Adaptive Transceiver Design and Performance Analysis for Ofdm Systems.

With the enormous demand for wireless access to the Internet for packet data and voice applications. Wireless Local Area Networks (WLANs) and Wireless Metropolitan Area Networks (WMANs) are becoming ubiquitous. As is the case in all wireless systems; applications carried over these networks are subject to impairments such as path-loss, shadowing and fading in the wireless channel. These impairments lead to transmission errors and consequently, packet loss, which degrades the Quality of Service (QoS) perceived by a user. In this study, we focus on coded orthogonal frequency division multiplexing (OFDM)-based WLANs and WMANs in order to propose a way to improve these shortcomings in wireless communication. It has been shown that an adaptive transceiver can provide considerable improvements in the performance of OFDM systems: however, the design of adaptive OFDM transceiver can be very complex and challenging due to estimation errors and limited knowledge of channel information. Hence, we explore and propose a solution to improve design of adaptive OFDM transceiver. The fading characteristics of the indoor wireless channel are very different from those of the mobile case. In indoor wireless systems, the transmitter and receiver are stationary and people are moving between, whereas in mobile systems, the user is often moving through an environment. Therefore, we propose a new model for a time varying indoor channel in order to fit the Doppler spectrum measurements. In the second part of the dissertation, time and frequency synchronization problems in an OFDM inner receiver are presented. In the burst packet mode OFDM systems, synchronization needs to be done very fast to avoid reduction of system capacity and also must be very must be very accurate to minimize interferences. We analyzed effects of estimation error on the system performance and proposed adaptive synchronization methods based on adaptive windowing and Kalman filtering to mitigate estimation errors with feasible complexity. For several different channel environments, our numerical results showed that the proposed methods can significantly decrease synchronization errors without the need for prior knowledge of channel conditions. In the third part of the dissertation, we propose an enhanced DFT-based minimum mean square error (MMSE) channel estimator using the Kalman smoother. In practical OFDM systems with virtual carriers (VCs), conventional DFT-based approaches are not directly applicable as they induce a spectral leakage owing to VCs, which results in an error floor for the mean square error (MSE) performance. We applied Kalman smoothing to minimize the leakage effect and the time domain MMSE weighting was also used to suppress the channel noise. Finally, using request to send (RTS) and clear to send (CTS) mechanism, we introduce a method to improve throughput performance by adaptively changing constellation size and power distribution across the sub-carriers without sacrificing throughput due to explicit feedback. Based on the theoretical analysis, part of this complex maximization problem approximately reduced to Lagrange equation and the objective function can be solved by a simple iterative algorithm. Our simulation results, using the proposed channel model, show that this algorithm combined with proposed estimation methods is a promising approach in solving throughput optimization problems with practical impairments.