Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper introduces a new maximum likelihood (ML) solution for the code-aided (CA) timing recovery problem in square-quadrature amplitude modulation (QAM) transmissions and derives, for the very first time, its CA Cramér–Rao lower bounds (CRLBs) in closed-form expressions. The channel is assumed to be slowly time varying so that it can be considered as constant over the observation interval. By exploiting the full symmetry of square-QAM constellations and further scrutinizing the Gray-coding mechanism, we express the likelihood function of the system explicitly in terms of the code bits’ a priori log-likelihood ratios (LLRs). The timing recovery task is then embedded in the turbo iteration loop, wherein increasingly accurate estimates for such LLRs are computed from the output of the soft-input soft-output decoders and exploited at a per-turbo-iteration basis in order to refine the ML time delay estimate. The latter is then used to better resynchronize the system, through feedback to the matched filter, so as to obtain more reliable symbol-rate samples for the next turbo iteration. In order to properly benchmark the new CA ML estimator, we also derive for the very first time the closed-form expressions for the exact 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 CA estimation against the traditional nondata-aided scheme, thereby highlighting the potential performance gains in time synchronization that can be achieved owing to the decoder assistance. Over a wide range of practical signal-to-noise ratios (SNRs), CA estimation becomes even equivalent to the completely data-aided scheme in which all the transmitted symbols are perfectly known to the receiver. Moreover, the new CA ML estimator almost reaches the underlying CA CRLBs, even for small SNRs, thereby confirming its statistical efficiency in practice. It also enjoys significant improvements in computational complexity as compared to the most powerful existing ML solution, namely the combined sum-product and expectation-maximization algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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 FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

In this paper, we present a new time delay maximum likelihood 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. [ABSTRACT FROM PUBLISHER]

The main contribution of our work consists in developing for the first time a method of estimating the direction of arrival (DOA) parameters assuming noncircular and temporally and spatially correlated signals. This new approach, based on a significant enhancement of the two-sided instrumental variable signal subspace fitting (IV-SSF) method, outperforms its classical version in terms of lower bias and error variance. Moreover, it will be shown that our new method is statistically more efficient than the MODE method especially in the case of partly and fully coherent signals where only the extended and the classical two-sided IV-SSF methods are applicable. We also derive an explicit expression for the stochastic Cramér–Rao bound (CRB) of the DOA estimates from temporally and spatially correlated signals generated from noncircular sources. The new CRB is compared to those of circular temporally correlated and noncircular independent and identically distributed signals to show that the CRB obtained assuming both noncircular sources and temporally correlated signals is lower than the CRBs derived considering only one of these two assumptions. This illustrates the potential gain that both noncircularity and temporal correlation provide when considered together. It will also be proven that the difference between the three CRBs increases with the number of snapshots. However, as the signal-to-noise ratio (SNR) increases, the CRBs merge together and decrease linearly. Moreover, at low SNR values it will be shown that temporal correlation is more informative about the unknown DOA parameters than noncircularity. Finally, the CRB derived assuming noncircular and temporally correlated signals depends on the noncircularity rate, the circularity phase separation, and the DOA separation. [ABSTRACT FROM AUTHOR]

In this paper, we derive for the first time analytical expressions for the exact Cramér–Rao lower bounds (CRLB) for symbol timing recovery of binary phase shift keying (BPSK), minimum shift keying (MSK), and square QAM-modulated signals. It is assumed that the transmitted data are completely unknown at the receiver and that the shaping pulse verifies the first Nyquist criterion. Moreover the carrier phase and frequency are considered as unknown nuisance parameters. The time delay remains constant over the observation interval and the received signal is corrupted by additive white Gaussian noise (AWGN). Our new expressions prove that the achievable performance holds irrespective of the true time delay value. Moreover, they corroborate previous attempts to empirically compute the considered bounds thereby enabling their immediate evaluation. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problem of data-aided (DA) and nondata-aided (NDA) per-antenna signal-to-noise ratio (SNR) estimation over wireless single-input multiple-output (SIMO) channels from linearly modulated signals. Under constant channels and additive white Gaussian noise (AWGN), we first derive the DA maximum-likelihood (ML) SNR estimator in closed-form expression. The performance of the DA ML estimator is analytically carried out by deriving the closed-form expression of its bias and variance. Besides, in order to compare its performance with the fundamental limit, we derive the DA Cramér-Rao lower bound (CRLB) in closed-form expression. In the NDA case, the expectation-maximization (EM) algorithm is derived to iteratively maximize the log-likelihood function. The performance of the NDA ML estimator is empirically assessed using Monte Carlo simulations. Moreover, we introduce an efficient algorithm, which applies to any one/two-dimensional M-ary constellation, to numerically compute the NDA CRLBs. In this paper, the noise components are assumed to be spatially uncorrelated over all the antenna elements and temporally white. In both cases, we show that our new inphase and quadrature I/Q-based estimators offer substantial performance improvements over the single-input single-output (SISO) ML SNR estimator due to the optimal usage of the statistical dependence between the antenna branches, and that it reaches the corresponding CRLB over a wide SNR range. We also show that the use of the I/Q-based ML estimators can lead to remarkable performance improvements over the moment-based estimators for the same antenna-array size. Moreover, it is shown that SIMO configurations can contribute to decreasing the required number of iterations of the EM algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we derive for the first time the analytical expressions of the exact Cramér–Rao lower bounds (CRLBs) of the carrier frequency and the carrier phase from square quadrature amplitude modulated (QAM) signals, assuming the noise power and the signal amplitude to be completely unknown. The signal is assumed to be corrupted by additive white Gaussian noise (AWGN). The main contribution of this paper consists in deriving the analytical expressions for the non-data-aided (NDA) Fisher information matrix (FIM) for higher-order square QAM-modulated signals. We prove that the problem of estimating the synchronization parameters is separable from the one of estimating the signal and the noise powers by showing that the FIM is block diagonal. Besides, we show analytically that the phase CRLB is higher than the frequency CRLB, implying that it is much easier to estimate the frequency than the distortion phase. It will be seen that the CRLBs differ widely from one modulation order to another in the medium SNR range. The newly derived expressions corroborate previous attempts to numerically or empirically compute the considered CRLBs as well as their asymptotical expressions derived only in special SNR regions. [ABSTRACT FROM PUBLISHER]

Considering orthogonal frequency-division multiplexing transmissions, analytical expressions for the Cramér-Rao lower bounds for the subcarrier signal-to-noise ratio (SNR) estimates are derived. The transmitted signal is assumed to be corrupted by additive white Gaussian noise on the different subcarriers. The channel is assumed to be slowly time-varying over the observation interval, so that it can be assumed constant. It will be shown that the achievable performance on subcarrier SNR estimation can be significantly improved by exploiting the mutual information (the same experienced noise power) between the different tones. [ABSTRACT FROM AUTHOR]