Wireless communication systems with high data rate are strongly desired by a variety of applications. Then, MIMO-OFDM is one of the promising technologies satisfying the need for high throughput. Orthogonal frequency division multiplexing (OFDM) [1][2] possesses the ability to resist multipath channel by simple frequency-domain equalizer; thus, it is adopted by many wireless standards, e.g. IEEE 802.11a/g/n, IEEE 802.16a, digital video broadcasting (DVB), and etc. Multiple-input multiple-output (MIMO) [3] makes use of multiple transmitter and receiver antennas to transmit independent data streams simultaneously for increasing diversity and spectral efficiency. Consequently, the combination of MIMO and OFDM is widely discussed in recent years and has been used by some wireless broadband systems, such as IEEE 802.11n [4][5] and IEEE.802.16a.
To popularize this communication technology, we need a receiver architecture that is small, cheap and less power-consuming. A direct-conversion receiver (DCR)[6]
is a good choice that meets these requirements so that it becomes a trend in industry and academic world nowadays. This kind of architecture, however, introduces some radio frequency (RF) imperfections such as the direct current (DC) offset, frequency offset, in-phase/quadrature (I/Q) imbalance, phase noise, and etc [2][7][8][9][10].
Some digital signal processing algorithms are needed to deal with these RF imperfections in baseband to ease the requirements of analog front-end devices. In this thesis, we are going to dig into the problems of compensation for I/Q imbalance and estimation for channel in MIMO-OFDM systems.
The I/Q imbalance means the mismatch between I and Q branches in amplitudes and phases [10][11]. It is commonly mentioned in the direct-conversion receiver, which uses analog quadrature down-mixing. Then, there are two major factors in I/Q imbalance. For one thing, the imperfect local oscillator (LO) results in
frequency-independent I/Q imbalance. The complex carrier generated by imperfect LO is not a true quadrature signal, and the amplitudes of carrier signal for I/Q branches are not equal in practice. For another, I/Q imbalance results from the unavoidable mismatch among all the analog elements on I/Q branches. The signals of I and Q branches are processed by individual analog devices, such as amplifiers, low-pass filter (LPF), A/D converters, and etc. Nevertheless, it is difficult to produce two analog devices with exactly identical responses or properties, especially when the bandwidth (BW) of the system is large, e.g. 40 MHz mode of IEEE 802.11n[4][5].
This kind of I/Q imbalance is frequency-dependent; in other words, the I/Q imbalance effects tend to vary with frequency. The frequency-independent and frequency-dependent I/Q imbalance have the received signal interfered by its image signal as Fig.1-1 shows. In the next chapter, the system model and effects of I/Q imbalance are about to be explained in detail.
fc
(a) Received signal in passband
f
(b) Received signal which is interfered by image signal in baseband
In fact, there are tons of literatures focusing on the problem of I/Q imbalance and the compensation scheme; see [11]-[24] and references therein. However, most of them put emphasis on frequency-independent I/Q imbalance, and only a few of them [11]-[17] provide compensation schemes for frequency-dependent I/Q imbalance, which is much more complicated. Based on the assumption that desired signal and image interference are statistically independent, some methods make use of the blind source separation techniques to extract the desired signal [11][12][18][19]. However, the finite training sequences used to estimate the coefficients for compensation do not always satisfy the assumption. Therefore, decision-directed methods are usually needed for these algorithms to adaptively converge the desired solutions. It, nevertheless, can not be applied directly in the MIMO systems because the MIMO systems involve multi-user detection (MUD) procedures. Some other adaptive compensation methods, such as [20], also encounter the same difficulty. Then, [16]
derives an adaptive MMSE solution of joint MUD and I/Q mismatch cancellation in frequency domain for MIMO-OFDM systems. It independently calculates the coefficients of detection and compensation for each subcarrier, but the coefficients of neighboring subcarriers are highly correlated. Accordingly, we should take account of this property to improve speed of convergence.
On the other hand, some researchers develop the compensation algorithms by means of the training symbols known by the receiver in advance, like [21]-[24].
Reference [21] analyzes the frequency-dependent I/Q imbalance in the presence of frequency-offset. Still, some restrictions on the training sequence exist and a finite impulse response (FIR) filter is adopted to correct the frequency-dependent I/Q mismatch. Truly, a FIR filter is not suitable for OFDM systems because it increases the effective channel length experienced by transmitted signal. If the effective channel
length is larger than the guard interval (GI), we have to show the great concern about the inter-symbol interference (ISI) which is a troublesome issue in OFDM systems.
Then, speaking of Reference [22], it only discusses frequency-independent I/Q imbalance problem on the basis of channel smoothness criterion. Moreover, both [21]
and [22] ignore the noise contribution when they analyze these RF imperfections, and it doesn’t conform to the real case. Different from [21] and [22], assuming the noise at receiver is an additive white Gaussian noise (AWGN), references [23][24] propose techniques that jointly estimates the I/Q imbalance and other RF impairments based on the maximum likelihood (ML) criterion. However, the joint estimators of [23][24]
are only for the frequency-independent I/Q mismatch and can not be directly applied to MIMO-OFDM systems.
In this thesis, we propose a joint ML estimators of I/Q imbalance and channel for MIMO-OFDM systems, such as IEEE 802.11n [4][5], and the problems of I/Q mismatch compensation and channel estimation are considered at the same time. The basic idea of our I/Q compensation scheme is similar to [23][24]. What is more, we acquire three advantages of our algorithms. First of all, we derived the compensation algorithms not only for frequency-independent but also for frequency-dependent I/Q imbalances. Secondly, in addition to SISO-OFDM systems, it is able to work in the MIMO-OFDM systems with tone-interleaved training sequences for MIMO channel estimation [4]. Thirdly, the property of high correlation between neighboring subcarriers is adopted when we both calculate the channel response and I/Q imbalance parameter of each subcarrier.
The remainder of this thesis organized as follows. Chapter 2 introduces the system model of I/Q imbalance. Then, we propose ML channel estimators and the compensation schemes not only for frequency-independent I/Q imbalance in Chapter
results are demonstrated in Chapter 5. Finally, Chapter 6 concludes the thesis.