T-BLAST for wireless communications - IEEE Xplore

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 52, NO. 3, MAY 2003

T-BLAST for Wireless Communications: First Experimental Results Mathini Sellathurai, Member, IEEE, and Simon Haykin, Fellow, IEEE

Abstract—In earlier papers [1]–[5], we described a novel multitransmit, multireceive (MTMR) antenna system for wireless communications. This new system, turbo Bell-Labs layered space–time (T-BLAST) architecture, combines the benefits of layered space–time coding concepts and turbo principles in the multitransmit, multireceive antenna system design. In particular, the random layered space–time codes designed by using a set of block convolutional codes and random space–time interleavers and the space–time turbo-like decoding operation allow T-BLAST to realize the benefits of MTMR systems in a computationally feasible manner. The goal of this paper is to present experimental results of T-BLAST based on real-life data collected using the Bell-Labs experimental multiple antenna system with eight transmit and five and six receive antennas. The experimental results show the practical virtues of T-BLAST. Index Terms—Bell-Labs layered space-time (BLAST) architecture, interference cancellation, space–time codes, turbo processing.

I. INTRODUCTION

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NE of the major concerns of wireless communications research is to provide techniques that use a scarce resource, namely, the available transmission spectrum, efficiently. A groundbreaking technique for wireless research, offering tremendous potential to increase systems capacity and spectrum efficiency, is the multitransmit, multireceive (MTMR) antenna schemes popularized as Bell-Labs layered space-time (BLAST) architecture. The first version of BLAST, originated by Foschini [6], used a novel diagonal layered space–time architecture, hence the terminology D-BLAST. However, from a practical perspective, D-BLAST is inefficient for short packet transmissions due to its boundary space–time wastage. The challenge is to design an MTMR wireless communications system that is capable of achieving a high spectral efficiency and yet maintains a manageable system complexity. Along the series of BLAST innovations, vertical BLAST (V-BLAST), described in [7], was the first practical system demonstrated in real time. In V-BLAST, every antenna transmits its own independent substream of data using a simple vector- and linear-decoding structure. By using this Manuscript received May 23, 2002; revised August 28, 2002, November 22, 2002, and January 21, 2003. The authors are grateful to the Natural Sciences and Engineering Research Council of Canada and Dr. R. A. Valenzuela, Department of Wireless Communications Research, Lucent Technologies, Bell Laboratories, Holmdel, NJ, USA, for financial support. M. Sellathurai was with McMaster University, Hamilton, ON L8S 4K1, Canada. She is currently with the Communications Research Center (CRC) of Canada, Ottawa, ON K2H 8S2, Canada. S. Haykin is with McMaster University, Hamilton, ON L8S 4K1, Canada (e-mail: [email protected]). Digital Object Identifier 10.1109/TVT.2003.810986

architecture, it has been demonstrated that up to 50% of the channel capacity can be achieved even with no channel coding. However, V-BLAST suffers from two limitations. • It demands more receive than transmit antennas. The ability to work with fewer receivers than transmitters is necessary in most cellular communications systems since the base station is typically designed with more antennas than mobile hand-communication devices. • No built-in space–time codes are used to overcome deep fades from any of the transmit antennas. The next major innovation in this series is turbo BLAST, or T-BLAST for short, which uses a relatively simple layered space–time encoder and an iterative joint detector–decoder that is turbo-like in operation. Due to the structure of the space–time encoder and the turbo-like operation of the receiver, T-BLAST offers the following combination of features: • a spectral efficiency attained in the course of two to four iterations of the receiver, which is significantly superior to that attainable with V-BLAST; • a built-in capability of accommodating the multiple-antenna configuration, including the case of fewer receive than transmit antennas, which is achieved with manageable computational complexity. These novel features of T-BLAST are confirmed by the real-life experiments presented here. The data set was acquired with the cooperation of the Department of Wireless Communications Research, Lucent Technologies, Bell Laboratories at Holmdel, New Jersey, USA, and was collected using the indoor narrow-band BLAST test bed located on the second floor of the Crawford-Hills Laboratory. Basic to the operation of T-BLAST is the idea of iterative processing, which has also found applications in channel equalization and multiuser detection [10]–[13]. The rest of the paper describes the experimental setup and the real-life experimental results. For a detailed exposition of T-BLAST architecture, readers are referred to references [1]–[5].

II. EVALUATION TESTS This section discusses the performance of T-BLAST using the Bell-Labs test bed, described in [14], with eight transmit and five and six receive antennas. It begins by first describing the indoor narrow-band test bed and receiver digital signal-processing (DSP) operations. Next, the bit- and frame-error performances of T-BLAST configuration are presented.

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SELLATHURAI AND HAYKIN: T-BLAST FOR WIRELESS COMMUNICATIONS

Fig. 1. Bit-error performance of T- BLAST n = 8 and n = 6 with convolutional code of rate R = 1=2 constraint length 3 and of 4-QAM modulated SNR = 9 dB.

A. Indoor Test Bed The antenna arrays consist of wire dipoles mounted in various (horizontal- and vertical-polarization) arrangements, with about half-wave length separation between adjacent elements. The system operates at a carrier frequency of 1.95 GHz with 30-kHz signal bandwidth. Detailed descriptions of the indoor BLAST test-bed hardware and measurement approach are given in [7] and [14]. The measurements were taken in the Bell-Labs building in Crawford Hill, NJ, in a laboratory of 12 24 , located on the second floor of the building. For all measurements, the transmitter was located in the southwest corner of the lab and was facing north. The receiver was located in the northeast corner of the lab and was facing south. The measurements were taken while people were walking around the area and 100 continuously measured channel conditions were considered in this paper. At each channel condition, a packet of 132 4-QAM modulated symbols per antenna is transmitted at a rate of 25 k-symbols/s. Among those 132 symbols, the first 32 are dedicated to synchronization and training. 1) Synchronization: The first 16 symbols are used for frame and symbol-timing recovery. 2) Training sequence: The next 16 symbols (symbols 17 to 32) are used for matrix channel-response estimation. For each substream, mutually orthogonal and equal powertraining sequences are generated by using 16-dimensional Hadamard sequences. 3) Information symbols: The last 100 symbols are used for information transmission. In the transmit end, each substream of 100 information bits is independently encoded , constraint using a convolutional code with rate length 3, and then interleaved using space–time interleavers. The space interleavers are designed according to the D-BLAST architecture but with no edge wastage [5]. Eight independent time interleavers are chosen randomly

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Fig. 2. Number of errors versus packets in T-BLAST n = 8 and n = 6 with convolutional code of rate R = 1=2 and constraint length 3 and 4-QAM modulation SNR = 9 dB.

and no attempt is made to optimize their design. The dimensions of the time interleavers are 200 b. B. Receiver At the receiving end, the signal-detection process involves the following sequence of operations: 1) Frame initialization: The receiver waits until it finds a sufficiently strong signal to indicate the start of data transmission. 2) Symbol synchronization: The sampled received signal is cross-correlated with a predefined synchronization sequence and the condition that results in highest cross correlation is used to establish symbol synchronization. The received signal is oversampled with four samples per symbol period. Binary Barker sequences are used for synchronization due to their good autocorrelation properties. 3) Hardware-induced intersymbol interference (ISI) mitigation: The spectrum shaped with an analog low-pass filter is usually distorted by the radio-frequency front-end of the transmitter during the transmission process. The ISI caused by the spectrum shaping and its distortions is mitigated by a precalculated fixed-coefficient FIR filter. 4) Channel estimation: The matrix channel response is estimated by using a mutually orthogonal 16-dimensional Hadamard sequence transmitted between the parallel antennas for training purpose. 5) Information recovery: The iterative detection and decoding receiver described in Section II is used to recover the transmitted signals. In this scheme, we separate the receiver into two stages: soft interference-cancellation detector and a set of parallel single-input–single-output (SISO) channel decoders. Extrinsic information learned from one stage is applied to the other stage iteratively until the receiver converges. These two stages of pro-

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Fig. 3.


Signal–space diagram at the receiver output in T-BLAST for packet 1

n = 8 and n = 6 with convolutional code of rate R = 1=2 and constraint length 3 and 4-QAM modulation SNR = 9 dB.

cessing are separated by the corresponding space–time interleavers and de-interleavers. C. Experiments With (8,6)-BLAST This section discusses the performance of T-BLAST using the Bell-Labs test bed with eight transmit and six receive antennas. In particular, we describe the bit- and frame-error rate performance obtained using real-life received signals. The estimated signal-to-noise ratio (SNR) of the real-life system is roughly 9 dB. Bit Error Rate (BER): Fig. 1 displays the BER performance of T-BLAST at each iteration of the receiver. Here the instantaneous (broken trace) and average (solid trace) BER are demonstrated at each iteration. Subplots 1 and 2, respectively, show the BER performance before decoding the detected coded signals (200 b/packet) and the decoded information signals (98 information bits + 2 flushed b/packet) at the first iteration. As expected, the second scheme performs better due to the coding gain. However, the performance at the first iteration is poor since each substream (transmit antenna) sees all the other (seven) parallel substreams as interference. The subsequent subplots 3–6 show the bit-error performance at iterations 2–5. These figures clearly illustrate a significant performance improvement in the course of a few iterations. The first three iterations are sufficient to achieve a significant performance gain. Evidently, the performance gain due to the subsequent iterations (4 and 5) is minimal. Frame Error: In Fig. 2, we show the corresponding bit-error traces in each packet and the packets in error. As in Fig. 1, subplot 1 shows the receiver bit-error trace (out of 200 b/packet) at each packet with no channel encoding. The subsequent subplots 2–6 show the bit-error traces (out of 98 information b/packet) at iterations 1–5. The iterative action of the receiver significantly reduces the packet error rate.

MSE at the receiver output of T-BLAST for packet 1 n = 8 and n = 6 with convolutional code of rate R = 1=2 and constraint length 3 and 4-QAM modulation SNR = 9 dB. Fig. 4.

With no channel encoding, 100–150 bits were detected incorrectly in each packet; indeed a 100% packet-error rate was occasionally observed. With channel encoding, the number of errors in each packet is reduced to below 15 and the packet-error rate is reduced by over 25%. In fact, the packet-error rate is further reduced to 17% at iteration 2. Thereafter, only about 4 packets are corrupted and among the corrupted packets, only 1–2 bits are in error per packet. Even though the frame-error performance has converged at iteration 3, the appearance and disappearance of errors are observed between packets, from iteration 3 to 6. However, a packet-error rate of 4–5% is maintained. For example, at iteration 3, packets 7, 35, 50, and 75 are in error whereas at iteration 5, packets 15, 35, 50, and 75 are in error. Signal–Space Diagram: Another measure of convergence of the iterative receiver is the mean square error (MSE) between the detected signals and the transmitted constellation points in the signal–space diagram. Here we consider two examples. First, we show a packet of data that has converged to zero bit error in three iterations. The second example shows the appearance and disappearance of errors from one iteration to the next. Example 1: Perfect Convergence: In this example, we illustrate a perfect convergence behavior. Fig. 3 displays the softdecoded signals in the signal–space diagram for packet 1. Here the and axes, respectively, represent the real and imaginary parts of the 4-QAM signal. Subplot 1 shows the positions of the 200 8 coded bits, whereas subplots 2–9 show the positions of the 98 8 decoded bits at iterations 1–8 of the T-BLAST receiver. The corresponding MSEs of the detected soft bits are shown in Fig. 4. A bit error occurs if the MSE exceeds 1. MSE between 0 and 1 means that a bit is classified appropriately but has a residual error that can propagate through the soft interference-cancellation receiver. Subplot 1 shows the MSE before decoding. Almost all the bits have residual error and about 30% are detected incorrectly. The figure illustrates how the test error


Fig. 5.

Signal–space diagram at the receiver output for packet 2 in T-BLAST

n = 8 and n = 6 with convolutional code of rate R = 1=2 and constraint length 3 and 4-QAM modulation SNR = 9 dB.

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MSE at the receiver output of T-BLAST for packet 2 n = 8 and n = 6 with convolutional code of rate R = 1=2 and constraint length 3 and 4-QAM modulation SNR = 9 dB. Fig. 6.

decreases as new iterations are added to the receiver. In particular, all bits have been detected correctly in three iterations. Example 2: Appearance and Disappearance of Errors: In this example, we illustrate the appearance and disappearance of errors from one iteration to the next. Fig. 5 displays the soft decoded signals in the signal–space diagram for packet 2. The corresponding MSEs of the detected soft bits are shown in Fig. 6. From the figures, we observe the following: • the residual errors do not converge within eight iterations; • all the bit errors are corrected at iteration 3. However, a single bit error appears in the fourth, sixth, and eighth iterations and disappears during the fifth and seventh iterations, which demonstrates the appearance and disappearance of errors in the course of convergence. In both examples, little benefit results from increasing the number of iterations beyond three; thus, it is reasonable to accept this number as the practical number of iterations in terms of both error reduction and receiver complexity. D. Experiments With (8,5)-BLAST The above tests were repeated with eight transmit and five receive antennas. Even though the performance decreased from the previous experiment, good convergence behavior is still observed. Figs. 7 and 8 display the soft-decoded signals in the signal–space diagram and the corresponding MSEs of the detected soft bits, respectively. In contrast to the previous experiment, five iterations were needed to achieve a perfect convergence. Moreover, this point is also borne out by the bit-error rate performance, Fig. 9, over 100 packets versus the number of itand 6 plotted using solid traces. Note erations for both that the broken traces show the corresponding performances assuming no channel coding.

Fig. 7. Signal–space diagram at the receiver output for packet 1 in T-BLAST n = 8 and n = 5 with convolutional code of rate R = 1=2 and constraint length 3 and 4-QAM modulation SNR = 9 dB.

E. Simulated Results: BER versus SNR This section compares the performance of 4-QAM modulated T-BLAST and horizontal-coded V-BLAST using indoor real-channel measurements. We refer to horizontal-coded V-BLAST when each of the substreams is provided with an amount of channel coding equal to that used in T-BLAST. Note that V-BLAST does not use any space–time or iterative decoding. We synthesize the received signal using the measured channel characteristics and evaluate the performance of T-BLAST over a wide range of SNRs using various BLAST

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Fig. 8. MSE at the receiver output of T-BLAST for packet 1 n = 8 and n = 5 with convolutional code of rate R = 1=2 and constraint length 3 and of 4-QAM modulation SNR = 9 dB.

combinations. In all the experiments presented here, it is assumed that the exact channel matrix is known. The antenna and , 6, 7, and 8. configurations considered are Fig. 10 displays the BER performance of T-BLAST (solid trace) and coded V-BLAST (broken trace) for measured real-life channels (using thin curves) and simulated i.i.d complex Gaussian matrix channels (using thick curves). The antenna configurations of 8 transmit and 5–8 receive antennas and the T-BLAST gives us the best performance within the first 10 iterations. The figure reveals a major limitation of the V-BLAST system: the inability to work with fewer receive antennas than transmit antennas. In terms of T-BLAST performance, the following observations can be made from Fig. 10. The bit-error performance of T-BLAST improves with increasing number of receivers (due to the added diversity), with T-BLAST outperforming V-BLAST in all four cases. Moreover, the performances of indoor measured channels are very close to that of i.i.d. Gaussian matrix channels. In indoor rich-scattering environments, it is typical to get well-conditioned channels [7] and [8]. III. DISCUSSION Previously, the BLAST test bed has been used to demonstrate uncoded V-BLAST architecture for 8 transmit and 12 receive antennas [7]. However, a major limitation of V-BLAST is its inability to work with fewer receive antennas than transmit antennas. The ability to work with fewer receivers than transmitters is necessary in most cellular systems since the base station is typically designed with more antennas than mobile transceivers. In contrast, T-BLAST accommodates any multiple-antenna configuration, including the case of fewer receive antennas than transmit antennas with manageable computational complexity. The experimental results using real-life indoor wireless communication data with eight transmit and

Fig. 9. BER performances for n = 8 and n = 6, 5 with convolutional code of rate R = 1=2 and constraint length 3, and 4-QAM modulation SNR = 9 dB.

Fig. 10. Bit-error performance for n = 8 and n = 5, 6, 7 and 8 using convolutional code with rate R = 1=2 and constraint length 3 and 4-QAM modulation using measured indoor channels (thin curves) and i.i.d. Guassian channels (thick curves).

5 and 6 receive antennas presented here confirm the practical virtues of T-BLAST. ACKNOWLEDGMENT The experiments were carried out while the first author of this paper was at Bell Labs, Lucent Technologies, Crawford Hill, NJ as a visiting researcher in 2000. The cooperation and help provided by Dr. D. Samardzija, Lucent Technologies, in the experiments are gratefully acknowledged. REFERENCES [1] M. Sellathurai and S. Haykin, “Turbo-BLAST for high speed wireless communications,” in Proc. Wireless Commun. Network Conf., vol. 1, Chicago, IL, Sept. 2000, pp. 315–320.


[2] [3]

[4] [5] [6] [7]

[8] [9] [10] [11] [12] [13] [14]

, “A nonlinear iterative beamforming technique for wireless communications,” in 33rd ASILOMAR Conf. Signals, Systems, and Computers, vol. 2, Pacific Grove, CA, Oct. 1999, pp. 957–961. , “Turbo-BLAST: A novel technique for multi-transmit multi-receive wireless communications,” in Multiaccess, Mobility, and Teletraffic for Wireless Communications: Kluwer, 2000, vol. 5, pp. 13–24. , “A simplified diagonal BLAST architecture with iterative parallelinterference cancellation,” in Proc. IEEE Int. Conf. Commun. (ICC), vol. 10, Helsinki, Finland, June 2001, pp. 3067–3071. , “Turbo-BLAST for wireless communications: Theory and experiments,” IEEE Trans. Signal Proc., vol. 50, pp. 2538–2546, Oct. 2002. J. G. Foschini, “Layered space-time architecture for wireless communication in a fading environment when using multi element antennas,” Bell Labs Tech. J., 1996. G. D. Golden, J. G. Foschini, R. A. Valenzuela, and P. W. Wolniansky, “Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture,” Electron. Lett., vol. 35, pp. 14–15, Jan. 1999. D. Gesbert, H. Bolcskei, D. Gore, and A. Paulraj, “MIMO wireless channels: Capacity and performance prediction,” Proc. IEEE Globecom’02, vol. 1, pp. 1083–1087, Nov. 2000. C. Berrou, A. Glavieux, and P. Thitmajshima, “Near Shannon limit error-correcting coding and decoding: Turbo codes,” in Proc. Int. Conf. Commun., Geneva, Switzerland, May 1993, pp. 1064–1070. J. Hagenauer, “The turbo principle: Tutorial introduction and state of the art,” in Int. Symp. Turbo Codes, Best, France, Sept. 1997. X. Wang and V. Poor, “Iterative (Turbo) soft interference cancellation and decoding for coded CDMA,” IEEE Trans. Commun., vol. 47, pp. 1046–1061, July 1999. K. M. Chugg, A. Anaastasopoulos, and X. Chen, Iterative Detection: Adaptivity, Complexity Reduction and Application: Kluwer, 2001. M. Tuchler, R. Koetter, and A. C. Singer, “Turbo equalization: Principles and new results,” IEEE Trans. Commun., vol. 50, pp. 754–767, May 2002. H. Zheng and D. Samardzija, “Performance evaluation of indoor wireless video system using BLAST test-bed,” in The 35th Annu. Conf. Inform. Sciences Syst., Baltimore, MD, Mar. 2001.

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Mathini Sellathurai (S’95–M’01) received the Ph.D. degree in electrical engineering from McMaster University, Hamilton, ON, Canada, in 2001 and the Technical Licentiate degree in electrical engineering from the Royal Institute of Technology, Stockholm, Sweden, in 1997. She is currently with Communications Research Centre of Canada, Ottawa, ON, Canada, as a Senior Research Scientist. Her research interests include the applications of adaptive signal processing to space–time wireless communications, satellite communications, and broadband multimedia systems. Dr. Sellathurai was awarded the doctoral price in engineering and computer sciences from the Natural Sciences and Engineering Research Council of Canada for her Ph.D. dissertation.

Simon Haykin (F’86) received the B.Sc. degree (First Class Honors) in 1953, the Ph.D. degree in 1956, and the D.Sc. degree in 1967, all in electrical engineering from the University of Birmingham, Birmingham, U.K. His research interests include signal processing, neural networks and adaptive filters and their applications in radar and communication systems, adaptive hearing systems, and computational neuroanatomy. In 1980, he was elected Fellow of the Royal Society of Canada. He was awarded the McNaughton Gold Medal, IEEE (Region 7), in 1986 and was the recipient of the Booker Gold Medal from URSI. He is the founding Director of the Communications Research Laboratory at McMaster University, Hamilton, ON, Canada. In 1996, he was awarded the title "University Professor," the first faculty member in the Faculty of Engineering at McMaster University to be awarded this prestigious title.