Cecilia Carbonelli

Sparse Channel Estimation with Zero Tap Detection

Algorithms for the estimation of a channel whose impulse response is characterized by a large number of zero tap coefficients are developed and compared. Estimation is conducted in a two-stage fashion where an estimate of the non-zero taps is followed by channel estimation. Tap detection is transformed into an equivalent on-off keying detection problem. Several tap detection algorithms are investigated which tradeoff between complexity and performance. The proposed methods are compared to an unstructured least squares channel estimate as well as a structured approach based on matching pursuit. Three schemes in particular are developed: a sphere decoder based scheme, a Viterbi algorithm based method and a simpler iterative approach. The latter offers a better tradeoff between estimation accuracy and computational cost. A joint estimation and zero tap detection scheme is also considered. All solutions exhibit a significant gain in terms of mean-squared error and bit error rate over conventional schemes which do not exploit the sparse nature of the channel, as well as the matching pursuit approach which does endeavor to exploit the sparsity

M-PPM noncoherent receivers for UWB applications

The paper investigates ultra-wideband receivers for applications in sensor and control networks, M-ary pulse position modulation is considered and the detector operates with no knowledge of the channel response. It is shown that the optimum decision rule consists of comparing the signal energies received in the M possible pulse positions in a symbol interval and deciding for that position corresponding to the maximum energy. Simple synchronization algorithms are proposed and their performance is assessed by simulation over realistic multipath channels. The algorithms are non-data-aided (blind) and have a feedback structure in which the error signal is based on energy measurements from the received waveform. The receiver performance degradations due to timing errors are found to be marginal

Sparse channel estimation with zero tap detection

Algorithms for the estimation of a channel whose impulse response is characterized by a large number of negligible tap coefficients are developed and compared. Exploiting this sparsity, the estimation problem is transformed into an equivalent on-off keying detection problem, whose solution gives an indication on the position of the zero taps. The proposed schemes are compared to the standard least squares estimate via simulations in terms of mean square error and bit error rate. A scheme based on sphere decoding appears to give the best performance while maintaining moderate complexity.

A Simple Sparse Channel Estimator for Underwater Acoustic Channels

Underwater acoustic channels exhibit very long delay spreads, but with limited multipath. To this end, channel estimation for sparse underwater acoustic channels is examined. Based on the on-off keying techniques developed in [1], a new iterative scheme for sparse channel estimation is provided. The method is of lower complexity than that of [1], exploiting features of underwater acoustic channels. The strategy iterates between zero-tap detection and structured channel estimation. The new sparse channel estimation scheme is compared to the Matching Pursuit method. It is shown that the proposed algorithm outperforms the latter over realistic underwater acoustic channels and has comparable computational complexity. Furthermore, the new estimation method does not require channel order information, in contrast to Matching Pursuit.

Clustered ML Channel Estimation for Ultra-Wideband Signals

The multipath capture of ultrawideband (UWB) communications systems can be dependent on the accuracy of channel estimation. Furthermore, inefficient modeling of the channel often leads to over-parametrization and increased channel estimation error. Relying on a channel model based on clusters, this letter proposes a maximum likelihood estimation strategy which exploits the properties of the UWB channel and offers performance improvement of about 2 dB over less parametric schemes. The robustness of the proposed algorithm in the presence of pulse distortion is investigated and the corresponding BER degradation is observed to be small even when experimental data are employed.

Semi-blind ML synchronization for UWB transmitted reference systems

Timing acquisition is one of the most critical issues in ultra-wideband (UWB) communications and poses a serious challenge to the deployment of impulse radio as a viable wireless technology. In most systems, timing acquisition is achieved by transmitting a training sequence at the beginning of each data burst. In this work, a semi-blind synchronization scheme based on maximum-likelihood (ML) techniques to recover both symbol and frame timing is studied. The proposed algorithm compares favorably to previously proposed UWB acquisition strategies and can be successfully employed in conjunction with both coherent and non-coherent detection strategies.

Low complexity synchronization for UWB noncoherent receivers

This paper investigates the operation of noncoherent detectors for UWB applications. Binary pulse-position modulation is assumed and it is shown that, if no channel information is exploited for detection purposes, application of the generalized maximum ratio test leads to an energy capture receiver. A simple synchronization scheme is proposed for such a receiver, and its performance is assessed by simulation over realistic multipath channels. Bit-error-rate degradations due to synchronization errors are found to be marginal.

Performance Analysis of MIMO OFDM ML Detection in the Presence of Channel Estimation Error

In this work the effects of channel estimation errors on the performance of a maximum likelihood detector in a MIMO OFDM system are assessed. The analysis is conducted in the framework of a long term evolution air interface and is based on a robust pilot aided channel estimator. To this end the correlation coefficient between the estimate and the real channel is computed and the impact of interpolation errors on the detector is evaluated as a function of key design parameters such as the pilot spacing, the filter length, and the number of transmit antennas. The effect of partial knowledge of the channel statistics at the receiver side is also studied.

Performance analysis of PRS-based synchronization algorithms for LTE positioning applications

Among the user equipment (UE) assisted position applications in Long Term Evolution (LTE), the observed time-difference of arrival (OTDOA) method which uses measurements reported by the UE physical layer provides the highest precision. The UE measurement task is based on dedicated positioning pilot signals and translates to a time synchronization problem, which is the focus of this paper work. Our analysis comprises the illustration of the proposed synchronization algorithm, its performance benchmarking in AWGN and multipath channels, the derivation of theoretical bounds and simulation results in a realistic LTE cellular scenario.

On the Performance of an LTE SFBC System in Frequency-Selective Channels in the Presence of Channel Estimation Errors

The uncoded performance of an Alamouti space-frequency block code operating in a frequency-selective channel is studied in the presence of channel estimation errors. The Alamouti Maximum Likelihood (ML) symbol detector with linear combining is considered at the receiver. Imperfect channel estimates are provided by a realistic Wiener interpolator. The impact of channel estimation errors and correlation mismatch on the uncoded performance of the system is evaluated as a function of key system parameters, such as the anticipated power delay profile at the channel estimator, the actual, i.e. true, power delay profile, the pilot spacing and the Wiener filter length. The role of partial knowledge of the channel statistics is investigated as well.