Jin He

Abstract:

Proliferation of mobile data applications has increased the demand for wireless communication systems offering high throughput, wide coverage, and improved reliability. The main challenges in the design of such systems are the limited resources—such as constrained transmission power, scarce frequency bandwidth, and limited implementation complexity—and the impairments of the wireless channels, including noise, interference, and fading effects. Multiple-Input Multiple-Output

(MIMO) communication has been shown to be one of the most promising emerging wireless technologies that can efficiently boost the data transmission rate, improve system coverage, and enhance link reliability. MIMO is now widely adopted by many mainstream wireless industry standards including 3GPP WCDMA/HSDPA, LTE, EVDO, WiFi, and WiMAX. By employing multiple antennas at both transmitter and receiver sides, MIMO techniques enable a new dimension—the spatial dimension—that can be utilized in different ways to combat the impairments of wireless channels. Spatial diversity provided by multiple antennas is one of the diversity techniques, which are known to be the most effective tool against fading effects of wireless channels. Spatial multiplexing exploits independent fading effects to create additional degrees of freedom, thus achieving higher capacity. Spatial diversity benefits different systems and channel types, from single user systems to multiuser systems, and from flat-fading to frequency-selective channels.

This thesis focuses on precoding and equalization techniques for flat-fading MIMO broadcast channels, with applications in spread spectrum communication systems. First, a novel linear precoding technique, Coordinated Interference-aware Beamforming (CIB), is introduced that utilizes channel side information at the transmitter.

This technique features low complexity of implementation as well as a closed form solution. Under constrained transmitter power, CIB balances multiuser interference, spatial channel interference, and noise effects.

Optimality analysis and simulation show that the achievable sum rate of CIB bridges the performance of zero-forcing precoding and matched filtering techniques at high and low signal to noise ratios, respectively. Furthermore, the complexity of CIB is similar to that of other linear non-adaptive techniques. CIB also allows flexible configurations on the number of antennas at the transmitter and receiver sides.

Next, for more complicated spread spectrum systems with frequency-selective broadcast channels, we show that the properly extended CIB can precode the signals well and inherits the benefits of CIB for flat-fading channels. The similarity of the results in this case with the flat-fading channel cases validates such CIB extension. As a pure equalization technique, generalized RAKE receiver technique is discussed. A novel finger placement strategy, serving as an important part of the generalized RAKE receiver is proposed. This strategy outperforms finger placement algorithms proposed in the original generalized RAKE receiver techniques.

We then introduce a novel nonlinear eigenvalue decomposition based lattice precoding technique (EDLP), for single user flat-fading channels. EDLP is a variant of dirty-paper coding and benefits from lattice reduction ideas, Tomlinson-Harashima precoding (THP), and linear precoding/equalization techniques. This technique achieves full diversity and a significant power gain with an implementation complexity similar to linear techniques. In the end of this thesis, we discussed the state-of-the-art of the precoding/equalization techniques for flat-fading multiuser MIMO broadcast channels and proposed a BDZF-EDLP technique based on EDLP and block-diagonal zero-forcing linear precoding technique. This technique focuses on balancing the uncoded error probability and transmitted power trade-off, and the computational complexity at the transmitter and receivers. As a result, such technique achieves full receive diversity with complexity similar to linear precoding. It also has valid scalability since its complexity grows linearly.

Advisor: Masoud Salehi