In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved grey wolves optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for "GWO embedding IS"), guarantess global optimality and offers higher resolution capabilities over multi-carrier and multi-path single-input multiple-output (SIMO) channels. Traditional GWO initializes randomly the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the preys, i.e., find the correct AoAs (angles of arrival) and TDs (time delays), thereby affecting its search efficiency. Whereas GWOEIS ensures faster convergence by providing initial estimates based on a simplified IS function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed. Moreover, GWOEIS reaches the Cramér-Rao lower bound (CRLB) even at low SNR levels.

We propose a comprehensive evaluation of ETSI/DECT-2020 New Radio (NR) as an IMT-2020 candidate technology for the mMTC (connection density) and URLLC (reliability) use cases. Several independent evaluation groups assessed DECT-2020 NR performance against the IMT-2020 requirements and INRS played a key role within the Canadian Evaluation Group (CEG) in evaluating the technology via simulation. In order to develop 5G link- and system-level simulators, it is necessary to identify and understand all key characteristics of DECT technology. Nevertheless, the mMTC use case offers D2D communications, which made simulation analysis extremely cumbersome. In this paper, we also explain the different maneuvers in order to fasten the simulations and reduce complexity while respecting all DECT specifications. Simulation results provided from link- and system-level simulations for both URLLC and mMTC use cases show that DECT-2020 component RIT meets the IMT-2020 requirements.

In this paper, we tackle the problem of joint angles and time delays estimation (JADE) in a non-data aided (NDA) scenario where no pilot symbols are available at the receiver. A differential evolution (DE) technique is proposed in the context of maximum likelihood (ML) estimation is proposed to solve the resulting multi-dimensional optimization problem. DE is a metaheuristic global optimization algorithm-based on population, that finds the optimum iteratively by trying to improve a candidate solution based on an evolutionary process. We introduce the improved DE using a pseudo-pdf for easier generation of individuals. Simulations results show that the proposed solution is significantly more efficient in terms of global convergence than the classic differential evolution algorithm (CDEA) as well in terms of RMSE. Moreover, due to a very useful approximation, we are able to reduce even further the computational complexity of the proposed technique without any significant performance loss. Computer simulations also show the distinct advantage of the new NDA-DE approach over the existing techniques. Most remarkably, it also approaches the Cramér-Rao lower bound (CRLB) at medium and high SNR levels.

This paper investigates the problem of fast time-varying 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) in both the data-aided (DA) and non-data-aided (NDA) case. The DA ML estimates are found in closed-form expressions and then used to initialize the expectation maximization (EM) algorithm that is used to iteratively maximize the LLF in the NDA case. We also introduce an alternative initialization procedure that requires less pilot symbols as compared to the DA ML-based solution without incurring a significant performance loss. Simulation results show that the proposed EM-based estimator converges within few iterations providing accurate estimates for all multipath gains, thereby resulting in significant BER gain as compared to the DA least square (LS) technique.

In this paper, we investigate the problem of fast time-varying multipath channel estimation over orthogonal frequency-division multiplexing (OFDM)-type transmissions. We do so by tracking each complex gain variation using a polynomial-in-time expansion. To that end, we derive the log-likelihood function (LLF) in the non-data-aided (NDA) case. Since the LLF is extremely nonlinear, we opt for the expectation maximization (EM) concept to find its global maximum. Simulation results show that the new estimator is able to converge to the global maximum within few iterations only and to provide accurate estimates for all multipath gains, thereby resulting in significant BER and link-level throughput gains.

In this paper, we investigate maximum likelihood (ML) time delay (TD) and carrier frequency offset (CFO) synchronization in multi-node decode-and-forward (DF) cooperative relaying systems operating over time-varying channels (TVCs). This new synchronization scheme is embedded into a distributed multiple input multiple output (MIMO)-relay beamforming transceiver structure to avoid the drawbacks of multidimensional ML estimation at the destination and to minimize the overhead cost. The new technique can be jointly implemented with any Doppler spread estimator in an iterative scheme using a time-constant channel (TCC) based synchronization method at the initialization step. The resulting TD and CFO estimates along with the channel estimates are then fed into a distributed MIMO-relay beamforming transceiver of K single-antenna nodes, for pre-compensation at each node of the transmitted signals, to ensure constructive maximum ratio combining (MRC) at the destination. Simulation results show significant synchronization accuracy improvement over previous distributed multi-node synchronization techniques assuming TCCs. The latter translates into noticeable gains in terms of useful link-level throughput, more so at higher Doppler or with more relaying nodes.

In this paper, we investigate maximum likelihood (ML) time delay (TD) and carrier frequency offset (CFO) synchronization in multi-node decode-and-forward cooperative relaying systems operating over time-varying channels. This new synchronization scheme is embedded into a distributed multiple input multiple output (MIMO)-relay beamforming transceiver structure to avoid the drawbacks of multidimensional ML estimation at the destination and to minimize the overhead cost. By accounting for a perfect Doppler spread value, the new synchronization solution delivers accurate TD and CFO estimates. For real-world operation, however, this new technique can be jointly implemented with any Doppler spread estimator in a new iterative scheme using a time-constant channel (TCC)-based synchronization method at the initialization step. The resulting TD and CFO estimates along with the channel estimates are then fed into a distributed MIMO-relay beamforming transceiver of K single-antenna nodes, for pre-compensation at each node of the transmitted signals, to ensure constructive maximum ratio combining (MRC) at the destination. Simulation results show significant synchronization accuracy improvement over previous distributed multi-node synchronization techniques assuming TCCs. The latter translates into noticeable gains in terms of useful link-level throughput, more so at higher Doppler or with more relaying nodes.

In this paper, we tackle the problem of joint angle and delays estimation (JADE) of multiple reflections of a known signal impinging on multiple receiving antennae. Based on the importance sampling (IS) concept, we propose a new non-iterative maximum likelihood (ML) estimator that enjoys guaranteed global optimality and enhanced high-resolution capabilities for both single- and multi-carrier models. The new ML approach succeeds in transforming the original multi-dimensional optimization problem into multiple two-dimensional ones thereby resulting in huge computational savings. Moreover, it does not suffer from the off-grid problems that are inherent to most existing JADE techniques. By exploiting the sparsity feature of a carefully designed pseudo-pdf that is intrinsic to the new estimator, we also propose a novel approach that enables the accurate detection of the unknown number of paths over a wide range of practical signal-to-noise ratios (SNRs). Computer simulations show the distinct advantage of the new ML estimator over state-of-the art JADE techniques both in the single- and multi-carrier scenarios. Most remarkably, they suggest that the proposed IS-based ML JADE is statistically efficient as it almost reaches the Camér-Rao lower bound (CRLB) even in the adverse conditions of low SNR levels. Using real-world channel measurements collected from four access points (APs) with IEEE 802.11ac standard's setup parameters in an indoor environment, we also show that the proposed ML estimator achieves a localization performance below 15 cm accuracy.

In this paper, we investigate maximum likelihood (ML) time delay (TD) and carrier frequency offset (CFO) synchronization (i.e., estimation and pre- compensation) in decode-and-forward (DF) cooperative systems operating over time-varying channels (TVCs). The new technique is embedded at each relay node in order to avoid the drawbacks of multidimensional ML estimation at the destination and to minimize the overhead cost. By accounting for a perfect Doppler spread value, the new synchronization solution delivers accurate TD and CFO estimates at each relay. The resulting TD and CFO estimates along with the channel estimates are then exploited by the MIMO relay for precompensation at each node of the distributed transmit beamforming signals to ensure constructive maximum ratio combining (MRC) at the destination. Simulation results show significant synchronization accuracy improvement over previous distributed multi-node synchronization techniques assuming time-constant channels (TCCs). The latter translates into noticeable gains in terms of useful (i.e., after accounting for incurred overhead) link-level throughput, more so at higher Doppler.

In this paper, we propose a new code-aided (CA) maximum likelihood (ML) approach for time synchronization in turbo-coded systems. The time delay estimate is refined at each turbo iteration owing to the increasingly accurate estimates for the log-likelihood ratios (LLRs) of the coded bits. The refined time delay estimate is then used by the matched filter (MF) in order to provide the soft-input soft-output (SISO) decoders with more reliable symbol-rate samples for the next turbo iteration. Simulation results show the remarkable performance improvements of CA estimation against the traditional non-data-aided (NDA) estimation scheme. Moreover, the new CA ML estimator (MLE) enjoys significant advantage in computational complexity over existing ML CA solutions.

In this paper, we address the problem of direction of arrival (DOA) estimation from turbo-coded square-QAM-modulated transmissions. We devise a new code-aware direction finding concept, derived from maximum likelihood (ML) theory, wherein the soft information provided by the soft-input soft-output decoder, in the form of log-likelihood ratios, is exploited to assist the estimation process. At each turbo iteration, the decoder output is used to refine the ML DOA estimate. The latter is in turn used to perform a more focused receiving beamforming thereby providing more reliable information-bearing sequences for the next turbo iteration. In order to benchmark the new estimator, we also derive the analytical expressions for the exact Cramer-Rao lower bounds (CRLBs) of code-aided (CA) DOA estimates. Simulation results will show that the new CA direction finding scheme lies between the two traditional schemes of completely non-data-aided and data-aided (DA) estimations. Huge performance improvements are achieved by embedding the direction finding and receive beamforming tasks into the turbo iteration loop. Moreover, the new CA DOA estimator reaches the new CA CRLBs over a wide range of practical SNRs thereby confirming its statistical efficiency. As expected intuitively, its performance further improves at higher coding rates and/or lower modulation orders.

This paper tackles the problem of code-aided (CA) timing recovery in turbo-coded square-QAM transmissions. Owing to a simple recursive construction process, some hidden properties of Gray-coded (GC) square-QAM constellations are demystified and the code bits' a priori log- likelihood ratios (LLRs) are explicitly incorporated in the log-likelihood function (LLF). Then, by splitting the underlying LLF into the sum of two analogous terms, we derive for the very first time the closed-form expressions for the exact Cramer-Rao lower bounds (CRLBs) of the underlying turbo synchronization problem. Computer simulations will show that the new closed-form CRLBs coincide exactly with their empirical counterparts evaluated previously using exhaustive Monte-Carlo simulations. They will also show unambiguously the remarkable performance improvements of the CA scheme against the traditional non-data-aided (NDA) one. Over a wide range of practical SNRs, the new CA CRLBs reach those of the completely data-aided (DA) scheme in which all the transmitted symbols are perfectly known to the receiver.

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 code-aided (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 non-data-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.

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) [13] with few received samples.