Optimal Pilot-to-Data Power Ratio Simulator for MIMO-OFDM Using NI LabVIEW 7.1

Author(s):
Jeffrey G. Andrews - University of Texas at Austin
Taeyoon Kim - University of Texas at Austin

Industry:
Telecommunications

Products:
LabVIEW

The Challenge:
Allocating a fixed amount of power in MIMO-OFDM, such as IEEE 802.16 and 802.11n, to data and pilot symbols to transmit data and estimate the channel.

The Solution:
Using National Instruments LabVIEW 7.1 to help design MIMO-OFDM systems.

Abstract

Orthogonal frequency division multiplexing (OFDM) can be used with a multiple-input, multiple-output (MIMO) system that uses multiple antennas both at the transmitter and receiver to improve communication quality and capacity. Pilot-symbol-aided or decision-directed channel estimation must be used to track channel variations in MIMO-OFDM systems. While pilot symbols facilitate channel estimation, they reduce the transmit energy for data symbols under a fixed total transmit power constraint. Thus, the optimal pilot-to-data power ratio (PDPR) in MIMO-OFDM systems can be used to maximize the capacity.

In order to model the MIMO-OFDM system in a graphical and fast simulation, we developed the MIMO-OFDM simulator in the NI LabVIEW simulation package. Using this simulator, one can see the bit error rate (BER) performance of the system and the channel capacity lower bound according to a given pilot-to-data power ratio (PDPR) with three different types of pilot patterns.

Introduction

Due to its robustness to frequency selective fading and low computational complexity, OFDM is an attractive solution for high-data rate wireless communications. We can use OFDM in conjunction with a MIMO transceiver to increase the diversity gain and/or the system capacity (1), (2). Thus, MIMO-OFDM is considered a key technology in emerging high-data rate wireless systems, such as 4G, IEEE 802.16 (3), and IEEE 802.11n.

In MIMO-OFDM systems, we must use pilot-symbol-aided or decision-directed channel estimation to track the instantaneous channel state information (CSI) at the receiver to coherently detect the received signal and to perform diversity combining or spatial interference suppression (4). Pilot symbols facilitate channel estimation, but in addition to consuming bandwidth, they reduce the transmitted energy for data symbols per OFDM symbol under a fixed total transmit power condition. This suggests a tradeoff between the power allowed to data symbols and the accuracy of the channel estimation when the total transmit power is fixed.

The optimal pilot-to-data power ratio (PDPR) for MIMO-OFDM systems with these three different pilot patterns is derived from an information-theoretic capacity point of view (5). This optimal PDPR can maximize the system capacity by simply adjusting the power between pilot and data symbols. This application demonstrates the optimal PDPR simulator for MIMO-OFDM. By using this simulator, we can get the optimal PDPR according the system parameters and see their expected performance according to various PDPR settings.

System Model

The system under consideration in our optimal PDPR simulator is a spatial multiplexing MIMO-OFDM system with Mt transmit antennas and Mr receive antennas. The number of subcarriers is K, and the number of nonzero taps of the impulse response is L for each channel. At each transmit antenna, data and pilot symbols are modulated on a set of subcarriers by the OFDM modulator. Because interference from other transmit antennas causes a large amount of interference in MIMO-OFDM systems, the pilot symbol should be transmitted in special ways such as independent pilot pattern (IPP), scattered pilot pattern (SPP), and orthogonal pilot pattern (OPP) to guarantee the orthogonality between transmit antennas (6).

Optimal PDPR Simulator for MIMO-OFDM Systems

The user can select the output from the symbol error rate (SER) performance and the information theoretic capacity. Also, the user can manually choose the PDPR, otherwise the optimal PDPR is calculated and used for simulation. In simulation part, we use Rayleigh fading channel, minimum mean square error (MMSE) channel estimator, and MMSE receiver. At this time, we can use only two and four transmit and receive antennas for simulation. We can easily extend this number by updating the pilot symbol allocation part, because the number of antennas is related with the pilot symbol allocation.

Next, the system presents and analyzes the simulation results from the optimal PDPR simulator for MIMO-OFDM systems. For the simulation, IEEE 802.11n system is considered. IEEE 802.11n uses 2x2 or 4x4 MIMO-OFDM systems. The number of subcarrier is 64, and four subcarriers are used for pilot symbols. We assume that L is 3 for fixed L case and the total power budget for transmission power is 100mW. The number of pilot symbols is 4 and 4Mt for the IPP/OPP and SPP cases, respectively.

The capacity of the IPP and OPP cases show the same results. An overall observation is that the capacity is not especially sensitive to the PDPR as long as it is in a certain region. For example, a quasi-optimal region is about 0.1~0.3 in 2x2 802.11n systems with IPP or OPP. Therefore, the optimal power for pilot symbols to maximize the capacity of the 2x2 802.11n system with 100mW power budget is about 20mW power for IPP / OPP cases and about 30mW power for SPP case. For 4x4 802.11n system, about 20mW and 35mW power should be allocated to pilot symbols for IPP/OPP case and SPP case, respectively.

Therefore, by using our PDPR simulator we can find optimal PDPR and adopt it to design the system in order to increase the system capacity.

LabVIEW Offers Easy Design and Implementation

In this application, we developed and demonstrated the optimal PDPR simulator. There is a tradeoff between the power for data symbols and the accuracy of the channel estimation in MIMO-OFDM systems when the total transmit power is fixed. The simulation results from the optimal PDPR simulator for maximizing capacity showed that independent and orthogonal pilot patterns show the same performance, and their performance is superior to a frequency-scattered pilot pattern. Also, the capacity is not highly sensitive to the PDPR across a fairly broad range. For example, if 15 to 30 percent of total transmit power is assigned to pilot symbols in 2x2 MIMO-OFDM system, the capacity obtained over this range is almost same as optimal capacity.

The PDPR simulator can find optimal PDPR according to the system parameters and see the expected capacity of the system with different pilot patterns according to the PDPR. This optimal PDPR simulator can control the system configuration to maximize the capacity of the system by simply adjusting the power between pilot and data symbols in MIMO-OFDM systems.

Using LabVIEW, we found it easy to design and implement the wireless systems. It is very convenient to design and update the whole wireless system by looking it in graphical design environment. Also, it is very easy to construct the GUI of the simulator using LabVIEW. The flexibility of LabVIEW also made it an attractive option for us because we can easily update this simulator by reprogramming and add more sub-VIs without changing whole systems. 

References

(1) G. L. Stuber, J. R. Barry, S. W. McLaughlin, Y. G. Li, M. A. Ingram, and T. G. Pratt, “Broadband MIMO-OFDM wireless communications,” Proceedings of the IEEE, vol. 92, pp. 271–294, Feb. 2004.

(2) H. Bolcskei, D. Gesbert, and A. J. Paulraj, “On the capacity of OFDMbased spatial multiplexing systems,” IEEE Trans. on Communications, vol. 50, pp. 225–234, Feb. 2002.

(3) A. Ghosh, D. R.Wolter, J. G. Andrews, and R. Chen, “Broadband wireless access with WiMax/802.16: Current performance benchmarks and future potential,” IEEE Communications Magazine, vol. 43, pp. 129–136, Feb. 2005.

(4) Y. G. Li, N. Seshadri, and S. Ariyavisitakul, “Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels,” IEEE Journal on Sel. Areas in Communications, vol. 17, pp. 461–470, Mar. 1999.

(5) T. Kim and J. G. Andrews, “Pilot-to-data power ratio for maximizing the capacity of MIMO-OFDM,” IEEE Trans. on Communications, Submitted 2004.

(6) A. Dowler and A. Nix, “Performance evaluation of channel estimation techniques in a multiple antenna OFDM system,” in Proc., IEEE Veh. Technology Conf., vol. 2, Oct. 2003, pp. 1214–1218.

For more information, contact:

Taeyoon Kim

Graduate Student

University of Texas at Austin

1 University Station CO803

Austin, TX 78712

Tel: (512) 832-8886

E-mail: tykim@ece.utexas.edu

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