Book Description

Wireless communication devices are currently enjoying increasing popularity and widespread use. The constantly growing number of users, however, results in the shortage of the available spectrum. Various techniques have been proposed to increase the spectrum efficiency of wireless systems to solve the problem. Multiple Input Multiple Output (MIMO) is one solution that employs multiple antennas at the transmitter and receiver. The MIMO algorithms are usually highly complex and computationally intensive. This results in increased power consumption and reduced battery lifespan. This thesis investigates the complexity - performance trade-off of two MIMO algorithms. Space Time Block Coding (STBC) is a MIMO-based algorithm, which efficiently exploits spatial and temporal diversity. Recently, it has been specified in a number of 3G standards. However, not much attention has been paid to the implementation issues of this algorithm. One such issue, clipping of the Analog to Digital Converter (ADC) at the receiver, is described in the first part of the thesis (chapter 3). A small amount of clipping in an ADC can improve dynamic range and reduce the power consumption. However, the increased clipping distortion of the signal, can adversely affect the overall performance of the system. It will be shown in this dissertation that STBC are more sensitive to clipping, compared to the uncoded single antenna systems. Two receiver structures are considered: Direct Conversion (DC) structure, where the ADCs impose a square clipping function, and a Log-Polar structure, where ADC induces a circular clipping function. Log-Polar receivers were found to be clipping insensitive for the given target Symbol Error Rate (SER) of 1*10-3. This makes Log-Polar receivers an obvious choice for the system designers. The second part of the thesis (chapter 4) addresses the complexity problem associated with the QR decomposition algorithm, which is frequently used as a faster alternative to channel inversion in a MIMO scheme. Channel tracking can be employed with QR equalization in order to reduce the pilot overhead of a MIMO system in a non-stationary environment. QR decomposition is part of the QR equalization method and has to be performed in every instance that the channel estimate is obtained. The high rate of the QR decomposition, a computationally intensive technique, results in a high computational complexity per symbol. Some novel modifications are proposed to address this problem. Reducing the repetition rate of QR decompositions and tracking R (the upper triangular matrix) directly, while holding unitary matrix Q fixed, can significantly reduce complexity per symbol at the expense of some introduced error. Additional modification of the CORDIC algorithm (a square root- and division-free algorithm used to perform QR decomposition) results in more than 80% of computational complexity savings. Further, Minimum Mean Squared Error (MMSE) detection is applied to Least Mean Squared (LMS) based R tracking and channel tracking algorithms and then compared in complexity and performance to the Recursive Least Squares Decision Feedback Equalizer (RLS-DFE) tracking system in [1]. The R tracking scheme is shown to achieve more accurate channel estimates compared to the channel tracking scenario, but this advantage does not translate into better Bit Error Rate (BER) results due to errors on the first layer of the detector. Both LMS strategies have an inferior BER performance compared to the DFE RLS-based system of [1], and surprisingly the LMS schemes show no significant complexity improvement.