MMSE estimation of basis expansion model for rapidly time-varying channels

In this paper, we propose an estimation technique for rapidly time-varying channels. We approximate the time-varying channel using the basis expansion model (BEM). The BEM coefficients of the channel are needed to design channel equalizers. We rely on pilot symbol assisted modulation (PSAM) to estimate the channel (or the BEM coefficients of the channel). We first derive the optimal minimum mean-square error (MMSE) interpolation based channel estimation technique. We then derive the BEM channel estimation, where only the BEM coefficients are estimated. We consider a BEM with a critically sampled Doppler spectrum, as well as a BEM with an oversampled Doppler spectrum. It has been shown that, while the first suffers from an error floor due to a modeling error, the latter is sensitive to noise. A robust channel estimation can then be obtained by combining the MMSE interpolation based channel estimation and the BEM channel estimation technique. Through computer simulations, it is shown that the resulting algorithm provides a significant gain when an oversampled Doppler spectrum is used (an oversampling rate equal to 2 appears to be sufficient), while only a slight improvement is obtained when the critically sampled Doppler spectrum is used. 1

Blind Receiver Design for OFDM Systems Over Doubly Selective Channels

We develop blind data detectors for orthogonal frequency-division multiplexing (OFDM) systems over doubly selective channels by exploiting both frequency-domain and time-domain correlations of the received signal. We thus derive two blind data detectors: a time-domain data detector and a frequency-domain data detector. We also contribute a reduced complexity, suboptimal version of a time-domain data detector that performs robustly when the normalized Doppler rate is less than 3%. Our frequency-domain data detector and suboptimal time-domain data detector both result in integer least-squares (LS) problems. We propose the use of the V-BLAST detector and the sphere decoder. The time-domain data detector is not limited to the Doppler rates less than 3%, but cannot be posed as an integer LS problem. Our solution is to develop an iterative algorithm that starts from the suboptimal time-domain data detector output. We also propose channel estimation and prediction algorithms using a polynomial expansion model, and these estimators work with data detectors (decision-directed mode) to reduce the complexity. The estimators for the channel statistics and the noise variance are derived using the likelihood function for the data. Our blind data detectors are fairly robust against the parameter mismatch

Joint channel estimation and data detection for OFDM systems over doubly selective channels

In this paper, a joint channel estimation and data detection algorithm is proposed for OFDM systems under doubly selective channels (DSCs). After representing the DSC using Karhunen-Loève basis expansion model (K-L BEM), the proposed algorithm is developed based on the expectationmaximization (EM) algorithm. Basically, it is an iterative algorithm including two steps at each iteration. In the first step, the unknown coefficients in K-L BEM are first integrated out to obtain a function which only depends on data, and meanwhile, a maximum a posteriori (MAP) channel estimator is obtained. In the second step, data are directly detected by a novel approach based on the function obtained in the first step. Moreover, a Bayesian Cramer-Rao Lower Bound (BCRB) which is valid for any channel estimator is also derived to evaluate the performance of the proposed channel estimator. The effectiveness of the proposed algorithm is finally corroborated by simulation results. ©2009 IEEE.published_or_final_versionThe 20th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2009), Tokyo, Japan. 13-16 September 2009. In Proceedings of the 20th PIMRC, 2009, p. 446-45

Equalization Techniques of Control and Non-Payload Communication Links for Unmanned Aerial Vehicles

In the next years, several new applications involving unmanned aerial vehicles (UAVs) for public and commercial uses are envisaged. In such developments, since UAVs are expected to operate within the public airspace, a key issue is the design of reliable control and non-payload communication (CNPC) links connecting the ground control station to the UAV. At the physical layer, CNPC design must cope with time- and frequency-selectivity (so-called double selectivity) of the wireless channel, due to lowaltitude operation and flight dynamics of the UAV. In this paper, we consider the transmission of continuous phase modulated (CPM) signals for UAV CNPC links operating over doubly-selective channels. Leveraging on the Laurent representation for a CPM signal, we design a two-stage receiver: the first one is a linear time-varying (LTV) equalizer, synthesized under either the zero-forcing (ZF) or minimum mean-square error (MMSE) criterion; the second one recovers the transmitted symbols from the pseudo-symbols of the Laurent representation in a simple recursive manner. In addition to LTV-ZF and LTV-MMSE equalizers, their widely-linear versions are also developed, to take into account the possible noncircular features of the CPM signal. Moreover, relying on a basis expansion model (BEM) of the doubly-selective channel, we derive frequency-shift versions of the proposed equalizers, by discussing their complexity issues and proposing simplified implementations. Monte Carlo numerical simulations show that the proposed receiving structures are able to satisfactorily equalize the doubly-selective channel in typical UAV scenarios