- Research Article
- Open Access
Implementation of a Smart Antenna Base Station for Mobile WiMAX Based on OFDMA
© Seungheon Hyeon et al 2009
- Received: 1 August 2008
- Accepted: 12 February 2009
- Published: 4 March 2009
We present an implementation of a mobile-WiMAX (m-WiMAX) base station (BS) that supports smart antenna (SA) functionality. To implement the m-WiMAX SA BS, we must address a number of key issues in baseband signal processing related to symbol-timing acquisition, the beamforming scheme, and accurate calibration. We propose appropriate solutions and implement an m-WiMAX SA BS accordingly. Experimental tests were performed to verify the validity of the solutions. Results showed a 3.5-time (5.5 dB) link-budget enhancement on the uplink compared to a single antenna system. In addition, the experimental results were consistent with the results of the computer simulation.
- Medium Access Control
- Orthogonal Frequency Division Multiple Access
- Antenna Element
- Automatic Gain Control
- Smart Antenna
Modern mobile communication requires not only a high data rate transmission but also a relatively fast mobility. The mobile WiMAX (m-WiMAX) based on orthogonal frequency division multiple access (OFDMA) is believed to be a solution that addresses both of these requirements . Moreover, the application of smart antenna (SA) technologies to OFDMA is regarded as a key solution for increasing the data rates and the mobility of fourth generation (4G) wireless communication systems operating in frequency-selective fading environments. However, there are several things to consider in baseband signal processing when implementing SA systems in OFDMA. These include the performance of symbol-timing acquisition, the beamforming scheme, and accurate calibration.
The SA system enlarges cell coverage through beamforming. However, to obtain effectively enlarged cell coverage, performance of the initial acquisition and symbol synchronization should also be enhanced. Since initial acquisition is performed prior to calculating the weight vector, an algorithm to enlarge the acquisition coverage is required. Moreover, in the contention-based ranging used in m-WiMAX, since classification of the ranging signal by the user is impossible prior to decoding, it is difficult to properly apply a weight to the desired ranging signal.
Various beamforming algorithms for OFDMA communications have been investigated [2, 3]. However, most of the research focuses on beamforming per subcarrier using the conventional single-carrier beamforming algorithm. This approach causes high computational loads and increases system complexity.
The calibration technique is essential for the SA system to apply a proper beamforming weight to the transmission. Without an accurate calibration technique, the advantages of SA technology cannot be provided in the downlink . More specifically, even if the optimal weight vector is computed from the received signal, downlink (DL) beamforming can never be optimized without accurate calibration. The primary reason is that the beamforming parameter for the DL is, in most cases, heavily dependent upon the parameter values computed during the uplink (UL). Thus, the overall communication quality of the SA base-station (BS) system cannot be improved without a proper calibration technique.
In this paper, we propose solutions for these problems and implement an m-WiMAX SA BS accordingly. In Section 2, we propose our solutions, and Section 3 shows the implementation of the m-WiMAX SA BS. Each signal-processing module is described in detail in this section. The performance of the m-WiMAX SA BS is presented in comparison to the conventional single-antenna BS in Section 4, and computer-simulation results are shown to verify our experimental results. Finally, we conclude this paper in Section 5.
This section addresses some essential problems that must be considered when implementing the m-WiMAX SA BS. These include the performance of symbol-timing acquisition, an optimized beamforming scheme, and accurate calibration. For SA BS to provide effective coverage, the coverage of the symbol-timing acquisition must be enhanced. The optimized beamforming scheme is essential to implement an SA BS. Finally, to provide proper downlink and uplink beamformings, a pragmatic procedure for automatic calibration is required for the SA BS. In the following subsections, we propose solutions to these problems.
2.1. Ranging Processing
The problem of ranging arises because the propagation delays between the SA BS and each of the mobile stations (MSs) in a given cell is different, so the arrival time of the signal associated with each of the subscribers cannot be the same. Beamforming gain can be obtained in the SA BS only when symbol time synchronization is performed properly. Thus, proper symbol time synchronization is a prerequisite if the SA BS is to enhance communication capacity and cell coverage.
Time synchronization, which is used to compensate for differences in propagation delays, is referred to as "ranging" in the mobile-WiMAX system. Each subscriber randomly selects a ranging code, allocates that code to the ranging channel, and transmits it in the form of a ranging symbol. The BS then checks whether or not the ranging code has been transmitted in a given uplink frame at each frame time throughout the code detection procedure. When the BS detects the ranging code transmitted by a subscriber, it finds the ranging code index and estimates the propagation delay associated with that MS.
where denotes . The mean and variance of the detection variable increase linearly in accordance with the number of antenna elements, as shown in (5). This means that the SNR of the ranging code detector increases in proportion to , where the SNR of the ranging channel receiver is defined as .
On the contrary, if the signal of each antenna is descrambled with a code that is different from the one transmitted by the target subscriber, approaches zero due to the correlation characteristics of the ranging codes.
2.2. Beamforming Scheme
The conventional beamforming algorithms for OFDMA use samples in time to estimate the statistical characteristic of the spatial channel [2, 3]. This approach avoids the effect of frequency selective fading. However, it is difficult to obtain enough samples to estimate the statistical characteristic of a spatial channel in an m-WiMAX waveform which is a packet-based communication. Note that the spatial-channel basis is independent of both time and frequency in narrowband communications. Therefore, we can obtain enough samples to estimate the spatial-channel basis in both the time and frequency domains.
The problem of calibration occurs because the phase characteristics of the radio frequency (RF)/intermediate frequency (IF) chains associated with each antenna are different in both the receiving (RX) and transmitting (TX) modes. Several calibration techniques for the SA system have been proposed [8–11]. Of these techniques, we chose to use  because it offers simple and accurate calibration. Although the experimental data in  was obtained using the CDMA2000 1x standard, it is noteworthy that this technique can be applied to the OFDMA standard. Another advantage is that this technique can be applied while the SA system is operating.
The chosen calibration technique requires the installation of an additional antenna which is used to TX or RX a test signal to or from each antenna element for RX and TX calibrations. This additional antenna transmits the test signal through an RX carrier frequency and receives the test signal through a TX carrier frequency. The calibration is performed separately, since the RX and TX modes exist separately in the frame format of mobile WiMAX. By using a test signal orthogonal to the RX/TX signal, the influence on the SA BS can be minimized when the calibration operation is performed.
The additional calibration antenna generates and transmits a test signal.
Each RX path in the SA system receives the signal simultaneously.
The calibration processor calculates a calibration value for each RX path in the SA system.
An exact numerical analysis of the procedure is given in . The phase delay of the wireless path between each antenna and the additional antenna can be calculated by making a connection between each antenna path and the additional antenna path with a cable. The phase difference between each antenna RX path is obtained by correlating the received signal from each antenna path with the test signal.
The calibration processor generates N (the number of antenna elements) orthogonal test signals for each TX path of the SA system.
Each path transmits the signals.
The additional calibration antenna receives the signals.
The calibration processor calculates the calibration value for each TX path of the SA system.
As shown in , the phase difference between each antenna and the reference antenna is almost eliminated using the calibration. As a result, a proper beam pattern can be obtained.
The array antenna was implemented using five patch-type elements. The element spacing was a half-wavelength (6.52 cm). Four elements were used for transmitting and receiving signals, and the other element was used for calibration.
The signal processing modules presented in this section were integrated into the m-WiMAX SA BS. A photograph of the entire SA BS is provided with a description of the experimental environment in the next section.
In this section, experimental results obtained from the implemented m-WiMAX SA BS are presented, including the symbol-timing estimation probability for the ranging process, the accuracy of the phase-delay compensation for the calibration, and throughput. In addition, various computer simulations supported the validity of our experimental results.
In this paper, we addressed three key issues in implementing the m-WiMAX SA BS: ranging, beamforming, and calibration.
First, the proposed ranging process significantly reduced calculation loads using IFFT instead of a correlation operation. Moreover, the proposed process achieved diversity gain in the received signals from each antenna path.
Second, the proposed beamforming scheme addressed the lack of samples in OFDM-based packet communications. The proposed scheme used time and frequency samples for obtaining the statistical properties of the spatial channel.
Finally, the calibration method, which can be applied while the SA system is operating, was proposed. Although additional antenna chains are required, the proposed method provided fast and accurate performance.
The experimental results and computer simulations verified the validity of these solutions. As shown in Section 4, the proposed solutions can be applied to the m-WiMAX SA BS. In addition, the m-WiMAX SA BS increased the link-budget by 5.5 dB.
It should be noted that the experiments described in this paper represent lab tests only, which might be quite different from the outdoor environments in which m-WiMAX is used. As shown in Figure 10, the MS in our lab tests was located just 4-5 meters away from the BS in a direct line of sight. Since a mobile fading environment cannot easily be set up in the laboratory, we checked the proposed beamforming scheme in fading environments through various computer simulations. As shown in Figures 2 and 4, it is clear that the proposed beamforming scheme provided a remarkable improvement in mobile fading environments as well as in the static circumstances of the lab tests. Another limitation of the experimental tests was that the calibration performance was not verified in the throughput tests shown in Figure 14. Note that as the calibration was used for downlink beamforming, the uplink performance shown in this paper does not confirm the validity of the proposed calibration procedure except that the phase differences at each antenna channel were equalized as shown in Figures 12 and 13. Future tests could include the downlink measurements to verify the actual performance of the proposed calibration procedure.
This work was partly supported by the IT R&D program of MIC/IITA (2007-S001-01, Implementation of Advanced-MIMO system) and the HY-SDR research center at Hanyang University, Seoul, South Korea under the ITRC program of MIC, South Korea.
- WiMAX Forum : Mobile WiMAX—part I: a technical overview and performance evaluation. http://www.wimaxforum.org/
- Li Y, Sollenberger NR: Adaptive antenna arrays for OFDM systems with cochannel interference. IEEE Transactions on Communications 1999, 47(2):217-229. 10.1109/26.752127View ArticleGoogle Scholar
- Chen Y-F, Li C-P: Adaptive beamforming schemes for interference cancellation in OFDM communication systems. Proceedings of the 59th IEEE Vehicular Technology Conference (VTC '04), May 2004, Milan, Italy 1: 103-107.Google Scholar
- Wennström M, Öberg T, Rydberg A: Effects of finite weight resolution and calibration errors on the performance of adaptive array antennas. IEEE Transactions on Aerospace and Electronic Systems 2001, 37(2):549-562. 10.1109/7.937468View ArticleGoogle Scholar
- van de Beek JJ, Sandell M, Börjesson PO: ML estimation of timing and frequency offset in OFDM systems. IEEE Transactions on Signal Processing 1997, 45(7):1800-1805. 10.1109/78.599949MATHView ArticleGoogle Scholar
- Fu X, Minn H: Initial uplink synchronization and power control (ranging process) for OFDMA systems. In Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '04), November-December 2004, Dallas, Tex, USA. Volume 6. IEEE Communications Society; 3999-4003.Google Scholar
- Choi S, Shim D: A novel adaptive beamforming algorithm for a smart antenna system in a cdma mobile communication environment. IEEE Transactions on Vehicular Technology 2000, 49(5):1793-1806. 10.1109/25.892584View ArticleGoogle Scholar
- Litva J, Lo TK: Digital Beamforming in Wireless Communications. Artech House, Norwood, Mass, USA; 1996.Google Scholar
- Mano S, Katagi T: A method for measuring amplitude and phase of each radiating element of a phased array antenna. Journal of the Institute of Electronics and Communication Engineers of Japan 1982, 65(5):555-560.Google Scholar
- Nishimori K, Cho K, Takatori Y, Hori T: Automatic calibration method of adaptive array for FDD systems. Proceedings of the IEEE Antennas and Propagation Society International Symposium (APS '00), July 2000, Salt Lake, Utah, USA 2: 910-913.Google Scholar
- Hyeon S, Yun Y, Choi S: Novel automatic calibration technique for smart antenna systems. Digital Signal Processing 2009, 19(1):14-21. 10.1016/j.dsp.2007.10.006View ArticleGoogle Scholar
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