# Bit Error Rate Approximation of MIMO-OFDM Systems with Carrier Frequency Offset and Channel Estimation Errors

- Zhongshan Zhang
^{1}Email author, - Lu Zhang
^{2}, - Mingli You
^{2}and - Ming Lei
^{1}

**2010**:176083

https://doi.org/10.1155/2010/176083

© Zhongshan Zhang et al. 2010

**Received: **23 February 2010

**Accepted: **16 September 2010

**Published: **23 September 2010

## Abstract

The bit error rate (BER) of multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems with carrier frequency offset and channel estimation errors is analyzed in this paper. Intercarrier interference (ICI) and interantenna interference (IAI) due to the residual frequency offsets are analyzed, and the average signal-to-interference-and-noise ratio (SINR) is derived. The BER of equal gain combining (EGC) and maximal ratio combining (MRC) with MIMO-OFDM is also derived. The simulation results demonstrate the accuracy of the theoretical analysis.

## 1. Introduction

Spatial multiplexing multiple-input multiple-output (MIMO) technology significantly increases the wireless system capacity [1–4]. These systems are primarily designed for flat-fading MIMO channels. A broader band can be used to support a higher data rate, but a frequency-selective fading MIMO channel is met, and this channel experiences intersymbol interference (ISI). A popular solution is MIMO-orthogonal frequency-division multiplexing (OFDM), which achieves a high data rate at a low cost of equalization and demodulation. However, just as single-input single-output- (SISO-) OFDM systems are highly sensitive to frequency offset, so are MIMO-OFDM systems. Although one can use frequency offset correction algorithms [5–10], residual frequency offsets can still increase the bit error rate (BER).

The BER of SISO-OFDM systems impaired by frequency offset is analyzed in [11], in which the frequency offset is assumed to be perfectly known at the receiver, and, based on the intercarrier interference (ICI) analysis, the BER is evaluated for multipath fading channels. Many frequency offset estimators have been proposed [8, 12–14]. A synchronization algorithm for MIMO-OFDM systems is proposed in [15], which considers an identical timing offset and frequency offset with respect to each transmit-receive antenna pair. In [10], where frequency offsets for different transmit-receive antennas are assumed to be different, the Cramer-Rao lower bound (CRLB) for either the frequency offsets or channel estimation variance errors for MIMO-OFDM is derived. More documents on MIMO-OFDM channel estimation by considering the frequency offset are available at [16, 17].

However, in real systems, neither the frequency offset nor the channel can be perfectly estimated. Therefore, the residual frequency offset and channel estimation errors impact the BER performance. The BER performance of MIMO systems, without considering the effect of both the frequency offset and channel estimation errors, is studied in [18, 19].

This paper provides a generalized BER analysis of MIMO-OFDM, taking into consideration both the frequency offset and channel estimation errors. The analysis exploits the fact that for unbiased estimators, both channel and frequency offset estimation errors are zero-mean random variables (RVs). Note that the exact channel estimation algorithm design is not the focus of this paper, and the main parameter of interest is the channel estimation error. Many channel estimation algorithms developed for either SISO or MIMO-OFDM systems, for example, [20–22], can be used to perform channel estimation. The statistics of these RVs are used to derive the degradation in the receive SINR and the BER. Following [10], the frequency offset of each transmit-receive antenna pair is assumed to be an independent and identically distributed (i.i.d.) RV.

This paper is organized as follows. The MIMO-OFDM system model is described in Section 2, and the SINR degradation due to the frequency offset and channel estimation errors is analyzed in Section 3. The BER, taking into consideration both the frequency offset and channel estimation errors, is derived in Section 4. The numerical results are given in Section 5, and the conclusions are presented in Section 6.

*Notation*.
and
are transpose and complex conjugate transpose. The imaginary unit is
.
and
are the real and imaginary parts of
, respectively.
represents the angle of
, that is,
. A circularly symmetric complex Gaussian RV with mean
and variance
is denoted by
.
is the
identity matrix, and
is the
all-zero matrix.
is the
all-zero vector.
is the
th entry of vector
, and
is the
th entry of matrix
.
and
are the mean and variance of
.

## 2. MIMO-OFDM Signal Model

Input data bits are mapped to a set of complex symbols drawn from a typical signal constellation such as phase-shift keying (PSK) or quadrature amplitude modulation (QAM). The inverse discrete fourier transform (IDFT) of these symbols generates an OFDM symbol. Each OFDM symbol has a useful part of duration seconds and a cyclic prefix of length seconds to mitigate ISI, where is longer than the channel-response length. For a MIMO-OFDM system with transmit antennas and receive antennas, an vector represents the block of frequency-domain symbols sent by the th transmit antenna, where . The time-domain vector for the th transmit antenna is given by , where is the total transmit power and is the IDFT matrix with entries for . Each entry of is assumed to be i.i.d. RV with mean zero and unit variance; that is, for and .

Note that if and are satisfied simultaneously, we assume that there is no correlation between and . Otherwise the correlation between and is nonzero.

In this paper, and are used to represent the initial phase and normalized frequency offset (normalized to the OFDM subcarrier spacing) between the oscillators of the -th transmit and the th receive antennas. The frequency offsets for all are modeled as zero-mean i.i.d. RVs. (Multiple rather than one frequency offset are assumed in this paper, with each transmit-antenna pair being impaired by an independent frequency offset. This case happens when the distance between different transmit or receive antenna elements is large enough, and this big distance results in a different angle-of-arrive (AOA) of the signal received by each receive antenna element. In this scenario, once the moving speed of the mobile node is high, the Doppler Shift related to different transmit-receive antenna pair will be different.)

where , and is a vector of additive white Gaussian noise (AWGN) with . Note that the channel state information is available at the receiver, but not at the transmitter. Consequently, the transmit power is equally allocated among all the transmit antennas.

## 3. SINR Analysis in MIMO-OFDM Systems

where is derived from by replacing with and and are the residual IAI and AWGN components of , respectively (When is large enough and the frequency offset is not too big (e.g., ), from the Central-Limit Theorem (CLT) [23, Page 59], the IAI can be approximated as Gaussian noise.).

### 3.1. SINR Analysis without Combining at Receive Antennas

The SINR is derived for the th transmit signal at the th receive antenna. The signals transmitted by antennas other than the th antenna are interference, which should be eliminated before demodulating the desired signal of the th transmit antenna. Existing interference cancelation algorithms [24–27] can be applied here.

- (1)
- (2)
- (3)
- (4)
- (5)

- (1)
- (2)
- (3)

For signal demodulation in MIMO-OFDM, signal received in multiple receive antennas can be exploited to improve the receive SINR. In the following, equal gain combining (EGC) and maximal ratio combining (MRC) are considered.

### 3.2. SINR Analysis with EGC at Receive Antennas

### 3.3. SINR Analysis with MRC at Receive Antennas

## 4. BER Performance

The BER as a function of SINR in MIMO-OFDM is derived in this section. We consider -ary square QAM with Gray bit mapping. In the work of Rugini and Banelli [11], the BER of SISO-OFDM with frequency offset is developed. The BER analysis in [11] is now extended to MIMO-OFDM.

where and are specified by signal constellation, is the average SINR of the th transmit antenna, and is the error function (Please refer to [28] for the meaning of and .).

where , , , and . Since obtaining a close-form solution of (23) appears impossible, an infinite-series approximation of is developed. In [11], the average is expressed as an infinite series of generalized hypergeometric functions.

where is based on instead of . We first define and , which will be used in the following subsections. We next give a recursive definition for for the following reception methods: (1) demodulation without combining, (2) EGC, and (3) MRC.

Note that the SINR for each combining scenario (i.e., without combining, EGC, or MRC) is a function of the second-order statistics of the channel and frequency offset estimation errors (although the interference also comprises the fourth-order statistics of the frequency offset estimation errors, they are negligible as compared to the second-order statistics for small estimation errors). Any probability distribution with zero mean and the same variance will result in the same SINR. Therefore, the exact distributions need not be specified. However, when the BER is derived by using an infinite-series approximation, the actual distribution of the frequency offset estimation errors is required. In [31], it is shown that both the uniform distribution and Gaussian distribution are amenable to infinite-series solutions with closed-form formulas for the coefficients. In the following sections, the frequency offset estimation errors are assumed to be i.i.d. Gaussian RVs with mean zero and variance [10].

### 4.1. BER without Receiving Combining

### 4.2. BER with EGC

### 4.3. BER with MRC

### 4.4. Complexity of the Infinite-Series Representation of BER

Infinite-series BER expression (27), (29), or (32) must be truncated in practice. The truncation error is negligible if the number of terms is large enough: Reference [31] shows that when the number of terms is as large as 50, the finite-order approximation is good. In this case, a total of multiplication and summation operations are needed to calculate the BER for each combining scheme.

## 5. Numerical Results

Our theoretical BER approximations are accurate at low SNR with/without diversity combining. However, the simulation and theory results diverge as the SNR increases, especially when is large. For example, in Figure 9, with 16QAM, when ( , ) and , about 1 dB difference exists between the simulation and the theoretical result for either EGC or MRC at high SNR. This discrepancy is due to several reasons. As the SNR increases, the system becomes interference limited. When , , and are not large enough, the interferences may not be well approximated as Gaussian RVs with zero mean. In addition, with either EGC or MRC reception, the phase rotation or channel attenuation of the receive substreams should be estimated, and their estimation accuracy will also affect the combined SINR. The instant large phase or channel estimation error also contributes a deviation to the BER when using EGC or MRC.

## 6. Conclusions

The BER of MIMO-OFDM due to the frequency offset and channel estimation errors has been analyzed. The BER expressions for no combining, EGC, and MRC were derived. These expressions are in infinite-series form and can be truncated in practice. The simulation results show that the truncation error is negligible if the number of terms is large than 50.

## Appendices

## A. BER without Combining

## B. BER of EGC

## C. BER of MRC

## Declarations

### Acknowledgments

This paper has been presented in part at the IEEE Globecom 2007 [32]. Although the conference paper was a brief version of this journal paper and they have the same results and conclusion, this journal paper provides a more detailed proof to each result appeared in the IEEE ICC 2007 paper.

## Authors’ Affiliations

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