We investigate the uplink scenario in massive multiple input multiple output (MIMO) communication systems using dense uniform linear arrays (ULAs) of antenna elements that are tightly packed within a confined space and equipped with low-resolution analog-to-digital converters (ADCs). We tackle the problem of power consumption reduction and hardware simplification while simultaneously improving the performance of quantized systems by exploring spatial oversampling. Due to the subwavelength inter-element spacing in dense ULAs, extrinsic spatial thermal noise correlations arise from the significant coupling between adjacent antenna terminals. In addition to this correlated extrinsic noise, the noise figure caused by hardware imperfections profoundly impacts signal recovery and cannot be simply neglected in system performance analysis. We propose a low-resolution multi-user detection method based on a modified version of the vector approximate message passing (VAMP) framework. We also conduct a state evolution analysis to characterize the asymptotic behaviour of the proposed algorithm. We demonstrate that spatial oversampling in the context of low-resolution communication substantially enhances system performance, bringing it closer to the ideal scenario with infinite-resolution ADCs. This reveals the benefits of spatial oversampling as an effective strategy for enhancing the performance of low-resolution massive MIMO systems. We also thoroughly analyze the impact of noise figure on signal recovery under spatial oversampling, thereby highlighting its significance in system design considerations1.

This paper explores the idea of usingredirectivereconfigurable intelligent surfaces (RedRIS) to overcome many of the challenges associated with the conventionalreflectiveRIS. We develop a framework for jointly optimizing the switching matrix of the lens-type RedRIS ports along with the active precoding matrix at the base station (BS) and the receive scaling factor. A joint non-convex optimization problem is formulated under the minimum mean-square error (MMSE) criterion with the aim to maximize the spectral efficiency of each user. In the single-cell scenario, the optimum active precoding matrix at the multi-antenna BS and the receive scaling factor are found in closed-form by applying Lagrange optimization, while the optimal switching matrix of the lens-type RedRIS is obtained by means of a newly developed alternating optimization algorithm. We then extend the framework to the multi-cell scenario with single-antenna base stations that are aided by the same lens-type RedRIS. We further present two methods for reducing the number of effective connections of the RedRIS ports that result in appreciable overhead savings while enhancing the robustness of the system. The proposed RedRIS-based schemes are gauged against conventional reflective RIS-aided systems under both perfect and imperfect channel state information (CSI). The simulation results show the superiority of the proposed schemes in terms of overall throughput while incurring much less control overhead.

Approximate message passing (AMP) algorithms are devised under the Gaussianity assumption of the measurement noise vector. In this work, we relax this assumption within the vector AMP (VAMP) framework to arbitrary independent and identically distributed (i.i.d.) noise priors. We do so by rederiving the linear minimum mean square error (LMMSE) to accommodate both the noise and signal estimations within the message passing steps of VAMP. Numerical results demonstrate how our proposed algorithm handles non-Gaussian noise models as compared to VAMP. This extension to general noise priors enables the use of AMP algorithms in a wider range of engineering applications where non-Gaussian noise models are more appropriate.

This paper investigates distributed estimation problems with factorized structures over factor graphs. By building upon the recent progress in the approximate message passing (AMP) paradigm, this paper extends the vector AMP (VAMP) algorithm to the distributed scenario where multiple agents collaboratively estimate the same signal using different measurement channels. We do so by deriving the new collaborative linear minimum mean square error (LMMSE) messages within the estimation steps through message passing. The new algorithm — coined D-VAMP — allows distributed agents to be heterogeneous thereby handling a broader class of practical applications. Our numerical results demonstrate the trade-off between the reconstructed accuracy and the level of heterogeneity measured in terms of the number of correlated agents and signal-to-noise ratio.

Machine learning (ML)-aided wireless communication studies are initiating the investigation of the domain generalization capabilities of deep neural networks (DNNs) when applied to communication problems. They do so by adopting the out-of-distribution (OOD) performance evaluation by comparing it to the in-distribution (ID) performance as usually done within the ML community. In this paper, we demonstrate that such blind adoption can yield a misleading OOD performance analysis of DNNs unless wireless communication metrics are involved in the OOD evaluation. By analyzing the OOD performance of an end-to-end (E2E) ML communication system over additive white Gaussian noise (AWGN) channels in terms of bit error rate (BER), we show that smaller (resp. larger) BER gaps between ID and OOD performance do not necessarily translate into a high (resp. low) reconstruction accuracy. Our results suggest that the comparison between ID and OOD performances is not enough to judge whether the OOD performance is acceptable or not. The ID and OOD performances of E2E communication systems should instead be carried out based on wireless metrics.

This paper develops a linear minimum mean-square error (LMMSE) channel estimator that takes advantage of the mutual coupling in antenna arrays. We model the mutual coupling through multiport networks and express the singleuser multiple-input multiple-output (MIMO) communication channel in terms of the impedance and scattering parameters of the antenna arrays. It is shown that appropriately accounting for mutual coupling through the developed physically consistent model leads to remarkable improvements in terms of channel estimation performance. We demonstrate the gains in our algorithm in a rich-scattering environment using a connected array of slot antennas both at the transmitter and receiver sides.

With the proliferation of deep learning techniques for wireless communication, several works have adopted learning-based approaches to solve the channel estimation problem. While these methods are usually promoted for their computational efficiency at inference time, their use is restricted to specific stationary training settings in terms of communication system parameters, e.g., signal-to-noise ratio (SNR) and coherence time. Therefore, the performance of these learning-based solutions will degrade when the models are tested on different settings than the ones used for training. This motivates our work in which we investigate continual supervised learning (CL) to mitigate the shortcomings of the current approaches. In particular, we design a set of channel estimation tasks wherein we vary different parameters of the channel model. We focus on Gauss-Markov Rayleigh fading channel estimation to assess the impact of non-stationarity on performance in terms of the mean square error (MSE) criterion. We study a selection of state-of-the-art CL methods and we showcase empirically the importance of catastrophic forgetting in continuously evolving channel settings. Our results demonstrate that the CL algorithms can improve the interference performance in two channel estimation tasks governed by changes in the SNR level and coherence time.

We present a unified model for connected antenna arrays with a large number of tightly integrated (i.e., coupled) antennas in a compact space within the context of massive multiple-input multiple-output (MIMO) communication. We refer to this system as tightly-coupled massive MIMO. From an information-theoretic perspective, scaling the design of tightly-coupled massive MIMO systems in terms of the number of antennas, the operational bandwidth, and form factor was not addressed in prior art. We investigate this open research problem using a physically consistent modeling approach for far-field (FF) MIMO communication based on multi-port circuit theory. In doing so, we turn mutual coupling (MC) from a foe to a friend of MIMO systems design, thereby challenging a basic percept in antenna systems engineering that promotes MC mitigation/compensation. We show that tight MC widens the operational bandwidth of antenna arrays thereby unleashing a missing MIMO gain that we coin “bandwidth gain”. Furthermore, we derive analytically the asymptotically optimum spacing-to-antenna-size ratio by establishing a condition for tight coupling in the limit of large-size antenna arrays with quasi-continuous apertures. We also optimize the antenna array size while maximizing the achievable rate under fixed transmit power and inter-element spacing. Then, we study the impact of MC on the achievable rate of MIMO systems under line-of-sight (LoS) and Rayleigh fading channels. These results reveal new insights into the design of tightly-coupled massive antenna arrays as opposed to the widely-adopted “disconnected” designs that disregard MC by putting faith in the half-wavelength spacing rule.

We present a unified model for connected antenna arrays with a large number of tightly integrated (i.e., coupled) antennas in a compact space within the context of massive multiple-input multiple-output (MIMO) communication. We refer to this system as tightly-coupled massive MIMO. From an information-theoretic perspective, scaling the design of tightly-coupled massive MIMO systems in terms of the number of antennas, the operational bandwidth, and form factor was not addressed in prior art. We investigate this open research problem using a physically consistent modeling approach for far-field (FF) MIMO communication based on multi-port circuit theory. In doing so, we turn mutual coupling (MC) from a foe to a friend of MIMO systems design, thereby challenging a basic percept in antenna systems engineering that promotes MC mitigation/compensation. We show that tight MC widens the operational bandwidth of antenna arrays thereby unleashing a missing MIMO gain that we coin “bandwidth gain”. Furthermore, we derive analytically the asymptotically optimum spacing-to-antenna-size ratio by establishing a condition for tight coupling in the limit of large-size antenna arrays with quasi-continuous apertures. We also optimize the antenna array size while maximizing the achievable rate under fixed transmit power and inter-element spacing. Then, we study the impact of MC on the achievable rate of MIMO systems under line-of-sight (LoS) and Rayleigh fading channels. These results reveal new insights into the design of tightly-coupled massive antenna arrays as opposed to the widely-adopted “disconnected” designs that disregard MC by putting faith in the half-wavelength spacing rule.

This paper introduces a novel information-theoretic approach for studying the effects of mutual coupling (MC), between the transmit and receive antennas, on the overall performance of single-input-single-output (SISO) near-field communications (NFC). By incorporating the finite antenna size constraint using Chu’s theory and under the assumption of canonical-minimum scattering (CMS), we derive the MC between two radiating volumes of fixed sizes. Expressions for the self and mutual impedances are obtained by the use of the reciprocity theorem. Based on a circuit-theoretic two-port model for SISO radio communication systems, we first establish its input-output relationship where the noise depends on the self/mutual impedances of the antennas, unlike the conventional assumption of independent additive white Gaussian noise. We then characterise the achievable data rate for a given pair of transmit and receive antenna sizes, thereby providing an upper bound on the system performance under physical size constraints. Through the lens of these findings, we shed new light on the influence of MC on the information-theoretic limits of near-field communications using compact antennas.

This paper introduces a new algorithmic solution to the massive unsourced random access (mURA) problem. The proposed uncoupled compressed sensing (UCS)-based scheme relies on slotted transmissions and takes advantage of the inherent coupling provided by the users’ spatial signatures in the form of channel correlations across slots to completely eliminate the need for concatenated coding. As opposed to all existing methods, the proposed solution combines the steps of activity detection, channel estimation, and data decoding into a unified mURA framework. It capitalizes on the bilinear vector approximate message passing (Bi-VAMP) algorithm, tailored to fit the inherent constraints of mURA. Exhaustive computer simulations demonstrate that the proposed scheme outperforms recent coupled and uncoupled mURA schemes in massive connectivity/MIMO setup.

This paper introduces a novel information-theoretic approach for studying the effects of mutual coupling (MC), between the transmit and receive antennas, on the overall performance of single-input-single-output (SISO) near-field communications. By incorporating the finite antenna size constraint using Chu’s theory and under the assumption of canonical-minimum scattering, we derive the MC between two radiating volumes of fixed sizes. Expressions for the self and mutual impedances are obtained by the use of the reciprocity theorem. Based on a circuit-theoretic two-port model for SISO radio communication systems, we establish the achievable rate for a given pair of transmit and receive antenna sizes, thereby providing an upper bound on the system performance under physical size constraints. Through the lens of these findings, we shed new light on the influence of MC on the information-theoretic limits of near-field communications using compact antennas.

We derive an achievability bound in an uplink setting where N single-antenna devices, of which a random subset of Kausers are active in each transmission period, attempt to update a base-station (BS), equipped with M antennas, with their status packets. Motivated by emerging applications of massive connectivity we consider the asymptotic scenario where both the total number of users and the number of antennas at the BS grow large at a fixed ratio $\zeta = \frac{M}{N}$. Under maximal-ratio combining and perfect channel state information at the receiver, we find that the achievable rate approaches ${\log _2}\left( {1 + \frac{M}{{{K_a}}}} \right)$ in the large system limit. We explore the trade-offs between this achievable rate and the freshness of the status packets using the age of information (AoI) metric. In the limiting regime, we find that the penalty one pays for increasing the data rate is a rise in the minimum AoI obtainable. Finally, we compare recent massive unsourced random access (URA) schemes against the newly established bound.

This paper introduces a novel approach of utilizing the reconfigurable intelligent surface (RIS) for joint data modulation and signal beamforming in a multi-user downlink cellular network by leveraging the idea of backscatter communication. We present a general framework in which the RIS, referred to as modulating intelligent surface (MIS) in this paper, is used to:$i$) beamform the signals for a set of users whose data modulation is already performed by the base station (BS), and at the same time,$ii$) embed the data of a different set of users by passively modulating the deliberately sent carrier signals from the BS to the RIS. To maximize each user’s spectral efficiency, a joint non-convex optimization problem is formulated under the sum minimum mean-square error (MMSE) criterion. Alternating optimization is used to divide the original joint problem into two tasks of:$i$) separately optimizing the MIS phase-shifts for passive beamforming along with data embedding for the BS- and MIS-served users, respectively, and$ii$) jointly optimizing the active precoder and the receive scaling factor for the BS- and MIS-served users, respectively. While the solution to the latter joint problem is found in closed-form using traditional optimization techniques, the optimal phase-shifts at the MIS are obtained by deriving the appropriate optimization-oriented vector approximate message passing (OOVAMP) algorithm. Moreover, the original joint problem is solved under both ideal and practical constraints on the MIS phase shifts, namely, the unimodular constraint and assuming each MIS element to be terminated by a variable reactive load. The proposed MIS-assisted scheme is compared against state-of-the-art RIS-assisted wireless communication schemes and simulation results reveal that it brings substantial improvements in terms of system throughput while supporting a much higher number of users.

Using ideas from Chu and Bode/Fano theories, we characterize the maximum achievable rate over the single-input single-output wireless communication channels under a restriction on the antenna size at the receiver. By employing circuit-theoretic multiport models for radio communication systems, we derive the information-theoretic limits of compact antennas. We first describe an equivalent Chu’s antenna circuit under the physical realizability conditions of its reflection coefficient. Such a design allows us to subsequently compute the achievable rate for a given receive antenna size thereby providing a physical bound on the system performance that we compare to the standard size-unconstrained Shannon capacity. We also determine the effective signal-to-noise ratio (SNR) which strongly depends on the antenna size and experiences an apparent finite-size performance degradation where only a fraction of Shannon capacity can be achieved. We further determine the optimal signaling bandwidth which shows that impedance matching is essential in both narrowband and broadband scenarios. We also examine the achievable rate in presence of interference showing that the size constraint is immaterial in interference-limited scenarios. Finally, our numerical results of the derived achievable rate as function of the antenna size and the SNR reveal new insights for the physically consistent design of radio systems.

We investigate the age-limited capacity of the Gaussian many channel with total$N$users, out of which a random subset of$K_{a}$users are active in any transmission period, and a large-scale antenna array at the base station (BS). In an uplink scenario where the transmission power is fixed among the users, we consider the setting in which both the number of users,$N$, and the number of antennas at the BS,$M$, are allowed to grow large at a fixed ratio$\zeta = {M}/{N}$. Assuming perfect channel state information (CSI) at the receiver, we derive the achievability bound under maximal ratio combining. As the number of active users,$K_{a}$, increases, the achievable spectral efficiency is found to increase monotonically to a limit$\log _{2}\left ({1+\frac {M}{K_{a}}}\right)$. Further extensions of the analysis to the zero-forcing receiver as well as imperfect CSI are provided, demonstrating the channel estimation penalty in terms of the mean squared error in estimation. Using the age of information (AoI) metric, first coined by Kaul et al., as our measure of data timeliness or freshness, we investigate the trade-offs between the AoI and spectral efficiency in the context massive connectivity with large-scale receiving antenna arrays. As an extension of Liu and Yu, based on our large system analysis, we provide an accurate characterization of the asymptotic (finite system size) spectral efficiency as a function of the number of antennas and the number of users, the attempt probability, and the AoI. It is found that while the spectral efficiency can be made large, the penalty is an increase in the minimum AoI obtainable. The proposed achievability bound is further compared against recent massive MIMO-based massive unsourced random access (URA) schemes.

We put forward a new algorithmic solution to the massive unsourced random access (URA) problem, by leveraging the rich spatial dimensionality offered by large-scale antenna arrays. This paper makes an observation that spatial signature is key to URA in massive connectivity setups. The proposed scheme relies on a slotted transmission framework but eliminates the need for concatenated coding that was introduced in the context of the coupled compressive sensing (CCS) paradigm. Indeed, all existing works on CCS-based URA rely on an inner/outer tree-based encoder/decoder to stitch the slot-wise recovered sequences. This paper takes a different path by harnessing the nature-provided correlations between the slot-wise reconstructed channels of each user in order to put together its decoded sequences. The required slot-wise channel estimates and decoded sequences are first obtained through the hybrid generalized approximate message passing (HyGAMP) algorithm which systematically accommodates the multiantenna-induced group sparsity. Then, a channel correlation-aware clustering framework based on the expectation-maximization (EM) concept is used together with the Hungarian algorithm to find the slot-wise optimal assignment matrices by enforcing two clustering constraints that are very specific to the problem at hand. Stitching is then accomplished by associating the decoded sequences to their respective users according to the ensuing assignment matrices. Exhaustive computer simulations reveal that the proposed scheme can bring performance improvements, at high spectral efficiencies, as compared to a state-of-the-art technique that investigates the use of large-scale antenna arrays in the context of massive URA.

This paper tackles the problem of joint active and passive beamforming optimization for an intelligent reflective surface (IRS)-assisted multi-user downlink multiple-input multiple-output (MIMO) communication system under both ideal and practical IRS phase shifts. We aim to maximize the spectral efficiency of the users by minimizing the sum mean square error (MSE) of the users’ received symbols. For this, a joint non-convex optimization problem is formulated under the sum minimum mean square error (MMSE) criterion. Alternating minimization is used to break the original joint optimization problem into the separate optimization of the active precoding matrix for the base station (BS) and the matrix of phase shifts for the IRS. While the MMSE active precoder is obtained in closed-form, the IRS phase shifts are optimized iteratively using a modified version (developed in this paper) of the vector approximate message passing (VAMP) algorithm. Moreover, the underlying joint optimization problem is solved under two different models for the IRS phase shifts, namely by assuming$i$) a unimodular (i.e., ideal) constraint on the reflection coefficients and$ii$) a more practical reflection elements termination by a variable reactive load (which inherently introduces the phase-dependent amplitude attenuation in the IRS phase shifts). Simulation results are presented to illustrate the performance of the proposed method under both perfect and imperfect channel state information (CSI) and to show the effect of the practical constraint on the system throughput. The results validate the superiority of the proposed method over the state-of-the-art techniques both in terms of throughput and computational complexity.

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.

This paper tackles the problem of millimeter-wave (mmWave) channel estimation in massive MIMO communication systems. A new Bayes-optimal channel estimator is derived using recent advances in the approximate belief propagation Bayesian inference paradigm. By leveraging the inherent sparsity of the mmWave MIMO channel in the angular domain, we recast the underlying channel estimation problem into that of reconstructing a compressible signal from a set of noisy linear measurements. Then, the generalized approximate message passing (GAMP) algorithm is used to find the entries of the unknown mmWave MIMO channel matrix. Unlike all the existing works on the same topic, we model the angular-domain channel coefficients by Laplacian distributed random variables. Furthermore, we establish the closed-form expressions for the various statistical quantities that need to be updated iteratively by GAMP. To render the proposed algorithm fully automated, we also develop an expectation-maximization (EM) based procedure that can be easily embedded within GAMP's iteration loop in order to learn all the unknown parameters of the underlying Bayesian inference problem. The computer simulations show that the proposed combined EM-GAMP algorithm under a Laplacian prior exhibits improvements both in terms of channel estimation accuracy, achievable rate, and computational complexity, as compared to the Gaussian mixture prior that has been advocated in the recent literature. In addition, it is found that the Laplacian prior speeds up the convergence time of GAMP over the entire signal-to-noise ratio range.

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.

This paper presents two new implementations of the maximum likelihood (ML) time delay estimation (TDE) from multi-carrier (MC) Direct-Sequence Spread Spectrum (DSSS) in multipath MIMO transmissions that will characterize future 5G radio interface technologies (RITs). The first TDE, based on expectation maximization (EM), provides accurate estimates of the delays when a good initialisation of the parameters is available. The second TDE returns the global maximum of the compressed likelihood function (CLF) using the importance sampling (IS) technique without requiring any initialization. Interestingly, in the non-data-aided (NDA) case, temporal, spatial (transmit and receive), and frequency samples have the same impact on estimation accuracy and performance bound which depends on the product of these dimensions regardless of the channel correlation type. Furthermore, we cope with such channel correlations that arise in practice and, hence, become very challenging both in estimation and CRLB derivation in the data-aided (DA) case, but that have been so far overlooked in previous works.

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.

Code-domain non-orthogonal multiple access (NOMA), a much more sophisticated and efficient generalization of code division multiple access (CDMA), is a promising candidate for future 5G transceivers. Precisely, the CDMA Spatio-Temporal Array-Receiver (STAR) transceiver lends itself to very flexible reconfiguration and adaptation to most NOMA-type radio access technologies. It also possesses among other assets extremely high temporal synchronization capabilities. In this paper, we tackle the hardware feasibility of STAR and provide a proof of concept for a STAR-based time-delay estimator (TDE) through an FPGA-based real-time operational prototype running on a MiniBee Software-Defined-Radio (SDR) platform. We propose a modular, versatile, and reconfigurable architecture for the most basic and simplest “canonic” version of STAR at very low usage of FPGA resources, thereby paving the way for the quick implementation of extended and more complex configurations of this powerful transceiver. The real-time performances of the new prototype in terms of time-delay tracking accuracy compared to the original reference MATLAB version confirm the high precision and robustness of our new design to quantization errors and to all other hardware implementation imperfections.

A novel MIMO cognitive transceiver (CTR) for LTE-downlink communication system is devised in this work. We consider the cognition concept from the perspective of providing a highly reliable communication to the mobile user anytime anywhere. Rather than handling spectrum allocation (the common perspective of cognitive radio), we consider the channel estimation as the reconfiguration parameter of the proposed CTR. The developed cognitive transceiver is capable of selecting the best channel identification scheme between the conventional least squares (LS) estimator and the recently proposed maximum likelihood (ML) estimator. the proposed CTR is also able to toggle between the conventional pilot-assisted or data-aided (DA) mode and the non-data-aided with pilot (NDA with pilot) mode that relies on both reference and data symbols to track the channel variations. The decision rules of the new CTR that identify the best combination couple of pilot-use and channel-identification modes are drawn after running extensive and exhaustive LTE-downlink link-level simulations. The proposed CTR outperforms all static transceivers, in terms of link-level performance, for any given operating conditions such as SNR, mobile speed, channel type, and channel quality indicator (CQI). The new proposed CTR offers significant link-level throughput gains against the LS channel estimator working in a pilot-assisted mode in most operating conditions and the improvement gains can reach as much as 100% at low SNR and high mobility!

This paper addresses the problem of Doppler spread and carrier frequency offset (CFO) estimation under flat-fading Rayleigh channels. We develop a new low-cost and robust approximate maximum likelihood (ML) estimator for these two key parameters that builds upon an elegant two-ray approximation model of the channel's covariance matrix. The latter is then inverted analytically thereby yielding a closed-form expression for the underlying log-likelihood function that is prone to easy evaluation by the fast Fourier transform. Computer simulations show that the new estimator is accurate over wide ranges of the Doppler spread and CFO parameters. Moreover, it outperforms many state-of-the-art techniques under the adverse conditions of short data records and/or low SNR thresholds. Most prominently, it exhibits an unprecedented robustness to the Doppler spectrum shape of the channel since it does not require its a priori knowledge.

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.

In this paper, we address the problem of time delay estimation (TDE) from single-carrier (SC) or multi-carrier (MC) direct-sequence spread spectrum (DSSS) multipath transmissions in the presence of multiple transmit and/or receive antennas that will characterize future 5G radio interface technologies (RITs), such as coded-domain nonorthogonal multiple access. We derive for the first time a closed-form expression for the Cramer-Rao lower bound (CRLB) and develop two maximum likelihood (ML) multipath TDEs for SC DSSS single-input multiple-output (SIMO) in the non-data-aided (NDA) case. The first TDE, based on iterative expectation maximization (EM), provides accurate estimates whenever a good initial guess of the parameters is available at the receiver. The second TDE implements the ML criterion in a non-iterative way and finds the global maximum of the compressed likelihood function using the importance sampling (IS) technique without requiring any initialization. We also extend both the SC DSSS SIMO CRLB and the two new SC DSSS SIMO ML NDA TDEs to MC DSSS RITs and to multiple-input multiple-output structures with any diversity versus multiplexing pre-coding type before generalizing them all to the data-aided (DA) case. Simulations suggest that the EM TDE is suitable for large observation in space, time, and/or frequency, whereas the IS TDE is preferred in the opposite case of very short data records. Moreover, we show in the NDA case, both analytically and by simulations, that spatial (transmit and receive), temporal, and frequency samples interchangeably have the same impact on estimation accuracy and performance bound regardless of the channel correlation type and amount present in each dimension. Furthermore, we are able to properly cope with such channel correlations that do indeed arise in practice and, hence, become very challenging both in estimation and CRLB derivation in the DA case, but that have been so far overlooked in previous works.

Abstract In this paper, we design a new single‐input multiple‐output context‐aware cognitive transceiver (CTR) that is able to switch to the best performing modem in terms of link‐level throughput. On the top of conventional adaptive modulation and coding, we allow the proposed CTR to make best selection between three different pilot‐utilization modes: conventional data‐aided (DA) or pilot‐assisted, non‐DA (NDA) or blind, and NDA with pilots, which is a newly proposed hybrid version between the DA and NDA modes. We also enable the CTR to make best selection between two different channel identification schemes: conventional least‐squares (LS) and newly developed maximum‐likelihood estimators. Depending on whether pilot symbols can be exploited or not at the receiver, we further enable the CTR to make the best selection among two data detection modes: coherent or differential. Owing to exhaustive link‐level simulations on the downlink of a long‐term evolution system, we draw out the optimal decision rules in terms of the best combination triplet of pilot‐use, channel‐identification, and data‐detection modes that yield the best link‐level throughput as function of channel type, mobile speed, signal‐to‐noise ratio, and channel quality indicator. The proposed CTR offers a link‐level throughput gains improvement as high as 700%compared with DA LS for VehB channel type at a mobile speed of 100 km/h in the low signal‐to‐noise ratio region. For VehA channels, its throughput gain improvements can reach 114%. For PedA and PedB channels, the proposed CTR provides throughput enhancements of about 66%and 330%, respectively. Moreover, realistic simulations at the system‐level of the long‐term evolution‐HetNet network suggest that the new context‐aware CTR outperforms the conventional transceiver (i.e., pilot‐assisted LS‐type channel estimation with coherent detection) by as much as 50% and 60% gains in average and cell‐edge (i.e., five percentile) throughputs, respectively, in the high‐clustering case with type‐B channels. In the low clustering scenario, the average and cell‐edge throughput gain improvements offered by the proposed CTR exceed 80% and 90% for type‐B channels. Copyright © 2016 John Wiley & Sons, Ltd.

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, we design a new single-input multiple-output (SIMO) context-aware cognitive transceiver (CTR) that is able to switch to the best performing modem in terms of link-level throughput. On the top of conventional adaptive modulation and coding (AMC), we allow the context-aware CTR to make best selection among three different pilot-utilization modes: conventional decision-aided (DA) or pilot-assisted, non-DA (NDA) or blind, and NDA with pilot which is a newly proposed hybrid version between the DA and NDA modes. We also enable the CTR to make best selection between two different channel identification schemes: conventional least-square (LS) and newly developed maximum-likelihood (ML) estimators. Depending on whether pilot symbols can be properly exploited or not at the receiver, we further enable the CTR to make best selection among two data detection modes: coherent or differential. Owing to extensive and exhaustive simulations on the downlink (DL) of a long-term evolution (LTE) heterogeneous network (HetNet), we are able draw out the decision rules of the new CTR that identify the best combination triplet of pilot-use, channel-identification, and data-detection modes to achieve the best link-level throughput at any operating condition in terms of channel type, mobile speed, signal-to-noise ratio (SNR), and channel quality indicator (CQI). Realistic extensive simulations at the system level suggest that the new context-aware CTR outperforms the conventional transceiver (i.e., pilot-assisted LS-type channel estimation with coherent detection) by as much as 40 and 45% gains in average and cell-edge (i.e., 5-percentile) total throughput per macro-area with 10 pico-cells each, respectively.

In this paper, we consider the problem of joint phase and carrier frequency offset (CFO) estimation for turbo-coded systems. We derive for the first time the closed-form expressions for the exact Cramér-Rao lower bounds (CRLBs) of these estimators over turbo-coded square-QAM-modulated single- or multi-carrier transmissions. In the latter case, the derived bounds remain valid in the general case of adaptive modulation and coding (AMC) where the coding rate and modulation order vary from one subcarrier to another depending on the corresponding channel quality information (CQI). In particular, we introduce a new recursive process that enables the construction of arbitrary Gray-coded square-QAM constellations. Some hidden properties of such constellations will be revealed, owing to this recursive process, and carefully handled to decompose the system's likelihood function (LF) into the sum of two analogous terms. This decomposition makes it possible to carry out analytically all the statistical expectations involved in the Fisher information matrix (FIM). The new analytical CRLB expressions corroborate the previous attempts to evaluate the underlying bounds empirically. In the low-to-medium signal-to-noise ratio (SNR) region, the CRLB for code-aided (CA) estimation lies between the bounds for completely blind [non-data-aided (NDA)] and completely data-aided (DA) estimation schemes, thereby highlighting the effect of the coding gain. Most interestingly, in contrast to the NDA case, the CA CRLBs start to decay rapidly and reach the DA bounds at relatively small SNR thresholds. It will also be shown that contrary to the CRLB of the phase shift, the CRLB of the CFO improves in a multi-carrier system as compared to its counterpart in a single-carrier system. The derived bounds are also valid for LDPC-coded systems and they can be evaluated in the same way when the latter are decoded using the turbo principal.

In this contribution, we derive for the first time the closed-form expressions for the Cramér-Rao lower bounds (CRLBs) of the signal-to-noise ratio (SNR) estimates from turbo-coded square-QAM transmissions. By exploiting the structure of the Gray mapping, we are able to factorize the likelihood function thereby linearizing all the derivation steps for the FIM elements. The analytical CRLBs coincide exactly with their empirical counterparts validating thereby our new analytical expressions. Numerical results suggest that the CLRBs for code-aided (CA) SNR estimates range between the CRLBs for non-data-aided (NDA) SNR estimates and those for data-aided (DA) ones, thereby highlighting the effect of the coding gain. At sufficiently high SNR levels, the three CRLBs coincide. The derived bounds are also valid for LDPC-coded systems and they can be evaluated in the same way when the latter are decoded using the turbo principal.

We consider the problem of joint phase and carrier frequency offset (CFO) estimation from turbo-coded square-QAM modulated signals. We derive for the first time the closed-form expressions for the exact Cramér-Rao lower bounds (CRLBs) of this estimation problem. In particular, we introduce a new recursive process that enables the construction of arbitrary Gray-coded square-QAM constellations. Some hidden properties of such constellations will be revealed and carefully handled in order to decompose the likelihood function (LF) into the sum of two analogous terms. This decomposition makes it possible to carry out analytically all the statistical expectations involved in the Fisher information matrix (FIM). The new analytical CRLB expressions corroborate the previous attempts to evaluate the underlying perfromance bounds empirically. In the low-to-medium signal-to-noise ratio (SNR) region, the CRLB for code-aided (CA) estimation lies between the bounds for completely blind [non-data-aided (NDA)] and completely data-aided (DA) estimation schemes, thereby highlighting the coding gain potential in CFO and phase estimation. Most interestingly, in contrast to the NDA case, the CA CRLBs start to decay rapidly and reach the DA bounds at relatively small SNR thresholds. The derived bounds are also valid for LDPC-coded systems and they can be evaluated in the same way when the latter are decoded using the turbo principal.

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.

In this paper, we propose a new signal-to-noise-ratio (SNR) maximum likelihood (ML) estimator over time-varying single-input multiple-output (SIMO) channels, for both data-aided (DA) and non-data-aided (NDA) cases. Unlike the classical techniques which assume the channel to be slowly time-varying and, therefore, considered as constant during the observation period, we address the more challenging problem of instantaneous SNR estimation over fast time-varying channels. The channel variations are locally tracked using a polynomial-in-time expansion. In the DA scenario, the ML estimator is developed in closed-form expression. In the NDA scenario, however, the ML estimates of the per-antenna SNRs are obtained iteratively, with very few iterations, using the expectation-maximization (EM) procedure. Our estimator is able to accurately estimate the instantaneous SNRs over a wide range of average SNR. We show through extensive Monte-Carlo simulations that the new estimator outperforms previously developed solutions.

This paper addresses the problem of Doppler spread estimation in Rayleigh flat fading channels using a new low-cost and robust maximum likelihood (ML) technique. Relying on a an elegant approximation of the channel covariance matrix by a two-ray model, we are able to invert the overall approximate covariance matrix analytically thereby obtaining a low-cost closed-form approximation of the likelihood function. We show by computer simulations that the new estimator is accurate over a wide Doppler spread range and that it outperforms many state-of-the-art techniques. In contrast to the latter, it exhibits an unprecedented robustness to the Doppler spectrum shape of the channel since it does not require its a priori knowledge.

In this paper, we develop a new implementation of maximum likelihood (ML) time delay estimation from direct-sequence CDMA (DS-CDMA) multipath transmissions. The formulation of the problem obtained from the DS-CDMA post-correlation model (PCM) leads to a non-linear likelihood function which is maximized by the iterative expectation maximization (EM) algorithm. In this approach, we avoid eigen-decomposition of the autocorrelation matrix widely used for multiple parameters estimation. Instead, we transpose the problem of multidimensional maximization to simpler one- dimensional maximizations carried out in parallel, thereby reducing the computational cost considerably. We also extend the application of the proposed single-carrier (SC) algorithm to multicarrier (MC)-CDMA systems by exploiting the frequency gain over subcarriers. Simulations suggest that the proposed EM-based algorithm provides accurate estimates even in the challenging case of closely-spaced delays from one transmitted symbol.

This paper derives for the first time the stochastic Cramer-Rao lower bounds (CRLBs) for direction of arrival (DOA) estimates of any linearly-modulated signals, in presence of additive white circular complex Gaussian noise (AWCCGN). The transmitted symbols are assumed to be completely unknown at the receiver side. The channel is slowly time-varying and assumed to introduce a constant distortion phase during the observation interval. Simulation results show that the CRLBs hold almost the same for all the modulation schemes. We also show that, contrarily to uniform lineair array (ULA) systems, the knowledge of the channel distortion phase does not bring any additional information to the achievable performance in uniform circular array (UCA) systems.

In this paper we derive a new method of DOA estimation for ULA configurations using the annihilating filter technique. The new method is non-data-aided (NDA) and does not therefore impinge on the whole throughput of the system. The noise components are assumed spatially and temporally white. The transmitted signals are also assumed to be temporally and spatially white (across the transmitting sources). The new method is compared in performance to the root-MUSIC algorithm, a powerful DOA estimation technique for ULA configurations. Simulations will show that the new method performs well over a wide SNR range. The main advantage of the new method is that it succeeds in accurately estimating the DOAs for fast moving sources or for short data snapshots, and even from a single snapshot where the root-MUSIC method fails completely.

In this paper, we propose a joint estimator of the Ricean K-factor and the signal-to-noise ratio (SNR) for singleinput multiple-output (SIMO) systems. The second-order moment and the fourth-order cross-moment matrix of the received signal envelope are used to estimate the desired parameters. The K-factor is estimated using the kurtosis of the Ricean channel gain while the SNR is obtained by separately estimating the powers of the useful signal and the additive noise. Two versions are developed depending on the value of the kurtosis of the transmitted data. Unlike the autocorrelation-based K-factor and SNR joint estimator developed in [1] that is tailored for unmodulated signals, the proposed method applies to any digital modulation and does not request the knowledge of the maximum Doppler spread. Simulation results are included to illustrate the performance behavior of the new estimator.

In this paper, we present a new time delay estimator for multipath environments using the importance sampling (IS) method. The new technique allows finding the maximum of the compressed likelihood function in an efficient manner. The main idea consists in generating realizations of a random variable distributed according to a function that approximates the actual compressed likelihood function and then computing the mean of the plausible realizations. We avoid eigen-decomposition operation that is widely used in the conventional high-resolution methods. We show through computer simulations that the new algorithm provides accurate estimates for closely spaced unknown time delays. Moreover, the method does not suffer from lack of convergence and initialization problems that arise with other iterative implementations of the maximum likelihood estimator.

In this paper, we derive for the first time analytical expressions for the Cramer-Rao lower bounds (CRLBs) of timing recovery estimators from binary phase shift keying (BPSK) and square quadrature amplitude modulation (QAM) transmissions. The bounds are derived in the presence of additive white Gaussian noise (AWGN). Moreover the carrier phase and frequency are considered as unknown nuisance parameters. Our new analytical expressions reveal that the CRLBs do not depend on the corresponding time delay parameter and that they do not change widely from one modulation order to another. They also corroborate previous works that computed them empirically and provide a meaningful tool for their quick and easy evaluation.

We address the problem of direction of arrival (DOA) finding for uniform linear arrays (ULAs). We derive a new and very simple method for estimating the DOA of a single source based on the covariance matrix of the received signal. The new method is non-data-aided (NDA) and does not therefore impinge on the whole throughput of the system. The noise components are assumed spatially and temporally white. The new method is derived in closed form and it exhibits exactly - over a wide practical SNR range - the same performance of the popular root-MUSIC algorithm, a powerful DOA estimation technique for ULA configurations. Therefore, the new estimator offers a way for a rapid and very easy DOA evaluation; making it very attractive for practical implementation as compared to the root-MUSIC algorithm that relies on the heavy operation of eigen decomposition.

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In this paper, we develop for the first time a method of estimating the DOA parameters assuming noncircular and spatially and temporally correlated signals. The new approach is based on the two-sided IV-SSF method (instrumental variable signal with subspace fitting). It will be shown that our newly developed method outperforms the classical two-sided IV-SSF in terms of lower bias and error variance. Its performance improvement increases as the noncircularity rate increases. Moreover, this improvement is more prominent at low SNR values. We also derive for the first time an analytical expression for the stochastic Cramér-Rao bound (CRB) of the DOA estimates from spatially and temporally correlated signals generated from noncircular sources. The new CRB is compared to that of circular and temporally correlated signals. It will be shown that the CRB obtained assuming both noncircular sources and temporally correlated signals is lower than the CRB derived considering only the temporal correlation. This illustrates the potential gain that both the noncircularity and the temporal correlation provide when considered together.

In this paper, we present a new non-data-aided (NDA) maximum likelihood (ML) time delay estimator based on importance sampling (IS). We show that a grid search and lack of convergence from which most iterative estimators suffer can be avoided. It is assumed that the transmitted data are completely unknown at the receiver. Moreover the carrier phase is considered as an unknown nuisance parameter. The time delay remains constant over the observation interval and the received signal is corrupted by additive white Gaussian noise (AWGN). We use importance sampling to find the global maximum of the compressed likelihood function. Based on a global optimization procedure, the main idea of the new estimator is to generate realizations of a random variable using an importance function, which approximates the actual compressed likelihood function. We will see that the algorithm parameters affect the estimation performance and that with an appropriate parameter choice, even over a small observation interval, the time delay can be accurately estimated at far lower computational cost than with classical iterative methods.

In this paper, we derive for the first time analytical expressions for the inphase/quadrature (I/Q) non-data-aided (NDA) Cramér-Rao lower bounds (I/Q NDA CRLBs) of the direction of arrival (DOA) estimates from square quadrature amplitude (QAM)-modulated signals corrupted by additive white circular complex Gaussian noise (AWCCGN) with any antenna configuration. Yet the main contribution embodied by this paper consists in deriving for the first time analytical expressions for the NDA Fisher information matrix (FIM) and then for the stochastic CRLB of the NDA DOA estimates in the case of square QAM-modulated signals. It will be shown that in the presence of any unknown phase offset (i.e., non-coherent estimation), the ultimate achievable performance on the NDA DOA estimates holds almost the same irrespectively of the modulation order. However, the NDA CRLBs obtained in the absence of the phase offset (i.e., coherent estimation) vary, in the high SNR region, from one modulation order to another.

In this paper, we derive for the first time analytical expressions for the exact Cramér-Rao lower bounds on the variance of unbiased non-data-aided (NDA) signal-to-noise ratio (SNR) estimators of square QAM-modulated signals. The channel is assumed to be constant over the observation interval and the received signal is supposed to be corrupted by additive white Gaussian noise (AWGN). The derived expressions corroborate previous attempts to numerically compute the considered CRLBs. It will be shown that the NDA CRLBs differ widely from one modulation order to another especially at moderate SNR levels.

In this paper, we develop a new method for signal-to-noise ratio (SNR) estimation when multiple antenna elements receive linearly-modulated signals in complex additive white Gaussian noise (AWGN) spatially uncorrelated between the antenna elements. We also derive extensions of other existing moment-based SNR estimators to the single-input multiple-output (SIMO) configuration. The new SIMO SNR estimation technique is non-data-aided (NDA) since it is a moment-based method and does not rely, therefore, on the a priori knowledge or detection of the transmitted symbols; it does not require the a priori knowledge of the modulation type or order. The new method is shown by Monte Carlo simulations to clearly outperform the best NDA moment-based SNR estimation methods in terms of normalized root mean square error (NRMSE) over QAM-modulated transmissions, namely the M2M4method and the estimators referred to, in this paper, as the GT and the M6methods, even when we extend them to the SIMO configuration.

In this paper, we derive analytical expressions for the exact Cramer-Rao lower bounds (CRLBs) for the joint estimation of the carrier frequency, the carrier phase, the noise power and the signal amplitude of square quadrature amplitude (QAM) modulated signals. The channel is assumed to be slowly timevarying so that it can be assumed constant over the observation interval. The signal is assumed to be corrupted by additive white Gaussian noise (AWGN). The closed-form expressions for the corresponding modified Cramer-Rao lower bounds (MCRLBs) are also derived in this paper.

This paper addresses the stochastic Cramer-Rao lower bound (CRLB) for the non-data-aided (NDA) direction of arrival (DOA) estimation of square quadrature amplitude (QAM)-modulated signals when the transmitted symbols are supposed to be completely unknown to the receiver. These signals are assumed to be corrupted by additive white circular complex Gaussian noise (AWCCGN). The channel is supposed to be slowly time-varying so that it can be assumed constant over the observation interval. The main contribution of this paper consists in deriving an explicit expression for the Fisher information matrix (FIM) in the case of a single square QAM modulated waveform and an analytical expression for the stochastic CRLB of the NDA DOA estimates. It will be shown that the achievable performance on the DOA estimates hold almost the same irrespectively of the modulation order.

In this paper, we address the problem of non-data-aided SNR estimation in wireless SIMO channels. We derive the per-antenna ML SNR estimator using the expectation-maximization (EM) algorithm under constant channels and additive white Gaussian noise (AWGN). The new method is valid for any arbitrary constellation. It is NDA and, therefore, does not impinge on the hole throughput of the system. We obtain two non linear vector equations which are tackled by a less complex approach based on the EM algorithm. The noise components are assumed to be spatially uncorrelated over all the antenna elements and temporally white with equal power. Besides, in order to evaluate our EM-ML SNR estimator, we derive the Cramer-Rao lower bound (CRLB) in the DA case. Monte Carlo simulations show, that our new estimator offers, a substantial performance improvement over the SISO ML SNR estimator due to the optimal usage of the mutual information between the antenna branches, and that it reaches the derived DA CRLBs. To the best of our knowledge, we are the first to derive the ML per-antenna SNR estimators as well as the CRLBs in the NDA and the DA case, respectively, both over SIMO channels.

In this paper, considering a multicarrier transmissions system, we propose two techniques of maximum-likelihood (ML) subcarrier signal-to-noise ratio (SNR) estimation, in both data-aided (DA) and non-data-aided (NDA) schemes, and derive the corresponding Crame¿r-Rao lower bounds (CRLBs). The channel gains and phases are assumed to be constant over the observation window and the received signal is assumed to be corrupted by additive white Gaussian noise (AWGN). The proposed SNR estimators both exploit the mutual information between the different subcarriers and reach the corresponding CRLBs, as shown by Monte-Carlo simulations.

Considering orthogonal frequency division multiplexing (OFDM) transmissions, we derive analytical expressions for the Cramer-Rao bounds for the subcarrier signal-to-noise ratio (SNR) estimates. The channel coefficients of the different subcarriers are assumed to be constant over the observation interval and the received signal is assumed to be corrupted by additive white Gaussian noise (AWGN). We will show that exploiting the mutual information between the different tones improves the achievable performance of subcarrier SNR estimators.

In this paper, we derive analytical expressions for the inphase/quadrature Cramer-Rao lower bounds (I/Q CRLB) for the non-data-aided (NDA) signal-to-noise ratio (SNR) estimation of square quadrature amplitude modulated (QAM) signals. The channel is supposed to be slowly time-varying so that it can be assumed constant over the observation interval. The signal is assumed to be corrupted by additive white Gaussian noise (AWGN). We will demonstrate that the shape of the bounds differs greatly as the modulation order changes.