A NEW IMPORTANCE-SAMPLING ML ESTIMATOR OF TIME DELAYS AND ANGLES OF ARRIVAL IN MULTIPATH ENVIRONMENTS

ABSTRACT In this paper, the importance sampling (IS) concept is exploited for the first time in the context of maximum likelihood (ML) estimation of both the time delays and angles of arrival (AoAs) in multipath propagation environments. The global maximum of the compressed likelihood function (CLF) is found empirically with a low computational cost. Simulations suggest that the new IS-based ML-type estimator outperforms, in terms of accuracy, the main state-of-the-art techniques published on the topic. It is also able to reach the Cramér-Rao-lower bound (CRLB

ML-Type EM-Based Estimation of Fast Time-Varying Frequency-Selective Channels Over SIMO OFDM Transmissions

This paper investigates the problem of fast time-varying frequency-selective (i.e., multipath) channel estimation over single-input multiple-output orthogonal frequency-division multiplexing (SIMO OFDM)-type transmissions. We do so by tracking the variations of each complex gain coefficient using a polynomial-in-time expansion. To that end, we derive the log-likelihood function (LLF) both in the data-aided (DA) and non-data-aided (NDA) cases. The DA maximum likelihood (ML) estimates over fast SIMO OFDM channels are derived here for the first time in closed-form expressions and hereby shown to be limited to applying over each receive antenna the DA least squares (LS) estimator tailored in [1] to fast SISO OFDM channels. This DA ML is used to initialize periodically, over a relatively large number of data blocks (i.e., with further reduced and relatively close-to-negligible pilot overhead compared to DA ML), a new expectation maximization (EM) ML-type solution we developed here in the NDA case to iteratively maximize the LLF. We also introduce an alternative regularized DA ML (RDM) initialization solution no longer requesting - in contrast to DA ML - more per-carrier pilot frames than the number of paths to further reduce overhead without incurring significant performance losses. Simulation results show that the proposed hybrid ML-EM estimator (i.e., combines all new NDA ML-EM and DA ML or RDM versions) converges within few iterations, thereby providing very accurate estimates of all multipath channel gains. Most importantly, this increased estimation accuracy translates into very significant BER and link-level per-carrier throughput gains over the best representative benchmark solution available so far for the problem at hand, the SISO DA LS technique in [1] with its new generalization here to SIMO systems

Low-complexity DOA estimation from short data snapshots for ULA systems using the annihilating filter technique

Abstract This paper addresses the problem of DOA estimation using uniform linear array (ULA) antenna configurations. We propose a new low-cost method of multiple DOA estimation from very short data snapshots. The new estimator is based on the annihilating filter (AF) technique. It is non-data-aided (NDA) and does not impinge therefore on the whole throughput of the system. The noise components are assumed temporally and spatially white across the receiving antenna elements. The transmitted signals are also temporally and spatially white across the transmitting sources. The new method is compared in performance to the Cramér-Rao lower bound (CRLB), the root-MUSIC algorithm, the deterministic maximum likelihood estimator and another Bayesian method developed precisely for the single snapshot case. Simulations show that the new estimator performs well over a wide SNR range. Prominently, the main advantage of the new AF-based method is that it succeeds in accurately estimating the DOAs from short data snapshots and even from a single snapshot outperforming by far the state-of-the-art techniques both in DOA estimation accuracy and computational cost

CLOSED-FORM CRAMER-RAO LOWER BOUNDS FOR DOA ESTIMATION FROM TURBO-CODED SQUARE-QAM-MODULATED TRANSMISSIONS

ABSTRACT This paper tackles the problem of the direction of arrival (DOA) estimation in turbo-coded systems. We derive for the first time the closed-form expressions for the Cramér-Rao lower bounds (CRLBs) of the codeaided (CA) DOA estimates from arbitrary square-QAM modulated signals. We succeed in factorizing the likelihood function of the system into two analogous terms linearizing thereby all the derivation steps of the Fisher information (FI) element. Simulation results demonstrate that the CRLB for the CA DOA estimates lies between its counterparts in nondata-aided (NDA) and data-aided (DA) estimation schemes. Moreover, the DOA CA CRLB improves by decreasing the coding rate highlighting thereby the potential gain in estimation performance stemming from the proper exploitation of the decoder output