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Known interference (KI) is commonly used in many scenarios, where the original interference data is known to the receiver in advance. Unfortunately, due to the impacts of hardware imperfections and propagation environment, the received KI may suffer frequency offset, phase noise, and multi-path propagation with different time delays and channel gains. However, perfect time-frequency synchronization and channel estimation are hard to implement in practice, making it challenging to cancel the KI completely. In this paper, the comprehensive effects of synchronization and channel impairments on KI cancellation are investigated. First, the normalized time synchronization error is modeled as the inter-symbol interference (ISI), and the frequency synchronization error and phase noise are modeled as the inter-frequency interference (IFI). Subsequently, the closed-form expression of KI cancellation ratio (KICR) is derived for the multi-path block fading channel, by jointly considering imperfect time-frequency synchronization and channel estimation, as well as phase noise. Furthermore, assuming the channel and synchronization parameters estimation reach their corresponding Cramer-Rao bounds (CRBs), the upper bound of KICR and the lower bound of channel capacity loss are analyzed for the single-path channel. Simulation results confirm the theoretical analysis that the KICR is more sensitive to phase noise compared to time-frequency synchronization and channel estimation errors. In addition, it is also proved that lower KI power is less sensitive to the implementation imperfections. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the waterfall performance of finite-length low-density parity-check (LDPC) codes for asymmetric memoryless channels by considering variations of channel parameters: the observed bit error rate $P_{\text{obs}}$ and mutual information $I_{\text{obs}}$ during the real-time transmission. Moreover, we propose to use the variation of a new channel parameter: the maximal achievable channel coding rate $R_{\text{obs}}$ at finite blocklengths to estimate the waterfall performance. Simulation results demonstrate that our estimations can well predict the waterfall performance of LDPC codes. In particular, the estimation based on $R_{\text{obs}}$ can fit the waterfall performance for various code lengths. For estimations based on $P_{\text{obs}}$ and $I_{\text{obs}}$ , their accuracy increases as the codeword length increases and no curve fitting is required. [ABSTRACT FROM AUTHOR]

This paper addresses the channel estimation problem for three-dimensional (3-D) massive multiple-input multiple-output (MIMO) systems, where the base station (BS) is equipped with a two-dimensional uniform planar array (UPA) to serve a number of user equipments (UEs). To implement with low hardware complexity, the number of available radio-frequency (RF) chains at BS is constrained to be much smaller than the number of antennas. The theoretical analysis of sparse property for 3-D massive MIMO channel reveals that there exists two kinds of sparse structures in beam-domain channel vector, namely the common sparsity structure among sub-arrays of UPA and block sparsity structure per sub-array. Based on this property, a novel structured sparse Bayesian learning framework is proposed to estimate the channel between BS and UE reliably. Moreover, an adaptive spatial interpolation scheme is proposed to further reduce the number of required RF chains at BS while maintaining the estimation performance. The simulation results show that the proposed scheme provides stable estimation performance for a variety of scenarios with different numbers of RF chains, transmit signal-to-noise ratios, Rician factors, and angular spreads, and outperforms the reference schemes significantly. [ABSTRACT FROM AUTHOR]

High mobility and multipath propagation induce Doppler and delay dispersion, respectively. When both phenomena affect the transmitted signal, the communication channel is known as a doubly selective channel (DSC). In multicarrier systems under this scenario, intersymbol interference (ISI) and intercarrier interference (ICI) are ineluctably present, severely degrading the system's performance. Therefore, much effort has been invested in researching convenient waveforms that enable the receiver self-interference cancellation, while making use of the available space, time, and frequency diversity. In this paper, a multicarrier multiple-input multiple-output (MIMO) system for communication in DSCs is presented. By using the virtual trajectory (VT) representation of the MIMO channel, direct conversion of Doppler diversity into additional virtual antennas in the receiver is achieved. This concept is developed for a coherent system with one-tap channel estimation (CE) and equalization algorithms of low complexity as well as for incoherent detection using differential space-frequency block codes (DSFBCs), where pilot signals are not used. For both systems, runtime cancellation and/or inversion of complex interference channel matrices is avoided. Diversity of the doubly selective MIMO channel is exploited without sacrificing compatibility with current multicarrier MIMO systems. The presented system offers robustness in DSCs with minimal extra complexity when compared to conventional approaches. The results demonstrate that MIMO orthogonal frequency division modulation (MIMO-OFDM) can be still implemented in extremely high Doppler scenarios if VT reception is incorporated. [ABSTRACT FROM AUTHOR]

Transceiver hardware impairments (e.g., phase noise, high power amplifier nonlinearities, in-phase/quadrature-phase imbalance, and quantization errors) degrade the performance of channel estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Although compensation methods can be exploited to mitigate the impact of hardware impairments, there always remains residual impairments that will distort the training pilots and received signals. In this paper, we reformulate the channel estimation with transceiver impairments into a sparse recovery problem from a Bayesian perspective and propose an efficient channel estimation algorithm. The proposed algorithm can effectively deal with the perturbation in channel estimation problem caused by the transmitter hardware impairments with small amount computation times. Simulation results demonstrate the superior performance of the proposed algorithm compared to the conventional orthogonal matching pursuit algorithm (OMP) based channel estimation method and Bayesian inference method. [ABSTRACT FROM AUTHOR]

A rigorous model for automatic modulation classification (AMC) in cognitive radio systems is proposed in this paper. This is achieved by exploiting the Kalman filter (KF) integrated with an adaptive interacting multiple model (IMM) for resilient estimation of the channel state information. A novel approach is proposed, in adding up the square root singular values of the decomposed channel using the singular value decompositions algorithm. This new scheme, termed Frobenius eigenmode transmission, is chiefly intended to maintain the total power of all individual effective eigenmodes, as opposed to keeping only the dominant one. The analysis is applied over multiple-input multiple-output (MIMO) antennas in combination with a Rayleigh fading channel using a quasi-likelihood ratio test algorithm for AMC. The expectation-maximization is employed for recursive computation of the underlying estimation and classification algorithms. Novel simulations demonstrate the advantages of the combined IMM-KF structure when compared to the perfectly known channel and maximum likelihood estimate, in terms of achieving the targeted optimal performance with the desirable benefit of less computational complexity loads. [ABSTRACT FROM AUTHOR]

In this paper, the achievable rate of the multi-cell multi-user generalized spatial modulation uplink is analyzed in the presence of realistic pilot contamination. A practical channel model associated with arbitrary transmit and receive correlation matrices is assumed for each user and base station (BS). Imperfect channel estimation is assumed with pilot contamination. The performance of a single-cell based minimum mean squared error (SMMSE) combiner is analyzed and compared to the ubiquitous zero forcing (ZF) combiner as well as matched filtering (MF). The transmit antenna (TA) index detection and the classic amplitude/phase modulation signal detection process are carried out separately for the sake of low complexity. Moreover, an algorithm based on order statistics is proposed for calculating the antenna detection probability conditioned on the actual TA. Finally, an approximation of the achievable rate is derived by exploiting the characteristics of massive multiple-input multiple-outputs (MIMO). Simulation results show that SMMSE has the best performance, followed by ZF, and then MF. Furthermore, the relationships between the system's achievable rate and three systems’ parameters, namely the number of BS antennas, the signal-to-noise-ratio (SNR), and the interference factor of pilot contamination, are presented. The performance of different number of activated antennas is also compared. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is considered as a multiple access technique for supporting massive number of devices with high spectral efficiency. Simultaneous wireless information and power transfer (SWIPT) is an efficient solution for fully autonomous and sustainable communication networks. In this paper, application of SWIPT in networks employing cooperative NOMA (CNOMA) is investigated with imperfect channel state information (CSI). Performance of the CNOMA network with SWIPT is investigated in a multi-tier downlink scenario with macro-base stations (MBSs) and femto-base stations (FBSs). The distribution of MBSs and FBSs follow Poisson point process model. Unlike solution in the existing literature, instead of using only the superimposed signal for energy harvesting (EH), in this work, the cooperating node/user employs EH using interference from the adjacent base stations. Expressions for outage probability and throughput are derived at the user pair (comprising a cell-center user and a cell-edge user) served using NOMA. Comparison of the proposed EH system with the existing solutions is highlighted. Monte Carlo simulations are carried out to validate the analytical results. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an intelligent reflecting surface (IRS) aided millimeter wave (mmWave) communication system using hybrid precoding of subarray-connected structure at the base station. To maximize the received signal power of the user, we jointly optimize the active beamforming at the BS and passive beamforming at the IRS, respectively. Exploiting the rank-one property of mmWave channels, we transform the original problem into an approximate optimization problem and give the closed-form solution of the approximated problem. Simulation results show that our proposed scheme can effectively improve the received signal quality of the user with a high energy efficiency. [ABSTRACT FROM AUTHOR]

Recently, the reconfigurable intelligent surface (RIS) aided communication system has emerged as a promising candidate for future wireless communications. The existing channel estimation methods for RIS-aided millimeter-wave systems assume that the RIS is ideal without blockage. However, in practice, RIS may be blocked by the rain, snow, or dust, which will cause absorption and scattering of the incident/reflected signals and change the channel characteristics. In this paper, we formulate the system model of RIS-aided multi-user communications considering the blocked RIS. Then, we propose a variational Bayesian based channel estimation algorithm that is robust to RIS blockage, where we can simultaneously estimate the channel information and the RIS blockage. Simulation results demonstrate the superior performance of the proposed algorithm to existing ones. [ABSTRACT FROM AUTHOR]

In this paper, we study an uplink non-orthogonal multiple access (NOMA) wireless system with short-packet communications (SPC) in the presence of channel estimation errors and residual transceiver hardware impairments. Two NOMA decoding schemes are considered, namely fixed-order decoding (i.e., mean-power based decoding) and dynamic-order decoding (i.e., instantaneous-power based decoding). We first derive the closed-form exact and asymptotic expressions of the cumulative distribution functions (CDF) of the signal-to-interference-plus-noise ratio (SINR) associated with the two decoding schemes. Based on this, we analyze the average block error-rate (BLER) achieved in the finite block-length regime. Next, we formulate maximum throughput optimization problems with respect to the length of the packet. This problem is shown to be quasi-concave, which facilitates the finding of the optimal block-length via a search algorithm. Analytical comparisons between the NOMA-SPC system and its OMA-SPC counterpart are also performed. Simulation results are provided to validate the analysis. [ABSTRACT FROM AUTHOR]

Faster-than-Nyquist signaling (FTNS) has emerged as a promising technique to increase communication capacity in bandwidth-limited channels. However, the presence of FTN-induced inter-symbol interference (FTN-ISI) in the received observations, is detrimental to channel estimation (CE) and data detection in terms of computational complexity and performance. This paper copes with these problems by incorporating linear pre-equalization (LPE) and composite precoding formed by linear spectral precoding and Tomlinson-Harashima precoding (THP), into the FTNS. Specifically, LPE completely pre-equalizes the FTN-ISI, while spectral precoding resolves the LPE-caused signal spectral broadening by introducing proper artificial ISI, which is pre-equalized by THP. Channel-induced ISI, as the only ISI component in the observations, is estimated and equalized using the classical frequency-domain low-complexity schemes. We show that there are four advantages of the LPE-aided CE over the CE designed for the FTN transmissions without FTN-ISI pre-equalization, namely lower pilot overhead, simpler yet optimal pilot sequence design, lower mean-squared error of CE, and more robust against the FTN-ISI. Simulation results show that our scheme improves the performance of CE and detection, compared to existing FTN frequency-domain CE and equalization schemes. [ABSTRACT FROM AUTHOR]

An emerging technology namely the intelligent reflecting surface (IRS) can be deployed in millimeter wave (mmWave) communication to overcome the large propagation loss and huge power consumption issues. However, the large number of passive IRS elements without signal processing abilities induces high pilot overhead for channel estimation (CE). In this paper, the angular spread feature (or cluster feature) is exploited to formulate the CE problem as a structured sparse signal recovery problem. Then, a structured orthogonal matching pursuit (S-OMP) algorithm is proposed to efficiently solve the problem by utilizing the structure of the channel matrix in row and column without a priori knowledge of the angular spread. Simulation results demonstrate that S-OMP reduces about 40% to 50% pilot overhead at low signal-to-noise ratio (SNR) (SNR= 0 dB) while maintaining the same accuracy of CE compared with the existing methods. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) massive antenna array has shown great potential in gaining high-accuracy localization for user equipments (UEs). Yet, mmWave signals suffer from non-line-of-sight (NLOS) propagation and fast time-scale fading in practice, which will affect UE localization performance. It is non-trivial to reveal the impact of multipath propagation and fast time-scale fading on mmWave-based localization, and gain guidelines for harnessing NLOS propagation. In this paper, we aim to provide a unified framework for performance analysis of mmWave-based UE localization and channel estimation. Firstly, closed-form Cramer-Rao lower bounds for UE localization and channel estimation, respectively, are derived to shed lights on their performance limits. Secondly, the effect of system elements (e.g., channel correlation and fast time-scale fading) on UE localization performance is quantitatively analyzed. In addition, information contribution from NLOS channel is quantified to reveal the impact of multipath propagation on UE localization. Finally, we show that the NLOS propagation-caused localization error can be alleviated by using a NLOS model folding-based localization scheme1, which strikes a balance between computational complexity and localization performance. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of channel estimation in a single cell massive multiple-input multiple-output (mMIMO) system with distributed antenna arrays (DAA) under the availability of limited pilots. Specifically, we propose to use pilots that are not only orthogonal within a cluster but also minimally correlated across clusters. We show that pilot sets that form mutually unbiased orthonormal bases (MUOB) minimize both inter- and intra-cluster pilot contamination. This also allows us to optimally choose the number of pilots needed to serve a given number of user equipments (UEs) within a DAA-mMIMO system. Following this, we develop an access point (AP) centered UE clustering algorithm to optimally cluster UEs for MUOB pilot allocation. Our experiments reveal that in a DAA-mMIMO system, MUOB not only offers better fairness compared to the traditional orthogonal pilot reuse scheme in terms of channel estimation error and per-UE throughput, but also delivers a higher sum system throughput. [ABSTRACT FROM AUTHOR]

This paper proposes a convex optimization problem, based on which the optimal strategy of false data injection (FDI) attacks is obtained to intrude machine-type-communications (MTC) networks from the perspective of an attacker, aiming to seek effective defensive measures based on a good understanding of attackers’ behaviour. We consider a target tracking example, which is a typical application of MTC networks. Specifically, as a type of MTC devices, smart sensors each have the ability of perception, calculation and communication and all of them can form a sensor network. In this network, its sensors and transmission channels are vulnerable to FDI attacks, resulting in the degradation of system estimation performance. In order to maximize the estimation error covariance of MTC network, the attacker needs to decide which sensors and channels to intrude due to limited energy budget. The estimation error covariance of the MTC network is calculated, based on which a convex optimization problem to obtain the optimal attack strategy is proposed. Simulation results demonstrate that the optimal attack strategy maximizes the transient mean-square deviation and estimation error covariance of the MTC network. [ABSTRACT FROM AUTHOR]

The recent concept of beamspace multiple input multiple output (MIMO) with discrete lens array can utilize beam selection to reduce the number of radio-frequency chains (RF) required in terahertz (THz) massive MIMO systems. However, to achieve the capacity-approaching performance, beam selection requires information on a beamspace channel of large size. This is difficult to obtain since the user mobility usually leads to the fast variation of THz beamspace channels, and the conventional real-time channel estimation schemes involve unaffordable pilot overhead. To solve this problem, in this paper, we propose the a priori aided (PA) channel tracking scheme. Specifically, by considering a practical user motion model, we first excavate a temporal variation law of the physical direction between the base station and each mobile user. Then, based on this law and the special sparse structure of THz beamspace channels, we propose to utilize the obtained beamspace channels in the previous time slots to predict the prior information of the beamspace channel in the following time slot without channel estimation. Finally, aided by the obtained prior information, the time-varying beamspace channels can be tracked with low pilot overhead. Simulation results verify that to achieve the same accuracy, the proposed PA channel tracking scheme requires much lower pilot overhead and signal-to-noise ratio (SNR) than the conventional schemes. [ABSTRACT FROM AUTHOR]

The frequency-domain pilot multiplexing technique (FDPMT) has recently emerged as an appealing technique for channel estimation in single-carrier frequency-domain equalization (SC-FDE) systems due to the high spectral efficiency it promises. However, the performance of the FDPMT system is significantly affected by the signal distortion level, which depends on the pilot position selection (PPS) strategy at the transmitter and the signal reconstruction (SR) ability of the receiver. In this paper, the relationship between the interference pattern and the loss of equally spaced tones is discovered. By exploiting this relationship, we propose a novel PPS method and a novel SR method. The proposed PPS method, which is based on the minimization of the most severe intersymbol interference (ISI) experienced by those subblocks decimated from the transmitted signal block, dispenses with a priori knowledge of the channel statistics and imposes very low computational cost to the transmitter. The proposed SR method, which is based on the maximum-likelihood criterion, can be carried out subblock by subblock in parallel and sample by sample within each subblock, saving the computational complexity and time consumption of the receiver. Simulation results in terms of bit error rate (BER) corroborate the superiority of both proposed methods over their existing counterparts in various channels of interest. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage self-interference (SI) cancelation for full-duplex multi-input–multi-output (MIMO) communications systems. By exploiting the SI channel sparsity, a compressed-sensing-based SI channel estimation technique is developed and used in the first SI-cancelation radio-frequency (RF) stage to reduce the SI power prior to the receiver low-noise amplifier (LNA) and the analog-to-digital converter (ADC) to avoid overloading. Subsequently, a subspace-based algorithm is proposed to jointly estimate the coefficients of both the residual SI and intended channels and transceiver impairments for the second SI-cancelation baseband stage to further reduce the residual SI. Unlike other previous works, the intended signal is taken into consideration during the estimation process to reduce the overhead. It is demonstrated that the SI channel coefficients can be perfectly estimated with no knowledge of the intended signal, and only a few training symbols are needed for ambiguity removal in intended-channel estimation. Simulation results show that the proposed algorithms outperform the least square (LS) algorithms and offer the remarkable signal-to-residual-SI-and-noise ratio (SINR) approaching the signal-to-noise ratio (SNR). [ABSTRACT FROM PUBLISHER]

In this paper, we consider a multiple-input single-output (MISO) outdoor visible light communications (VLC) system in which the channel state information (CSI) at the receiver side is unknown and where the channel coefficients are independent and lognormal distributed. For such a system, we propose to take advantage of the repetition coding scheme in the space dimension and to add a fixed nonzero pilot bit to the head of the block to ensure the reliable blind estimation of channel coefficients. Then, a symbol-by-symbol maximum-likelihood (ML) receiver with linear complexity for the repetition-coded MISO outdoor VLC is developed, and the bit error performance is derived by integrating the conditional error function on the dominant decision region. It is proved that the training ML receiver can assure full-diversity achievement. In addition, optimal power allocation between the pilot bit and data bits is proven to be a concave problem, and an optimal power allocation scheme is presented. Simulation results demonstrate that the training receivers can achieve full diversity and that the optimal power allocation scheme can improve the error performance significantly. [ABSTRACT FROM PUBLISHER]

A detailed understanding of the dynamic processes of vehicular radio channels is crucial for its realistic modeling. In this paper, we present multipath components (MPCs) tracking results from a channel sounder measurement with 1-GHz bandwidth at a carrier frequency of 5.7 GHz. We describe in detail the applied algorithms and perform a tracking performance evaluation based on artificial channels and on measurement data from a tunnel scenario. The tracking performance of the proposed algorithm is comparable to the tracking performance of the state-of-the-art Gaussian mixture probability hypothesis density filter but with a significantly lower complexity. The fluctuation of the measured channel gain is followed very well by the proposed tracking algorithm, with a power loss of only 2.5 dB. We present statistical distributions for the number of MPCs and the birth/death rate. The applied algorithms and tracking results can be used to enhance the development of geometry-based channel models. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a non-data-aided (NDA) method to estimate the noise variance for orthogonal frequency division multiplexing (OFDM) system in frequency-selective channels. The proposed method is developed based on the signal correlation introduced by the use of the cyclic prefix (CP) and uses the difference between two discrete Fourier transform (DFT) outputs in the CP interval to estimate the noise variance. Analytical result shows that the obtained noise variance is an unbiased estimate in a quasi-static fading channel. Simulation results show that, even in the case in which the maximum delay spread is equal to the CP length and the channel is not quasi-static, the proposed method still works well. [ABSTRACT FROM PUBLISHER]

In this paper, we study the secure multiantenna transmission with artificial noise (AN) under imperfect channel state information (CSI) in the presence of spatially randomly distributed eavesdroppers. We derive the optimal solutions of the power allocation between the information signal and the AN for minimizing the secrecy outage probability (SOP) under a target secrecy rate and for maximizing the secrecy rate under an SOP constraint, respectively. Moreover, we provide an interesting insight that channel estimation error affects the optimal power allocation strategy in opposite ways for the two given objectives. When the estimation error increases, more power should be allocated to the information signal if we aim to decrease the rate-constrained SOP, whereas more power should be allocated to the AN if we aim to increase the SOP-constrained secrecy rate. [ABSTRACT FROM PUBLISHER]

A single-carrier frequency-domain equalization (SC-FDE) system operating at 60-GHz millimeter-wave band suffers from in-phase (I) and quadrature (Q) imbalance, which refers to the mismatch between the I and Q branches in analog front-end processing. The I/Q imbalance incurs unwanted image interference on top of the desired signal and can severely degrade system performance if left uncompensated. In this paper, a training-based approach is proposed to estimate and compensate for the I/Q imbalance in the SC-FDE system. First, we derive the likelihood function capturing the I/Q imbalance and multipath channel. By iteratively maximizing the likelihood function, we can simultaneously and separately estimate the I/Q imbalance and the multipath channel. Then, with the estimate of the I/Q imbalance, the corresponding image interference is removed by simple substitution. Finally, we use the channel estimate to conduct equalization in the frequency domain, as is done in the conventional SC-FDE without the I/Q imbalance. It is shown through simulations compliant with the IEEE 802.11ad Standard that the proposed approach can effectively mitigate the adverse effect of I/Q imbalance and, consequently, can achieve the performance very close to the ideal case without I/Q imbalance. [ABSTRACT FROM PUBLISHER]

Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising candidate to support the requirements of the fifth-generation (5 G) and beyond networks. However, the capacity of the fronthaul network dramatically influences its performance. While wired fronthaul links can be seen as the optimal choice, they may not be practically feasible. Exploiting the enormous bandwidth available in the millimeter-Wave (mmWave) band to support the fronthaul links paves the way to achieve the full potential of CF mMIMO systems. In this paper, we investigate the uplink (UL) performance of CF mMIMO systems supported by mmWave-fronthaul networks. Using tools from stochastic geometry, we derive analytical expressions for both the distribution of the provided fronthaul capacity and the average UL data rates. We show that although increasing the density of blockages degrades the average UL data rates, increasing the density of CPUs can limit such effect. Moreover, the obtained results reveal that the network deployment should be adjusted according to the available fronthaul bandwidth and the density of blockages. In particular, for a given fronthaul bandwidth, increasing the density of APs beyond a certain limit would not achieve further improvement in the UL data rates. Besides, increasing the number of antennas per AP may even cause a degradation in the system performance. [ABSTRACT FROM AUTHOR]

The impulsive noise and the time-varying channel are two major detrimental factors which greatly constrain the performance of underwater acoustic communications (UACs). Utilizing the joint sparsity of the impulsive noise and the channel impulse response, the paper proposes the generalized approximate message passing (GAMP)-based sparse Bayesian learning (SBL) frameworks for joint impulsive noise mitigation and channel estimation and tracking for orthogonal frequency division multiplexing (OFDM) UACs. Firstly, the SBL framework for the joint estimation is employed. To reduce the computational complexity, the GAMP is introduced into the expectation-maximization (EM) algorithm, and a low-complexity GAMP based SBL framework is formulated without performance degradation. To further estimate and track the impulsive noise and channel state information in the slow time-varying scenarios, we propose a novel GAMP-based temporal SBL framework. The factor graph and GAMP are used to achieve the approximated estimation for the posterior statistics of both the channel state information and the impulsive noise. The algorithm formulates the message passing scheduling for the EM algorithm to solve the joint multiple sparse signal recovery problem. Simulations and sea-trial results demonstrate that the proposed algorithms significantly improve the performance in terms of the mean square error of channel estimation, impulsive noise estimation, bit error rate and computational complexity compared with their corresponding SBL-based counterparts. [ABSTRACT FROM AUTHOR]

In this paper, we introduce channel prediction to wireless communications aided by intelligent reflecting surface (IRS) in order to reduce the overhead brought by full channel estimation. Hopefully, fine-grained control of reflection coefficients on IRS can still be maintained with sufficiently accurate channel prediction. Innovatively, we consider channel prediction as an analogy to image super-resolution in computer vision since elements on an IRS are normally two-dimensionally arranged, which makes the popular deep learning techniques efficient solution. To this end, we first solve the element control problem via semi-definite relaxation (SDR) and then design an end-to-end deep-learning model for channel prediction. With estimation for only part of the entire channel state information (CSI), the total channel estimation overhead can be significantly reduced. We visually show that the purposed deep-learning model maintains considerably high accuracy for channel prediction, which makes the proposed channel-prediction method outperforms the existing group-based method in terms of achievable rate. Moreover, the tradeoff between channel estimation overhead and channel prediction accuracy is also investigated with numerical results. [ABSTRACT FROM AUTHOR]

Consider a base station (BS) relying on a massive antenna array, which transmits information to multiple vehicles of vehicular networks. In order to jointly consider both the communication resource consumption and the road-traffic efficiency, we define a metric given by the BS's downlink power normalized by the vehicular velocity. We refer to it as the Power to Velocity Ratio (PVR). The prime objective of this paper is to minimize either the maximum individual vehicle PVR or the entire system's PVR by optimizing the power allocation at the BS, while guaranteeing the information requirements of the vehicles both under the total transmit power constraint and driving velocity constraint. As for the individual vehicle PVR, a closed-form power allocation expression is derived by assuming that the transmit power constraint is non-restrictive. Based on this an optimal power allocation algorithm is proposed for arbitrary finite transmit power constraints. As for the system PVR, the non-convex problem formulated is first transformed into a convex problem and then the optimal solution is found by conceiving an efficient iterative algorithm. Our simulation results show that the proposed algorithms indeed succeed in achieving the optimal individual vehicle or system PVR. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep neural network (DNN) aided codebook based beamforming scheme for frequency-division-duplex (FDD) millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) systems under multipath. Different from the time-division-duplex (TDD) systems, for FDD systems the reciprocity between the down-link (DL) and up-link (UL) channel state information (CSI) does not hold in general, requiring an extra CSI feedback stage. Based on the previous theoretical analysis and measurements, however, partial reciprocities, including the spatial directional angles and number of propagation paths, do exist for FDD mmWave systems. Given such partial reciprocities, we propose a beamforming scheme based on a predefined codebook. Furthermore, we consider the channels with multipath where multiple codewords can be selected for some mobile stations (MSs) to utilize more than one propagation paths, which improves the link reliability. We then propose the corresponding beamforming strategy that not only identifies the number of paths, but also reconstructs the individual single-path channels which are easier to deal with at the beamforming stage. To select the optimal codewords for these single-path channels with lower complexity, we then present a DNN-aided approach that predicts the optimal codewords. The integration of DNN resolves not only the issue of power leakage, but also angular ambiguity in the codebook. Simulation results demonstrate that the performance of the proposed DNN-aided approach is very close to that of an exhaustive search. [ABSTRACT FROM AUTHOR]

IEEE 802.11p standard defines wireless technology protocols that enable vehicular transportation and manage traffic efficiency. A major challenge in the development of this technology is ensuring communication reliability in highly dynamic vehicular environments, where the wireless communication channels are doubly selective, thus making channel estimation and tracking a relevant problem to investigate. In this paper, a novel deep learning (DL)-based weighted interpolation estimator is proposed to accurately estimate vehicular channels especially in high mobility scenarios. The proposed estimator is based on modifying the pilot allocation of the IEEE 802.11p standard so that more transmission data rates are achieved. Extensive numerical experiments demonstrate that the developed estimator significantly outperforms the recently proposed DL-based frame-by-frame estimators in different vehicular scenarios, while substantially reducing the overall computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we study a beam tracking problem for millimeter wave (mmWave) channels in massive multiple-input and multiple-output communication systems. We formulate the beam tracking problem as a stochastic filtering problem by modeling the dynamic state equation and the observation equation. Then, given prior information of the channel, we propose an efficient grid-based Bayesian beam tracking algorithm by leveraging the sparsity of the mmWave channel. Our proposed algorithm examines neighboring transmit and receive beam pairs and exploits multiple observations on the angular domain representation in the sense of the Bayesian statistics. Numerical results show that the proposed Bayesian method with multiple observations outperforms conventional schemes in terms of both the beam tracking accuracy and computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, we propose three new compressive sensing-based algorithms for channel estimation in millimeter wave hybrid MIMO systems over doubly (time and frequency) selective channels. Utilizing the basis expansion model (BEM) for effectively representing doubly selective channels, we first propose a BEM-based block orthogonal matching pursuit (BBOMP) algorithm, which can work with any training sequence structure. Next, we present the second algorithm to reduce the complexity of the BBOMP method by employing a special training sequence which results in a computationally efficient block sparse sensing matrix. Finally, in order to further decrease the computational complexity, we propose the third algorithm, where the channel estimation is split into two separate steps of tap detection and gain estimation. The proposed algorithms exploit the entire available training sequence to estimate all channel parameters, and can capture channel variations across the entire training frames without requiring a feedback channel. The Cramer-Rao lower bound and the computational complexity of the proposed algorithms are also addressed. Intensive computer simulations are conducted to evaluate the accuracy of the proposed approaches, showing that they can significantly improve the mean squared error performance compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

A novel compressed sensing-aided generalized space-frequency index modulation (CS-GSFIM) scheme is conceived for the large-scale multi-user multiple-input multiple-output uplink (LS-MU-MIMO-UL). Explicitly, the information bits are mapped both to the spatial- and frequency-domain indices, where we treat the activation patterns of the transmit antennas and of the subcarriers separately. Specifically, our indexing strategy strikes a flexible tradeoff between the throughput, performance, and complexity. In order to further increase the system's achievable rate, CS-aided pre-processing is applied to the subcarriers. An upper bound of the average bit error probability of the proposed system using the optimal maximum likelihood (ML) detector is derived, which is shown to be tight by our simulation results at moderate to high signal-to-noise ratios (SNR). Then, we design a CS-aided reduced-complexity detector, namely the reduced search-space based iterative matching pursuit (RSS-IMP), which significantly reduces the detection complexity compared to the ML detection and makes the proposed design a feasible one for LS-MU scenarios. Furthermore, the simulation results presented in this paper demonstrate that the proposed RSS-IMP detector significantly reduces the detection complexity, while attaining better performances than both the conventional MU-MIMO-OFDM (orthogonal frequency division multiplexing) system using the ML detector and the proposed system using the minimum mean square error detector. We also characterize the performances of the proposed system in the presence of channel estimation errors. Our simulation results show that the proposed CS-GSFIM system is more robust to imperfect channel than the conventional MU-MIMO-OFDM system. In order to achieve a near-capacity performance, soft-input soft-output decoders are designed for the proposed CS-GSFIM system using both the ML and the RSS-IMP multi-user detectors for detecting all users. [ABSTRACT FROM AUTHOR]

Of the wide palette of 5G features, ultra-wide bandwidth and large-scale antenna configuration are regarded as the essential enabling technology components for millimeter wave (mmWave) communication. Accurate knowledge of delay and angle information of multipath components is essential for many applications in mmWave systems. There is a strong need for a low computation-cost channel estimation algorithm for such systems, where typically adopted far-field and narrowband assumptions might be violated. In this paper, a generic yet novel beamspace uniform circular array (UCA) beamforming algorithm with successive cancellation scheme is proposed to jointly detect the impinging azimuth angle and delay of each multipath component. The proposed algorithm is computationally cheap and it works for ultra-wideband UCA systems in the near-field conditions as well as the traditional narrowband systems in the far-field conditions. Both numerical simulations and experimental validation results are provided to demonstrate the effectiveness and robustness of the proposed algorithm, compared to the state-of-art works. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) systems consist of a large number of antennas and radio frequency (RF) chains, which cause enormous circuit power consumption at the RF front ends. Since high-resolution analog-to-digital converters are the main contributor in the RF circuit power consumption, one-bit massive MIMO is considered as one of the potential solutions. The channel state information (CSI) acquisition in the presence of pilot contamination is a challenging task in such systems. In this paper, we present an iterative semi-blind-based channel estimation scheme for pilot contaminated one-bit multi-cell multi-user massive MIMO systems, which comprise of two stages: initialization and iteration. The initial channel estimate uses pilots in the initialization stage, which is further refined in the iteration stage with the help of both pilot and a few data symbols. We derive lower bounds on the uplink achievable rate with both perfect and imperfect CSI availability at the base station. Through simulations, we show that the proposed estimator achieves a considerable improvement in mean square error, bit-error-rate, power efficiency, and spectral efficiency from one-bit pilot-aided estimators at the cost of a nominal increase in computational complexity. Moreover, the proposed scheme in one-bit system achieves almost the same spectral efficiency as that of a pilot-aided estimator in an infinite-resolution-based massive MIMO system for lower values of signal-to-noise-ratio (SNR). [ABSTRACT FROM AUTHOR]

Ambient backscatter, an emerging green communication technology, has aroused great interest from both academia and industry. One open problem for ambient backscatter communication (AmBC) systems is channel estimation for a massive-antenna reader. In this paper, we focus on a channel estimation problem in AmBC systems with a uniform linear array at the reader consisting of large number of antennas. We first design a two-step method to jointly estimate channel gains and direction of arrivals (DoAs), and then refine the estimates through angular rotation. Additionally, Cramér–Rao lower bounds are derived for both the modulus of the channel gain and the DoA estimates. Simulations are then provided to validate the analysis, and to show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Adaptive channel coding and power control for practical free-space optical communication systems are proposed in this paper. Particularly, we first assume that the channel state information (CSI) is perfectly known at the transmitter, and propose adaptive transmission schemes in which coding rate is adjusted either independently or jointly with transmit power according to the channel conditions. Moreover, an optimization problem is developed to attain power consumption minimization under free space optical (FSO) practical constraints, i.e., target bit-error rate, target outage probability, and maximum transmit power. We then solve the optimization problem and derive closed-form expressions for throughput and average transmit power for both adaptive schemes over the Gamma–Gamma atmospheric turbulence channels. Our results demonstrate the superiority of the proposed adaptive schemes over non-adaptive schemes, especially under strong turbulence conditions. In the second part of this paper, we move toward a more realistic scenario where the channel is not perfectly known to Tx/Rx and is estimated by the sequence of received signals. Therefore, we investigate the effect of channel estimation inaccuracies on the performance of the proposed schemes and show that the accuracy of the channel estimator depends on the length of the sequence and, for a sufficiently large length of the observation window, it achieves performance close to the receiver with known CSI. Extensive analytical derivations are provided to investigate the performance of the proposed adaptive schemes under channel estimation error. Our analytical results can be used for calculating and tuning of the optimum length of the observation window without resorting to Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate secure communication in a massive multiple-input multiple-output (MIMO) system with multiple users and multiple eavesdroppers (Eve) under both pilot spoofing attack (PSA) and uplink jamming. Specifically, Eve impairs the normal channel estimation by sending identical pilot sequences with the legitimate users. Based on the impaired channel estimation, the base station adopts linear processing schemes for uplink data reception, which is jammed by Eve, and downlink confidential information transmission. We first evaluate the impact of the PSA on the achievable rate with linear processing, and then propose a double channel training based scheme to combat PSA. By using the channel estimation difference in two training phases, the presence of the PSA can be detected and accurate legitimate channel estimation can be obtained by removing the effect of Eve's channel. Furthermore, we analyze the channel estimation errors and derive a closed-form expression of the minimum mean square error precoding scheme to maximize the minimum achievable secrecy rate, which outperforms the conventional linear precoding counterparts. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid pilot-aided channel estimation technique for mitigating the effect of pilot contamination for the uplink of multi-cell multiuser massive MIMO systems. The proposed hybrid pilot is designed such that it enjoys the complementary advantages between time-multiplexed (TM) pilot and time-superimposed (TS) pilot and, thereby, allows superior solution to the conventional pilot schemes. We mathematically characterize the impact of hybrid pilot on the massive MIMO uplink by deriving a closed-form approximation for the uplink achievable rate. In the large-number-of-antennas regime, we obtain the asymptotically optimal solution for the hybrid pilot by jointly designing the TM pilot and the TS pilot. It is shown that either TM pilot or TS pilot has the advantages for large frame-size and limited frame-size transmission, respectively, while the hybrid pilot scheme can offer a superior performance to that employing either TM pilot or TS pilot. Numerical results demonstrate the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

In massive multiple-input and multiple-output (M-MIMO) systems, one of the key challenges in the implementation is the large-scale matrix inversion operation, as widely used in channel estimation, equalization, detection, and decoding procedures. Traditionally, to handle this complexity issue, several low-complexity matrix inversion approximation methods have been proposed, including the classic Cholesky decomposition and the Neumann series expansion (NSE). However, the conventional approaches failed to exploit neither the special structure of channel matrices nor the critical issues in the hardware implementation, which results in poorer throughput performance and longer processing delay. In this paper, by targeting at the correlated M-MIMO systems, we propose a modified NSE based on tridiagonal matrix inversion approximation (TMA) to accommodate the complexity as well as the performance issue in the conventional hardware implementation, and analyze the corresponding approximation errors. Meanwhile, we investigate the very-large-scale integration implementation for the proposed detection algorithm based on a Xilinx Virtex-7 XC7VX690T FPGA platform. It is shown that for correlated massive MIMO systems, it can achieve near minimum mean square error performance and 630 Mb/s throughput. Compared with other benchmark systems, the proposed pipelined TMA detector can get high throughput-to-hardware ratio. Finally, we also propose a fast iteration structure for further research. [ABSTRACT FROM AUTHOR]

We consider a two-way half-duplex relaying system where multiple pairs of single-antenna users exchange information assisted by a multiple-antenna relay. Taking into account the practical constraint of imperfect channel knowledge, we study the achievable sum spectral efficiency (SE) of the amplify-and-forward protocol, assuming that the relay employs maximum ratio processing. We derive a closed-form expression for the sum SE for arbitrary system parameters and a large-scale approximation for the sum SE when the number of relay antennas $M$ becomes sufficiently large. In addition, we study how the transmit power reduces with $M$ to maintain a desired SE. Our results show that by using a large number of relay antennas, the transmit powers of the user, relay, and pilot symbol can be scaled down proportionally to $1/M^\alpha$ , $1/M^\beta$ , and $1/M^\gamma$ for certain combinations of $\alpha$ , $\beta$ , and $\gamma$ , respectively. This elegant power scaling law reveals a fundamental tradeoff between the transmit powers of the user/relay and pilot symbol. Finally, capitalizing on the new expressions for the sum SE, novel power allocation schemes are designed to further improve the sum SE. [ABSTRACT FROM AUTHOR]

This paper investigates the energy efficient downlink transmission in distributed massive multiple-input–multiple-output systems, where massive antennas are clustered on geographically distributed remote radio heads (RRH). A closed form of the ergodic achievable rate is derived for a specific RRH-user association and imperfect channel state information to assist the formulation of its system energy efficiency (EE). By assuming minimum user achievable rate requirements and a practical power consumption model including both the transmit power and the antenna circuit power, an optimization problem is formulated to maximize the system EE with respect to the power allocation, the antennas activation, and the RRH-user association in a combinatorial manner. The formulated problem is a mixed-integer nonlinear programming problem, which is NP-hard and usually has no feasible solution. To solve it, an efficient algorithm is proposed by utilizing hierarchical decomposition technique and iterative successive convex approximation. The feasibility as well as the effectiveness of the proposed algorithm are analyzed and proved through analysis and intensive computer simulations. Results show that the system EE can be greatly improved by the proposed scheme. [ABSTRACT FROM AUTHOR]

The fifth-generation technology is emerging with various real-time applications that demand high data rate. The number of connected devices is expected to increase. However, spectral and energy resources are limited. Therefore, it is challenging to meet users satisfaction. Thus, the necessity for a structure that can sustain real-time performance of wireless communication systems is pressing. This paper presents an efficient radio resource allocation scheme using regularized particle filter with optimized kernel size and kernel bandwidth, and Monte Carlo Markov chain move step based on metropolis algorithm is used to estimate next state of channel. Channel state is calculated from the temporal channel correlation using maximum likelihood estimation. Throughput for the next user state is predicted using posterior probability with normalized weights. The main aim of this paper is to observe the user throughput. The problem is modeled as throughput maximization subject to channel state. The optimization problem is formulated as a non convex optimization and solved using branch and bound method. It has been shown in the simulation results that the average throughput is increased by ${\text{5.94}\%}{\text{--52.98}\%}$ compared to sequential importance sampling with resampling (SISR) method. Therefore, the RPF scheme has proved to be efficient by its remarkable increase in user throughput compared to the SISR particle filter algorithm. [ABSTRACT FROM AUTHOR]

Considering that wireless unidirectional full-duplex (FD) transmission is one of the FD transmissions over 5G mobile wireless networks, this paper proposes a joint receive and transmit relay beamformer design for systems consisting of a source, a FD relay, an intended destination, and an eavesdropper. The source and destination are single-antenna nodes, whereas the FD relay is a multi-antenna node. Assuming that full channel state information (CSI) is available and the eavesdropper processes two blocks of received data at a time, the joint optimization problem is reformulated as a semidefinite relaxation (SDR) problem (in terms of relay transmit beamformer), which is tight in the sense that the rank-one beamformer can always be recovered from the SDR solution. In order to significantly minimize the computational cost of the SDR implementation, a novel semi-analytical approach based on dual optimization is proposed to obtain the optimum transmit beamformer. When the number of data blocks is arbitrary, we propose a successive convex approximation approach for solving the original non-convex optimization problem and show that a stationary solution can be efficiently obtained. We then propose a distributionally robust beamformer design for the case of an arbitrary number of blocks, assuming that the means and covariance matrices of the eavesdropper's channels are known. Computer simulations show that the proposed semi-analytical solution matches with that obtained from numerical optimization of the SDR problem, the FD scheme significantly outperforms its half-duplex counterpart, and the proposed robust design provides robustness against CSI uncertainty. [ABSTRACT FROM AUTHOR]

Along with the evolution of wireless technologies, users are expected to have high-throughput and low-latency indoor mobile applications, such as augmented reality and virtual reality. Therefore, the use of millimeter-wave for low-latency communications becomes attractive. Under low-latency criteria, a fast multi-link establishment is desired. In this paper, we present a novel concurrent multi-beam training jointly with a proposed multi-beam codebook. First, we propose a progressive multi-beam estimation (PMBE). This algorithm makes multiple coarse beams emerge within a few training steps. Second, we propose a Fast Fourier Transform (FFT)-based codebook which can be considered as a Discrete Fourier Transform based codebook built in a bit-reversal scrambler, thus forming a scattering multi-beam codebook. This FFT-based codebook design is optimally implemented on a sub-connected architecture, a hardware-efficient architecture. For fast multi-link establishment, we design our concurrent multi-beam training by combining the proposed PMBE jointly with the proposed FFT-based codebook, so as to further improve multiplexing gain. Since multiplexing gain can occur immediately, even under strictly-limited training steps, the high data rate can be achieved. Simulation results show that when reaching more than 95% capacity gain of exhaustive channel estimation, our work only spends 1% training steps of the exhaustive case. [ABSTRACT FROM AUTHOR]

Orthogonal time frequency space (OTFS) modulation was shown to provide significant error performance advantages over orthogonal frequency division multiplexing (OFDM) in delay–Doppler channels. In order to detect OTFS modulated data, the channel impulse response needs to be known at the receiver. In this paper, we propose embedded pilot-aided channel estimation schemes for OTFS. In each OTFS frame, we arrange pilot, guard, and data symbols in the delay–Doppler plane to suitably avoid interference between pilot and data symbols at the receiver. We develop such symbol arrangements for OTFS over multipath channels with integer and fractional Doppler shifts, respectively. At the receiver, channel estimation is performed based on a threshold method and the estimated channel information is used for data detection via a message passing algorithm. Thanks to our specific embedded symbol arrangements, both channel estimation and data detection are performed within the same OTFS frame with minimum overhead. We compare through simulations the error performance of OTFS using the proposed channel estimation and OTFS with ideally known channel information and observe only a marginal performance loss. We also demonstrate that the proposed channel estimation in OTFS significantly outperforms OFDM with known channel information. Finally, we present extensions of the proposed schemes to multiple-input multiple-output (MIMO) and multi-user uplink/downlink. [ABSTRACT FROM AUTHOR]

Channel estimation is of great challenge for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. The underlying reason can be attributed to two facts. One is the huge dimension of MIMO channel matrix, since mmWave systems typically employ large antenna arrays at both the transmitter and receiver to realize sufficient link margin. The other fact is the limited number of radio-frequency chains available for transmitter/receiver array, due to the power and cost limitations. This paper proposes a two-stage accurate channel estimation scheme for mmWave MIMO. Initially, we utilize sparse Bayesian learning to obtain a coarse channel estimate. Then we refine the channel estimate by iteratively maximizing the likelihood function of channel parameters. It is shown through simulations that the proposed scheme significantly outperforms the counterparts, and can achieve accurate channel estimation performance. [ABSTRACT FROM AUTHOR]

Generalized frequency division multiplexing (GFDM) as a sub-carrier filtered scheme can flexibly handle various different application scenarios of the fifth-generation system. However, due to the intrinsic self-interference resulting from the filter, the channel estimation becomes very hard, particularly when GFDM works with a multi-input multi-output (MIMO) system. In this paper, a new GFDM-dual filter (GFDM-DF) transmission scheme is proposed, which can guarantee the orthogonality of sub-carriers, so that the intrinsic self-interference can be eliminated. We first divide information symbols into odd-numbered and even-numbered sub-carrier symbols. Then, the orthogonality conditions for GFDM-DF are derived. According to the orthogonality conditions, we choose two different filters for odd-numbered and even-numbered sub-carriers. The signal-to-interference ratio of either the conventional GFDM or GFDM-DF is also evaluated. Moreover, based on the characteristics of the receiving matrix, we further design a low-complexity GFDM-DF receiver. Numerical results show that GFDM-DF can work well with the channel estimation and the MIMO system and its performance is better than that of the conventional GFDM system. [ABSTRACT FROM AUTHOR]

In this paper, the exact closed-form signal-to-interference-noise ratio (SINR) expression, for the joint impact of carrier frequency offset (CFO) and phase noise (PHN), is derived for each channel link in a multi-relay hybrid satellite-terrestrial broadcast system, employing the Walsh Hadamard (WH) based spread-orthogonal frequency multiplexing (S-OFDM) and OFDM transmission scheme at satellite terminal and terrestrial repeaters (relays) respectively. Additionally, bit error rate and spectral efficiency are analyzed for such hybrid system with $M$ -relay, based on the obtained SINR expressions. To examine that, an adaptive decode and forward (ADF) strategy is employed at relay terminals. Also, three-state Fontan Land Mobile Satellite and six-path typical urban terrestrial channel models are considered. This analysis is performed to support variable bit rate (VBR) quality of service at the physical layer of Digital Video Broadcast Satellite Handheld (DVB-SH)-A architecture, the provision of which is absent in the DVB-SH standard. VBRs are assumed for S-OFDM. The results illustrate that both single-relay and three-relay ADF-based VBR WH-SOFDM yield an identical performance in the presence of the combined effect of CFO and PHN, under both performance measures. However, the improvement in performance for the three-relay strategy is observed in the presence of CFO only. Further, a pilot-based channel estimation technique is performed at the receiver, and the estimated error variance is evaluated for every channel link. Later, the block-by-block system computational complexity of single-relay and $M$ -relay ADF-based VBR WH-SOFDM is evaluated. It is shown that the complexity of the $M$ -relay ADF strategy is rate dependent and almost $M-1$ times the single-relay strategy. Hence, an increase in the number of relays would not yield any significant performance gain, but would enhance the system computational complexity considerably. Therefore, for high data rate transmission, the single-relay ADF-based WH-SOFDM scheme could be a suitable candidate for hybrid satellite-terrestrial communication systems in future. [ABSTRACT FROM AUTHOR]

This paper investigates the downlink (DL) rate of multicell massive multiuser multiple-input and multiple-output systems over Ricean fading channels that takes into account channel estimation errors. To acquire channel state information at all users, beamforming training (BT) is examined. Considering both maximum-ratio transmission (MRT) and zero forcing, this paper derives closed-form expressions on the lower bound of the achievable rates for two cases, with or without BT. With the obtained expressions, Bernoulli's inequality is invoked to find the ranges for the length of DL pilots such that the sum spectral efficiency of the scheme with BT is superior to that of the scheme without BT, and vice versa. Various power scaling laws concerning DL data and pilot transmit powers and uplink pilot transmit power are analyzed. Numerical results corroborate the accuracy of the closed-form expressions. In particular, the results show that employing BT with MRT processing is only preferred in environments having a high signal-to-noise ratio, low mobility, and small Ricean $K$ -factors. [ABSTRACT FROM AUTHOR]