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Physical-layer key generation (PKG) based on channel reciprocity has recently emerged as a new technique to establish secret keys between devices. Most works focus on pairwise communication scenarios with single or small-scale antennas. However, the fifth generation (5G) wireless communications employ massive multiple-input multiple-output (MIMO) to support multiple users simultaneously, bringing serious overhead of reciprocal channel acquisition. This paper presents a multi-user secret key generation in massive MIMO wireless networks. We provide a beam domain channel model, in which different elements represent the channel gains from different transmit directions to different receive directions. Based on this channel model, we analyze the secret key rate and derive a closed-form expression under independent channel conditions. To maximize the sum secret key rate, we provide the optimal conditions for the Kronecker product of the precoding and receiving matrices and propose an algorithm to generate these matrices with pilot reuse. The proposed optimization design can significantly reduce the pilot overhead of the reciprocal channel state information acquisition. Furthermore, we analyze the security under the channel correlation between user terminals (UTs), and propose a low overhead multi-user secret key generation with non-overlapping beams between UTs. Simulation results demonstrate the near-optimal performance of the proposed precoding and receiving matrices design and the advantages of the non-overlapping beam allocation. [ABSTRACT FROM AUTHOR]

The directions and gains of the paths with significant power can reconstruct mm-wave channel estimation, attributed to spatial sparsity resulting from severe propagation loss. Leveraging this feature, the two-dimensional beamspace method of direction estimation (2-D BMODE) is developed to estimate the path directions, including angles of departure and arrival. The least-squares(LS) method is then employed to estimate the path gains. Different from the beamspace two-dimensional multiple signal classification (MUSIC) method, the method proposed in this paper significantly ruduces computational complexity without spectral search. Moreover, the DODs and DOAs can be paired automatically by reduced-dimension(RD) MUSIC. Meanwhile, the performance of this method deteriorates when the covariance matrix of gains isn’t of full column rank. This paper also enhances the performance using spatial smoothing technique and oblique projecting to overcome the influence of rank deficiency. The 2-D BMODE method, as illustrated in this paper, outperforms in terms of both unity in computational complexity and estimation accuracy. Numerical simulations validate the advantages and demonstrate the method can serve as a superior alternative.

Compressive sensing (CS) has been widely used in vehicular technology including compressive spectrum sensing, sparse channel estimation, and vehicular communications. The complete procedure of CS consists of sparse representation, sparse measurement, and sparse recovery. In the CS, measurement matrix $\mathbf {\Phi }$ is utilized to sample a sparse signal, while sensing matrix $\mathbf {\Psi }$ is exploited for sparse recovery. It is important to properly design the sensing matrix that significantly affects the signal recovery performance. This paper addresses the issue of the sensing matrix design for the CS with multiple measurement vectors (MMV). We first study sufficient conditions for the MMV-CS and then develop three sensing matrix design approaches in different situations. The proposed methods have their roots in the principle of minimum variance distortionless response (MVDR) beamformer. This allows us to optimally determine the sensing matrix in the framework of the MVDR, which amounts to minimizing local cumulative cross-coherence between $\mathbf {\Phi }$ and $\mathbf {\Psi }$ while keeping the columns of $\mathbf {\Psi }$ highly correlated to their counterparts of $\mathbf {\Phi }$. Simulation results are presented to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In multi-user multiple-input–multiple-output (MU-MIMO) systems that employ pilot-symbol aided channel estimation, the pilot-to-data power ratio (PDPR) has a large impact on the system performance. In this paper, we consider the problem of setting the PDPR in multi-cell MU-MIMO systems in the presence of channel estimation errors, intercell interference and pilot contamination. To analyze and address this problem, we first develop a model of the multi-cell MU-MIMO system and derive a closed-form expression for the mean squared error of the uplink received data symbols. Building on this result, we then propose two decentralized PDPR-setting algorithms based on game theoretic approaches that are applicable in multi-cell systems. We find that both algorithms converge to a Nash equilibrium and provide performance improvements over systems that do not properly set the PDPR, while they maintain different levels of fairness. [ABSTRACT FROM AUTHOR]

Situational awareness in wireless networks refers to the availability of position information on transmitters and receivers as well as information on their propagation environments to aid wireless communications. In millimeter wave massive multiple-input multiple-output communication systems, situational awareness can significantly improve the quality and robustness of communications. In this paper, we establish a model that describes the statistical dependencies between channel state information and the position, orientation, and clock offset of a user equipment along with the locations of features in the propagation environment. Based on this model, we introduce COMPAS (COncurrent Mapping, Positioning, And Synchronization); an inference engine that can provide accurate and reliable situational awareness in millimeter wave massive multiple-input multiple-output communication systems. Numerical results show that COMPAS is able to infer the positions of an unknown and time-varying number of features in the propagation environment and, at the same time, estimate the position, orientation, and clock offset of a user equipment. [ABSTRACT FROM AUTHOR]

For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is usually used to reduce the complexity and cost, which poses a very challenging issue in channel estimation. In this paper, deep convolutional neural network (CNN) is employed to address this problem. We first propose a spatial-frequency CNN (SF-CNN) based channel estimation exploiting both the spatial and frequency correlation, where the corrupted channel matrices at adjacent subcarriers are input into the CNN simultaneously. Then, exploiting the temporal correlation in time-varying channels, a spatial-frequency-temporal CNN (SFT-CNN) based approach is developed to further improve the accuracy. Moreover, we design a spatial pilot-reduced CNN (SPR-CNN) to save spatial pilot overhead for channel estimation, where channels in several successive coherence intervals are grouped and estimated by a channel estimation unit with memory. Numerical results show that the proposed SF-CNN and SFT-CNN based approaches outperform the non-ideal minimum mean-squared error (MMSE) estimator but with reduced complexity, and achieve the performance close to the ideal MMSE estimator that is very difficult to be implemented in practical situations. They are also robust to different propagation scenarios. The SPR-CNN based approach achieves comparable performance to SF-CNN and SFT-CNN based approaches while only requires about one-third of spatial pilot overhead at the cost of complexity. The results in this paper clearly demonstrate that deep CNN can efficiently exploit channel correlation to improve the estimation performance for mmWave massive MIMO systems. [ABSTRACT FROM AUTHOR]

In conventional full-duplex communications, dedicated symbols are transmitted to estimate both the self-interference channel and the desired signal channel in order to perform self-interference cancellation (SIC) and to coherently detect the desired signal. However, inaccurate channel estimation will produce residual self-interference and degrade the detection performance. In this paper, we exploit a Gaussian mixture model (GMM) clustering to design a full-duplex transceiver (FDT), which is able to detect the desired signal without requiring digital-domain channel estimation and SIC. The frame structure of the designed FDT contains two successive phases: labeling phase and data transmission phase. In particular, the designed FDT performs cluster labeling in the labeling phase and performs GMM clustering based on an expectation-maximization (EM) algorithm in the data transmission phase. Furthermore, the theoretical analysis about the detection performance, computational complexity, and convergence performance for the designed FDT are studied. Finally, simulation results show that the bit error rate (BER) of the designed FDT is closed to the performance of the FDT with a maximum likelihood (ML) detector and perfect channel knowledge meanwhile is superior to the BER performance of the FDT with a ML detector and a least square (LS) or least mean square (LMS) channel estimator. [ABSTRACT FROM AUTHOR]

Visible light communications (VLC) is an emerging technology with a promise of viable solution to spectrum crunch problem in conventional radio-frequency (RF) bands. In this paper, we consider a downlink multiuser VLC network where users randomly change their location and vertical orientation. In order to increase the spectral efficiency, we consider the non-orthogonal multiple access (NOMA) transmission to serve multiple users simultaneously. In particular, we propose individual and group-based user ordering techniques for NOMA with various user feedback schemes. In order to reduce the computational complexity and link overhead, feedback on the channel quality is proposed to be computed using a mean value of the vertical angle (instead of the exact instantaneous value), as well as the distance information. In addition, a two-bit feedback scheme is proposed for the group-based user scheduling, which relies on both the distance and the vertical angle, and differs from the conventional one-bit feedback of the distance only. The outage probability and sum-rate expressions are derived analytically, which show a very good match with the simulation data. Numerical results verify that the practical feedback scheme with the mean vertical angle achieves a near-optimal sum-rate performance, and the two-bit feedback significantly outperforms the one-bit feedback. [ABSTRACT FROM AUTHOR]

This paper presents a family of training preambles for offset quadratic-amplitude modulation based filter-bank multi-carrier (FBMC) modulations with low peak-to-average power ratio (PAPR) property. We propose to use binary Golay sequences as FBMC preambles and analyze the maximum PAPR for different numbers of zero guard symbols. For both the PHYDYAS and Hermite prototype filters with overlapping factor of 4, as an illustration of the proposed preambles, we show that a preamble PAPR less than 3 dB can be achieved with probability of one, when three or more zero guard symbols are inserted in the vicinity of each preamble. [ABSTRACT FROM AUTHOR]

In future 5G user-centric ultra-dense networks (UUDN), demands of high data rate and high spectrum efficiency are effectively met by dual connectivity (DC) technology. However, due to huge increase of base stations (BSs) and mobile users (MUs), it becomes difficult for BSs to quickly and precisely select the codeword and provide DC to MUs. Hence, different from some traditional methods, this correspondence paper aims to improve the network performance using the method of machine learning. First, we model the random distribution of BSs by homogeneous Poisson point processes, where each MU is served by millimeter-wave channel. Second, the probabilities that macro cell BS or small cell BS serves the MU are further derived to get the average sum rate (ASR) in UUDN. Third, inspired by ML, we utilize an iterative support vector machine (SVM) classifier to select the codewords of BSs, with sequential minimal optimization (SMO) algorithm used for training all link samples in UUDN. Then, an iterative SVM-SMO classification algorithm is proposed to achieve a highly efficient performance of DC, where the convergence and complexity are also discussed. The sample training and simulation at last are evaluated by Google TensorFlow. The simulation verified that our proposed algorithm gets a higher ASR than the traditional channel estimation based algorithm. In addition, the results also show a lower computational complexity can be achieved by the proposed algorithm as well. [ABSTRACT FROM AUTHOR]

Channel estimation is a key feature for free space optical (FSO) wireless communication systems which are required for a high QoS and high data rate system. Aiming at the problem of FSO wireless channel estimation, this paper proposes an implicit sequence channel estimation scheme to eliminate dc offset, and studies the key factors affecting the system performance. The optimal power allocation factor is derived based on the maximum SNR criterion. The system performance parameters such as the bit error rate (BER), mean square error (MSE), and computational complexity are evaluated. The results show that under the same dc bias condition, the MSE is estimated to decrease significantly after using the method of this paper. With the increase of dc offset d, the system error rate is significantly reduced and more approaching d = 0 (i.e., no dc offset). The proposed algorithm effectively mitigates the dc offset impact on the system and the BER is significantly improved while the MSE is reduced to 50% of the traditional state-of-the-art method. The proposed work can provide a technical solution to the FSO system and give support to technological development. [ABSTRACT FROM AUTHOR]

In diffusion-based communication, for molecular systems, the achievable data rate depends on the stochastic nature of diffusion, which exhibits a severe inter-symbol-interference (ISI). Multiple-input multiple-output (MIMO) multiplexing improves the data rate at the expense of an inter-link interference (ILI). This paper investigates training-based channel estimation schemes for diffusive MIMO (D-MIMO) systems and corresponding equalization methods. Maximum likelihood and least-squares estimators of mean channel are derived, and the training sequence is designed to minimize the mean square error (MSE). The numerical validations in terms of MSE are compared with Cramér–Rao bound derived herein. Equalization is based on decision feedback equalizer (DFE) structure as this is effective in mitigating diffusive ISI/ILI. Zero-forcing, minimum MSE, and least-squares criteria have been paired to DFE, and their performances are evaluated in terms of bit error probability. D-MIMO time interleaving is exploited as an additional countermeasure to mitigate the ILI with remarkable performance improvements. The configuration of nano-transceivers is not static but affected by a Brownian motion. A block-type communication is proposed for D-MIMO channel estimation and equalization, and the corresponding time-varying D-MIMO MC system is numerically evaluated. [ABSTRACT FROM AUTHOR]

In this paper, we study the design of secure communication for time-division duplex multi-cell multi-user massive multiple-input-multiple-output (MIMO) systems with active eavesdropping. We assume that the eavesdropper actively attacks the uplink pilot transmission and the uplink data transmission before eavesdropping the downlink data transmission of the users. We exploit both the received pilot’s and the received data signals for uplink channel estimation. We show analytically that when both the number of transmit antennas and the length of the data vector tend to infinity, the signals of the desired user and the eavesdropper lie in different eigenspaces of the received signal matrix at the base station, provided their signal powers are different. This finding reveals that decreasing (instead of increasing) the desired user’s signal power might be an effective approach to combat a strong active attack from an eavesdropper. Inspired by this observation, we propose a data-aided secure downlink transmission scheme and derive an asymptotic achievable secrecy sum-rate expression for the proposed design. For the special case of a single-cell single-user system with independent and identically distributed fading, the obtained expression reveals that the secrecy rate scales logarithmically with the number of transmit antennas. This is the same scaling law as for the achievable rate of a single-user massive MIMO system in the absence of eavesdroppers. The numerical results indicate that the proposed scheme achieves significant secrecy rate gains compared with alternative approaches based on matched filter precoding with artificial noise generation and null space transmission. [ABSTRACT FROM AUTHOR]

Massive machine-type communication (mMTC) is a newly introduced service category in 5G wireless communication systems to support a variety of Internet-of-Things (IoT) applications. In recovering sparsely represented multi-user vectors, compressed sensing-based multi-user detection (CS-MUD) can be used. CS-MUD is a feasible solution to the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, active user detection (AUD) and channel estimation (CE) should be performed before data detection. In this paper, we propose the expectation propagation-based joint AUD and CE (EP-AUD/CE) technique for mMTC networks. The EP algorithm is a Bayesian framework that approximates a computationally intractable probability distribution to an easily tractable distribution. The proposed technique finds a close approximation of the posterior distribution of the sparse channel vector. Using the approximate distribution, AUD and CE are jointly performed. We show by numerical simulations that the proposed technique substantially enhances AUD and CE performances over competing algorithms. [ABSTRACT FROM AUTHOR]

Compressive random access (CRA) is a random access scheme that has been considered for massive machine-type communication (MTC) with non-orthogonal spreading sequences, where the notion of compressive sensing (CS) is used for low-complexity detectors by exploiting the sparse device activity. In this paper, we study the application of power-domain non-orthogonal multiple access (NOMA) to CRA in order to improve the performance of CRA (or increase the number of devices to be supported). We consider Gaussian spreading sequences and derive design criteria through a large-system analysis to determine the power levels for power-domain NOMA. From simulation results, we can confirm that the number of incorrectly detected device activity can be reduced by applying NOMA to CRA. [ABSTRACT FROM AUTHOR]

This paper aims at the problem of time-of-flight (ToF) estimation using channel state information (CSI) obtainable from commercialized multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless local area network (WLAN) receivers. It was often claimed that the CSI phase is contaminated with errors of known and unknown natures rendering the ToF-based positioning difficulty. To search for an answer, we take a bottom-up approach by first understanding CSI, its constituent building blocks, and the sources of error that contaminate it. We then model these effects mathematically. The correctness of these models is corroborated based on the CSI collected in extensive measurement campaign, including radiated, conducted, and chamber tests. Knowing the nature of contaminations in the CSI phase and amplitude, we proceed with introducing pre-processing methods to clean CSI from those errors and make it usable for range estimation. To check the validity of the proposed algorithms, the MUSIC super-resolution algorithm is applied to post-processed CSI to perform range estimates. The results substantiate that a median accuracy of 0.7, 0.8, and 0.9 m is achievable in a highly multipath line-of-sight environment where the transmitter and the receiver are 5, 10, and 15 m apart. [ABSTRACT FROM AUTHOR]

In this paper, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS). Specifically, we put forth two MUD schemes, termed random sparsity learning multiuser detection (RSL-MUD) and structured sparsity learning multiuser detection (SSL-MUD) for the time-slotted and non-time-slotted grant-free MaDMA systems, respectively. In RSL-MUD, active users generate and transmit data packets with random sparsity. In SSL-MUD, we introduce a sliding-window-based detection framework, and the user signals in each observation window naturally exhibit structured sparsity. We show that by exploiting the sparsity embedded in the user signals, we can recover the user activity state, the channel, and the user data in a single phase, without using pilot signals for channel estimation and/or active user identification. To this end, we develop a message-passing-based statistical inference framework for the BS to blindly detect the user data without any prior knowledge of the identities and the channel state information (CSI) of active users. The simulation results show that our RSL-MUD and SSL-MUD schemes significantly outperform their counterpart schemes in both reducing the transmission overhead and improving the error behavior of the system. [ABSTRACT FROM AUTHOR]

Channel state information (CSI) plays a vital role in wireless communication systems. However, the CSI acquisition overhead is an enormous obstacle to realize the system performance improvements promised by massive connectivity and massive multiple-input-multiple-output (MIMO). To alleviate this overhead, this paper proposes a remote channel inference framework by probing the channels occupied by a source base station (BS) and inferring the channels of target BSs at geographically separated sites. The work generalizes existing literature which mainly focuses on utilizing the CSI linear correlations of adjacent antennas, by adopting a model-free deep learning framework to investigate non-linear dependence among remote CSI. The existence of such cross-BS CSI dependence is first shown by calculating the mutual information between remote channels, and the Cramér-Rao lower bound of remote CSI inference performance based on a one-ring channel model. Inspired by this finding, modern deep learning approaches are leveraged to perform remote channel inference in heterogeneous networks for both single user and multi-user scenarios. The simulation results based on ray tracing data show evident performance advantages over conventional methods, under both homogeneous and heterogeneous frequency coverage. The proposed framework achieves beamformer inference accuracy within 4.6% of the genie-aided optimum at the cost of sweeping only two beams. [ABSTRACT FROM AUTHOR]

Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms. In this correspondence paper, a modified extreme learning machine (ELM) that can process tensorial data, or ELM model with tensorial inputs (TELM), is proposed to handle the channel interpolation task. The TELM inherits many good properties from ELMs. Based on the TELM, the Tucker decomposed extreme learning machine is proposed for further improving the performance. Furthermore, we establish a theoretical argument to measure the interpolation capability of the proposed learning machines. Experimental results verify that our proposed learning machines can achieve comparable mean squared error (MSE) performance against the traditional ELMs but with 15% shorter running time, and outperform the other methods for a 20% margin measured in MSE for channel interpolation. [ABSTRACT FROM AUTHOR]

Secret key generation based on channel measurements has emerged as a meaningful approach to enhance the security of wireless links. The key generation mechanism takes advantage of wireless channel reciprocity and uses the mutual information between legitimate users’ channel measurements. However, the rate at which key bits are generated depends on the channel coherence time. Furthermore, eavesdroppers may obtain information to generate the secret key bits if their channels become correlated to the legitimate channels. In this paper, we propose a new approach for key generation that uses MIMO precoding and role switching by legitimate users during the key generation process in order to prevent an eavesdropper whose channel is correlated with the legitimate channel to acquire information that can be used to generate the encryption key, while also ensuring that the rate at which uncorrelated key bits are generated does not depend on the channel coherence time. The proposed approach is supported by an information theoretic analysis and is illustrated with numerical results obtained from simulations showing that, even when the legitimate and eavesdropper channels are correlated, the bit mismatch rate at the eavesdropper does not improve with increasing SNR, while that at the legitimate users improves. [ABSTRACT FROM AUTHOR]

This paper proposes a robust beamforming (BF) scheme to enhance the physical layer security (PLS) of a multibeam satellite system operating at Ka band. By considering the effects of beam gain, path loss, and rain attenuation, we first formulate a constrained optimization problem to maximize the minimal achievable secrecy rate of multiuser under the assumption of imperfect eavesdropping channel and the constraint of total transmit power on the satellite. Since the problem is non-convex and mathematically intractable, we exploit the sequential convex approximation approach and S-procedure method to transform the original problem into one with a series of linear matrix inequality and second-order cone constraints, and then propose an iterative scheme to obtain the optimal BF weight vectors. Finally, simulation results showing the effectiveness and superiority of the proposed algorithm are provided. [ABSTRACT FROM AUTHOR]

In this paper, we propose regularized block diagonalization (RBD) precoding using artificial noise (AN) for physical layer security in downlink multi-user MIMO wireless networks. We derive secrecy sum rate and asymptotic secrecy sum rate for the proposed scheme. The optimum power allocation for legitimate users and the AN signal are derived in closed form for optimal asymptotic secrecy sum rate. Our analysis shows that to achieve best performance, it is more efficient to degrade the performance of the eavesdropper than improving legitimate users’ rates. We also study the impact of error in channel estimation on the system and derive closed-form SINR and secrecy sum-rate expressions for this case. Simulations show that the secrecy sum rate of RBD precoding outperforms the secrecy sum rate of regularized channel inversion by 0.5 bits/s/Hz. We also show that in the presence of channel estimation error, our approximate closed forms for secrecy sum rate are very closed to the actual secrecy sum rate over a wide range of SNR and channel estimation error values. [ABSTRACT FROM AUTHOR]

This correspondence paper considers a multiple-input single-output non-orthogonal multiple access (MISO-NOMA) system that spatially multiplexes NOMA signals formed by superimposing two user symbols in the power domain. We aim to design practical beamforming (BF), power allocation (PA), and user clustering methods for the system by minimizing the transmit power under the rate constraints. The two-step BF and conditionally optimal PA are devised in closed-form expressions that facilitate user clustering based on message passing and effective channel correlation. The results show that the MISO-NOMA system with the proposed BF, PA, and user clustering methods outperforms the benchmark systems of comparable complexity. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a novel channel estimation (CE) method for multiple-input and single-output (MISO) systems in visible light communication (VLC). Direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) is commonly used in VLC where half of the available subcarriers are spent to guarantee a real-valued output after the inverse fast Fourier transform operation. Besides, dedicated subcarriers are typically used for CE, thus, many resources are wasted and the spectral efficiency is degraded. We propose a superimposed training approach for CE in MISO DCO-OFDM VLC scenarios. Analytical expressions of mean squared error (MSE) and spectral efficiency are derived when the least squares estimator is considered. This analysis is valid for outdoor and indoor scenarios. For the CE error, simulation results of MSE show a perfect match with analytical expressions. Moreover, results prove that this technique guarantees a larger spectral efficiency than previous schemes where dedicated pilots were used. Finally, the optimal data power allocation factor is also analytically derived. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of recovering a group sparse vector from a small number of linear measurements. In the past, the common approach has been to use various “group sparsity-inducing” norms such as the Group LASSO norm for this purpose. By using the theory of convex relaxations, we show that it is also possible to use $\ell _1$ -norm minimization for group sparse recovery. We introduce a new concept called group robust null space property (GRNSP), and show that, under suitable conditions, a group version of the restricted isometry property (GRIP) implies the GRNSP, and thus leads to group sparse recovery. When all groups are of equal size, our bounds are sometimes less conservative than known bounds. Moreover, our results apply even to situations where the groups have different sizes. When specialized to conventional sparsity, our bounds reduce to one of the well-known “best possible” conditions for sparse recovery. This relationship between GRNSP and GRIP is new even for conventional sparsity, and substantially streamlines the proofs of some known results. Using this relationship, we derive bounds on the $\ell _p$ -norm of the residual error vector for all $p \in [1,2]$ , and not just when $p = 2$. When the measurement matrix consists of random samples of a sub-Gaussian random variable, we present bounds on the number of measurements, which are sometimes less conservative than currently known bounds. [ABSTRACT FROM AUTHOR]

Modeling of lightning return-stroke channel affects significantly the estimation of fast-front overvoltages arising across overhead line insulators, thus, also the lightning performance of power lines. This paper introduces a simplified method for estimating the minimum lightning currents causing shielding failure flashover and backflashover of overhead transmission line insulation, as affected by lightning channel equivalent impedance. The validity of the proposed methodology is demonstrated through ATP-EMTP simulations of typical 66 up to 765-kV overhead transmission lines with variable tower ground resistance. An assessment is made of the effects of lightning channel equivalent impedance on the lightning performance of overhead lines, that is, shielding failure flashover and backflashover rates. Lightning channel equivalent impedance values of 1000 and 400 Ω are suggested, respectively, for shielding failure and backflashover studies, based on the results obtained in this paper together with a discussion on the literature data regarding channel impedance. [ABSTRACT FROM AUTHOR]

In this paper, we experimentally compared the performance of bit-loading discrete multi-tone (DMT) and pre-equalized discrete-Fourier-transform spread (DFT-Spread) DMT in intensity modulation with direct detection (IM/DD) system for 2-km optical interconnect for the first time. Pre-equalization is applied to compensate the high-frequency power fading in wideband communication system. However, it remains to be verified whether zero-forcing (ZF) channel estimation based complete pre-equalization can achieve the best performance for system with serious high frequency fading. In an IM/DD system with 120 Gbit/s data rate, we demonstrated that the bit error rate of DFT-Spread DMT signal with optimized quadratic curve fitting based partial pre-equalization outperforms ZF based complete pre-equalization after 2-km standard single mode fiber (SSMF) transmission. What's more, we experimentally compared the performance of pre-equalized DFT-Spread DMT and Chow–Cioffi–Bingham (CCB) algorithm-based bit loading DMT. The results show that the receiver sensitivity of pre-equalized DFT-Spread DMT is improved by 2.5 dB compared with CCB algorithm-based bit-loading DMT at hard decision forward error correction threshold of 3.8 × 10−3 after 2-km SSMF transmission. [ABSTRACT FROM AUTHOR]

The identification of maximum tolerable levels for potential mismatches is critical when designing communication systems. In this paper, we derive maximum tolerable levels for time-interleaved analog-to-digital-converter (TI-ADC) gain mismatch in orthogonal frequency-division multiplexing (OFDM) systems. To this end, we first analytically evaluate the bit error rate (BER) for square quadrature amplitude modulated-OFDM systems that are impaired by: 1) the gain mismatch of a TI-ADC and 2) the channel estimation errors (CEEs) of a zero-forcing equalizer. Our analysis includes the cases of a frequency-selective Rayleigh fading channel and a wired channel. Next, built on the obtained BER expressions, a threshold is established on the gain mismatch level, at which an error floor caused by the gain mismatch is below a given BER value at high signal-to-noise ratios in the absence of CEEs. Finally, numerical results further show that if and only if we set the gain mismatch level below 0.25 of this threshold, there is essentially no BER performance degradation compared with the mismatch-free case. [ABSTRACT FROM AUTHOR]

In this paper, we consider the uplink of massive MIMO systems where the channel capacity is almost deterministic and varies slowly over time. We exploit this fact, by appropriately choosing the length of the codeword only once based on an initial estimate of the uplink channel capacity fed back by the base station (BS). The user terminal (UT) then transmits the codeword over consecutive coherence time intervals without waiting for any acknowledgement (ACK/NACK) from the BS. The BS sends only a single ACK/NACK after it completes decoding. Therefore, the proposed method has significantly less latency and signaling overhead when compared with stop-and-wait type hybrid automatic repeat request (HARQ) methods (as in LTE uplink) where a lot of delay is introduced as the UT needs to wait for the BS to signal the ACK/NACK for every retransmission. Analysis reveals that for sufficiently large number of antennas at the BS, the average codeword length of the proposed method (in number of coherence intervals) is the same as the average number of retransmissions (in number of coherence intervals) required by incremental redundancy (IR)-based HARQ and also that the energy efficiency of the proposed method and IR-HARQ is the same. [ABSTRACT FROM AUTHOR]

In this paper, we consider the uplink channel parameter estimation problem in the presence of pilot contamination for massive multiple-input-multiple-output (MIMO) systems. We propose a parallel factor (PARAFAC)-based estimation scheme, which exploits the low-rank property of massive MIMO channels caused by the finite scattering in a physical environment. Specifically, we first parameterize the channel in terms of three parameters, i.e., fading coefficients, directions of arrival (DOAs), and delays; thereby, the channel is characterized via three equivalent PARAFAC models. Then, the proposed PARAFAC-based scheme is developed, which jointly estimates these three channel parameters using an alternating least squares (ALS) algorithm. Therein, we certify the identifiability of the three channel parameters of the PARAFAC models to mitigate the pilot contamination and state the convergence of the ALS algorithm, which guarantees that the three channel parameters can be uniquely determined with the proposed scheme. Moreover, to further reduce the computational complexity, two advanced schemes are proposed by antenna selection and reducing the estimation frequency of DOAs and delays, respectively. Simulation results show that the proposed schemes can achieve both low computational complexities and close to optimal Cramer–Rao Bound performance. [ABSTRACT FROM AUTHOR]

In existing communication systems, the channel state information of each user equipment (UE) should be repeatedly estimated when it moves to a new position or when another UE takes its place. The underlying ambient information, including the specific layout of potential reflectors, which provides more detailed information about all UEs’ channel structures, has not been fully explored and exploited. In this paper, we rethink the mm-wave channel estimation problem in a new and indirect way, i.e., instead of estimating the resultant composite channel response at each time, and for any specific location, we first conduct the ambient perception exploiting the fascinating radar capability of a mm-wave antenna array and then accomplish the location-based sparse channel reconstruction. In this way, the sparse channel for a quasi-static UE arriving at a specific location can be rapidly synthesized based on the perceived ambient information, thus greatly reducing the signaling overhead and online computational complexity. Based on the reconstructed mm-wave channel, single-beam mm-wave communication is designed and evaluated which shows an excellent performance. Such an approach, in fact, integrates the radar with communication, which may possibly open a new paradigm for future communication system design. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) is one of the most promising techniques for next generation wireless communications due to its superior capability to provide high spectrum and energy efficiency. Considering the very large number of antennas employed at the base station, however, the pilot overhead for downlink channel estimation becomes unaffordable in frequency division duplex (FDD) multiuser massive MIMO systems. In this paper, we propose an information-theoretic metric to design the pilot for downlink channel estimation in FDD multiuser massive MIMO systems. By exploiting the low-rank nature of the channel covariance matrix, we first derive the minimum number of pilot symbols required to ensure perfect channel recovery, which is much less than the number of antennas at the base station. Further, under a general channel model that the channel vector of each user follows a Gaussian mixture distribution, the pilot symbols are designed by maximizing the weighted sum of the Shannon mutual information between the measurements of the users and their corresponding channel vectors on the complex Grassmannian manifold. Simulation results demonstrate the effectiveness of the proposed information-theoretic pilot design for the downlink channel estimation in FDD massive MIMO systems. [ABSTRACT FROM AUTHOR]

This paper presents the design and the experimental demonstration of transmission of spectrally efficient frequency division multiplexing (SEFDM) signals, using a single 5-GHz channel, from 81 to 86 GHz in the E-band frequency allocation. A purpose-built E-band SEFDM experimental demonstrator, consisting of transmitter and receiver GaAs microwave integrated circuits, along with a complete chain of digital signal processing is explained. Solutions are proposed to solve the channel and phase offset estimation and equalization issues, which arise from the well-known intercarrier interference between the SEFDM signal subcarriers. This paper shows the highest transmission rate of 12 Gb/s over a bandwidth varying between 2.67 to 4 GHz depending on the compression level of the SEFDM signals, which results in a spectral efficiency improvement by up to 50%, compared to the conventional orthogonal frequency division multiplexing modulation format. [ABSTRACT FROM AUTHOR]

The use of multichannel data in line spectral estimation (or frequency estimation) is common for improving the estimation accuracy in array processing, structural health monitoring, wireless communications, and more. Recently proposed atomic norm methods have attracted considerable attention due to their provable superiority in accuracy, flexibility, and robustness compared with conventional approaches. In this paper, we analyze atomic norm minimization for multichannel frequency estimation from noiseless compressive data, showing that the sample size per channel that ensures exact estimation decreases with the increase of the number of channels under mild conditions. In particular, given $L$ channels, order $K \left ({\log K}\right) \left ({1+\frac {1}{L}\log N}\right)$ samples per channel, selected randomly from $N$ equispaced samples, suffice to ensure with high probability exact estimation of $K$ frequencies that are normalized and mutually separated by at least $\frac {4}{N}$. Numerical results are provided corroborating our analysis. [ABSTRACT FROM AUTHOR]

In heterogeneous vehicular networks, non-orthogonal multiple access (NOMA) with effective resource allocation improves spectrum efficiency by allowing multiple users to share the same channel. However, channel estimation errors caused by high mobility in vehicular networks affect system robustness and link reliability. As a result, resource allocation in high-mobility scenarios is a challenging issue. In this paper, robust resource allocation is studied to improve both the throughput performance and reliability of NOMA-based heterogeneous vehicular networks. A cascaded Hungarian channel assignment algorithm is proposed to simplify the formulated resource allocation problem with reliability requirements into a robust power allocation problem with chance constraints. With the approximation of non-central Chi-square distribution, the chance constraints are transformed into deterministic constraints. Furthermore, the optimal power allocation for the transformed problem is obtained in consideration of the requirements in NOMA. Simulation results illustrate the effectiv- eness of the proposed robust resource allocation scheme and its improvement over existing schemes. [ABSTRACT FROM AUTHOR]

This paper considers a Massive MIMO network with $ {L}$ cells, each comprising a base stations (BS) with $ {M}$ antennas and $K$ single-antenna user equipments. Within this setting, we are interested in deriving approximations of the achievable rates in the uplink and downlink under the assumption that single-cell linear processing is used at each BS and that each intracell link forms an uncorrelated MIMO Rician fading channel matrix; that is, with a deterministic line-of-sight (LoS) path and a stochastic non-LoS component describing a spatial uncorrelated multipath environment. The analysis is conducted assuming that $ {N}$ and $ {K}$ grow large with a given ratio ${\textbf {N/K}}$ under the assumption that the data transmission in each cell is affected by channel estimation errors, pilot contamination, an arbitrary large scale attenuation and LoS components. Numerical results are used to prove that the approximations are asymptotically tight, but accurate for systems with finite dimensions. The asymptotic results are also used to evaluate the impact of LoS components. In particular, we exemplify how the number of antennas for achieving a target rate can be substantially reduced with LoS links of only a few dBs of strength. [ABSTRACT FROM AUTHOR]

Analog beamforming is an attractive and cost-effective solution to exploit the benefits of massive multiple-input-multiple-output systems, requiring only one up/down-conversion chain. However, the presence of only one chain imposes a significant overhead in estimating the channel state information required for beamforming, when conventional digital channel estimation (CE) approaches are used. As an alternative, this paper proposes a novel CE technique, called periodic analog CE (PACE), which can be performed by analog hardware. By avoiding digital processing, the estimation overhead is significantly lowered and does not scale with the number of antennas. PACE involves the periodic transmission of a sinusoidal reference signal by the transmitter, the estimation of its amplitude and phase at each receive antenna via analog hardware, and using these estimates for beamforming. To enable such non-trivial operation, two reference tone recovery techniques and a novel receiver architecture for PACE are proposed and analyzed, both theoretically and via simulations. The results suggest that in sparse, wide-band channels and above a certain signal-to-noise ratio, PACE-aided beamforming suffers only a small loss in beamforming gain and enjoys a much lower CE overhead, in comparison to conventional approaches. The benefits of using PACE-aided beamforming during the initial access phase are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, a low complexity channel estimation method is proposed for two-dimensional (2-D) massive MIMO systems with uniform rectangular arrays of antennas. Instead of assuming independent fading between different transmit–receive antenna pairs, an array-structure-based physical channel model that characterizes the realistic scattering environments via the angles and gains associated with different propagation paths is studied. A 2-D Fourier-transform (FT) based on elevation and azimuth steering factors is applied to derive the spatial spectrum of received signals in angular domain at base station. Based on the spectrum analysis, the spatial spectrum of the received data is used to estimate the channel parameters, including direction of arrival (incident azimuth and elevation angles) and the path amplitude of each resolvable path. Since the channel coefficients are estimated in spatial domain and the proposed 2-D FT can be realized by fast-Fourier-transform, the computational complexity is reduced significantly. Simulation results are provided to verify the accuracy and the complexity of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing mmWave systems based on the sparse nature of the angular channel and leveraging the compressed sensing (CS) tools. The angular support is recovered without any discretization by treating the angular channel in a continuous framework which resolves the limited angular resolution of the discrete sparse channel models used in the previous CS-based channel estimators. The power leakage problem is also addressed by modeling the continuous angular channel as a multi-band signal with the bandwidth of each sub-band being proportional to the amount of power leakage caused by the limited antenna array length. The RF combiner is implemented using a network of low-power switches for antenna subset selection based on a multi-coset sampling pattern. Simulation results validate the low reconstruction error and good performance of the proposed channel estimator. [ABSTRACT FROM AUTHOR]

Due to the rapid growth of free-space optical (FSO) communication over the last few years, there is an exigency to develop diversity-multiplexing tradeoff (DMT) metric in order to compare and design new multiple-input multiple-output (MIMO) FSO schemes. This paper introduces the DMT for MIMO-FSO systems that utilize the intensity modulation and direct detection technique for which the channel gains are real and positive, contrary to MIMO radio-frequency (MIMO-RF) systems where the channel gains are complex. Log-normal, gamma–gamma, and negative exponential channel models are used in this paper to cover weak to saturated turbulence environments. It is shown that at zero multiplexing gain ($r$), the log-normal channel offers maximum but finite diversity order that depends on the block length within which the channel matrix is constant. On the other hand, negative exponential channel offers minimum diversity order. However, for $r \geq 1$ , DMT curves under all channel models are of the same nature; and for a given $r$ , the diversity is half as that observed in the MIMO-RF channels. To gain additional insights into the DMT of an MIMO-FSO system, we also develop upper and lower bound expressions of DMT and determine the minimum block length at which these bounds are identical. [ABSTRACT FROM AUTHOR]

With the growing use of underwater acoustic communications (UWAC) for both industrial and military operations, there is a need to ensure communication security. A particular challenge is represented by underwater acoustic networks (UWANs), which are often left unattended over long periods of time. Currently, due to the physical and performance limitations, the UWAC packets rarely include encryption, leaving the UWAN exposed to external attacks faking legitimate messages. In this paper, we propose a new algorithm for message authentication in a UWAN setting. We begin by observing that, due to the strong spatial dependency of the underwater acoustic channel, an attacker can attempt to mimic the channel associated with the legitimate transmitter only for a small set of receivers, typically just for a single one. Taking this into account, our scheme relies on trusted nodes that independently help a sink node in the authentication process. For each incoming packet, the sink fuses beliefs evaluated by the trusted nodes to reach an authentication decision. These beliefs are based on the estimated statistics of the channel parameters, which are chosen to be the most sensitive to the transmitter-receiver displacement. Our simulation results show accurate identification of an attacker’s packet. We also report results from a sea experiment demonstrating the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper studies the performance of wireless power transfer (WPT) in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems operating in rainy or non-rainy conditions. Accounting for rainfall effects, path loss, and small-scale fading, a comprehensive channel model suitable for modeling the energy propagation in mmWave massive MIMO systems is first developed. Based on this model, a framework for the channel estimation necessary at the hybrid data-and-energy access point (HAP) is provided, and various analytical results on the estimated channel matrix are obtained. Then, using the law of energy conservation, the downlink energy transferred by the HAP and harvested by the user equipments (UEs) is analyzed, and investigated in several important scenarios. The results reveal that the asymptotic harvested energy increases linearly with the number of HAP antennas and the number of UEs, whereas it decreases exponentially with the rain parameters which monotonically increase with the operating frequency. It is also demonstrated that severe rain attenuation can even make the WPT impossible. Afterwards, the scenario where UEs are randomly distributed is investigated, and important insights are gained. In particular, for a WPT system with coverage radius $R_{\mathrm {n}}$ and exclusion radius $R_{\mathrm {e}}$ , the average asymptotic harvested energy significantly increases with decreasing $R_{\mathrm {e}}$ and $R_{\mathrm {n}}$ , which confirms that small-cells configurations will be viable solutions for enhancing WPT performances in mmWave massive MIMO networks. [ABSTRACT FROM AUTHOR]

In this paper, downlink (DL) precoding and power allocation strategies are identified for a time-division-duplex multi-cell multi-user massive full-dimension MIMO network. Utilizing channel reciprocity, DL channel state information feedback is eliminated and the DL multi-user MIMO precoding is linked to the uplink (UL) direction of arrival (DoA) estimation through the estimation of signal parameters via rotational invariance technique. Assuming non-orthogonal/non-ideal spreading sequences of the UL pilots, the performance of the UL DoA estimation is analytically characterized and the characterized DoA estimation error is incorporated into the corresponding DL precoding and power allocation strategy. The simulation results verify the accuracy of our analytical characterization of the DoA estimation and demonstrate that the introduced multi-user MIMO precoding and power allocation strategy outperforms the existing zero-forcing-based massive MIMO strategies. [ABSTRACT FROM AUTHOR]

This paper examines the role of full-duplex radio for securing wireless network from a new perspective. It first studies the secrecy capacity of two single-antenna full-duplex users against a multi-antenna eavesdropper (Eve) who has the perfect knowledge of the channel state information (CSI) from users to Eve. It is shown that if Eve uses a basic matched-filtering, the probability of zero secrecy (or outage) can be made small by a large jamming power from both users and a small gain of residual self-interference (RSI) power. But if Eve uses the optimal matched-filtering, the probability of outage grows rapidly as either the jamming power from the users increases or the number of antennas on Eve increases, regardless of the RSI gain. To prevent any Eve from obtaining its CSI, this paper then proposes a novel anti-eavesdropping channel estimation (ANECE) method, which allows users to obtain their own CSI while keeping all Eves in handicap. This method also prevents Eves from colluding with each other at any layer. The design of ideal pilots for ANECE for multiple multi-antenna users and multiple broadband multi-antenna users is discussed. It is also shown that the capacity of Eve with any number of antennas but without its CSI can be virtually eliminated over a time window corresponding to the number of antennas at the transmitter for each realization of the CSI. [ABSTRACT FROM AUTHOR]

The millimeter-wave (mmWave) full-dimensional (FD) MIMO system employs planar arrays at both the base station and the user equipment and can simultaneously support both azimuth and elevation beamforming. In this paper, we propose atomic-norm-based methods for mm-wave FD-MIMO channel estimation under both uniform planar arrays (UPA) and non-uniform planar arrays (NUPA). Unlike existing algorithms, such as compressive sensing (CS) or subspace methods, the atomic-norm-based algorithms do not require to discretize the angle spaces of the angle of arrival and angle of departure into grids, thus provide much better accuracy in estimation. In the UPA case, to reduce the computational complexity, the original large-scale atomic norm minimization problem is approximately reformulated as a semi-definite program (SDP) containing two decoupled two-level Toeplitz matrices. The SDP is then solved via the alternating direction method of multipliers where each iteration involves only closed-form computations. In the NUPA case, the atomic-norm-based formulation for channel estimation becomes nonconvex and a gradient-decent-based algorithm is proposed to solve the problem. Simulation results show that the proposed algorithms achieve better performance than the CS-based and subspace-based algorithms. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) communication and network densification hold great promise for achieving high-rate communication in next-generation wireless networks. Cloud radio access network (CRAN), in which low-complexity remote radio heads (RRHs) coordinated by a central unit (CU) are deployed to serve users in a distributed manner, is a cost-effective solution to achieve network densification. However, when operating over a large bandwidth in the mmWave frequencies, the digital fronthaul links in a CRAN would be easily saturated by the large amount of sampled and quantized signals to be transferred between the RRHs and the CU. To tackle this challenge, we propose in this paper a new architecture for the mmWave-based CRAN with advanced lens antenna arrays at the RRHs. Due to the energy focusing property, the lens antenna arrays are effective in exploiting the angular sparsity of mmWave channels, and thus help in substantially reducing the fronthaul rate and simplifying the signal processing at the multi-antenna RRHs and the CU, even when the channels are frequency-selective. We consider the uplink transmission in a mmWave CRAN with lens antenna arrays and propose a low-complexity quantization bit allocation scheme for multiple antennas at each RRH to meet the given fronthaul rate constraint. Furthermore, we propose a channel estimation technique that exploits the energy focusing property of the lens array and can be implemented at the CU with low complexity. Finally, we compare the proposed mmWave CRAN using lens antenna arrays with a conventional CRAN using uniform planar arrays at the RRHs, and show that the proposed design achieves significant throughput gains, yet with much lower complexity. [ABSTRACT FROM AUTHOR]

Full-duplex has emerged as a promising technology that enables a communication node to transmit and receive at the same time and same frequency band. One limitation of full-duplex is that the self-interference (SI) is very strong. In this paper, an effective SI cancellation scheme operated in the digital domain is proposed. It is facilitated by the property that the SI channel is reciprocal and the transmitted data is known by both the transmitter and the receiver. It converts the strong SI to the inter-symbol interference through a combination of the signals received in successive time slots. This conversion leads to a reduction in the number of independent signal flows but can be partially compensated for by transmitting more bits using spatial modulation and involving more time slots in the SI cancellation, which enhances the achievable rate. To achieve optimal performance, the transmitted symbols at one node need to be artificially rotated. We also derive a closed-form expression for an upper bound on the average bit error rate, and its error-free information transmission capability is investigated. Monte Carlo simulations over Rayleigh fading channels between two nodes are conducted and advantages are revealed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a preamble optimization method for the frequency-division multiplexing (FDM)-structured preamble in multiple-input multiple-output systems employing offset quadrature amplitude modulation-based filter bank multicarrier. Specifically, we formulate an optimization problem based on the periodic preamble structure to minimize the mean square error (MSE) of the channel estimation. For two transmit antennas, we find the relationship between preambles and intrinsic interferences from neighboring symbols to achieve the minimum MSE, and derive the optimal closed-form solution. For more than two transmit antennas, we convert the original optimization problem into a quadratically constrained quadratic program and obtain the suboptimal solution by relaxing the nonconvex constraint. The simulation results demonstrate that, in terms of MSE and bit error rate performances, the proposed method outperforms the conventional FDM method at all signal-to-noise ratio (SNR) regimes and outperforms the interference approximation method-complex method at low-to-medium SNR regimes with lower preamble overhead. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access scheme to be used in fifth-generation wireless networks. In this paper, multiple-input and multiple-output (MIMO) techniques are applied to NOMA systems by considering two types of imperfect channel state information—channel distribution information (CDI) and channel estimation uncertainty. Based on the two considered channel models, the power allocation and beamforming vectors are jointly designed to maximize the system utility of MIMO-NOMA, subjected to probabilistic constraints. Due to the implementation of successive interference cancellation in NOMA, the power allocation coefficients of the users in each cluster become coupled, which complicates the rate outage probability constraints and results in two challenging non-convex problems. For the optimization problem under CDI, we propose an efficient successive convex approximation (SCA) algorithm based on first-order approximation and semidefinite programming (SDP). For the optimization problem under channel estimation uncertainty, a new algorithm for the joint power allocation and receive beamforming design is developed to maximize the system utility based on SCA and an efficient 1-D search. In addition, the convergence and the feasibility are discussed for the two formulated problems. Furthermore, an efficient method to find a feasible initial solution is provided. Finally, the presented simulation results validate that the proposed two algorithms outperform MIMO-orthogonal multiple access and MIMO fixed NOMA (MIMO F-NOMA) with fixed power allocation and beamforming. [ABSTRACT FROM AUTHOR]