713 DSP is used. With faster DSPs and

K. Kucuk · H. YigitDepartment of Electronic and Computer Education, Kocaeli University, Izmit, Kocaeli 41380, Turkeye-mail: [email protected]

H. Yigite-mail: [email protected]

A. Kavak (B)Wireless Communications and Information Systems Research Centre, Department of ComputerEngineering, Kocaeli University, Izmit, Kocaeli 41040, Turkeye-mail: [email protected]

M. KarakocTurkcell Telecommunication Services, Kartal, Istanbul 34880, Turkeye-mail: [email protected]

C. OzdemirDepartment of Electrical and Electronics Engineering, Mersin University, Mersin 33343, Turkeye-mail: [email protected]

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larger search resolutions, execution time could be significantly reduced as well. It providescomparable SINR performance with LMS and CM algorithms.

Keywords Adaptive arrays · CDMA2000 · Digital signal processor (DSP) · Direction ofarrival (DOA) estimation · Field programmable gate array(FPGA) · Smart antenna systems(SAS) · Software radio · 3G Systems

1 Introduction

Mobile communication systems have been growing exponentially and used widely all aroundthe world. Third-generation (3G) systems based on wideband code division multiple access(WCDMA) technique are aimed to provide worldwide communication capability with widerange of services (voice, data, video, message) at high speed rates. However, there existproblems such as transmission of mixed traffic signals through wireless propagation mediumat high rates, limited system capacity, and low coverage range of base stations. In solvingthese problems, researches have focused on software and cognitive radio, smart antenna andMIMO systems and advanced signal processing techniques. Software defined radio (SDR)is often described as a radio whose functionality can be defined in the software [1]. SDRrequires programmable processors to perform the signal processing necessary to transmitand receive base band information at radio frequencies [2]. This technology offers greaterflexibility and potentially longer product life, since the radio functionalities can be updatedvery cost effectively by software.

A Smart Antenna System (SAS) that employs an antenna array and advanced signal pro-cessing techniques at the base station performs spatial-filtering on signals received frommultiple users at distinct locations, adaptively adjust their beam patterns according to prop-agation dynamics [3]. It has been shown that SAS improves system capacity and spectralefficiency, expands coverage, and provides transfer of high data rate by efficiently utilizingthe given bandwidth [4]. It employs beamforming algorithms to track the desired user andsuppress the possible interference [5]. It requires both the software (beamforming algorithmin base band) and the hardware (RF transceiver, multiple antennas) associated with the addi-tional processing capability. One of the major challenges integrating a SAS into 3G CDMAbase stations is the development of beamforming algorithms that can provide the desiredperformance as well as having small computational load for the SDR implementation [6].

SDR implementation of beamforming algorithms on programmable processors such asdigital signal processors (DSPs), field programmable gate array (FPGAs), or special typeof ASICs facilitates upgrading of existing 3G base stations into smart antenna processingcapable base stations. DSPs can be viewed as special purpose CPUs providing fast instructionsequences, such as shift, add, and multiply, and therefore they are widely used in math-inten-sive signal processing applications [7]. FPGAs with their re-programmable logic gates andfunctions are more hardware oriented devices but preferred for more time critical applicationsrequiring high processing speed when compared to DSPs [8].

1.1 Related Works

The array processing algorithms used in wireless communications are usually designedto operate in a particular air interface. Various wireless standards in 2G and 3G systemswith different access techniques (FDMA, TDMA, CDMA, WCDMA, and CDMA2000)

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A Practical Space-Code Correlator Receiver 247

and modulation schemes motivate the investigation of array processing schemes that canbe software-implement able and adaptable for programmable processors. The code filteringapproach (CFA) proposed by Naguib [9] was the pioneering work in the field of beamformingfor CDMA systems. In CFA, traditional RAKE receiver for CDMA is replaced by a beam-former-RAKE receiver in which beamforming is applied to each finger, and then the outputsof from each finger is summed up. Since then, many research efforts have been put in thedevelopment of beamforming algorithms for CDMA systems with the emergence of 3Gwireless systems [10–17].

In [18], a normalized constant modulus algorithm (NCMA) was implemented using SPAR-TAN II FPGA from Xilinx to demonstrate digital beamforming capability of an FPGA. Asmart antenna base station transceiver (BTS) using TI’s C6711 DSP in beamformer weightvector estimation for CDMA2000 1X was introduced in [19]. The beamformer was installedin each channel card controlled by MPC860 based controller. The optimal weight vectorin C6711 was computed in such a way to maximize the signal to interference plus noiseratio (SINR) by using Lagrange’s formula for generalized eigenvector solution. Wu et al.[20] has implemented a smart antenna testbed utilizing a beamformer module that included4 TI C6701 DSPs on the same board, where a digital beamforming with phase compensationtechnique was used. That testbed was, however, unable to support CDMA signal due to thehigh chip rate required. Im and Choi [21] demonstrated a smart antenna implementationwhere C6701 based beamformer can compute weight vectors of 16 users in Korean CDMAWireless Local Loop (WLL) system by using Lagrange’s formula for generalized eigenvectorsolution. In his very earlier work [22], Choi used the linearized conjugate gradient method(LCGM) to implement beamforming on a general-purpose DSP with a clock frequency of30 MHz.

Introduction of the SUNBEAM project built on the work of the Advanced Communi-cations Technologies and Services (ACTS) program in TSUNAMI II project was given in[23], which proposed algorithms and analyzed the feasibility of implementation of arrayprocessing architectures in multi standard UMTS/GSM base stations. Two different types ofbeamforming algorithm were considered for the software radio implementation, which aresuitable for the uplink of UTRA-FDD: Vector RAKE [24] and MDIR [25]. Vector RAKEreceiver is based on the idea of code filtering approach proposed by Naguib [9]. The opti-mum beamformer weights in Vector RAKE are given by the Wiener solution [26]. MDIRperforms spatial cancellation followed by a RAKE receiver. MDIR was shown to outperformVector-RAKE receiver in both pedestrian and vehicular scenarios due to severe nature of theradio channel. However, when two receivers were compared from the processing require-ments perspective, which were expressed in terms of floating point operations on a DSP,Vector-RAKE was shown to be less complex than MDIR in terms of implementation withTI’s C6203 DSP.

A DSP based implementation of reverse link beamformers for a WCDMA using LeastMean Squares (LMS) and Recursive Least Squares (RLS) algorithms was presented in [27]Implementations were performed using fixed-point arithmetic on TI’s C6211 DSP processorthat has maximum clock rate of 167 MHz. Compared to RLS, LMS resulted in a low con-vergence rate requiring more than one radio frame (dedicated control channel—DPCCH).The performance of the algorithms was not tested for dynamic channel conditions in whichmultipath fading coefficients and DOAs change randomly.

Implementation of a setup module using TI’s C6203 DSP for possible plug-and-play oper-ation of a smart antenna with Node B of UTRA-UMTS system was presented in [28]. In thesetup module, two TI C6203 DSPs were used; one for the implementation of physical layer,and the other for the implementation of MAC, RLC and RRC layers. The weight vectors were

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computed for each user by using the normalized least mean squares (NLMS) algorithm. Thepilot bits in the uplink dedicated physical control channel DPCCH were used as temporalreference for the adaptive algorithm. The execution time of MAC, RLC, RRC layers wereshown to be much less than 5 ms. However, no result regarding the execution time of NLMSalgorithm in physical layer was mentioned. For real time operation most of the implemen-tation effort will be given to estimate beamformer weight vector, which needs to be carriedout in physical layer.

1.2 Contribution of this Work

In this paper, we propose a practical space-code correlator receiver for 3G CDMA and evalu-ate the feasibility of its SDR implementation using DSPs for CDMA2000 reverse link channel[29]. We use TI’s high performance C67x family floating-point DSPs (C6701, C6711, andC6713) for the implementation. Rather than building the whole hardware testbed whichrequires antenna array, RF/IF transceiver, ADC/DAC units, we only focus on the key part ofthe system, which is the software radio beamformer. We evaluate the performance of SDRimplementation of SCC receiver in terms of beamforming accuracy, weight vector computa-tion time (execution time), DSP resource utilization, and received SINR. Our proposed SCCalgorithm can be considered as the practical version of Vector RAKE receiver. Unlike non-blind type LMS [5] and blind type CM [5,30] algorithms, SCC algorithm does not requireany training sequence or learning parameter and its weight vector computation time is notaffected by the array topology and multipath fading conditions [31].

The remainder of the paper is organized as follows. In the following section, base band sig-nal model used in the simulations is given. In Sect. 3, the structure and mathematical model ofSCC beamforming receiver are presented. Section 4 explains the implementation setups andparameters. Section 5 presents results of DSP based software radio implementation includ-ing beamforming accuracy, DSP resource utilization, convergence time, and received SINR.Finally, the concluding remarks and discussions regarding the performance of the proposedSCC are given in Sect. 6.

2 Baseband Signal Model

We consider the reverse link signal format of CDMA2000 [29,32] as shown in Fig. 1. Forthe i th mobile user, bi (l) is the zero mean, independent identically distributed (i.i.d.) bitsequence with unit variance taking values {+1,−1}, which corresponds to traffic channeltransmitted thru the Q channel and hi (l) is the bit sequence composed of all “1”s, whichcorresponds to pilot channel transmitted thru the I channel. The index l denotes the symbolindex. After scaling with gains G1 and G2, the bit sequences in the I and Q channels for thei th user can be written as,

d(I )i (l) = G1 · h(l), (1)

d(Q)i (l) = G2 · b(l), (2)

and their complex representation is given by

di (l) = d Ii (l) + jd Q

i (l). (3)

Let us define c(I )i (l; m) and c(Q)

i (l; m) be the L-chip spreading codes of I and Q-channels,respectively, for the lth symbol of this user, where m is the chip index with m = 0 . . . L − 1.

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A Practical Space-Code Correlator Receiver 249

Walsh matrix with size 64

Interleaving ( 576 Symbol )

CDMA2000 RC1 format signal

Pilot (t) =1

pilot channel

Vocoder 9.6 Kbps

Convolutional Coding

R = 1/3 , K = 9

BPSKmodulation

0 → 1,1→ -1

G2 = 0.7

G1 = 1

b(t) x CIPN

b(t)b(t) x CQ

PN

Σ h(t)

Σh(t) x CI

PN

h(t) x CQPN

Σs(t)

Tc/2

Long PN

CQPN

½ chip delay

cIPN

s(t) = s(t) / ⏐s(t) ⏐

28.8 Kbps

GS

+

+

+

_

Fig. 1 The block diagram for base band signal model transmitted from a mobile in CDMA2000 format

These code sequences are identically distributed, random binary numbers taken from the set{+1,−1} with equal probability of unit variance. And the complex spreading code sequenceci (l; m) is

ci (l; m) = c(I )i (l; m) + jc(Q)

i (l; m). (4)

The corresponding continuous time spreading signal can be written as

ci (l; t) =L−1∑

m=0

ci (l; m) · p (t − [(l − 1)L + m]Tc) (5)

where, p(t) is the waveform for the pulse shaping filter and Tc is the chip period which isthe ratio of symbol period Tw over processing gain Nc(i.e.,Tc = Tw/L). Thus, the base bandform of then transmitted signal from [9] user i is

si (t) =∞∑

l=1

di (l) · ci (l; t) (6)

3 Space-Code Correlator (SCC) Receiver

It consists of two stages: code correlator and space correlator parts, and therefore named asspace-code correlator (SCC) [30] receiver as depicted in Fig. 2. From another perspective inthis structure, each finger of traditional RAKE receiver in CDMA systems is followed byspace correlator stage which utilizes predefined spatial correlation vectors (array responsevectors). In the code correlator stage, multipath components of received signal vector are

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Code Correlator #1

SpaceCorrelator #1M M

Code Correlator #2

SpaceCorrelator #2M M

Code Correlator #F

SpaceCorrelator #FM M

Reverse Link Beamformer

M

∑=

=F

ppF 1

ˆ1ˆ θθ

1θ

2θ

FθM

WU

Reverse Link search table

))

2τ

1τ

Fτ

Wireless Propagation Channel

a( 1)a( 2)a( 3)

a(θK)

X1

X2

XM

Z1

Z2

ZF

Mθ

θθ

Fig. 2 Space-code correlator receiver architecture for the implementation on a DSP

despread by the code of the desired user to obtain post correlation signal vector (Z’s inFig. 2). In the space correlator stage, post-correlation signal vector is correlated with thepredefined array response vectors that are stored in “Reverse Link Search Table” to yielddirection of arrival (DOA) estimations (θ ’s in Fig. 2).

Let f and F denote the multipath index and number of multipath, respectively, fromthe desired mobile to the base station as depicted in Fig. 2. The multipath channel inducescomplex path attenuation, αi, f = βi, f e jφi, f and time delay τi, f on the transmitted signalgiven by (6). Hence, signal received by M element antenna array at the base station can beexpressed as,

X (t) =NI∑

i=1

Fi∑

f =1

αi, f · si(t − τi, f

) · a(θi, f

) + n (t) , (7)

where NI is the number of interference users, Fi is the multipath number for the i th user,θi, f is the direction-of-arrival (DOA) of f th multipath signal of the i th user, and n(t) isAdditive White Gaussian Noise vector. Without loss of generality let i = 1 denote index forthe desired user. The desired, interference, and noise components are separated as following

X (t) =F1∑

f =1

α1, f · s1(t − τ1, f

) · a(θ1, f

)

︸ ︷︷ ︸desired

+NI∑

i=2

Fi∑

f =1

αi, f · si(t − τi, f

) · a(θi, f

)

︸ ︷︷ ︸interference

+n (t) , (8)

The received signal vector X(t) is first passed through code correlator blocks in which it isdespread with its code cI . For simplicity, we drop the term l in c1(l; t) and the post-correlationoutput vector of the pth finger can be written as,

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A Practical Space-Code Correlator Receiver 251

Zp (t) = 1

Tw

lTw+τ1,p∫

(l−1)Tw+τ1,p

X (t) · c∗1

(t − τ1,p

)dt (9)

Zp(l) =L−1∑

m=0

c∗1(l; m).

1

Tw

·(m+1)Tc+τ1,p∫

mTc+τ1,p

X(t) · p(t− [(l−1)L + m] · Tc−τ1,p)dt (10)

Assuming that the base band signal is sampled at Tc instants and the pulse shapingwaveform is chosen as rectangular function with unit amplitude, and then the above equationis simplified to

Zp(l) = 1

L

L−1∑

m=0

c∗1(l; m) · X(mTc + τ1,p) p = 1, 2, . . . , F. (11)

It is assumed that each multipath time delay (τl,p) required for despreading process iscomputed by a time delay search algorithm, which is beyond the scope of this work.

After code correlation process, spatial correlation between Zp(l) and array response vec-tors a(θ) is carried out. For this purpose, the range [0◦ 180◦] is divided into K DOAs, whichhave angular resolution of �θ◦ = 180◦/(K −1). Hence, we need to store K complex-valuedarray response vectors a(θ) with dimension Mx1 in the reverse link search table. The outputof space correlator for each multipath tries to find the maximum DOA as given by

θp = arg max∣∣∣a (θ)H · Zp (l)

∣∣∣2. (12)

Then, the mean DOA angle obtained as the average of F paths is given by [33]

θ = 1

F

F∑

p=1

θp. (13)

This estimated DOA can be used for both uplink and downlink beamforming provided thatthe user position changes very slowly during several symbol periods. The complexity andaccuracy of the SCC receiver depends on number of multipath (F), SNR level of multipath,number of antenna elements (M), and angular resolution of the DOA range to be scanned(�θ◦). For small multipath level and small DOA resolution, the algorithm is expected to findthe desired user’s DOA much accurately.

4 DSP Implementation

4.1 Implementation Setup

The hardware setup for the implementation of SCC receiver consists of a PC and DSP boards.In order to test the performances of various processor configurations, we prefer using threedifferent DSPs (Texas Instruments’ C6701, C6711 and C6713). The C6701 EVM supportsfour clock rates up to 133 MHz and includes a peripheral component interconnect (PCI) inter-face that enables host access to the onboard joint test action group (JTAG) controller, DSPhost port interface (HPI), and board control/status registers [34]. The C6711 DSK has clockrate of 150 MHz and communicates a PC via parallel port interface [35]. The C6713 DSP

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operates at 225 MHz and delivers up to 1350 MFLOPS/1800 MIPS with dual floating/fixed-point multipliers up to 450 million multiply accumulate operations per second (MMACS).The 6713 DSK uses universal serial bus (USB) communications for connections to the host[36].

Having higher operating frequency for a processor does not necessarily mean that thisprocessor provides better performance. Internal memory architecture of a processor, widthof data paths, and configurations of external memory also affect the performance of a proces-sor [37]. The algorithms are directly coded in assembly language using TI’s Code ComposerStudio (CCS) version 3.01 [38] and loaded into the DSP memory. Each DSP board’s perfor-mance is tested separately, i.e., one of the DSP boards is connected to the host PC at a time.We utilized Matlab’s tool for CCS utility [39] for the loading of received signal vector definedin (7) into the DSP memory and post processing of the obtained weight vector. The executiontime of the algorithm for computing weight vector is obtained by counting the clock cyclesfor the execution of the desired part of our assembly code (given in Table 1 below), which isachieved by setting break points in relevant part of the code via CCS. The assembly code iscompiled using CCS and its optimization is made by running the code in single steps.

4.2 Coding of the Algorithm in Assembly

The SCC algorithm whose architecture was given previously in Fig. 2 is based on performingcode correlation with the desired user’s code as given by (11) and then spatial correlation ofthis post-correlation signal with the predetermined array response vectors as given by (12).

Table 1 IEEE 32-Bit floating point assembly code of SCC algorithm implemented on TI C67x DSPs

SCC_Main_EntryMVK .S2 48,B13 Subroutine_4

|| MVK .S2 12,B0 ADDSP .L2X A10,B11,B11SCC_Code_Corr_Entry || SUB .S2 B2,1,B2

MVK .S2 5,B1 [B2]B .S2 Subroutine_4Subroutine_1 STW .D1 A11,*A15++[1]

|| STW .D2 B11,*B15++[1]|| MVK .S2 64,B2

Subroutine_2 SUB .S2 B1,1,B1LDW .D1 *A4++[5],A8 [B1]B .S2 Subroutine_3

|| ADDK .S2 20,B5|| SUB .S2 B2,1,B2

[B2]B .S2 Subroutine_2 Subroutine_5

|| STW .D2 B10,*B6++[1] || MPYSP .M2 B8,B8,B9SUB .S1 A4,A0,A4 ADDSP .L1X A9,B9,A10

|| SUB .S2 B1,1,B1 [B2]B .S2 Subroutine_5[B1]B .S2 Subroutine_1

|| SUB .L2 B0,1,B0|| ADD .S2X B4,A1,B4 [B0]B .S2 SCC_Space_Corr_Entry

SUB .S2 B0,1,B0[B0]B .S2 SCC_Code_Corr_Entry || MVK .S2 90,B1

SCC_Peak_Find_EntryMVK .S2 90,B0 LDW .D2X *B7++[1],A14

SCC_Space_Corr_Entry CMPLTSP.S2X A14,B14,B0MVK .S2 12,B1

Subroutine_3 [B0]B .S2 Subroutine_6MV .S A14,B14

|| MVK .S2 5,B2 Subroutine_6NOP hasn’t shown in this code [B1]B .S2 SCC_Peak_Find_Entry

SCC_Main_Entry

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A Practical Space-Code Correlator Receiver 253

Table 1 describes SCC algorithm’s implementation, which is directly coded in assembly usingCCS Tool for TI’s C67x DSPs. The code considers parallel operation of some instructions,and it is tried to be made as optimized as possible. It has four main blocks which are desiredpart of the assembly code for profiling code performance:

SCC_Main_Entry: This routine performs some initialization steps such as determinationof path number, size of the data to be processed, and controlling of other routines.SCC_Code_Corr_Entry: This routine performs code correlation of the received signal vec-tor with the desired user’s code as given by (11). Mathematically, it implements matrixmultiplication to yield post-correlation signal vector by using two subroutines.SCC_Space_Corr_Entry: It computes the inner product of post-correlation signal vectorwith predetermined spatial correlation vectors as given by (12), which is stored as KxMmatrix. Hence, the output of this routine is K complex-valued numbers stored in datamemory to be sent out to next routine. This routine includes three subroutines.SCC_Peak_Find_Entry: By taking the output of previous routine as input, this routinesearches the index (which is desired user’s DOA) for the maximum value of squaredamplitudes of K numbers.

4.3 Input Signal Parameters

The receiving antenna topology is a five-element antenna array with either uniform lineararray (ULA) or uniform circular array (UCA) configuration. In the signal modeling, wirelesschannel that has one direct path and one multipath component of the desired signal, andan interference signal is considered. Relevant parameters in each simulation run are set asfollows: For desired signal component, direction-of-arrival (DOA) of the direct path (θ1,1) isfixed at 32◦, while DOA of the multipath (θ1,2) and the interference signal (θ2,1) is generatedrandomly in the form of uniform distribution. The amplitude (β) and phase (φ) componentsof multipath fading parameters are represented by Rayleigh and uniform random variables,respectively. Multipath signal power is set to −5 dB, −10 dB, and −15 dB levels for testingthe performance of the algorithm. Interference signal is −10 dB below the direct path ofdesired signal.

5 Results

5.1 Beamforming Accuracy

First we check the DSP implemented beamformers’ ability to track the desired user’s direc-tion-of-arrival (DOA). Figure 3 shows a representative beamforming spectrum plotted usinga weight vectors that is obtained as a result of DSP implementation of the SCC beamformeron C6713 DSK. Here, we note that we performed repeated simulations on C6713 DSK andother DSPs (C6711 and C6701). We observed very similar beamforming plots all showingpeak in the direction of desired users’ direct path as long as direct path is spatially wellseparated from the multipath and interference. As seen from the Fig. 3, SCC beamformerimplemented on a DSP successfully pinpoints the desired user direction at 32◦.

5.2 Weight Vector Computation (Execution) Time

Weight vector computation time which is referred as DSP execution time of the algorithmis the time elapsed until a stopping criterion is attained while computing the beamforming

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Fig. 3 A representative beamforming spectrum of the SCC beamformer implemented on C6713 DSP

Table 2 Execution time of SCC algorithm for ULA configuration with 5-elements

DOA resolution (�θ ) C6701 C6711 C6713

0.5◦ 2◦ 0.5◦ 2◦ 0.5◦ 2◦

SCC_Code_Corr_Entry 1737303 434304 2261011 565404 2613504 653364SCC_Space_Corr_Entry 3976001 993330 5010804 1252710 5727960 1431990SCC_Peak_Find_Entry 6472 1620 12220 3060 9023 2250Total cycle 5740317 1430002 7286045 1823262 8359327 2090147Total time (ms) 43.16 10.75 48.57 12.15 37.15 9.28

weight vector using the code in Table 1. Execution time is determined using CCS profil-er menu by counting the clock cycles. The profiler analyzes program execution and showswhere the program spends its time. Several factors such as CPU clock speed, speed andwidth of external memory can affect execution time. In order to assess the performance ofthe SCC algorithm under varying multipath channel conditions with random DOA and fad-ing coefficient, code is run repeatedly many times. The results regarding clock cycles andcorresponding execution times for each DSP type with DOA resolutions of 0.5◦ and 2◦ aresummarized in Table 2. To determine the clock cycles for each entry, we used toggle break-points in our assembly code. Most of the clock cycle is wasted in the space correlator partwhile the least of the clock cycle is wasted in the peak finder part. Smallest execution timecan be achieved by using C6713 DSP. For �θ = 2◦, it is below 10 ms. However, largestexecution time is obtained by using C6711 DSP. The reason for this is the different hardwarearchitectures and speeds of DSPs.

5.3 Effect of DOA Search Resolution

SCC algorithm’s execution time is not affected by the change in multipath DOA (θi, f ) fadinglevel (αi, f ) and antenna array topology. However, the sensitivity of its execution time depends

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A Practical Space-Code Correlator Receiver 255

Fig. 4 DOA estimation errors of SCC algorithm implementation for �θ = 0.5◦ and ULA topology

on number of multipath (F) and DOA search resolution (�θ ). In order to demonstrate thesensitivity of SCC algorithm, simulations were run using DOA resolutions of �θ = 0.5◦ and�θ = 2◦. Using DOA search resolution of �θ = 2◦ reduces execution time approximatelyby four times when compared to using DOA search resolution of �θ = 0.5◦ as seen fromTable 2. Here, we note that smaller execution time could be obtained with larger DOA res-olutions at the expense of performance degradation in DOA estimation and SINR. For thisreason, we also examine the effect of DOA search resolution on the DOA estimation errorunder varying multipath SNR level and array topology.

Histograms for the DOA estimation errors obtained from 1000 repeated simulation runsare given in Figs. 4 and 5 for �θ = 0.5◦ and �θ = 2◦, respectively. In each simulation run,multipath DOAs and fading coefficients change randomly. Obviously, SCC algorithm per-forms poor for low SNR case. As observed from top subplots of Figs. 4 and 5, DOA estimationerror is noticeably spread over large angle range (implying large standard deviation) for lowSNR case, i.e., SNR = 5 dB . For both �θ = 0.5◦ and �θ = 2◦ cases, it varies between 0◦and 11◦. However, DOA estimation error is squeezed into low angle range (implying smallstandard deviation) for high SNR case, i.e., SNR = 15 dB. In this case, it is limited between 0◦and 6◦ for both DOA resolutions. Mean values of DOA estimation errors which are derivedfrom the histograms are given in Table 3 for both ULA and UCA topology. DOA resolutionof �θ = 0.5◦ leads to slightly smaller DOA estimation errors as compared to �θ = 2◦case for all multipath conditions. For example, while mean value of DOA estimation error is1.44◦ for �θ = 0.5◦, it is found as 2.21◦ for �θ = 2◦ when SNR is 15 dB and ULA is used.Another remarkable observation about DOA estimations errors is that SCC performs betterwhen UCA topology is employed as evident from Table 3. Execution time for �θ = 0.5◦is significantly larger than 10 ms duplexing internal of CDMA2000 system (see Table 2),hence, it is feasible to consider �θ = 2◦ for SCC implementations.

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Fig. 5 DOA estimation errors of SCC algorithm implementation for �θ = 2◦ for ULA topology

Table 3 Mean and standard deviation of DOA estimation errors with different DOA search resolutions(�θ = 0.5◦ and �θ = 2◦) in SCC beamformer implementation using TI C6713 DSP

SNR

5 dB 10 dB 15 dB

UCA ULA UCA ULA UCA ULA

�θ 2◦ Mean 3.57 3.83 2.25 2.74 1.41 2.21Std. 1.97 2.55 1.36 1.97 0.91 1.57

0.5◦ Mean 3.36 3.38 2.25 2.26 1.38 1.44Std. 1.86 2.03 1.23 1.42 0.75 1.02

5.4 DSP Resource Utilization

We evaluate the implementation of the SCC algorithm in terms of DSP resource utilization,i.e., memory occupancy and compare it with other algorithms such as LMS and CM. Resultsare summarized in Table 4. When implementations are examined from the program memoryusage perspective, in which the assembly code is placed, we see that C6701 EVM requiresthe smallest memory space. On the other hand, when examined from floating point instruc-tion execution and data memory occupancy perspectives, C6713 DSK requires the smallestmemory space.

5.5 Signal-to-Interference Plus Noise Ratio (SINR)

One of the measures that need to be studied for evaluation DSP based beamformer ishow much SINR can be obtained via the computed weight vectors. SINR values for each

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Table 4 DSP resource utilization for the implementation of various beamforming algorithms

DSP device MIPS MFLOPS Using internal programmemory

Using externaldata memory

LMS CM SCC

C6701 EVM 1064 at 133 MHz 798 at 133 MHz 0.88% 0.93% 0.98% 61%C6711 DSK 1200 at 150 MHz 900 at 150 MHz 14% 14.8% 15.6% 57.4%C6713 DSK 1800 at 225 MHz 1350 at 225 MHz 14% 14.8% 15.6% 15%

Table 5 Mean and standard deviation for SINR performance of the software radio beamformers

LMS CM SCC

UCA ULA UCA ULA UCA ULA

Received SINR (dB) Mean DSP C6701 16.86 12.46 16.02 15.94 15.58 14.11C6711 16.83 12.59 16.02 16.47 13.96 13.93C6713 16.86 13.85 16.02 16.24 13.96 13.96

Std. DSP C6701 0.019 5.59 0.028 1.16 6.26 6.80C6711 0.019 5.28 0.028 2.30 6.87 6.95C6713 0.019 5.01 0.028 2.13 6.55 5.98

computed weight vector are calculated as described in [40] and cumulative distributions ofthe SINR values are obtained. Mean and standard deviation derived from these cumulativedistributions are given in Table 5. For the comparison, we also provide results of our previousstudy [30] obtained for non-blind type LMS and blind-type CM beamformers. Although CMbeamformer’s SINR performance is slightly better than LMS and SCC, all three beamformersprovide nearly the same SINR values. SINR values for SCC are obtained under multipathSNR level 5 dB (which is our worst case). One important observation on the results is thatstandard deviation in SINR values obtained by SCC is larger when compared to LMS and CMfor both ULA and UCA topologies. This is because we have evaluated SCC’s performanceunder strong multipath condition (which is −5 dB below direct path). When multipath anddirect path DOAs do not fall apart, SCC is unable to precisely estimate both DOAs and thatprovides a weight vector different than the actual weight vector. However, we expect betterresults with SCC when weak multipath signal is considered. When compared from the DSPprocessor perspective, SINR performance of the beamformers is almost unaffected by theirimplementation on different processors considered here.

6 Conclusions

In this paper, we have proposed a practical space-code correlator (SCC) beamformer receiverfor CDMA2000 system and demonstrated its software radio implementation using TI’s C67xfamily floating point DSPs (C6701, C6711, C6713). At the base station receiver, a five-ele-ment ULA and UCA antenna configurations are considered. A simple wireless channel modelthat has one direct and one multipath component for the desired signal and an interferencesignal is used in generating the received signal vector at the antenna array. The input signalgeneration, initialization of the DSP memory, loading of the implemented codes onto theDSP memory, and finally post processing of processed data from the DSP are all handled viaMatlab link for CCS.

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The performance evaluation of the implemented beamformer is accomplished in terms ofvarious measures: Accuracy of beamforming spectrum, execution time (weight vector com-putation time), DSP resource utilization, and receive SINR. Also, the effect of DOA searchresolution on its performance is examined. Our assembly code for SCC is run repeatedlymany times to assess the performance under dynamic multipath channel conditions in whichDOA and fading coefficient of the multipath changes randomly from one simulation run toanother. Based on the results obtained from implementation of our SCC algorithm on TI’sC6701, C6711, and C6713 DSP platforms, following conclusions can be made:

• In general, SCC software radio beamformer can approximately point in the desired userdirection with a certain DOA error. Multipath SNR level and DOA search resolutionaffect the beamforming accuracy of SCC algorithm.

• Only C6713 results in the smallest execution time less than 10 ms (for �θ = 2◦), duplex-ing time in CDMA2000. However, implementations on C6701 and C6711 can computeweight vectors within the time period slightly greater than 10 ms. With faster DSPs andother programmable devices (such as FPGA) and optimization of the assembly codes, itis possible to reduce this execution time.

• SINR performance of the SCC is expected to improve better when desired signal compo-nent becomes stronger than the multipath signals. The advantage of our proposed SCCalgorithm is that unlike LMS and CM algorithms it does not require any learning param-eter or training sequence. Also, its convergence (weight vector computation) time is notaffected by the array topology and multipath fading coefficients.

Acknowledgements This work was supported by following: (1) DSP equipment and software donationfrom Texas Instrument’s European University Program, (2) Kocaeli University Scientific Research ProjectsUnit under grant 2005/58. The author’s would like to thank anonymous reviewers who helped to improve thequality of this paper by their careful reading.

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Author Biographies

Kerem Kucuk was born in Izmit, Turkey, in 1979. He received the B.Sc.and M.Sc. degrees from the Electronics and Computer Education De-partment, Kocaeli University, Kocaeli, Turkey, in 2002 and 2005, respec-tively. He is currently pursuing his Ph.D. degree at Kocaeli University.Currently, he is a research assistant with the Department of Electronicsand Computer Education, and a member of Wireless Communicationsand Information Systems (WINS) Research Center, University of Ko-caeli, Turkey. Kucuk was awarded “Excellence in Signal ProcessingAward” by Texas Instruments, in May 2006. His current research inter-ests include wireless communication, wireless sensor networks, MIMOsystems, software radio, smart antenna implementation, digital signalprocessor. His URL is http://wins.kocaeli.edu.tr/kkucuk/.

Adnan Kavak was born in Usak, Turkey, in 1970. He received theB.S. degree from the Electrical and Electronics Engineering Department,Middle East Technical University, Ankara, Turkey, in 1992. He receivedthe MS and PhD. degrees from the Electrical and Computer Engineer-ing Department, The University of Texas at Austin, TX, USA, in 1996and 2000, respectively. He was a satellite control engineer with TurksatSatellite Control Center, Ankara, Turkey, from December 1992 to May1994. He worked as a Senior Research Engineer at Wireless SystemsLaboratory, Samsung Telecommunications America in Richardson, TX,USA, from January 2000 to July 2001. He then joined Kocaeli University,Turkey, in August 2001 and worked as an Assistant Professor there untilMay 2005. Currently, he is the director of Wireless Communications andInformation Systems (WINS) Research Center, and an Associate Pro-fessor with the Computer Engineering Department, Kocaeli University,Turkey. His current research interests include 3G and beyond wireless

networks, software and cognitive radios, smart antenna systems, resource allocation in wireless networks, andwireless sensor networks.

Mustafa Karakoc was born on July 13, 1978, in Kutahya, Turkey.He received the B.S. and M.S. in Electronics and Computer Educa-tion Department from the Kocaeli University, Kocaeli, Turkey, in 2002and 2004 respectively. He is currently pursuing his Ph.D. degree atKocaeli University. Also, he was a research assistant with the Depart-ment of Electronics and Computer Education, University of Kocaeli,Turkey. He is currently working as a radio network engineer at TurkcellTelecommunication Services, Istanbul, Turkey. His research interests in-clude smart antenna implementation for CDMA cellular networks, digi-tal signal processors, array signal processing, and resource allocation inwireless networks.

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A Practical Space-Code Correlator Receiver 261

Halil Yigit was born on August 15, 1977, in Kocaeli, Turkey. He receivedthe B.S. and M.S. in Electronics and Computer Education Departmentfrom the Kocaeli University, Kocaeli, Turkey, in 2002 and 2005, respec-tively. He is currently pursuing his Ph.D. degree at Kocaeli University.Also, he has been working as a research assistant at the Department ofElectronics and Computer Education, Kocaeli University, Turkey since2003. He is interested in smart antennas, MIMO systems, array signalprocessing, and channel prediction.

Caner Ozdemir was born in Edremit, Turkey on March 29, 1971. Hereceived the B.S.E.E. degree in 1992 from the Middle East TechnicalUniversity (METU), Ankara, Turkey, and the M.S.E. and Ph.D. degreesin Electrical & Computer Engineering from the University of Texas atAustin in 1995 and 1998, respectively. From 1992 to 1993, he workedas a project engineer at the Electronic Warfare Programs Directorateof ASELSAN Electronic Industries Inc., Ankara, Turkey. From 1998 to2000, he worked as a research scientist at Electronic & Avionics Systems(ASTG) group of Allied Signal Inc., Columbia, Maryland. He joinedthe faculty of Mersin University in 2000 and is currently an AssociateProfessor in the department of Electrical & Electronics Engineering,Mersin, Turkey. Dr. Ozdemir is a recipient of URSI EMT-S Young Sci-entist Award in the 2004 International Symposium on ElectromagneticTheory in Pisa, Italy. He is the co-author of the encyclopedia titled “TheWiley Encyclopedia of RF and Microwave Engineering”. Dr. Ozdemir’sresearch interests are radar imaging and radar signal processing, time-frequency transforms and antenna design.

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