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Cell-free massive multi-input multi-output (MIMO) systems exhibit many characteristics that differ from those of traditional centralized massive MIMO systems, and there are still research gaps in the modeling of cell-free massive MIMO channels. In this paper, a geometry-based stochastic model (GBSM) that combines the double-ring model and hemisphere model to comprehensively consider the distribution of scatterers in the environment was proposed for cell-free massive MIMO channels. Combined with the line-of-sight (LoS) path, single scattering path, and double scattering path components, the channel matrix between the access points (APs) and the user was derived. The proposed model fully considers geometric parameters such as the arrival/departure direction, elevation angle, time delay, and distance, which can accurately characterize the channel. Then, we proved that the traditional channel model, standard block-fading model, and spatial basis expansion model (SBEM) adopted in a cell-free massive MIMO system could not describe the nonstationarity in the space, time, and frequency domains, whereas the proposed GBSM could. Statistical characteristics of the channel were analyzed, including the space cross-correlation function (CCF), time autocorrelation function (ACF), Doppler power spectral density (PSD), level crossing rate (LCR), and average fade duration (AFD). Then, we investigated the proposed model by simulating the space CCF, time ACF, Doppler PSD, LCR, and AFD under the conditions of two different scatterer densities. Through simulations and analyses, some new features of cell-free massive MIMO channels were identified, providing a theoretical basis for in-depth research on cell-free massive MIMO systems. Finally, the measurement-based scenario and the WINNER II channel model are compared to demonstrate that the GBSM is more practical to characterize real cell-free massive MIMO channels. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the uplink transmission for user-centric cell-free massive multiple-input multiple-output (MIMO) systems. The largest-large-scale-fading-based access point (AP) selection method is adopted to achieve a user-centric operation. Under this user-centric framework, we propose a novel inter-cluster interference-based (IC-IB) pilot assignment scheme to alleviate pilot contamination. Considering the local characteristics of channel estimates and statistics, we propose a location-aided distributed uplink combining scheme to balance the relationship among the spectral efficiency (SE), user equipment (UE) fairness and complexity, in which local partial minimum mean-squared error (LP-MMSE) combining is adopted for some APs, while maximum-ratio (MR) combining is adopted for the remaining APs. A corresponding AP selection scheme based on a novel proposed metric representing inter-user interference is proposed. We also propose a new fairness coefficient taking SE performance into account to indicate the UE fairness. Moreover, the performance of the proposed scheme is investigated under fractional power control and max–min fairness (MMF) power control. Simulation results demonstrate that the channel estimation accuracy of our proposed IC-IB pilot assignment scheme outperforms that of the conventional pilot assignment schemes. It is also shown that compared with the benchmark LP-MMSE combining, the proposed location-aided combining trades 13.45 % average SE loss for 26.61 % UE fairness improvement and 28.58 % complexity reduction when γ = 0.6 . And by adjusting the threshold γ , a good trade-off between the average SE, UE fairness and computational complexity can be provided by the proposed scheme. Furthermore, the proposed scheme with fractional power control can better demonstrate the advantages of trade-off performance than MMF power control and full power transmission. [ABSTRACT FROM AUTHOR]

This paper presents the application of intelligent reflecting surfaces (IRSs) in a cell-free massive MIMO network to enhance secure transmission in the system. Multiantenna access points (APs) need to transmit information to users safely and reliably via IRSs without fully knowing the channel state information (CSI) of a multiantenna eavesdropper (Eve) or the accurate beamforming information of a jammer. Specifically, through joint optimization of the AP active beamforming (ABF) and IRS passive beamforming (PBF), the information leaked to the Eve is limited by a set threshold, and restrictions on the IRS reflection phase are considered to maximize the weighted sum rate of the system's downlink transmission. To solve this multivariate-coupled nonconvex problem, we propose a joint precoding framework under imperfect CSI, using the generalized S-procedure, fractional programming (FP), and multidimensional complex quadratic transformation (MCQT) to transform the original complex nonconvex problem into an easily solvable convex optimization problem, and an alternating algorithm to obtain the optimal solution for precoding and IRS phase shifting. Simulation results show that the proposed scheme can significantly increase the weighted sum rate of the system compared to conventional antijamming methods while revealing that the scheme is effective in enhancing secure system transmission. [ABSTRACT FROM AUTHOR]

The quasi-static assumption of channels becomes invalid in a number of emerging applications of massive multiple-input multiple-output (MIMO) systems with high base station (BS)/user mobility, such as high speed train and unmanned aerial vehicle communications. In these situations, the time variation of channels shortens the channel coherence time and decreases the efficiency of traditional channel estimation schemes significantly. This paper focuses on the time-varying channel estimation problem in massive MIMO systems, where the mobility of BS/user is assumed to be high. The sparse property of the time-varying massive MIMO channels is analyzed, which reveals that the angular domain channels exhibit two kinds of spatial-temporal sparse (STS) structures, namely the temporal-common sparse structure and spatial-clustered sparse structure. By exploiting the STS structures, a novel structured variational Bayesian inference (VBI) framework is formulated to make a robust channel estimation during the training phase. A data-aided channel predication scheme is further proposed to combat the time variation of the channels during the data transmission phase without increasing the pilot consumption. The simulation results demonstrate the superiority of the proposed scheme in the time-varying scenarios with different BS/user mobilities. [ABSTRACT FROM AUTHOR]

In the fifth generation (5G) wireless communication, high‐speed railway (HSR) communication is one of the most challenging scenarios. By adopting massive multi‐input multioutput (mMIMO) technology in HSR communication, the design of the underlying communication system becomes more challenging. Some new channel characteristics must be studied, such as nonstationarity in space, time, and frequency domains. In this paper, two models are established for the two states of HSR. When the HSR stops, a three‐dimensional (3‐D) stationary channel model based on the single‐ring distribution of the scatterers is established. When the HSR moves, a 3‐D nonstationary geometric random channel model (GRCM) based on the cylinder distribution of the scatterers by introducing the birth and death process of the propagation path and time‐varying characteristics of the channel parameters is established. Moreover, a time evolution algorithm for time‐varying channel parameters is proposed, and a modularization study of key parameter update algorithms is conducted. Finally, based on the proposed 3‐D nonstationary geometric random channel model, important time‐varying channel statistical characteristics, such as time autocorrelation function (ACF), spatial cross‐correlation function (CCF), Doppler power spectral density (DPSD), and stationary interval are studied. Simulation results indicate that the properties of the proposed channel models, verified by simulation results, can match well with those of the theoretical model. Key Points: A 3‐D nonstationary geometric random channel model (GRCM) based on the cylinder distribution of the scatterers is establishedA time evolution algorithm for time‐varying channel parameters is proposedImportant time‐varying channel statistical characteristics, such as ACF, CCF, DPSD, and stationary interval, are studied [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]

In this paper, we consider the hybrid time switching (TS) and power splitting (PS) simultaneous wireless information and power transfer (SWIPT) protocol design in a full-duplex (FD) massive MIMO system. In this system, an FD base station (BS) serves a set of half-duplex (HD) users and a set of fixed HD sensors. The whole protocol can be divided into two phases based on the idea of TS. The first phase is Training Phase, which is designed for users uplink training and sensors energy harvesting as well as downlink training. Specifically, users transmit uplink pilots for beam-domain (BD) uplink channel estimation at the BS, and the BS transmits energy signals to sensors. Based on the idea of PS, sensors utilize the received energy signals for energy harvesting and BD downlink channel estimation. In the second phase, that is Information Transmission Phase, the BS intelligently schedules users and sensors based on the BD distributions of channels to mitigate self-interference and improve transmission spectral efficiency (SE). Then, the BS forms transmit beamformers for transmitting information to users and receive beamformers for receiving signals transmitted by sensors. By optimizing transmit powers at the BS during the two phases and the TS ratio, the system achievable sum-rate is maximized. Simulation results show the superiority of the proposed protocol on SE compared with conventional massive MIMO SWIPT protocol. [ABSTRACT FROM AUTHOR]

In this paper, we consider the hybrid time switching (TS) and power splitting (PS) simultaneous wireless information and power transfer (SWIPT) protocol design in massive MIMO system. In this system, the base station (BS) simultaneously serves a set of half-duplex (HD) sensor nodes which are uniformly distributed in its coverage. The whole protocol can be divided into two phases based on the idea of TS. The first phase is designed for sensor nodes energy harvesting as well as downlink training. During this phase, the BS transmits energy signals to the sensor nodes. Based on the idea of PS, the sensor nodes utilize the received energy signals for energy harvesting and downlink channel estimation. In the second phase, the BS schedules the sensors intelligently based on the beam-domain distributions of channels to mitigate interference between sensors and enhance transmission spectral efficiency. Then, the BS forms the receive beamformers for the reception of signals transmitted by sensors. By optimizing transmit powers at the BS and the TS ratio, the system achievable sum rate performance is maximized. Simulation results show the superiority of the proposed protocol on spectral efficiency compared with conventional massive MIMO SWIPT protocol. [ABSTRACT FROM AUTHOR]

In this paper, we consider beam-domain full-duplex (FD) transmission in massive multiple-input multiple-output (MIMO) system. By exploiting the channel spatial sparsity, massive MIMO channel is compressed in the beam-domain under certain selected basis spaces and as a result the channel dimension required to be estimated can be greatly reduced. According to the distribution of effective beams of uplink/downlink channel and self-interference (SI) channel, all the users are scheduled into several uplink and downlink groups. With the estimated effective beam-domain channel, we design the uplink and downlink beamforming matrices at the base station to suppress the SI, inter-user interference and inter-group interference. The achievable sum-rate of the proposed beam-domain FD massive MIMO transmission scheme is analyzed and the joint user and BS power allocation scheme is proposed to optimize the system achievable sum-rate. Simulation results show that the proposed beam-domain FD transmission scheme outperforms existing time division duplex (TDD) and frequency division duplex (FDD) massive MIMO and FD massive MIMO transmissions schemes on the spectral efficiency performance. [ABSTRACT FROM AUTHOR]

Co-time co-frequency uplink and downlink (CCUD) transmission was considered challenging in the cellular system due to the strong self-interference (SI) between the transmitter and receiver of base station (BS). In this paper, by investigating the beam-domain representation of channels based on the basis expansion model, we propose a beam-domain full-duplex (BDFD) massive multiple-input multiple-output (MIMO) scheme to make the CCUD transmission possible. The key idea of the BDFD scheme lies in intelligently scheduling the uplink and downlink user equipment (UE) based on the beam-domain distributions of their associated channels to mitigate SI and enhance transmission efficiency. We show that the BDFD scheme achieves significant savings in uplink/downlink training resource and achieves uplink and downlink sum capacities simultaneously as the number of BS antennas approaches infinity. The superiority of the BDFD scheme over the traditional time-division duplex (TDD)/frequency-division duplex (FDD) massive MIMO is evaluated through simulation for the macrocell environment. The results show that the spectral efficiency gain can even exceed 2 $\times$ in the specific scenarios, since the BDFD scheme utilizes the time-frequency resource more efficiently in both the training and data transmission phases. [ABSTRACT FROM PUBLISHER]

This paper proposes an amplify-and-forward-based massive multiple-input–multiple-output full-duplex relaying (AF-MM-FDR) protocol, where multiple source–destination pairs communicate simultaneously with a common full-duplex relay. A zero-forcing (ZF)-based relay transceiver design with semiblind gain control is presented, considering the problem of relay oscillation. The asymptotic expression of end-to-end signal-to-interference-plus-noise power (SINR) is derived under the general power scaling scheme. The scaling behaviors of echo interference (EI) and effective channel estimation error are determined. Scaling results show that the deployment of very large antenna arrays at the relay has the potential to eliminate the effect of EI due to the full-duplex operation, if the power scaling scheme is selected properly. Moreover, based on the asymptotic analysis, a low-complexity power control scheme is proposed to optimize the spectral efficiency of AF-MM-FDR. Simulation results show that the proposed power control scheme improves the spectral–energy efficiency tradeoff of AF-MM-FDR significantly. [ABSTRACT FROM AUTHOR]

This paper investigates the multi-pair full-duplex massive MIMO relaying, where multiple source-destination pairs communicate simultaneously with the help of a common full-duplex decode-and-forward relay. It is assumed that the sources and destinations are equipped with single antenna while the relay is equipped with very large antenna arrays. The asymptotic end-to-end signal-to-interference-plus-noise ratio (SINR) is analyzed under the general power scaling scheme, when maximal ratio combining/maximal ratio transmission scheme or zero-forcing reception/zero-forcing transmission scheme is employed at the relay. Theoretical results show that the effect of echo interference due to full-duplex operation can be eliminated by the large antenna arrays of the relay, if the power scaling scheme is selected properly. On the basis of the SINR expressions, the energy efficiency scaling law is derived. Furthermore, we present the power control scheme at the sources and relay to maximize the energy efficiency, subject to the required spectral efficiency and maximum power constraints. We show that the power control problem can be approximated as a geometric program problem and solved efficiently by the convex optimization tools. Numerical results are presented to validate the asymptotic analysis as well as the proposed power control scheme. The results demonstrate that the deployment of full-duplex relay is beneficial to achieve energy efficient transmission in massive MIMO relaying network. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) is a powerful supporting technology to meet the energy/spectral efficiency and reliability requirement of Internet of Things (IoT) network. However, the gain of massive MIMO relies on the availability of channel state information (CSI). In this paper, we investigate the channel estimation problem for frequency division duplex (FDD) massive MIMO system. By analyzing the sparse property of the downlink massive MIMO channel in the angular domain, a structured prior-based sparse Bayesian learning (SP-SBL) approach is proposed to estimate the downlink channels between base station (BS) and users reliably. The scheme can be implemented without the knowledge of channel statistics and angular information of users. The simulation results show that the proposed scheme outperforms the reference schemes significantly in terms of normalized mean square error (NMSE) for a variety of scenarios with different lengths of pilot sequence, transmit signal-to-noise ratios (SNRs), and angular spreads. [ABSTRACT FROM AUTHOR]

Energy supply and information backhaul are critical problems for wireless sensor networks deployed in remote places with poor infrastructure. To deal with these problems, this paper proposes an airborne massive multiple-input multiple-output (MIMO) system for wireless energy transfer (WET) and information transmission. An air platform (AP) equipped with a two-dimensional rectangular antenna array is employed to broadcast energy and provide wireless access for ground sensors. By exploiting the statistical property of air-terrestrial MIMO channels, the energy and information beamformers are jointly designed to maximize the average received signal-to-interference-plus-noise ratio (SINR), which gives rise to a statistical max-SINR beamforming scheme. The scheme does not rely on the instantaneous channel state information, but still requires large numbers of RF chains at AP. To deal with this problem, a heuristic strongest-path energy and information beamforming scheme is proposed, which can be implemented in the analog-domain with low computational and hardware complexity. The analysis of the relation between the two schemes reveals that, with proper sensor scheduling, the strongest-path beamforming is equivalent to the statistical max-SINR beamforming when the number of AP antennas tends to infinity. Using the asymptotic approximation of average received SINR at AP, the system parameters, including transmit power, number of active antennas of AP and duration of WET phase, are optimized jointly to maximize the system energy efficiency. The simulation results demonstrate that the proposed schemes achieve a good tradeoff between system performance and complexity. [ABSTRACT FROM AUTHOR]

In this paper, we consider the beam-domain hybrid time-switching (TS) and power-splitting (PS) simultaneous wireless information and power transfer (SWIPT) protocol design in full-duplex (FD) massive multiple-input multiple-output (MIMO) system, where the FD base station (BS) simultaneously serves a set of downlink half-duplex (HD) users (cellular users) and a set of fixed uplink HD users (fixed sensor nodes) which are uniformly distributed in its coverage area. In order to reduce the computational complexity, we investigate the beam-domain representation of massive MIMO channels based on the basis expansion model, and then the beam-domain SWIPT protocol which lies in intelligently scheduling the users and sensors based on the beam-domain distributions of their associated channels to mitigate SI and enhance transmission efficiency is designed for full-duplex massive MIMO system. The whole beam-domain hybrid TS and PS SWIPT protocol is divided into two phases based on the ideal of TS. The first phase is used for cellular users uplink training and sensor nodes energy harvesting as well as downlink training, wherein the cellular users transmit uplink pilots for uplink channel estimation at the BS, while the BS simultaneously transmits energy signals to the sensor nodes. Based on the idea of PS, the sensor nodes utilize the received energy signals for energy harvesting and downlink channel estimation. In the second phase, the BS forms the transmit beamformers for information transmission to the users as well as the receive beamformers for the sensors transmit their data to the BS simultaneously. By optimizing the TS ratio and transmit powers at the BS in two phases, the system achievable sum rate performance is maximized. Simulation results show the superiority of the proposed protocol on spectral efficiency compared with the existing massive MIMO SWIPT protocol. [ABSTRACT FROM AUTHOR]