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Achieving high channel estimation accuracy and reducing hardware cost as well as power dissipation constitute substantial challenges in the design of massive multiple-input multiple-output (MIMO) systems. To resolve these difficulties, sophisticated pilot designs have been conceived for the family of energy-efficient hybrid analog-digital (HAD) beamforming architecture relying on adaptive-resolution analog-to-digital converters (RADCs). In this paper, we jointly optimize the pilot sequences, the number of RADC quantization bits and the hybrid receiver combiner in the uplink of multiuser massive MIMO systems. We solve the associated mean square error (MSE) minimization problem of channel estimation in the context of correlated Rayleigh fading channels subject to practical constraints. The associated mixed-integer problem is quite challenging due to the nonconvex nature of the objective function and of the constraints. By relying on advanced fractional programming (FP) techniques, we first recast the original problem into a more tractable yet equivalent form, which allows the decoupling of the fractional objective function. We then conceive a pair of novel algorithms for solving the resultant problems for codebook-based and codebook-free pilot schemes, respectively. To reduce the design complexity, we also propose a simplified algorithm for the codebook-based pilot scheme. Our simulation results confirm the superiority of the proposed algorithms over the relevant state-of-the-art benchmark schemes. [ABSTRACT FROM AUTHOR]

Recently, there has been much interest in the use of convolutional neural networks (CNN) for mobile user localization in massive multiple-input multiple-output (MIMO) systems operating at millimeter wave (mmWave) frequencies. However, current CNN-based approaches cannot predict the confidence interval bounds for the localization accuracy. While the Bayesian neural network (BNN) method can be employed to estimate the model uncertainty, it entails a high computational cost. In this letter, the Monte Carlo (MC) dropout based method is proposed as a low-complexity approximation to BNN inference for capturing the uncertainty in a CNN-based mmWave MIMO outdoor localization system, without sacrificing accuracy. The proposed method is evaluated by means of simulations using a ray-tracing model of urban propagation at 28GHz. Results show that the localization uncertainty region can be properly determined and that their shape depends on the maximum power received at the user. [ABSTRACT FROM AUTHOR]

In this paper, we investigate secrecy-energy efficient hybrid beamforming (BF) schemes for a satellite-terrestrial integrated network, wherein a multibeam satellite system shares the millimeter wave spectrum with a cellular system. Under the assumption of imperfect angles of departure for the wiretap channels, the hybrid beamformer at the base station and digital beamformers at the satellite are jointly designed to maximize the achievable secrecy-energy efficiency, while satisfying signal-to-interference-plus-noise ratio constraints of both the earth stations (ESs) and cellular users. Since the formulated optimization problem is nonconvex and mathematically intractable, we propose two robust BF schemes to obtain approximate solutions with low complexity. Specifically, for the case of a single ES, we integrate the Charnes-Cooper approach with an iterative search algorithm to convert the original nonconvex problem into a solvable one and obtain the BF weight vectors. In the case of multiple ESs, by exploiting the sequential convex approximation method, we convert the original problem into a linear one with multiple matrix inequalities and second-order cone constraints, for which we obtain a solution with satisfactory performance. The effectiveness and superiority of the proposed robust BF design schemes are validated via simulations using realistic satellite and terrestrial downlink channel models. [ABSTRACT FROM AUTHOR]

In this paper, we present a new two-stage speech enhancement approach, specially conceived to reduce musical and other random noises without requiring their localization in the time–frequency domain. The proposed method is motivated by two observations: (1) the random scattering nature of the energy peaks corresponding to the musical noise in the spectrogram of the processed speech; and (2) the existence of correlation between Wiener filter gains calculated at different frequencies. In the first stage of the proposed method, a preliminary gain function is generated using the nonnegative matrix factorization algorithm. In the second stage, a modified gain function that is more robust to noise artefacts, and referred to as calibrated filter, is estimated by applying a DNN-based nonlinear mapping function to the preliminary gain function. To further decrease the variability of the estimated calibrated filter, we propose to expand the DNN-based extraction of frequency dependencies to a set of preliminary gain functions derived from spectral estimates based on a family of data tapers; the resulting calibrated filter is referred to as multi-filter. The evaluation of the proposed DNN-based calibrated filter models for speech enhancement, under different noise types and input SNR levels, shows substantial improvements in terms of standard speech quality and intelligibility measures when compared to uncalibrated filter. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid speech enhancement system that exploits deep neural network (DNN) for speech reconstruction and Kalman filtering for further denoising, with the aim to improve performance under unseen noise conditions. Firstly, two separate DNNs are trained to learn the mapping from noisy acoustic features to the clean speech magnitudes and line spectrum frequencies (LSFs), respectively. Then the estimated clean magnitudes are combined with the phase of the noisy speech to reconstruct the estimated clean speech, while the LSFs are converted to linear prediction coefficients (LPCs) to implement Kalman filtering. Finally, the reconstructed speech is Kalman-filtered for further removing the residual noises. The proposed hybrid system takes advantage of both the DNN based reconstruction and traditional Kalman filtering, and can work reliably in either matched or unmatched acoustic environments. Computer based experiments are conducted to evaluate the proposed hybrid system with comparison to traditional iterative Kalman filtering and several state-of-the-art DNN based methods under both seen and unseen noises. It is shown that compared to the DNN based methods, the hybrid system achieves similar performance under seen noise, but notably better performance under unseen noise, in terms of both speech quality and intelligibility. [ABSTRACT FROM AUTHOR]

In this work, we investigate the problem of secure communications in a downlink large-scale multi-antenna assisted simultaneous wireless information and power transfer (SWIPT) system, where a base station (BS) transmits signals to serve a number of information decoding (ID) and energy harvesting (EH) users. Considering that the EH users can potentially eavesdrop the ID users’ confidential information, we study the robust joint design of the hybrid analog-digital (A/D) beamforming (BF) matrices and of the artificial redundant signal (ARS) covariance matrix at the BS, where the aim is to maximize the worst-case sum secrecy rate for the ID users under a transmit power constraint, a nonlinear EH constraint and a unit-modulus constraint on the entries of the analog BF matrix. The corresponding optimization problem is very challenging due to the nonlinear and nonconvex objective function and constraints. Using innovative optimization techniques, we first transform the original problem into an equivalent but more tractable form, and then develop a novel joint iterative algorithm based on the penalty-concave-convex procedure (CCCP) for solving the resultant problem. We show that the proposed penalty-CCCP based algorithm for ARS-aided robust joint hybrid BF design converges to a Karush-Kuhn-Tucker solution of the original problem, and also analyze its computational complexity. Our simulation results verify that the resultant robust joint hybrid BF design algorithm relying on ARS significantly outperforms the conventional hybrid BF benchmark algorithms and efficiently achieves the performance of the fully-digital BF with reduced number of radio frequency chains and energy consumption. [ABSTRACT FROM AUTHOR]

Due to the severe pathloss experienced by electromagnetic waves in the millimeter wave (mmWave) band, a substantial challenge in their design is to have an adequate coverage area. With the objective of improving the coverage area and the sum rate attained, we conceive new full-duplex (FD) mmWave multiple-input multiple-output (MIMO) multiple-relay systems. Specifically, we propose a novel two-timescale analog-digital hybrid beamforming scheme for maximizing the sum rate, while reducing the system’s complexity and the channel state information (CSI) signalling overhead, as well as mitigating both the effects of self-interference and that of outdated CSIs caused by the associated delays. In the proposed scheme, the long-timescale analog beamforming matrices are designed based on the available channel statistics and updated in a frame-based manner, where a frame contains a fixed number of time slots. By contrast, the short-timescale digital beamforming matrices are optimized more frequently – namely for each time slot – based on the low-dimensional effective CSI matrices available on a real-time basis. We develop both an efficient analog beamforming algorithm based on the cut-set bound as well as on stochastic successive convex approximation (SSCA) and an innovative digital beamforming algorithm that relies on the theory of penalty dual decomposition (PDD), where our design objective is to maximize the system’s sum rate. Both the convergence properties and the computational complexity of the proposed algorithms are also examined. Our simulation results show that the proposed two-timescale hybrid beamforming design significantly outperforms the conventional beamformers both in terms of requiring a lower CSI-signalling overhead and a higher sum rate in the face of realistic outdated CSIs. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new hybrid automatic repeat request (HARQ) transmission protocol for broadcasting systems over free space optical (FSO) channels, which is applicable to both incremental redundancy (IR) and chase combining (CC) schemes. In traditional optical HARQ systems, upon receiving a non-acknowledgment (NACK) signal from a given user, the corresponding data packet is retransmitted from the central node to that user. However, in the proposed transmission protocol, called cooperative interuser retransmission protocol (CIRP), the optical users cooperate in the ARQ process. Specifically, instead of relying on the central node, a user who successfully decoded the original data packet and whose distance from the NACK issuing user is smaller than that of the central node, is invited to retransmit this packet to the latter user. This interuser cooperation, in fact, leverages the distance-dependent scintillation effect and path loss, which critically impact FSO channels and degrade the receiver's signal-to-noise ratio (SNR) as distance increases. We provide a detailed performance analysis of both the proposed and traditional protocols in terms of the achievable average sum rate and the next round probability of success. Our numerical results demonstrate that the former outperforms the latter for both IR and CC schemes under various turbulence conditions as well as in the presence of pointing errors. [ABSTRACT FROM AUTHOR]

Resource allocation algorithms are conceived for minimizing the energy consumption of multiuser mobile edge computing (MEC) systems operating in the face of interference channels, and where mobile users can offload their latency-sensitive tasks to the mobile edge server via a base station (BS). Latency-sensitive applications that benefit from MEC services can be divided into two major classes: 1) applications requiring uninterrupted execution and that cannot be fragmented and therefore require full offloading (FO); 2) applications which can benefit from fractional or partial offloading (PO). For each class of applications, we first formulate a joint optimization problem where the aim is to minimize the overall energy consumption across the sub-network subject to latency, transmission quality, computational budget and transmit power constraints. The proposed optimization problems are nonconvex, tightly coupled, and consequently challenging to solve. By exploiting binary relaxation, smooth approximation and auxiliary variables, we convert these problems into more tractable forms and subsequently develop novel algorithms based on the concave-convex procedure (CCCP) to solve them. Furthermore, by incorporating a measure of user priority, a reduced-complexity solution is proposed for the FO scheme. The benefits of our energy-efficient resource allocation algorithms for latency-sensitive MEC are demonstrated through simulations. [ABSTRACT FROM AUTHOR]

The long-range correction (LC) for treating electron exchange in density functional theory, combined with the Becke–Lee–Yang–Parr (BLYP) exchange-correlation functional, was used to determine (hyper)polarizabilities of polydiacetylene/polybutatriene oligomers. In comparison with coupled-cluster calculations including single and double excitations as well as a perturbative treatment of triple excitations, our values indicate that the tendency of conventional functionals to result in a catastrophic overshoot for these properties is alleviated but not eliminated. No clear-cut preference for LC-BLYP over Hartree–Fock values is obtained. This analysis is consistent with the calculations of Sekino et al. [J. Chem. Phys. 126, 014107 (2007)] on polyacetylene and molecular hydrogen oligomers. Thus, the performance of LC-BLYP with regard to (hyper)polarizabilities of quasilinear conjugated systems is now well characterized. [ABSTRACT FROM AUTHOR]

Hyper-Raman spectra of pure carbon tetrachloride in the liquid phase are recorded for different combinations of the polarizations of the incident and scattered lights and are compared to ab initio time-dependent Hartree-Fock simulations. Both the calculated intensities of the Raman and hyper-Raman spectra give indeed a quite satisfactory agreement with polarized experimental spectra. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

In this work, we examine the nature of the spiropyran/merocyanine metal interaction in the solid state and highlight its various linking possibilities. A ring opening isomerisation of spiropyran results in the colored merocyanine, which is only rarely observed in the solid state. Introducing metal salts (ZnCl2, ZnBr2, and CoCl2) to four different spiropyran compounds (i.e. 1,3,3-trimethylindolinobenzopyrylospiran, 1,3,3-trimethylindolino-β-naphthopyrylospiran, 1,3,3-trimethylindolino-8′-methoxybenzopyrylospiran, and 1,3,3-trimethylindolino-6′-nitrobenzopyrylospiran) leads successfully to the stabilisation of merocyanine. The ability to form different interactions as a complex, salt or a mixed form was discovered as well as the requirement of different synthetic pathways. Changing the solid form has a strong impact on the absorption properties, resulting in a tool which allows coloring of spiropyrans in the solid state covering a large scale spectrum from red to green. [ABSTRACT FROM AUTHOR]

We propose a novel dictionary learning technique for compressive sensing of speech signals based on the recurrent neural network. First, we exploit the recurrent neural network to solve an ℓ 0 -norm optimization problem based on a sequential linear prediction model for estimating the linear prediction coefficients for voiced and unvoiced speech, respectively. Then, the extracted linear prediction coefficient vectors are clustered through an improved Linde–Buzo–Gray algorithm to generate codebooks for voiced and unvoiced speech, respectively. A dictionary is then constructed for each type of speech by concatenating a union of structured matrices derived from the column vectors in the corresponding codebook. Next, a decision module is designed to determine the appropriate dictionary for the recovery algorithm in the compressive sensing system. Finally, based on the sequential linear prediction model and the proposed dictionary, a sequential recovery algorithm is proposed to further improve the quality of the reconstructed speech. Experimental results show that when compared to the selected state-of-the-art approaches, our proposed method can achieve superior performance in terms of several objective measures including segmental signal-to-noise ratio, perceptual evaluation of speech quality and short-time objective intelligibility under both noise-free and noise-aware conditions. [ABSTRACT FROM AUTHOR]

This paper studies the dynamic resource allocation (DRA) problem for LTE-based vehicle-to-infrastructure networks, where the goal is to minimize the total power consumption (TPC) in the downlink, subject to both power constraints and rate requirements. Under time-varying channel conditions, the TPC minimization takes the form of a discrete-time sequence of NP-hard combinational optimization problems. To solve these sequential problems, we propose a novel two-stage algorithm, named as DRA and precoding algorithm (DRA-Pre). In the first stage, the resource allocation problem (i.e., pairing of vehicle users to roadside units, and subcarrier allocation) is solved by applying the multi-value discrete particle swarm optimization method. This approach takes advantage of the channel correlation by exploiting the relationship between resource allocation solutions in adjacent time slots, which can improve the TPC performance. In the second stage, the precoding design problem is solved by a low-complexity algorithm, where the original problem is split into two subproblems, i.e., a rate max–min subproblem and a TPC minimization subproblem. Simulation results show that the proposed algorithm converges rapidly and significantly outperforms benchmark approaches in terms of TPC. [ABSTRACT FROM AUTHOR]

The joint design of hybrid beamforming matrices is conceived for multiuser mm-wave full-duplex (FD) multiple-input multiple-output (MIMO) relay-aided systems in the presence of realistic channel state information (CSI) errors. Specifically, considering a probabilistic CSI error model, we maximize the system’s worst-case sum rate by jointly optimizing the base station’s (BS’s) analog and digital beamforming matrices, plus the analog receive and transmit beamforming matrices of the relay station (RS) as well as its digital amplify-and-forward beamforming matrix under practical constraints. Explicitly, the transmit power constraints of the BS and RS, the residual self-interference power constraint of the RS, the per-user quality of service constraints, and the unit-modulus constraints on the analog beamforming matrix elements are all taken into account. Since the resultant optimization problem is very challenging due to its highly nonlinear objective function and nonconvex coupling constraints, we first transform it into a more tractable form. We then develop a novel joint optimization algorithm based on the penalty dual decomposition (PDD) technique to solve the resultant problem. The proposed PDD-based algorithm performs double-loop iterations: the inner loop updates the optimization variables in a block coordinate descent fashion, while the outer loop adjusts the Lagrange multipliers and penalty parameter, hence ensuring convergence to the set of stationary solutions of the original problem. Our simulations show that the mm-wave FD hybrid MIMO relay systems relying on our new algorithm significantly outperform both their non-robust FD and conventional half-duplex counterparts. [ABSTRACT FROM AUTHOR]

Investigates the vibrational hyperpolarizability of methane-[sub 4-n]fluorine [sub n] molecules. Implication of electronic and vibrational effects for nonlinear optical responses; Application of the Sadlej atomic basis set; Analysis of the potential energy surface of carbon-hydrogen and carbon-fluorine bonds.

Assesses the use of density functional schemes to determine the polarizabilities and hyperpolarizabilities of conjugated oligomers. Details on the geometries of polyacethylene chains; Overview of density functional theory; Information on the polarization charge induced by external electric field.

Analyzes the several parameter-independent relationship between vibrational and electronic hyperpolarizabilities. Origin of static vibrational polarizabilities and hyperpolarizabilities; Effect of electronic fields on electronic and nuclear motions; Rationalization of the relationship of vibrational and electronic properties.

Examines the effect of solid state interactions on the vibrational hyperpolarizability of polyacetylene chains. Application of coupled perturbed Hartree-Fock calculations; Comparison of the vibrational property to electronic property; Significance of collective and interchain modes on small oligomers.

Presents the crystal orbital calculations on stereoregular polymethineimine isomers. Geometry dependencies of the band structure, density of states (DOS) and polarizability; Effect of DOS on trans-transoid and cis-transoid isomers; Details on the configurational effects upon the first hyperpolarizability.

Examines differences between the exact sum-over-states and the canonical approximation for the calculation of static and dynamic hyperpolarizabilities of molecules. Effects of electronic and vibrational motions on electric fields; Proposed theoretical relationship between the vibrational and electronic first hyperpolarizabilities in push-pull conjugated systems.

Focuses on an ab initio coupled Hartree-Fock study carried out on polymethineimine (PMI) oligomers with an increasing number of unit cells. Effect of conformational differences; End-capping by a strong donor-accepted pair; Static electronic longitudinal polarizability; Computation of first hyperpolarizability.

Studies the linear and nonlinear polarizabilities of polydiacetylene and polybutatriene chains. Extrapolation of the polarizabilities to the infinite polymer limit; Electronic and vibrational components obtained; Sensitivity of the components to the difference in bond length alternation in different ways.

In this paper, we propose a low-complexity adaptive reduced-rank interference suppression scheme based on alternating low-rank decomposition techniques for navigation receivers. The proposed scheme, which employs a generalized sidelobe canceler structure, makes use of a projection matrix for rank reduction followed by a reduced-dimension receive filter. An alternating optimization strategy based on recursive least squares is devised to compute the basis vectors of the projection matrix and the receive filter adaptively. Simulation results show that the proposed algorithm achieves a better performance than existing methods with a reduced computational complexity. [ABSTRACT FROM AUTHOR]

This paper addresses the design of joint beamforming and jamming for a millimeter wave (mmWave) information surveillance system where a suspicious transmitter in the network sends messages to a suspicious receiver under the supervision of a surveillant controller (SC), which not only carries out the duty of a base station or other access point, but also legitimately monitor the suspicious link. Specifically, we seek to maximize the effective monitoring rate for information surveillance by jointly optimizing the analog transmit and receive beamforming vectors of the suspicious link, the analog jamming and monitoring beamforming vectors at the SC and the jamming signal’s power level under transmit power, successful monitoring, and self-interference power constraints at the SC, along with unit modulus constraint on the elements of the radio frequency analog beamforming vectors. The resulting optimization problem is quite challenging due to the tight coupling of the design variables in the objective function and constraints. To solve it, we develop a novel algorithm based on the penalty dual decomposition (PDD) technique, where the exacting constraints are penalized and dualized into the objective function as augmented Lagrangian components. The proposed PDD-based algorithm performs double-loop iterations, i.e., the inner loop resorts to the concave-convex procedure to update the optimization variables; while the outer loop adjusts the Lagrange multipliers and penalty parameter of the augmented Lagrangian cost function. We show that the proposed PDD-based joint beamforming and jamming algorithm converges to a stationary solution of the original problem. Based on our simulation results, the proposed algorithm achieves significantly better performance than the conventional beamforming and jamming algorithms. [ABSTRACT FROM AUTHOR]

In this letter, a novel diffusion Volterra (DV) algorithm is proposed for distributed in-network system identification in the presence of $\alpha$ -stable noise. The proposed algorithm is based on the logarithmic least mean $p$ th-power criterion, which makes it robust against impulsive interferences, at the price of increased complexity. To overcome this shortcoming, we further develop the diffusion interpolated Volterra algorithm, which provides computational savings and good performance in comparison with the DV algorithm. Simulations results show that the proposed adaptive algorithms achieve better performance than the state-of-the-art approaches for distributed nonlinear system identification in impulsive noise. [ABSTRACT FROM AUTHOR]

Wireless transmission relying on lens antenna arrays is becoming more and more attractive for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems using a limited number of radio frequency chains due to the lens’ energy-focusing capability. In this paper, we consider the joint design of the beam selection and precoding matrices in order to maximize the sum-rate of a downlink single-sided lens MU-MIMO mmWave system under transmit power constraints. We first formulate the optimization problem into a tractable form using the popular weighted minimum mean squared error (WMMSE) approach. To solve this problem, we then propose an efficient joint beam selection and precoding design algorithm based on the innovative penalty dual decomposition method. To reduce the design complexity, we also propose a simplified algorithm by combining the interference-aware beam selection scheme with the WMMSE approach. Simulation results demonstrate that our proposed algorithms can converge in a few iterations and achieve near-optimal performance when compared to the fully digital precoding scheme, thus enabling them to outperform the competing methods. [ABSTRACT FROM PUBLISHER]

In this paper, a novel technique for tight outer-approximation of the intersection region of a finite number of ellipses in 2-dimensional space is proposed. First, the vertices of a tight polygon that contains the convex intersection of the ellipses are found in an efficient manner. To do so, the intersection points of the ellipses that fall on the boundary of the intersection region are determined, and a set of points is generated on the elliptic arcs connecting every two neighbouring intersection points. By finding the tangent lines to the ellipses at the extended set of points, a set of half-planes is obtained, whose intersection forms a polygon. To find the polygon more efficiently, the points are given an order and the intersection of the half-planes corresponding to every two neighbouring points is calculated. If the polygon is convex and bounded, these calculated points together with the initially obtained intersection points will form its vertices. If the polygon is non-convex or unbounded, we can detect this situation and then generate additional discrete points only on the elliptical arc segment causing the issue, and restart the algorithm to obtain a bounded and convex polygon. Finally, the smallest area ellipse that contains the vertices of the polygon is obtained by solving a convex optimization problem. Through numerical experiments, it is illustrated that the proposed technique returns a tighter outer-approximation of the intersection of multiple ellipses, compared to conventional techniques, with only slightly higher computational cost. [ABSTRACT FROM AUTHOR]

Among the multicarrier modulation techniques considered as an alternative to orthogonal frequency division multiplexing (OFDM) for future wireless networks, a derivative of OFDM based on offset quadrature amplitude modulation (OFDM/OQAM) has received considerable attention. In this paper, we propose an improved joint estimation method for carrier frequency offset, sampling time offset, and channel impulse response, needed for the practical application of OFDM/OQAM. The proposed joint ML estimator instruments a pilot-based maximum-likelihood (ML) estimation of the unknown parameters, as derived under the assumptions of Gaussian noise and independent input symbols. The ML estimator formulation relies on the splitting of each received pilot symbol into contributions from surrounding pilot symbols, non-pilot symbols and additive noise. Within the ML framework, the Cramer-Rao bound on the covariance matrix of unbiased estimators of the joint parameter vector under consideration is derived as a performance benchmark. The proposed method is compared with a highly cited previous work. The improvements in the results point to the superiority of the proposed method, which also performs close to the Cramer-Rao bound. [ABSTRACT FROM AUTHOR]

Jointly detecting the signals from the source and relay in a spatial multiplexing (SM) multiple-input multiple-output (MIMO) relaying system improves the transmit reliability significantly. However, the existing joint detection schemes for SM MIMO relaying systems, which achieve full diversity, such as the near maximum likelihood (ML) decoder, suffer from high complexity. In this paper, we propose a weighted combining (WC) algorithm, which is applied before the detector in the SM MIMO decode-and-forward relaying system. The proposed algorithm merges the received signal vectors from the source and relay into a combined signal without expanding their dimension, and formulates an equivalent MIMO channel matrix for the combined signal, resulting in a lower complexity for the subsequent detection. We analyze the performance of the proposed WC algorithm with ML detection in terms of the diversity order and computational complexity. An approximate upper bound on the symbol error probability (SEP) for the proposed algorithm is also derived. Simulation results show that in symmetric networks, the proposed WC algorithm achieves substantially lower complexity, while maintaining an SEP performance similar to that of the benchmark NML decoder. The consistency of the derived upper bound on the SEP is also verified by simulations. [ABSTRACT FROM AUTHOR]

This paper investigates the design of robust nonlinear transceivers conceived for multiple-input multiple-output full-duplex wireless-powered relay networks in the face of realistic imperfect channel state information (CSI). A novel self-energy recycling aided relaying protocol is employed, whereby the relay node benefits from energy harvesting (EH) gleaned from the self-interfering link in addition to its primary energy. The proposed nonlinear transceiver relies on a Tomlinson-Harashima (TH) precoder along with an amplify-and-forward (AF) relaying matrix and a linear receiver, where the TH precoder is composed of a feedback matrix and a source precoding matrix. Two different criteria are considered for the robust design of the nonlinear transceiver in the presence of channel estimation errors modeled by the Gaussian distribution. The first one aims to minimize the mean-squared-error (MSE) at the destination subject to a transmit power constraint at the source and an EH constraint at the relay. The resultant optimization problem is converted to four subproblems and solved via an alternating optimization (AO) algorithm that iteratively updates the transceiver coefficients by sequentially addressing each subproblem, while keeping the other matrix variables fixed. The second design criterion aims to minimize the transmit power at the source under both MSE and EH constraints. Similarly, an AO-based iterative algorithm is proposed for solving this problem. Our simulation results show that the robust design advocated is capable of alleviating the effects of CSI errors, hence improving the robustness of the system over that of the corresponding linear designs. [ABSTRACT FROM PUBLISHER]

In this letter, the minimum number of required radio frequency (RF) chains for realizing any MIMO fully digital precoder (FDP) with the hybrid analog/digital precoder (HADP) architecture is investigated from a new perspective. First, in order to take full advantage of the digital domain, an extended system formulation of the hybrid structure is presented in which the digital precoder is taken from an abstract transformation space. Then, a constrained optimization problem is formulated, where the aim is to minimize the number of RF chains subject to an equality constraint on the multi-stream FDP and HADP outputs. We show that non-unique optimum solutions exist for only two RF chains. Based on these results, we further develop a single RF chain design. The implementation of the latter does not require extra hardware or computational complexity. We compare the proposed HADP design with recent works that require larger number of RF chains and demonstrate that significant improvement in performance can be achieved over direct HADP design. [ABSTRACT FROM PUBLISHER]

This paper considers the uplink of large-scale multiple-user multiple-input multiple-output millimeter wave systems, where several mobile stations (MSs) communicate with a single base station (BS) equipped with a large-scale antenna array, for application to fifth generation wireless networks. Within this context, the use of hybrid transceivers along with antenna selection can significantly reduce the implementation cost and energy consumption of analog phase shifters and low-noise amplifiers. We aim to jointly design the MS beamforming vectors, the hybrid receiving matrices (baseband and analog), and the antenna selection matrix at the BS in order to maximize the achievable system sum-rate under a set of constraints. The corresponding optimization problem is nonconvex and difficult to solve, mainly due to the receive antenna selection and constant modulus constraints on the analog receiving matrix. By exploiting the special structure of the problem and linear relaxation, we first convert this problem into three subproblems, which are solved via an alternating optimization method. The latter iteratively updates the antenna selection matrix, the transmit beamforming vectors, and the hybrid receiving matrices by sequentially addressing each subproblem while keeping the other variables fixed. Specifically, the antenna selection matrix is optimized via the concave–convex procedure; the weighted mean-square error minimization approach is used to find the solution for the transmit beamformer; and the hybrid receiver is obtained via manifold optimization. The convergence of the proposed algorithm is analyzed and its effectiveness is verified by simulation. [ABSTRACT FROM PUBLISHER]

This paper addresses joint transceiver design for secure downlink communications over a multiple-input multiple-output relay system in the presence of multiple legitimate users and malicious eavesdroppers. Specifically, we jointly optimize the base station (BS) beamforming matrix, the relay station (RS) amplify-and-forward transformation matrix, and the covariance matrix of artificial noise, so as to maximize the system worst-case secrecy rate in the presence of the colluding eavesdroppers under power constraints at the BS and the RS, as well as quality of service constraints for the legitimate users. This problem is very challenging due to the highly coupled design variables in the objective function and constraints. By adopting a series of transformation, we first derive an equivalent problem that is more tractable than the original one. Then, we propose and fully develop a novel algorithm based on the penalty concave-convex procedure (penalty-CCCP) to solve the equivalent problem, where the difficult coupled constraint is penalized into the objective and the resulting nonconvex problem is solved at each iteration by resorting to the CCCP method. It is shown that the proposed joint transceiver design algorithm converges to a stationary solution of the original problem. Finally, our simulation results reveal that the proposed algorithm achieves better performance than other recently proposed transceiver designs. [ABSTRACT FROM AUTHOR]

In this work, crystal structures of commercially available photochromic compounds, i.e., spiropyrans and spirooxazines, were investigated by single-crystal X-ray diffraction. A total of five new structures were obtained via isothermal evaporation experiments under different conditions, namely 1,3,3-Trimethylindolino-benzopyrylospiran (I), 1,3,3-Trimethylindolinonaphtospirooxaxine (II), 1-(2-Hydroxyethyl)-3,3-dimethylindolino-60-nitrobenzopyrylospiran (III), and 1,3,3-Trimethylindolino-80-methoxybenzopyrylospiran (IVa and IVb). Since the basic structure of a spiropyran/-oxazine does not present typical hydrogen bond accepting and donating groups, this study illustrates the importance of additional functional groups connected to this kind of molecules to induce specific intermolecular interactions. Our results show that possible hydrogen bonding interactions are rather weak due to the high steric demand of these compounds. These results are supported by a search of the Cambridge structural database focused on related structures. [ABSTRACT FROM AUTHOR]

We investigate the performance of a dual-hop two-way cognitive radio system, where the secondary users (SUs) exchange information in an underlay mode with the assistance of a half-duplex relay utilizing physical-layer network coding over finite GF(2). Moreover, we consider a practical scenario of interference from the primary users (PUs) affecting the relay and source nodes. The analysis provides a generalization of previous works as it considers an extended transmission system where the channels can consist of a combination of independent and identically distributed (i.i.d.) and independent but non-identically distributed (i.n.i.d.) Nakagami- $m$ fading models. In addition, unlike prior works, this paper focuses on the performance of both the PUs and the SUs. Closed-form expressions for the symbol error probability (SEP) and the outage probability of the intended PU are obtained. In addition, we derive exact closed-form expressions for the SEP with consideration of special cases of practical interest (e.g., no interference power, interference limited, and single dominant interference cases) for the SUs. Furthermore, an upper bound on the achievable rate of the secondary system is provided. Subsequently, a closed-form approximating expression for the SEP of the secondary system at high signal-to-noise ratios (SNRs) is obtained. Simulation results are provided and attest to the accuracy of the analytical results. [ABSTRACT FROM PUBLISHER]

We propose a blind adaptive reduced-rank widely linear beamforming algorithm using the generalized sidelobe canceller structure for interference suppression. A structured Krylov-subspace-based approach is devised to construct the dimensionality reducing transformation matrix, and a recursive least-squares algorithm is developed according to the widely linear constrained constant modulus criterion to update the reduced-rank filter. We analyze the convergence and complexity of the proposed algorithm and validate its performance gains through simulations. [ABSTRACT FROM AUTHOR]

In this paper, we propose joint transceiver design algorithms for the full-duplex $K$ -pair multiple-input multiple-output interference channel with simultaneous wireless information and power transfer. To mitigate and exploit the complex interference, we consider two important utility optimization problems, i.e., the sum power minimization problem and the sum-rate maximization problem. In the first problem, our aim is to minimize the total transmission power under both transmission rate and energy harvesting (EH) constraints. An iterative algorithm based on alternating optimization (AO) and with guaranteed monotonic convergence is proposed to successively optimize the transceiver coefficients. The algorithm consists of three main steps, where the concave–convex procedure (CCCP), the minimum mean-square error (MMSE) criterion, and the semidefinite relaxation technique are, respectively, employed to compute the vectors of power splitting ratios, the receiving matrices, and the transmitting beamforming vectors. Two simplified algorithms based on fixed beamformers, namely, the maximum ratio transmission and the maximum signal-to-interference-leakage beamformers are also proposed. In the second problem, our aim is to maximize the sum-rate under additional power and EH constraints. Due to the highly non-convex nature of this problem, we first reformulate it into an equivalent-weighted MMSE problem by introducing suitable weight factors, such that the global optima of the two problems are identical. Then, by utilizing the concept of AO and CCCP, we show that the equivalent problem can be efficiently solved. Again, with the aid of the fixed beamformers, two simplified algorithms are provided to reduce the computational complexity. Simulation results are presented to validate the effectiveness of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate a multiuser multiple-input single-output relay system with simultaneous wireless information and power transfer, where the received signal is divided into two parts for information decoding and energy harvesting (EH), respectively. Assuming that both base station (BS) and relay station (RS) are equipped with multiple antennas, we study the joint transceiver design problem for the BS beamforming vectors, the RS amplify-and-forward transformation matrix, and the power splitting (PS) ratios at the single-antenna receivers. The aim is to minimize the total transmission power of the BS and the RS under both signal-to-interference-plus-noise ratio and EH constraints. First, an iterative algorithm based on alternating optimization (AO) and with guaranteed convergence is proposed to successively optimize the transceiver coefficients. This AO-based approach is then extended into a robust transceiver design against norm bounded errors in channel state information (CSI), by using semidefinite relaxation and the S-procedure. Second, a novel design scheme based on switched relaying (SR) is proposed that can significantly reduce the computational complexity and overhead of the AO-based designs while maintaining a similar performance. In the proposed SR scheme, the RS is equipped with a codebook of permutation matrices. For each permutation matrix, a latent transceiver is designed, which consists of BS beamforming vectors, optimally scaled RS permutation matrix, and receiver PS ratios. For the given CSI, the optimal latent transceiver with the lowest total power consumption is selected for transmission. We propose concave-convex procedure-based and subgradient-type iterative algorithms, respectively, to design the latent transceivers under perfect and imperfect CSI. Simulation results are presented to validate the effectiveness of all the proposed algorithms. [ABSTRACT FROM PUBLISHER]

We consider a cooperative wireless network comprised of a source, a destination, and multiple relays operating in the presence of an eavesdropper, which attempts to tap the source–destination transmission. We propose a multirelay selection scheme for protecting the source against eavesdropping. More specifically, multirelay selection allows multiple relays to simultaneously forward the source's transmission to the destination, differing from the conventional single-relay selection, where only the best relay is chosen to assist in the transmission from the source to the destination. For the purpose of comparison, we consider the classic direct transmission and single-relay selection as benchmark schemes. We derive closed-form expressions of the intercept probability and the outage probability for the direct transmission, as well as for the single-relay and multirelay selection schemes over Rayleigh fading channels. It is demonstrated that as the outage requirement is relaxed, the intercept performance of the three schemes improves, and vice versa, implying that there is a security-versus-reliability tradeoff (SRT). We also show that both the single-relay and multirelay selection schemes outperform the direct transmission in terms of SRT, demonstrating the advantage of the relay selection schemes for protecting the source's transmission against the eavesdropping attacks. Finally, upon increasing the number of relays, the SRTs of both the single-relay and multirelay selection schemes significantly improve, and as expected, multirelay selection outperforms single-relay selection. [ABSTRACT FROM PUBLISHER]

In this paper, we present an overview on the topic of noise reduction in the short-time Fourier transform (STFT) domain. First, we briefly review the conventional literature in the single- and multichannel cases separately. In the single-channel scenario, we focus on the spectral subtractive methods, Wiener filter based methods, speech amplitude estimators and estimators of the complex STFT coefficients. In the multi-channel scenario, we investigate in short a selection of key beamforming approaches as well as conventional post-filtering methods. Next, a detailed survey of the most recent advances in the STFT-based noise reduction methods is provided. This includes STSA estimators with super-Gaussian priors, noise power spectral density (PSD ) estimation, estimation methods in the modulation domain, estimation of spectral phase and noise PSD matrix estimation for multi-channel applications. Finally, we summarize the presented material and draw important conclusions on each of the investigated topics. [ABSTRACT FROM PUBLISHER]

We study the performance of diffusion least-mean squares algorithms for distributed parameter estimation in multi-agent networks when nodes exchange information over wireless communication links. Wireless channel impairments, such as fading and path-loss, adversely affect the exchanged data and cause instability and performance degradation if left unattended. To mitigate these effects, we incorporate equalization coefficients into the diffusion combination step and update the combination weights dynamically in the face of randomly changing neighborhoods due to fading conditions. When channel state information (CSI) is unavailable, we determine the equalization factors from pilot-aided channel coefficient estimates. The analysis reveals that by properly monitoring the CSI over the network and choosing sufficiently small adaptation step-sizes, the diffusion strategies are able to deliver satisfactory performance in the presence of fading and path loss. [ABSTRACT FROM AUTHOR]

In this letter, we introduce a discriminative training algorithm of the basis vectors in the nonnegative matrix factorization (NMF) model for single-channel speech enhancement. The basis vectors for the clean speech and noises are estimated simultaneously during the training stage by incorporating the concept of classification from machine learning. Specifically, we consider the probabilistic generative model (PGM) of classification, which is specified by class-conditional densities, along with the NMF model. The update rules of the NMF are jointly obtained with the parameters of the class-conditional densities using the expectation–maximization (EM) algorithm, which guarantees convergence. Experimental results show that the proposed algorithm provides better performance in speech enhancement than the benchmark algorithms. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of amplify-and-forward (AF) relaying for multiple-input–multiple-output (MIMO) multiuser relay networks, where each source transmits multiple data streams to its corresponding destination with the assistance of multiple relays. Assuming realistic imperfect channel state information (CSI) of all the source–relay and relay–destination links, we propose a robust optimization framework for the joint design of the source transmit precoders (TPCs), relay AF matrices and receive filters. Specifically, two well-known CSI error models are considered, namely, the statistical and the norm-bounded error models. We commence by considering the problem of minimizing the maximum per-stream mean square error (MSE) subject to the source and relay power constraints (min–max problem). Then, the statistically robust and worst-case robust versions of this problem, which take into account the statistical and norm-bounded CSI errors, respectively, are formulated. Both of the resultant optimization problems are nonconvex (semi-infinite in the worst-case robust design). Therefore, algorithmic solutions having proven convergence and tractable complexity are proposed by resorting to the iterative block coordinate update approach along with matrix transformation and convex conic optimization techniques. We then consider the problem of minimizing the maximum per-relay power subject to the quality-of-service (QoS) constraints for each stream and the source power constraints (QoS problem). Specifically, an efficient initial feasibility search algorithm is proposed based on the relationship between the feasibility check and the min–max problems. Our simulation results show that the proposed joint transceiver design is capable of achieving improved robustness against different types of CSI errors when compared with nonrobust approaches. [ABSTRACT FROM AUTHOR]

In this paper, a cooperative wideband spectrum sensing scheme based on the expectation–maximization (EM) algorithm is proposed for the detection of a primary user (PU) system in multiantenna cognitive radio (CR) networks. Given noisy signal observations from $N$ secondary users (SUs) over multiple subbands at a fusion center (FC), prior works on cooperative spectrum sensing often use the set of received subband energy as decision statistics over the sensing interval. However, to achieve satisfactory performance, knowledge of the channel state information (CSI) and the noise variances at all the SUs is required by these algorithms. To overcome this limitation, our proposed method, which is referred to as joint detection and estimation (JDE), adopts the EM algorithm to jointly detect the PU signal and estimate the unknown channel frequency responses and noise variances over multiple subbands in an iterative manner. Various aspects of this proposed EM-JDE scheme are investigated, including a reliable initialization strategy to ensure convergence under practical conditions and a distributed implementation to reduce communication overhead. Under the assumption of perfect estimation for the channel frequency responses and noise variances, we further show that the proposed EM-JDE converges to the maximum-likelihood (ML) solution, which serves as an upper bound on its performance. Monte Carlo simulations over Rayleigh fading channels show that the proposed scheme significantly improves the performance of spectrum detection by exploiting the diversity of the spatially distributed SUs with multiple antennas. [ABSTRACT FROM AUTHOR]

Finding a tight ellipsoid that contains the intersection of a finite number of ellipsoids is of interest in positioning applications for wireless sensor networks (WSNs). To this end, we propose a novel geometrical method in 2-dimensional (2-D) space. Specifically, we first find a tight polygon, which contains the desired region and then obtain the tightest ellipse containing the polygon by solving a convex optimization problem. For demonstrating the usefulness of this method, we employ it in a distributed algorithm for elliptical outer-approximation of feasible sets in co-operative WSNs. Through simulations, we show that the proposed method gives a tighter bounding ellipse than conventional methods, while having similar computational cost. [ABSTRACT FROM PUBLISHER]