Orthogonal frequency division multiplexing (OFDM) became a popular transmission approach since it suppresses intersymbol interference (ISI) due to large channel delays. However, intercarrier interference (ICI) for high mobility receivers yields corrupted channel estimates for pilot-aided OFDM channel estimation. Thus, this paper presents a novel time-variant channel estimation approach to mitigate this system impairment. We linearize the time-variant channel and determine the expansion point by channel estimates corresponding to the current OFDM symbol, and we get the unknown channel slopes by an iterative data and channel estimation. Our algorithm combines a least squares or a minimum norm channel slope estimation and the detection of the channel paths. It sets any channel path power equal zero once the corresponding power estimate is smaller than a given threshold. Our algorithm exploits the large channel correlations between the cyclic prefix and the successive OFDM symbol optimally. These maximum correlations reason the superiority of ICI mitigation with cyclic prefixes compared to channel slope estimation with adjacent OFDM symbols, which needs at least twice the number of pilot symbols. Additionally, our iterative ICI mitigation reduces noise impairments.

This paper presents a novel method for the analytical mean time to lose lock (MTLL) computation of coherent second-order Langevin delay-locked loops (DLLs). Analytical MTLL computation is a key task for DLLs, since the computational complexity of numerical MTLL simulations is far too high in many operating ranges of the second-order Langevin DLLs. To obtain the crucial MTLL values analytically without simulations, we rewrite the Langevin stochastic differential equation (SDE) as a vector-valued Ornstein-Uhlenbeck (OU) SDE. It includes a Gaussian noise term, which yields as a solution of the vector-valued OU SDE a time-variant Gaussian distribution. Thus, the complementary error function yields the loss of lock probability and thereby the MTLL. If we replace the complementary error functions by suitable exponential approximations, we obtain a simple MTLL expression with an exponential function as dominant term. The simple exponential MTLL expression yields the optimum loop parameters corresponding to the maximum MTLL. Simulation results confirm that the optimum loop parameters corresponding to our analytical MTLL computation method and to the simplified exponential approximation coincide. Besides the crucial analytical MTLL results, the OU random processes yield additionally the likewise crucial analytical jitter results.

This paper presents a novel analytical derivation of the false alarm probability (FAP) and detection probability (DP) for non-line-of-sight (NLOS) detection of GNSS signals. In navigation systems, the NLOS propagation in for example urban environments causes positioning errors in the order of hundreds of meters. Thus, detection and mitigation of NLOS signals is a critical task for high accuracy navigation receivers. In this paper, we derive the FAP and DP of power-scaled detectors based on the multipath signals. The derivation considers frequency-selective fading channels taking sub-chip multipath interference, channel dynamics and initial frequency offsets into account. The results show a simple way to calculate the DP versus FAP. Simulation results verify the analytical probabilities and show the trade-off between temporal non-coherent versus temporal coherent averaging. Clearly, the optimum noise averaging depends on the length of the symbol sequence and channel dynamics. Additionally, the simulations show the improved DP of an antenna array at the receiver. Furthermore we see the influence of sub-chip interference in the simulation results.

Motivated by the possibility of decreasing the intersymbol interference (ISI) which is due to large delays of a multipath mobile radio channel, orthogonal frequency division multiplexing (OFDM) became very popular. However, the timevariance of the mobile radio channel induces intercarrier interference (ICI) yielding substantial channel estimation errors and thereby tremendous transmission impairments. Contrary to previous algorithms which resort to a linearization of the time-variant channel, we combat the ICI using eigenspaces of time-domain covariance matrices defined by the autocorrelation function of the Doppler spread. We perform a basis expansion using Slepian sequences and determine the basis coefficients of the time-variant channel by channel estimates from previous OFDM symbols. Once we know these basis coefficients, we obtain the necessary time-variant channel estimation by the Slepian sequences. These time-variant channel estimates allow a symbol detection in frequency domain which eliminates the ICI almost completely. Simulation results investigate both the signal to interference ratio (SIR) and the bit error ratio (BER) of our new ICI mitigation methods and reveal the superiority compared to previous algorithms for ICI reduction.

This paper presents a novel low complexity high resolution timing and Doppler acquisition algorithm for direct-sequence code division multiple access (DS-CDMA) systems. We apply the two-dimensional unitary estimation of signal parameters via rotational invariant techniques (ESPRIT) algorithm to the joint delay and Doppler acquisition task in multipath environments. Additionally, we use the advantageous beam space (BS) implementation of unitary ESPRIT, which reduces the computational complexity once again remarkably. The exploitation of a-priori knowledge according to the BS version of unitary ESPRIT also reduces the root mean square error (RMSE) at low signal to noise ratios (SNRs). Furthermore, we enhance the delay and Doppler multipath acquisition by the two-dimensional structured least squares (SLS) algorithm. Thus, our delay and Doppler RMSE reaches the Cramer-Rao bound (CRB) almost as good as maximum likelihood (ML) delay and Doppler acquisition. However, our algorithms save computational complexity compared to the ML algorithm by several orders of magnitude.

This paper presents a novel time-variant ML channel estimator for mobile radio navigation receivers. Our novel ML channel estimator enables the coherent noise averaging over several hundred codewords for time-variant channel phasors. Compared to the conventional incoherent summation of log-likelihood functions or compared to the conventional time-invariant log-likelihood functions, we avoid the squaring loss (SL) completely. The novel time-variant log-likelihood function compared to the conventional time-invariant log-likelihood function yields an SNR gain of up to 15 dB for an observation interval of 200ms. Additionally, since the novel time-invariant log-likelihood functions only require a Slepian subspace of a small dimension, the computational complexity of our novel time-variant ML channel estimation does not exceed the computational complexity of the conventional time-invariant ML channel estimation.

Traditionally, the low complexity maximum likelihood (ML) estimation of several closely-spaced superimposed signal replicas in a communications or navigation receiver is performed using expectation maximization (EM) or space alternating generalized expectation maximization (SAGE) methods. However, the componentwise decomposition of a multidimensional log-likelihood function according to the EM or SAGE algorithm fails for realistic time-variant channels. Thus, we consider the minimization of a multidimensional log likelihood function for the closely-spaced channel delays. To get this log-likelihood function, we use a decomposition of the highly time-variant channel path phasors based on Slepian's subspaces. The obtained log likelihood function enables a coherent averaging over several hundred codewords even for high-mobility receivers. Therefore, we avoid the squaring loss (SL) that appears for the conventional incoherent averaging and incoherent log-likelihood functions. Our simulations show that the minimization of coherent log-likelihood functions based on Slepian's functions yields a root mean square error (RMSE) of the line of sight (LOS) delay estimation that approximates the Cramer-Rao bound (CRB) rather closely.

In this paper, we present two extensions of the block conjugate gradient (BCG) algorithm, a method which exploits the concept of block Krylov subspaces. First, we extend the BCG algorithm such that it is more flexible concerning the dimension of the block Krylov subspace. Second, a computationally efficient preconditioned BCG (PBCG) algorithm is introduced which turns out to outperform the standard BCG algorithm concerning the complexity-performance ratio. Hence, we provide a powerful implementation for reduced-rank signal processing in the minimum mean square error (MMSE) sense. Simulation results show the gain in rank-flexibility and convergence speed