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Multiple-input-multiple-output (MIMO) systems are of interest for meeting the expected demand for higher data rates and lower delays in future wireless packet data systems. In such systems, it is optimal to simultaneously transmit to multiple users compared with a single user in a single-input-single-output system. In addition, multicarrier systems are of interest to combat frequency-selective fading that is experienced over the larger bandwidth that these future broadband systems will use. The use of dirty paper coding further complicates the matter, because the order in which the users are encoded will affect the rates that they can achieve. A well-designed cross-layer scheduling algorithm must take into account the multiple dimensions of this resource-allocation problem and other quality-of-service (QoS) parameters to fully exploit the communications channel. The scheduling problem is often expressed in terms of optimizing some utility function. Unfortunately, the search space for this optimization problem is extremely large, which prohibits an optimal exhaustive search. To this end, we investigate the use of genetic algorithms to reduce the complexity of the scheduling. This paper builds upon prior work that implements scheduling via genetic algorithms in the context of single-carrier systems using zero-forcing beamforming (ZFB). In this paper, we investigate how the genetic algorithm can be adapted to account for the effect of encoding order on the scheduling and how the scheduling can be extended to a multicarrier system. In particular, we investigate the maximum throughput and proportionally fair scheduling criteria. We demonstrate that the performance of the genetic algorithm is near optimal compared with an exhaustive search at a greatly reduced computational complexity. Furthermore, in the case of a multicarrier orthogonal frequency-division multiplexing (OFDM) system, an increase in capacity is shown relative to the single-carrier case. [ABSTRACT FROM AUTHOR]

The paper studies a reconfigurable intelligent surface (RIS)-assisted multi-user uplink massive multiple-input multiple-output (MIMO) system with imperfect hardware. At the RIS, the paper considers phase noise, while at the base station, the paper takes into consideration the radio frequency impairments and low-resolution analog-to-digital converters. The paper derives approximate expressions for the ergodic achievable rate in closed forms under Rician fading channels. For the cases of infinite numbers of antennas and infinite numbers of reflecting elements, asymptotic data rates are derived to provide new design insights. The derived power scaling laws indicate that while guaranteeing a required system performance, the transmit power of the users can be scaled down at most by the factor $\frac{1}{M}$ when $M$ goes infinite, or by the factor $\frac{1}{MN}$ when $M$ and $N$ go infinite, where $M$ is the number of antennas and $N$ is the number of the reflecting units. Furthermore, an optimization algorithm is proposed based on the genetic algorithm to solve the phase shift optimization problem with the aim of maximizing the sum rate of the system. Additionally, the optimization problem with discrete phase shifts is considered. Finally, numerical results are provided to validate the correctness of the analytical results. [ABSTRACT FROM AUTHOR]

A zero forcing (ZF) assisted single layer beamforming is developed for spatial modulation (SM) and space shift keying (SSK) modulation multiple input multiple output (MIMO) systems, termed as SZF-SM/SZF-SSK, to achieve both transmit beamforming and receive diversity gain in this paper. Specifically, the proposed SZF-SM/SZF-SSK maps part of the bits to spatial domain, simultaneously forming a ZF precoding assisted single layer data both in multiple input single output (MISO) and MIMO systems, that differs greatly from conventional ZF beamforming assisted MIMO systems. With MISO configuration, the ZF precoder of SZF-SM/SZF-SSK converges to the maximum ratio transmission (MRT), achieving only the transmit beamforming gain. With MIMO configuration, SZF-SM/SZF-SSK tranceiver could be further modified along with the MIMO channel to achieve single layer transmission and joint detection, attaining both the transmit beamforming and receive diversity gain in SM-MIMO system. The theoretical analysis and the simulation results of the proposed SZF-SM/SZF-SSK with MISO and MIMO configurations are presented in this paper respectively, which show an attractive performance gain over the conventional SM/SSK. Compared with the conventional ZF beamforming assisted MIMO systems and some other CSI assisted SM-MIMO schemes, the proposed scheme exhibits better performance, especially under highly correlated MIMO channels, rendering a new and practical way to achieve low rank MIMO transmission with good performance and high data rate. [ABSTRACT FROM AUTHOR]

In this paper, we design receivers for filter bank multicarrier-based (FBMC-based) massive MIMO considering practical aspects such as channel estimation and equalization. In particular, we propose a spectrally efficient pilot structure and a channel estimation technique in the uplink to jointly estimate all the users’ channel impulse responses. We mathematically analyze our proposed channel estimator and find the statistics of the channel estimation errors. These statistics are incorporated into our proposed equalizers to deal with the imperfect channel state information (CSI) effect. We revisit the channel equalization problem for FBMC-based massive MIMO, address the shortcomings of the existing equalizers in the literature, and make them more applicable to practical scenarios. The proposed receiver in this paper consists of two stages. In the first stage, a linear combining of the received signals at the base station (BS) antennas provides a coarse channel equalization and removes any multiuser interference. In the second stage, a per subcarrier fractionally spaced equalizer (FSE) takes care of any residual distortion of the channel for the user of interest. We propose an FSE design based on the equivalent channel at the linear combiner output. This enables the applicability of our proposed technique to small and/or distributed antenna setups such as cell-free massive MIMO. Finally, the efficacy of the proposed techniques is corroborated through numerical analysis. [ABSTRACT FROM AUTHOR]

An equation in the Appendix of a previous paper (IEEE Trans. Veh. Technol ., vol. 58, no. 7, pp. 3247–3262, Sep. 2009) contains an error, which propagates to a few more equations in the Appendix. This error does not affect the main body of the text or results presented in that paper, apart from a change to one equation. In this correspondence, we correct the error and provide a revised Appendix. [ABSTRACT FROM PUBLISHER]

Multiple signal classification (MUSIC) has been widely applied in multiple-input multiple-output (MIMO) receivers for direction-of-arrival (DOA) estimation. To reduce the cost of radio frequency (RF) chains at millimeter-wave bands, hybrid analog-digital structure has been adopted in massive MIMO transceivers. In this situation, received signals at the antennas are unavailable to the digital receiver, and as a consequence, the spatial covariance matrix (SCM), which is essential in MUSIC algorithm, cannot be obtained using traditional sample average approach. Based on our previous work, we propose a novel algorithm for SCM reconstruction in hybrid massive MIMO systems with multiple RF chains in this paper. By switching the analog beamformers to a group of predetermined DOAs, SCM can be reconstructed through the solutions of a set of linear equations. Furthermore, a low-complexity algorithm, as well as a careful selection of the predetermined DOAs, will be also presented in this paper. Simulation results show that the proposed algorithms can reconstruct the SCM accurately so that MUSIC algorithm can be well used in hybrid massive MIMO systems with multiple RF chains. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We assume partial offloading model and the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, respectively. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ analog and digital (A/D) hybrid beamforming. Moreover, we propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the high-dimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks. [ABSTRACT FROM AUTHOR]

Massive MIMO is a promising air interface technology for 5G wireless communications. Such antenna systems offer capabilities to utilize channel correlations to create beams suitable for user specific transmissions. Most of the prior research is devoted to user-specific transmission in such systems, where a narrow beam is formed towards a user to improve its reception. Meanwhile, public-channel transmission to multiple users at once by means of broad beams has been understudied. In this paper, we introduce the concept of array-size invariant (ASI) beamforming, enabling generation of broad beams from very large antenna arrays where all elements are transmitting at maximum power. The ASI technique offers the possibility to achieve a perfectly flat array factor by exploiting the additional degree of freedom coming from the dual-polarization beamforming. The proposed technique is applicable to both uniform linear and rectangular arrays. The benefits of the method are shown by means of several numerical examples. In particular, the practical usefulness of the ASI beamforming, in terms of coverage and energy efficiency, is illustrated on an example of designing a cell-specific broad beam for public-channel transmission in a realistic network deployment. [ABSTRACT FROM AUTHOR]

Millimeter-wave vehicle-to-vehicle communication systems with multiple-input multiple-output antenna array have drawn significant attention because it is expected to satisfy the growing bandwidth requirements for the cooperative intelligent transportation systems. However, maintaining high-quality communication links between communicating vehicles without high overhead is a challenging problem due to the high mobility of vehicles. In this paper, we propose a beam management algorithm with spatially distributed antenna subarrays instead of a single co-located antenna array. Unlike existing methods, we use distributed antenna subarrays to exploit the geometric relationship of the beam directions and the relative distance between communicating vehicles. We reduce the beam alignment errors by minimizing the sum of squared errors between the estimated beam direction after the beam training process and the refined beam direction derived from the measured position and velocity data. Additionally, a position data request protocol is devised to reduce the beam refining errors. Numerical results demonstrate that the proposed beam management algorithm successfully reduces the beam alignment errors and it is robust to position and velocity measurement errors. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) frequency spectrum offers orders of magnitude greater spectrum to mitigate the severe spectrum shortage in conventional cellular bands. To overcome the high propagation loss in the mmWave band, massive multiple-input and multiple-output (MIMO) can be adopted at both transmitter and receiver to provide large beamforming gains. At the same time, hybrid architecture is applied to reduce the huge power consumption caused by devices operating at radio frequency (RF). However, because of the hybrid architecture and large number of antennas, it is hard to obtain the channel state information (CSI) which is crucial for obtaining desirable beamforming gains. Off-grid error and sparsity pattern (SP) estimation error are two main limiting factors of the performance of most existing compressive sensing (CS) based channel estimation (CE) algorithms. Off-grid error presents when the true angle does not lie on the discretized angle grid of mmWave channel in the spatial domain. In this paper, we first propose a fast Bayesian matching pursuit method with ‘virtual sparsity’ to improve the accuracy of SP estimation and name it as the improved Bayesian matching pursuit (IBMP). Then an enhanced algorithm, named off-grid IBMP (OG-IBMP), is developed to mitigate the off-grid problem, followed by a theoretical analysis of OG-IBMP. This method iteratively updates the selected grid points and updates the corresponding parameters based on the maximum a posteriori (MAP) criterion. Numerical simulations are performed to validate our theoretical analysis and evaluate the performance of the proposed method. Compared to other existing methods, the results show that our proposed OG-IBMP algorithm greatly reduces the off-grid error and significantly enhances the accuracy of the SP estimation with low computational complexity. [ABSTRACT FROM AUTHOR]

As one of the three main application scenarios of 5 G cellular system, massive Machine-Type Communications (mMTC) has been regarded as the key solution to facilitate the IoT paradigm. One major bottleneck for accommodating mMTC is the severe congestion at the cellular random access channel when plenty of Machine-Type Devices (MTDs) send access requests concurrently while the preamble resources are limited. To remedy this issue, limiting the number of retransmissions and dropping access requests after the limit is reached can be an effective approach. Yet, the effect of the preamble retransmission limit $K$ on the optimal access performance of mMTC in cellular networks remains largely unexploited, which motivates the study in this paper. Specifically, in this paper, we start by characterizing the network steady-state points based on the limiting probability of successful transmission of access requests. We then obtain explicit expressions of the access throughput and the mean access delay of successfully-transmitted access requests as functions of $K$ and the number of preambles $M$. The maximum access throughput and the corresponding optimal backoff window size are further derived. It is shown that the maximum access throughput is independent of $K$ , while the mean access delay can be significantly reduced with a small $K$ , yet, at the expense of increased request dropping ratio. In addition, to improve both the throughput and delay performance, the analysis shows that more preambles should be allocated but the performance gain becomes marginal when $M$ is large. Therewith, an algorithm is proposed for determining the least number of preambles $M^\ast$ that maximizes the access throughput and the preamble resource utilization ratio. Numerical results show that a smaller preamble retransmission limit $K$ can further reduce $M^\ast$. [ABSTRACT FROM AUTHOR]

Multi-numerology transmission is a promising multiple access scheme for flexible communication systems, which can simultaneously support users with different service requirements. To this end, both inter- and intra-numerology interference (inter-NI and intra-NI) should be suppressed, which is not straightforward. This paper proposes a novel zero-forcing (ZF) precoding design, which perfectly eliminates all the interference, for massive multiple-input multiple-output (MIMO) systems with multi-numerology. First of all, we express the signal-to-interference-plus-noise ratio (SINR) of the multi-numerology system with spectrum sharing. From the SINR representation, we identify the channels that precoders must nullify to suppress inter-NI. After that, we propose a ZF precoder suppressing both inter-NI and intra-NI perfectly. The lower bound of ergodic achievable rate with perfect channel state information at the transmitter (CSIT) is analyzed. Based on the lower bound of ergodic achievable rate, we present the feasible rate region. Also, we propose two power allocation schemes: user fairness and the ergodic sum rate maximization. The simulation results demonstrate the advantage of the proposed precoding design and verify our analysis results. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is a passive antenna array composed of a large number of reflecting elements. In a RIS-assisted communication system, it is a challenging task to acquire accurate channel state information (CSI). In this paper, we propose a semi-blind channel estimation method for a RIS-assisted massive multiple-input single-output (MISO) system. Assuming a Gaussian priori on the data symbols, an iterative expectation maximization (EM)-based algorithm is developed to obtain the maximum likelihood (ML) estimate of the cascaded RIS channel. Different from existing pilot-based methods, the proposed semi-blind channel estimation method exploits the data symbols for channel estimation enhancement and only a fraction of full-pilot signaling overhead is required. Simulation results verify that the proposed method achieves significant improvement in the accuracy of the channel estimation while it noticeably reduces the training pilot overhead. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) and cell-free (CF) massive multiple-input multiple-output (MIMO) are two promising technologies for realizing beyond-fifth generation (5G) networks. In this paper, we study the uplink spectral efficiency (SE) of a practical spatially correlated RISs-aided CF massive MIMO system over Rician fading channels. Specifically, we derive the closed-form expression for characterizing the uplink SE of the system, which shows that increasing the number of RIS elements can improve the system performance. Moreover, the results unveil that the spatial correlation of RIS has a significant impact on the system while leading to the best performance gain at a half wavelength spacing. Finally, the accuracy of our analytical results are verified by Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS)-enhanced wireless communication is one of the critical technologies of the sixth generation mobile communication, which has recently emerged as an innovative solution to the realization of intelligent transportation. It can meet the increasing demand for high-speed railway (HSR) communications services. In this paper, we consider a train-ground TDD wireless mobile communication paradigm to deploy two IRSs on the roadside and the train roof to assist the wireless transmission for the HSR communication system. To achieve the more significant performance gain, in the uplink transmission phase, we propose the practical channel estimation framework of the MIMO system aided by double IRSs to acquire reliable channel state information (CSI) of each channel. To facilitate the processing of the three-dimension tensor, we leverage the trilinearity of the parallel factor decomposition to decompose the received signal into a two-dimensional matrix. The Doppler frequency offset compensating scheme and channel tracking scheme based on Kalman-Filter are proposed to further decrease the pilot overheads. Then, in the downlink phase, a block coordinate descent (BCD) algorithm is proposed to handle the data rate maximization problem of double IRSs communication system with the estimated CSI. We reformulate the original problem and decouple the double IRSs phase optimization problem into two single IRS phase optimization non-convex problems under the unit-modulus constraint. The Majorization-Minimization (MM) algorithm is proposed to approximate the optimal solution of the two optimization subproblems. Simulation results corroborate the effectiveness of the proposed channel estimation and phase shifts optimization scheme. [ABSTRACT FROM AUTHOR]

A quadrature spatial modulation with the fourth order transmit diversity (FO-QSM) scheme is presented in this paper. In this scheme, two dispersion-matrix (DM) sets are assigned at the transmitter, and $P$ out of $Q$ DMs are activated in each set to extend the real and imaginary parts of $P$ QSM signals respectively, to large number of transmit antennas. The two DM sets are constructed by performing Kronecker product between two simple extension matrices and the DMs of Sezginer-Sari-Biglieri (SSB) code. The framework designs of FO-QSM and the two DM sets make FO-QSM possess the properties: 1) it guarantees the fourth order transmit diversity without requiring any parameter optimization; 2) it has higher spectral efficiency than the newly proposed generalized space-time block coded spatial modulation (GSTBC-SM) scheme. Furthermore, a repeated computation reduced sphere decoding (RCR-SD) is proposed. RCR-SD is very suitable for the schemes constructed by a large number of DMs, including FO-QSM. By sorting the index vectors, the RCR-SD detector can efficiently reduce the repeated computations in the SD searching, which significantly reduces the decoding complexity without sacrificing optimal decoding performance. Simulation results show that FO-QSM has obvious better bit error rate (BER) performance than GSTBC-SM. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a two-hop simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay communication system with an energy-limited relay node. The relay node harvests energy based on the radio-frequency (RF) signal transmitted from the source node through the time-switching (TS) protocol and fully uses the harvested energy to precode and forward the information to the destination node. The non-linear energy harvesting models are considered at the relay node. With the consideration of the channel estimation error, the joint optimization of the TS factor, source and relay precoding matrices is proposed with robustness against the channel state information (CSI) mismatch to maximize the mutual information (MI) between the source and destination nodes. The optimal structure for the source and relay precoding matrices is derived to simplify the transceiver optimization problem. Numerical simulations show that the system performance provided by the proposed algorithms with robustness is better than the non-robust algorithm. [ABSTRACT FROM AUTHOR]

The paper proposes a multiple-input multiple-output-based power line communication (PLC) system subject to Rayleigh fading and corrupted by additive cyclostationary Gaussian noise. The proposed coherent receiver structure employs a block reception rule at the reception unit for detecting the space-time block-coded transmitted data symbols. A closed-form expression for the pairwise error probability (PEP) is derived using a characteristic function approach. Exact and asymptotic expressions for the average PEP are obtained for the special case of Alamouti code transmission which show that the PLC system under consideration offers full diversity. Numerical results depict the improvement in the system performance with increase in the number of receive diversity branches and the average signal-to-noise ratio per branch and decrease in the correlation value between the noise samples. [ABSTRACT FROM AUTHOR]

Providing seamless connectivity to unmanned aerial vehicle user equipment (UAV-UE) is very challenging due to the encountered line-of-sight interference and reduced gains of down-tilted base station (BS) antennas. For instance, as the altitude of UAV-UEs increases, their cell association and handover procedure become driven by the side-lobes of the BS antennas. In this paper, the performance of cellular-connected UAV-UEs is studied under 3D practical antenna configurations. Two scenarios are studied: scenarios with static, hovering UAV-UEs and scenarios with mobile UAV-UEs. For both scenarios, the UAV-UE coverage probability is characterized as a function of the system parameters. The effects of the number of antenna elements on the UAV-UE coverage probability and handover rate are then investigated. Moreover, a lower bound on the handover probability is derived as a function of the number of antenna elements and the UAV-UE mobility speed. Results reveal that the UAV-UE coverage probability under a practical antenna pattern is lower than that under a simple antenna model. Moreover, vertically-mobile UAV-UEs are susceptible to altitude handover due to consecutive crossings of the nulls and peaks of the antenna side-lobes. The effect of altitude handover is also shown to be effectively mitigated by serving UAV-UEs cooperatively from multiple ground BSs. [ABSTRACT FROM AUTHOR]

Recently, model-free power control approaches have been developed to achieve the near-optimal performance of cell-free (CF) massive multiple-input multiple-output (MIMO) with affordable computational complexity. In particular, deep reinforcement learning (DRL) is one of such promising techniques for realizing effective power control. In this paper, we propose a model-free method adopting the deep deterministic policy gradient algorithm (DDPG) with feedforward neural networks (NNs) to solve the downlink max-min power control problem in CF massive MIMO systems. Our result shows that compared with the conventional convex optimization algorithm, the proposed DDPG method can effectively strike a performance-complexity trade-off obtaining 1,000 times faster implementation speed and approximately the same achievable user rate as the optimal solution produced by conventional numerical convex optimization solvers, thereby offering effective power control implementations for large-scale systems. Finally, we extend the DDPG algorithm to both the max-sum and the max-product power control problems, while achieving better performance than that achieved by the conventional deep learning algorithm. [ABSTRACT FROM AUTHOR]

In cellular communication systems, it is conventional to assume the absence of a line of sight (LoS) path between the users and theirassociated access points (APs). This assumption however becomes questionable in the context of recent developments in the direction of cell-free (CF) massive MIMO systems. In the CF massive MIMO, the AP density is assumed to be comparable with the user density, which increases probability of existence of an LoS path between the users and their associated APs. In this paper, we analyze the performance of an uplink CF massive MIMO system, with a probabilistic LoS channel model. Here, we first derive the effective statistics of this channel model, and argue that their behaviour is fundamentally different from that of the conventional rich scattering channels. Utilizing these statistics, we next compare the rates achievable by CF massive MIMO systems, under both stream-wise and joint decoding at the central processing unit. Following this, we also discuss the centralized MMSE based data detection to obtain a complexity/ performance trade-off. Finally, using detailed Monte-Carlo simulations, we validate our analytical results, and evaluate the performance of the three data detection schemes. [ABSTRACT FROM AUTHOR]

As the number of antennas increases, the massive multiple-input multiple-output (MIMO) system can precisely point to user equipments (UEs) with narrow beams. Accurate and timely channel state information (CSI) feedback is crucial to keep UEs in service. Mobile UEs, however, may suffer from the narrow beam nature of the massive MIMO system since UEs can move out of the beam coverage. When intelligent reflecting surface (IRS) is applied to the massive MIMO system, the adjustment of the IRS cannot be frequent as the IRS is controlled remotely by the base station (BS). Limiting the number of CSI feedback and the number of both BS and IRS adjustments significantly reduces the overhead of transmission and computation to the system. In this paper, we consider the UEs’ mobility adaptation problem in an IRS assisted multiuser massive MIMO downlink system with infrequent CSI feedback. We propose a beam control method that adapts to UEs’ mobility. The problem is constructed as a sum rate problem where both the BS and IRS are taken into account to jointly optimize the beamforming matrices. Simulation results show that our proposed algorithm can converge quickly and provide satisfactory performance for mobile UEs. At the same time, our proposed algorithm reduces the frequency of updating the beamforming matrices effectively both at the BS and at the IRS. [ABSTRACT FROM AUTHOR]

This paper considers a novel algorithmic framework for the joint spectral sharing design of a colocated MIMO pulse radar and a MIMO communication system under signal direction mismatch, wherein both radar transceiver and communication transmitter designs are investigated, and the mutual information (MI) is maximized under a number of constraints to suppress the mutual interference of the coexistence system. The designed multi-variate formulation is non-convex, an iterative procedure is derived to compute a sequence of simpler sub-problems which admit a low complexity closed-form solution for each primal variable. The convergence of all devised algorithms is guaranteed. Finally, a thorough performance assessment is conducted to demonstrate the merits of the proposed solutions. [ABSTRACT FROM AUTHOR]

This study focuses on the downlink (DL) transmission of massive multiple-input-multiple-output (MIMO) with the support of simultaneous wireless information and power transfer (SWIPT) system based on a power-splitting (PS) scheme. To reach the green design target in the concept of wireless communication networks, this paper maximizes the system’s energy efficiency (EE) using joint system-level optimization. First, the closed-form expressions of the harvested energy and achievable data rate are first derived. Then, we formulated the EE maximization problem by jointly optimizing user equipment (UE) pilot transmission time allocation, the received PS ratios, a base station (BS) transmit power allocation, and a number of BS antennas while considering the minimum data rate as user’s quality-of-service (QoS) requirement and the maximum BS power transmission constraint. However, the formulated EE maximization problem is non-convex and non-linear which is difficult to solve. Hence, we propose an efficient low-complex alternative optimization (LCAO) algorithm to solve the non-convex problem iteratively with an acceptable computational complexity level. Simulation results are provided to evaluate the derived closed-form expressions and validate the proposed LCAO algorithm’s efficiency. The optimality, convergence, and complexity of the LCAO algorithm are analytically investigated. Results indicate that the proposed LCAO algorithm outperforms the equal power allocation (EPA) scheme by 15% better EE gain and 4% better than the max-min fairness scheme when the BS power transmission capacity is higher than 3 (Watt). [ABSTRACT FROM AUTHOR]

In this paper, we show how the absorption and re-radiation energy from molecules in the air can influence the Multiple Input Multiple Output (MIMO) performance in high-frequency bands, e.g., millimeter wave (mmWave) and terahertz. In more detail, some common atmosphere molecules, such as oxygen and water, can absorb and re-radiate energy in their natural resonance frequencies, such as 60 GHz, 180 GHz and 320 GHz. Hence, when hit by electromagnetic waves, molecules will get excited and absorb energy, which leads to an extra path loss and is known as molecular attenuation. Meanwhile, the absorbed energy will be re-radiated towards a random direction with a random phase. These re-radiated waves also interfere with the signal transmission. Although, the molecular re-radiation was mostly considered as noise in literature, recent works show that it is correlated to the main signal and can be viewed as a composition of multiple delayed or scattered signals. Such a phenomenon can provide non-line-of-sight (NLoS) paths in an environment that lacks scatterers, which increases spatial multiplexing and thus greatly enhances the performance of MIMO systems. Therefore in this paper, we explore the scattering model and noise models of molecular re-radiation to characterize the channel transfer function of the NLoS channels created by atmosphere molecules. Our simulation results show that the re-radiation can increase MIMO capacity up to 3 folds in mmWave and 6 folds in terahertz for a set of realistic transmit power, distance, and antenna numbers. We also show that in the high SNR, the re-radiation makes the open-loop precoding viable, which is an alternative to beamforming to avoid beam alignment sensitivity in high mobility applications. [ABSTRACT FROM AUTHOR]

In this paper, we compute an achievable rate of a multiple-input-multiple-output (MIMO) Gaussian Z-interference channel (ZIC), as shown in Fig. 1, when transmit node C has imperfect cognitive knowledge of the signal sent by transmit node A. First, we compute the capacity of this channel, assuming noncausal but noisy knowledge at node C of node A's signal. We then compute the achievable rate for a causal cognitive strategy: This achievable rate is derived using a two-phase transmission scheme, in which node C uses a combination of a linear MMSE (LMMSE) estimator and a dirty-paper code, and node D employs a combination of an LMMSE estimator and a partial interference canceler. The achievable rate is studied in two different cases: 1) Node C operates in full-duplex mode, and 2) node C operates in half-duplex mode. To quantify the performance of the proposed strategy, we compute simple lower and upper bounds on the capacity of this channel. Similar to an interference channel, the achievable rate of the cognitive ZIC nonmonotonically varies with the interference. Specifically, the achievable rate first decreases with the channel gain between nodes A and D and then begins to increase beyond a certain threshold. The difference in the achievable rate between full- and half-duplex transmissions is also numerically evaluated. [ABSTRACT FROM AUTHOR]

In this paper, a 3D geometry-based double-spherical terahertz (THz) channel model is proposed for ultra massive multiple-input multiple-output (MIMO) communications. To reflect real THz ultra massive MIMO communications, the characteristic of nano-material array antenna and the high loss of THz bands are considered in channel modeling. Based on the proposed geometry-based stochastic model (GBSM), power delay profile (PDP), space-time-frequency correlation function, and the Doppler power spectral density are derived and analyzed. Furthermore, the proposed model is validated by comparing the simulated results with the measurement reference. Influences of channel parameters, namely, antenna element spacing, carrier frequency, number and location of clusters, on the correlation functions are investigated. The results indicate that antenna element spacing and number of clusters have significant impacts on channel correlation, and the decreasing of correlation function becomes slower when antenna element spacing decreases. Moreover, location of clusters also affects the drop rate of correlation function. The influences of the number of clusters and concentration of intra-cluster multipath components (MPCs) on the Doppler power spectral density are analyzed. It is found that a larger number of clusters and a more concentrated distribution of intra-cluster MPCs increase the degree of Doppler spectrum variation. The proposed channel model and the corresponding statistical properties are insightful for designing and realizing THz ultra massive MIMO systems for 6G and beyond. [ABSTRACT FROM AUTHOR]

Identification of communication parameters, a major task of intelligent receivers, has important applications in intelligent systems, especially cognitive radio systems. Multiple antennas make the identification problem more challenging. In this paper, we focus on the problem of detecting the number of transmit antennas in multiple-input multiple-output (MIMO) cognitive radio systems. A novel identification algorithm is proposed to determine the number of transmit antennas for MIMO systems in the presence of alpha-stable noise. We first introduce the correlation matrix based on the fractional lower order statistics (FLOS) and provide a particular structure of FLOS-based correlation matrix. Then, the eigenvalues of the FLOS-based correlation matrix are employed to construct a test statistic and the central limit theorem is exploited to obtain the decision threshold. Finally, the transmit-antenna number is detected using a serial binary hypothesis test. Simulation results are demonstrated to evaluate the effectiveness of the proposed transmit-antenna number detection algorithm for MIMO systems in the presence of alpha-stable noise. [ABSTRACT FROM AUTHOR]

In the face of the unprecedented demand for higher data rates while coping with the constraints from physical size (i.e., windward area), it is profitable to deploy compact planar arrays (CPAs) on the base stations (BSs). But massively densified antenna deployment not only leads to antenna mutual coupling (MC), but also consumes additional energy due to the equipment of more radio frequency (RF) chains. This paper studies energy efficiency (EE) optimization for massive multi-input multi-output (MIMO) wireless systems with a CPA in the presence of MC. In comprehensive consideration of the spectral efficiency (SE) and power consumption, a joint optimization problem of beamforming and antenna subset (AS) is proposed. Our motivation is to relieve the effects of MC and reduce the relevant energy consumption by constructing irregular AS. Particularly, considering the nonconvexity of the objective and the discrete 0-1 constraints, an available iterative algorithm, named the energy-efficient beamforming and AS (EE-BF&AS) algorithm, is developed. Numerical results illustrate that when MC is modeled, the proposed EE-BF&AS algorithm outperforms conventional related approaches in EE. By using the AS switching strategy, the irregular AS can be constructed to make MC less serious and let the relevant energy consumption decrease. [ABSTRACT FROM AUTHOR]

The combination of unmanned aerial vehicles (UAVs) and millimeter wave (mmWave) multiple-input multiple-out (MIMO) system is regarded as a key enabling technology for beyond 5G networks, as it provides high data rate aerial links. However, establishing UAV-enabled mmWave MIMO communication is quite challenging due to the high hardware cost in terms of radio frequency (RF) chains. As a cost-effective alternative, a beamspace precoding with discrete lens arrays (DLA) architecture has received considerable attention. However, the underlying optimal design in beamspace precoding has not been fully exploited in UAV-enabled communication scenario. In this paper, the joint design of the UAV’s altitude and hybrid beamspace precoding is proposed for the UAV-enabled multiuser MIMO system, in which the DLA is exploited to reduce the number of the RF chain. In the proposed scheme, the optimization problem is formulated as a minimum weighted mean squared error (MWMSE) method. Then an efficient algorithm with the penalty dual decomposition (PDD) is proposed that aims to jointly optimize the altitude of UAV, beam selection and digital precoding matrices. Simulation results confirm the comparable performance of the proposed scheme and perform close to full-digital beamforming in terms of achievable spectral efficiency. [ABSTRACT FROM AUTHOR]

Decentralized baseband processing (DBP) architecture, which partitions the base station antennas into multiple antenna clusters, has been recently proposed to alleviate the excessively high interconnect bandwidth, chip input/output data rates, and detection complexity for massive multi-user multiple-input multiple-output (MU-MIMO) systems. In this paper, we propose a novel decentralized Gaussian message passing (GMP) detection for the DBP architecture. Based on the message passing rule, each antenna cluster iteratively calculated the local means and variances which are fused to generate the global symbol beliefs for demodulation. The state evolution framework of the decentralized GMP algorithm is presented under the assumptions of large-system limit and Gaussian sources. Analytical results corroborated by simulations demonstrate that the nonuniform antenna cluster partition scheme exhibits a higher convergence rate than the uniform counterpart. Simulation results illustrate that the proposed decentralized GMP detection outperforms the recently proposed decentralized algorithms. [ABSTRACT FROM AUTHOR]

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]

This paper presents multi-user (MU) linear precoding methods for massively distributed antenna system (DAS) with polarized antennas, where mobile stations (MSs) are spatially grouped and the distributed antenna (DA) ports with a polarized-antenna are connected to a central base station (BS). While the (slowly-changing) long-term channel state information (CSI) (i.e., large scale fading and polarization) can be accurately estimated at the transmit-side, the short-term CSI (instantaneous channel response) is available at the transmit-side with quantization errors due to the limited feedback. Accordingly, the utilization of a large number of DA ports is not always beneficial due to the imperfect short-term CSI at the transmitter (CSIT). In this paper, we first derive the asymptotic achievable rate and signal-to-leakage-and-noise ratio (SLNR) for the MU linear precoding in the massive DAS, which depends on the long-term CSIT and the number of feedback bits, and propose a transmit DA port selection based on the asymptotic SLNR performance. Here, the number of selected DA ports can be also optimized according to the long-term CSIT and the number of feedback bits. Furthermore, we also propose a polarization selection strategy at each DA port (equipped with multi-polarized antennas) so as to exploit the polarization diversity. Through the simulation, we confirm that the proposed MU precoding with the DA port selection outperforms the conventional precoding schemes for the DAS and, furthermore, the proposed polarization selection strategy achieves a rate performance comparable to the DAS with a (single polarized antenna) node density twice as large. [ABSTRACT FROM AUTHOR]

In a time-division duplex (TDD) massive multiple-input multiple-output (MIMO) system, the number of available orthogonal pilot sequences in each cell is limited. In this paper, we consider a massive MIMO system where the number of available orthogonal pilot sequences is less than the number of users. In order to optimize the long-term performance in estimating the uplink (UL) channels, the base station (BS) needs to judiciously decide the allocation of the available pilot sequences to different users in each training phase. In this paper, the pilot allocation problem is modeled as a partially observable Markov decision process with an exploitation-exploration tradeoff under the Gauss-Markov fading channels. We then investigate this problem within the restless multi-armed bandit (RMAB) framework and put forward a low-complexity method to derive the Whittle's index approximately. A simple index policy is then proposed to allocate the orthogonal pilots in a massive MIMO system. Numerical results demonstrate the superiority of the proposed index policy. [ABSTRACT FROM AUTHOR]

Linear dispersion (LD) based perfect space-time codes (STBCs) are an efficient means of increasing a multiple-input multiple-output (MIMO) system's overall diversity gain while maintaining the same spectral efficiency as a traditional spatial multiplexed (SM) MIMO system. Because the decoding procedure of LD codes traditionally requires the entire code to be received and decoded simultaneously, complexity increases proportional to the square of the MIMO array size. In this paper, we leverage the increased number of spatial and temporal layers at the decoder to dynamically reduce the complexity of a BLAST optimum ordering and successive interference cancellation (SIC) detector based on the instantaneous system capacity and data rate. The novel approach proposed in this paper is channel code and modulation agnostic, meaning the underlying constellation can be HEX or QAM and there is no feedback from a forward error correction (FEC) decoder, which makes the design useful in a wide range of MIMO systems employing LD codes with linear detectors. We investigate the method's bit error rate (BER) using MIMO dimensions up to $8 \times 8$ and bits per channel use (BPCU) up to 32. We analyze the system's run-time complexity and BER performance in software and implement perfect coding along with the novel method presented here in a custom MIMO orthogonal frequency division multiplexing (OFDM) system and test it over-the-air using an Ettus Research X310 software-defined radio (SDR) testbed. [ABSTRACT FROM AUTHOR]

This paper investigates a point-to-point millimeter-wave (mmWave) massive MIMO system in which the transmitter and receiver consist of $K_t$ and $K_r$ distributed antenna subarrays, respectively. All the subchannel matrices between each pair of subarrays at the transmitter and the receiver are assumed to be mutually independent and known at the transmitter and the receiver. To quantify the potential of spatial multiplexing in such a system, the multiplexing gain is defined and analyzed when the numbers of antennas at subarrays go to infinity while the transmit power is held fixed. In particular, assuming that all subchannels have the same average number of propagation paths $\bar{L}$ , the paper shows that an average maximum multiplexing gain of $K_rK_t\bar{L}$ can be achieved. This result means that the multiplexing gain under a distributed array architecture can statistically increase in proportion with $K_rK_t$. The multiplexing gain analysis is also extended to the multiuser scenario. Simulation results obtained with the hybrid analog/digital processing finally corroborate the analytical 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]

In the existing millimeter-wave (mmWave) wireless positioning systems, the method based on sparse Bayesian learning (SBL) uses the channel sparsity to estimate the parameters required for positioning, such as angle of arrival (AOA) and time delay, but most existing SBL solutions only consider the angle sparsity. In this paper, we consider the joint sparsity of the angle domain and time delay domain to propose an improved SBL algorithm by using a new two-dimensional adaptive grid refinement method in the SBL framework. This algorithm solves the grid mismatch problem caused by the fixed grid in the traditional SBL method, and reduces the algorithm complexity of the off-grid SBL (OGSBL) algorithm. We also obtain the Cramér-Rao bound (CRB) of AOA, time delay and position estimation based on the mmWave multipath signals to analyze the estimation errors. Simulation results show that the performance of the proposed algorithm is better than existing algorithms and can approach CRB. [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]

Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) wireless communication systems. In this paper, a novel three-dimensional (3D) space-time-frequency non-stationary theoretical channel model is first proposed for 6G THz wireless communication systems employing ultra-massive multiple-input multiple-output (MIMO) technologies with long traveling paths. Considering frequency-dependent diffuse scattering, which is a special property of THz channels different from millimeter wave (mmWave) channels, the relative angles and delays of rays within one cluster will evolve in the frequency domain. Then, a corresponding simulation model is proposed with discrete angles calculated using the method of equal area (MEA). The statistical properties of the proposed theoretical and simulation models are derived and compared, showing good agreements. The accuracy and flexibility of the proposed simulation model are demonstrated by comparing the simulation results of the relative angle spread and root mean square (RMS) delay spread with corresponding measurements. [ABSTRACT FROM AUTHOR]

Multiple-input-multiple-output (MIMO) systems significantly increase the network throughput and coverage probability for short-packet communications. In this paper, a tight lower bound on the average achievable rate of MIMO short-packet communication systems is theoretically derived, considering flat uncorrelated Rayleigh fading MIMO channels. The derivations are based on the eigenvalue analysis of a Wishart matrix and Jensen's inequality. Given the actual transmission rate, a tight approximation on the average block error rate (BLER) of MIMO short-packet communication systems is also provided, which is based on the moment generating function and inverse Laplace transform. Numerical results demonstrate that both our theoretically derived analytical average achievable rate lower bound and approximate average BLER are almost the same as the simulation results. [ABSTRACT FROM AUTHOR]

Thanks to the short wavelength at high frequency (HF), it is promising to combine massive multiple-input and multiple-output (MIMO) with HF communications. In particular, HF massive MIMO differs from the conventional MIMO in array structure and radio propagation. In this paper, we investigate the HF massive MIMO system with a compact planar array (CPA) deployed at the base station (BS) in the presence of electromagnetic mutual coupling (MC) for the asymptotic case under unlimited antenna elements assumption. Specifically, the HF sparse channel is analyzed asymptotically to show that the randomness characteristic of channel decreases in the asymptotic regime, which is conducive to the design of communication systems. Moreover, on the basis of the asymptotic analysis results, the optimal beamforming to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) is derived by applying random matrix theory. Numerical results verify the accuracy of the asymptotic analysis, indicate the performance superiority of the proposed scheme to the traditional minimum mean squared error (MMSE) and maximum ratio transmission (MRT) schemes, and reveal the robustness of the proposal to electromagnetic MC. [ABSTRACT FROM AUTHOR]

In this paper, a generalized uplink cell-free massive multiple-input multiple-output (mMIMO) system with hardware imperfections is investigated. Specifically, the hardware impairments consist of multiplicative phase drifts, additive distortion, and amplified thermal noise. Leveraging on a generic hardware impairment model, closed-form expressions for the channel estimation and achievable rate are presented. Based on these tractable results, we explore the uplink achievable rate surrounding the key design parameters, i.e., the channel estimation error, the number of antenna arrays, the local oscillator (LO) setup, and the variance of phase drifts. Aside from this, we also design a power optimization algorithm to maximize the sum-rate under variant channel use. The weighted minimum mean square error (MMSE) methodology is introduced to iteratively determine the solution in closed form. Numerical simulations validate the analytical analyses and examine the effectiveness of the proposed power control algorithm. [ABSTRACT FROM AUTHOR]

Even if massive multiple-input multiple-output (MIMO) can theoretically bring huge benefits, it incurs substantial hardware complexity and expensive hardware costs. To address these issues while maintaining the system performance simultaneously, we develop an asymmetrical transceiver architecture for massive MIMO systems in this paper by releasing the shackles on radio frequency (RF) chains. Specifically, we first develop the architecture for the asymmetrical transceiver where the number of receive RF chains is different from that of the transmit RF chains. Due to this unequal number of RF chains, channel inconsistency appears. To overcome the channel inconsistency and thus fully harness the system's downlink data transmission capability, we propose two uplink-to-downlink channel transfer algorithms. The cost and power consumption models for the asymmetrical transceiver are also developed and the uplink signal-to-noise loss due to the channel inconsistency is investigated. Through analyzing system spectral, cost, and energy efficiency, we demonstrate that the proposed asymmetrical transceiver-based massive MIMO system can achieve excellent downlink spectral efficiency while maintaining a reasonable energy efficiency. [ABSTRACT FROM AUTHOR]

This paper studies the sum spectral/energy efficiency (SE/EE) of a full-duplex (FD) cell-free (CF) massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) at the access points (APs) and the downlink (DL) users. We characterize the joint impact of the residual self-interference, inter-AP interference, uplink (UL)-to-DL interference, pilot contamination and quantization noise (QN) by deriving closed-form solutions for the UL/DL SE. It is shown that the UL SE loss due to the QN can be compensated by increasing the receiving antenna arrays. By contrast, in the DL, increasing the transmit antennas is ineffective in compensating for the DL SE loss. Furthermore, the analysis shows that the operating region of the FD CF massive MIMO is reduced as the ADCs’ resolution declines. We investigate the EE/SE trade-offs. [ABSTRACT FROM AUTHOR]

Free-space optics (FSO) communication is often unreliable, especially for long distances, as it suffers from attenuation due to foggy weather, atmospheric turbulence, and pointing errors. To avoid communication link failure, radio frequency (RF) communication, which is unaffected from these effects, is utilized when FSO communication is in outage. Diversity is known to counteract the adverse fading effects, therefore, this paper presents a transmit aperture/antenna selection (TAS) and selection combining (SC)-based multiple-input-multiple-output (MIMO) hybrid FSO/RF system. The proposed system consists of a MIMO FSO and RF sub-systems. The MIMO FSO sub-system is given higher priority to transmit and receive using the selected best link. The best link of the MIMO RF sub-system is used when the FSO sub-system is in outage. The probability density function (PDF) and cumulative distribution function (CDF) expressions for TAS/SC-based MIMO FSO and RF sub-systems are derived. The derived PDF and CDF expressions are used further to derive the exact and asymptotic expressions for outage probability, average symbol error rate (SER), and capacity of the hybrid system. From the asymptotic SER expressions, the diversity order of the proposed system is determined. The optimum values for the threshold SNR and transmit beam waist, which minimize the average SER, are obtained. Further, the obtained optimal values using analytical expressions are verified using numerical optimization technique. The effect of multiple apertures/antennas, pointing errors, and atmospheric attenuation over the system performance is analyzed extensively by varying the FSO and RF channel parameters. The results show that the proposed hybrid FSO/RF system is more reliable compared to the individual MIMO FSO and RF systems and achieves higher capacity compared to the MIMO RF system. [ABSTRACT FROM AUTHOR]

Multiple remote antenna units (RAUs) cooperate to serve a certain user in cell-free distributed massive multiple-input multiple-output (MIMO) systems. Due to the different distances between each RAU and the user, asynchronous reception effects will occur. This paper studies the impacts of asynchronous reception effects on cell-free distributed massive MIMO systems. We first derive the minimum-mean-square error (MMSE) channel estimation under the asynchronous reception effects. Given the estimated channel state information, we derive the closed-form expressions of the user achievable rates with maximum ratio transmission (MRT) and regularized zero-forcing (RZF) precoders, which are further used to analyze the impacts of asynchronous reception effects. The accuracy of the closed-form expressions are verified. Simulation results demonstrate that asynchronous reception effects cause a non-negligible drop on the system spectral efficiency. Moreover, the performance degradation is proportional to the subcarrier sequence number and even more serious with the increasing of signal-to-noise ratio (SNR) or the number of antennas per RAU. [ABSTRACT FROM AUTHOR]

When the wireless communication system evolves to massive multiple-input multiple-output (MIMO) system, the system becomes more and more complex. Hybrid beamforming architecture provides excellent trade-off between the system performance and the complexity for the massive MIMO system. Hybrid beamforming architecture can be classified as fully-connected (FC) and sub-connected (SC) architecture. Existing studies on the system performance comparison of the two architectures are limited. In this paper, we investigate the performance of the FC and SC architecture in the multi-user multiple-input single-output (MU-MISO) system. We derive the closed-form sum rate of two architectures under the same total transmission power constraint. From the results, we conclude that the system performance of FC architecture is always better than that of SC architecture. When the number of users is small or the signal noise ratio (SNR) is low, the performance difference between the FC and SC architecture is small. Our analysis can serve as a guideline of choosing hybrid beamforming architectures. [ABSTRACT FROM AUTHOR]