548 research outputs found
Multipath Parameter Estimation from OFDM Signals in Mobile Channels
We study multipath parameter estimation from orthogonal frequency division
multiplex signals transmitted over doubly dispersive mobile radio channels. We
are interested in cases where the transmission is long enough to suffer time
selectivity, but short enough such that the time variation can be accurately
modeled as depending only on per-tap linear phase variations due to Doppler
effects. We therefore concentrate on the estimation of the complex gain, delay
and Doppler offset of each tap of the multipath channel impulse response. We
show that the frequency domain channel coefficients for an entire packet can be
expressed as the superimposition of two-dimensional complex sinusoids. The
maximum likelihood estimate requires solution of a multidimensional non-linear
least squares problem, which is computationally infeasible in practice. We
therefore propose a low complexity suboptimal solution based on iterative
successive and parallel cancellation. First, initial delay/Doppler estimates
are obtained via successive cancellation. These estimates are then refined
using an iterative parallel cancellation procedure. We demonstrate via Monte
Carlo simulations that the root mean squared error statistics of our estimator
are very close to the Cramer-Rao lower bound of a single two-dimensional
sinusoid in Gaussian noise.Comment: Submitted to IEEE Transactions on Wireless Communications (26 pages,
9 figures and 3 tables
Channel estimation, data detection and carrier frequency offset estimation in OFDM systems
Orthogonal Frequency Division Multiplexing (OFDM) plays an important role in the implementation of high data rate communication. In this thesis, the problems of data detection and channel and carrier frequency offset estimation in OFDM systems are studied. Multi-symbol non-coherent data detection is studied which performs data detection by processing multiple symbols without the knowledge of the channel impulse response (CIR). For coherent data detection, the CIR needs to be estimated. Our objective in this thesis is to work on blind channel estimators which can extract the CIR using just one block of received OFDM data. A blind channel estimator for (Single Input Multi Output) SIMO OFDM systems is derived. The conditions under which the estimator is identifiable is studied and solutions to resolve the phase ambiguity of the proposed estimator are given.A channel estimator for superimposed OFDM systems is proposed and its CRB is derived. The idea of simultaneous transmission of pilot and data symbols on each subcarrier, the so called superimposed technique, introduces the efficient use of bandwidth in OFDM context. Pilot symbols can be added to data symbols to enable CIR estimation without sacrificing the data rate.
Despite the many advantages of OFDM, it suffers from sensitivity to carrier frequency offset (CFO). CFO destroys the orthogonality between the subcarriers. Thus, it is necessary for the receiver to estimate and compensate for the frequency offset. Several high accuracy estimators are derived. These include CFO estimators, as well as a joint iterative channel/CFO estimator/data detector for superimposed OFDM. The objective is to achieve CFO estimation with using just one OFDM block of received data and without the knowledge of CIR
Superimposed training-based channel estimation and data detection for OFDM amplify-and-forward cooperative systems under high mobility
In this paper, joint channel estimation and data detection in orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) cooperative systems under high mobility is investigated. Unlike previous works on cooperative systems in which a number of subcarriers are solely occupied by pilots, partial data-dependent superimposed training (PDDST) is considered here, thus preserving the spectral efficiency. First, a closed-form channel estimator is developed based on the least squares (LS) method with Tikhonov regularization and a corresponding data detection algorithm is proposed using the linear minimum mean square error (LMMSE) criterion. In the derived channel estimator, the unknown data is treated as part of the noise and the resulting data detection may not meet the required performance. To address this issue, an iterative method based on the variational inference approach is derived to improve performance. Simulation results show that the data detection performance of the proposed iterative algorithm initialized by the LMMSE data detector is close to the ideal case with perfect channel state information. © 2006 IEEE.published_or_final_versio
Coded DS-CDMA Systems with Iterative Channel Estimation and no Pilot Symbols
In this paper, we describe direct-sequence code-division multiple-access
(DS-CDMA) systems with quadriphase-shift keying in which channel estimation,
coherent demodulation, and decoding are iteratively performed without the use
of any training or pilot symbols. An expectation-maximization
channel-estimation algorithm for the fading amplitude, phase, and the
interference power spectral density (PSD) due to the combined interference and
thermal noise is proposed for DS-CDMA systems with irregular repeat-accumulate
codes. After initial estimates of the fading amplitude, phase, and interference
PSD are obtained from the received symbols, subsequent values of these
parameters are iteratively updated by using the soft feedback from the channel
decoder. The updated estimates are combined with the received symbols and
iteratively passed to the decoder. The elimination of pilot symbols simplifies
the system design and allows either an enhanced information throughput, an
improved bit error rate, or greater spectral efficiency. The interference-PSD
estimation enables DS-CDMA systems to significantly suppress interference.Comment: To appear, IEEE Transactions on Wireless Communication
Partial-Data Superimposed Training with Data Precoding for OFDM Systems
Superimposed training (ST) is a recently addressed technique used for channel estimation where known training sequences are arithmetically added to data symbols, avoiding the use of dedicated pilot subcarriers, and thus, increasing the available bandwidth compared with traditional pilot symbol assisted modulation schemes. However, the system handles data interference over channel estimation as a result of the ST process; also, data detection is degraded by pilot interference. Recent ST methods have analyzed the data interference and presented schemes that deal with it. We propose a novel superimposed model over a precoded data scheme, named partial-data superimposed training (PDST), where an interference control factor assigns the adequate information level to be added to the training sequence in orthogonal frequency division multiplexing systems. Also, a data detection method is introduced to improve the symbol error rate performance. Moreover, a capacity analysis of the system has been derived. Finally, simulation results confirm that performance of PDST is superior to previous proposals
Estimation and detection techniques for doubly-selective channels in wireless communications
A fundamental problem in communications is the estimation of the channel.
The signal transmitted through a communications channel undergoes distortions
so that it is often received in an unrecognizable form at the receiver.
The receiver must expend significant signal processing effort in order to be
able to decode the transmit signal from this received signal. This signal processing
requires knowledge of how the channel distorts the transmit signal,
i.e. channel knowledge. To maintain a reliable link, the channel must be
estimated and tracked by the receiver.
The estimation of the channel at the receiver often proceeds by transmission
of a signal called the 'pilot' which is known a priori to the receiver.
The receiver forms its estimate of the transmitted signal based on how this
known signal is distorted by the channel, i.e. it estimates the channel from
the received signal and the pilot. This design of the pilot is a function of the
modulation, the type of training and the channel. [Continues.
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