Joint CFO Estimation and Data Detection in OFDM systems

Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation technique that is widely used in wireless broadband communication systems. The spectral e ciency of OFDM is very high since the subcarriers are spaced as closely as possible while maintaining orthogonality. However, one of the major problems with OFDM that can cause performance degradation is carrier frequency o set (CFO) which impairs the orthogonality among OFDM subcarriers, as a consequence, results in inter-subcarrier interference. In this thesis, an iterative algorithm for joint CFO estimation and data detection in OFDM systems over frequency selective channels is proposed. The proposed algorithm is performing both CFO estimation and data detection in the frequency domain based on the Expectation-Maximization (EM) algorithm. The proposed algorithm can achieve the same bit-error-rate (BER) performance as that of its time-domain counterpart with much lower complexity. Simulation results show that the proposed algorithm can converge after three iterations and an estimate of CFO can be obtained with high accuracy

Iterative joint frequency offset and channel estimation for OFDM systems using first and second order approximation algorithms

[[abstract]]To implement an algorithm for joint estimation of carrier frequency offset (CFO) and channel impulse response (CIR) in orthogonal frequency division multiplexing (OFDM) systems, the maximum-likelihood criterion is commonly adopted. A major difficulty arises from the highly nonlinear nature of the log-likelihood function which renders local extrema or multiple solutions for the CFO and CIR estimators. Use of an approximation method coupled with an adaptive iteration algorithm has been a popular approach to ease problem solving. The approximation used in those existing methods is usually of the first order level. Here, in addition to a new first order approximation method, we also propose a second order approximation method. Further, for the part of the adaptive iteration algorithm, we adopt a new technique which will enable performance improvement. Our first order approximation method is found to outperform the existing ones in terms of estimation accuracies, tracking range, computation complexity, and convergence speed. As expected, our second order approximation method provides an even further improvement at the expense of higher computation complication.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子版[[countrycodes]]DE

Optimized Iterative (Turbo) Reception for QAM OFDM with CFO over Unknown Double-Selective Channels

A novel iterative (turbo) receiver is introduced, suitable for orthogonal frequency division multiplexing (OFDM) employing quadrature amplitude modulation (QAM) and receiver diversity. The system operates over a double-selective channel and includes a carrier frequency offset (CFO). We propose a maximum a posteriori probability expectation-maximization (MAP-EM) receiver with a different EM parameter division than standard methods. In such standard MAP-EM receivers, the E-step parameters correspond to the channel, while the M-step parameters correspond to the CFO and data symbols. This standard receiver parameter division results into a highly complex receiver for QAM, due to the large modulated symbol alphabet size, and the non-constant constellation symbol amplitude. In this paper, a new receiver framework introduces a different parameter division that leads to reduced complexity turbo receivers for QAM signaling, while still achieving close to optimal system performance. The new approach adapts the sum-product algorithm (SPA) parameter framework to the MAP-EM receiver. Thus, in the new receiver framework, the E-step parameters are data symbols, while the M-step parameters are the channel and the CFO. We evaluate the performance of the proposed receiver with and without automatic repeat request (ARQ), where in the former case packet combining applies to further improve performance