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In massive multiple-input multiple-output (MIMO) systems, accurate direction-of-arrival (DOA) estimation is important for the base station (BS) to perform effective downlink beamforming. So far, there have been few reports on DOA estimation considering gain-phase perturbations in massive MIMO systems. However, gain-phase perturbations indeed exist in practical applications and cannot be ignored. In this paper, an efficient method for two-dimensional (2-D) DOA estimation of incoherently distributed (ID) sources considering array gain-phase perturbations is proposed for massive MIMO systems. Firstly, a shift invariance structure is established in the subspace framework, and a constrained optimization problem is formulated to estimate the nominal azimuth and elevation DOAs as well as gain-phase perturbations with closed-form expressions, under the assumption that some of the BS antennas are well calibrated; secondly, the corresponding angular spreads are obtained with the aid of the estimated gain-phase perturbations. Theoretical analysis and an approximate Cramér-Rao bound are also provided. An improved estimation performance is achieved by the proposed method as demonstrated by numerical simulations.

We consider the problem of user activity detection for grant-free massive connectivity, where a base station, equipped with a large number of antennas, serves a large number of machine type devices. Two massive multiple-input multiple-output (mMIMO) systems are studied, namely, a collocated mMIMO with physically collocated antennas and a cell-free mMIMO with geographically separated antennas. By utilizing the sporadic transmission nature and the asymptotical orthogonality of channel vectors associated with different users, we propose a correlation-based sparse signal recovery scheme to detect active users. Simulation results are provided to evaluate the performance of the proposed method. Our results show that the cell-free mMIMO achieves much better user activity detection performance than the collocated mMIMO, and thus is more suitable for grant-free massive connectivity. An empirical analysis is performed to explain the superiority of the cell-free system over the collocated system.

This work proposes a new adaptive pilot pattern used for pilot arrangement at the mobile station and a new channel estimation at the base station. It addresses the bit error rate (BER) performance optimization of wireless channel estimation in massive multiple-input multiple-output systems. First, we propose a new adaptive pilot pattern (APP) that offers a lower BER than the conventional pilot patterns. Then, we suggest a new channel estimation algorithm based on the APP. It is called the shifted APP-channel estimation (SACE). As a result, APP guarantees an optimal BER performance for the different system configurations, channel models, and carrier frequencies. It offers better BER performance than conventional pilot patterns, such as the long-term evolution (LTE) pilot pattern. Moreover, the shifted APP-based channel estimation algorithm solves the error floor caused by the usage of multiple subcarriers. It also offers a signal-to-noise ratio (SNR) improvement that reaches 17 dB at BER $= 10^{-2}$ compared to the conventional minimum mean square error (MMSE) channel estimation.

Pilot contamination (PC) is a practical drawback of massive multiple-input multiple-output (mMIMO) systems. It prevents the exploitation of the inherent spectral-efficiency (SE) of mMIMO systems, especially in dense networks. In this letter, we consider multi-antenna users in a multi-cell network and propose a novel approach based on interference alignment (IA) to remove PC. We show that the extra degrees of freedom (DoFs), provided by multi-antennas at the users’ side, can be used efficiently when employed for pilot decontamination, especially in heavy-loaded networks. Thus, while the intra-cell interference is removed due to the properties of mMIMO the out-of-cell interference is eliminated by the proposed IA-based pilot decontamination. The proposed algorithm uses linear zero-forcing (ZF) and needs no continuous cooperation between the base stations (BSs). Moreover, the users need no channel state information (CSI). Our simulations show that the proposed method outperforms the conventional PC precoding algorithms significantly, particularly in heavy-loaded networks.

In this correspondence, we develop a novel frequency synchronization scheme for multiuser orthogonal frequency division multiplexing (OFDM) uplink with massive uniform linear array (ULA) at base station (BS). With proper user scheduling strategy, the directions of arrival (DOAs) of all users are considered to be disjunct. Our proposed method performs multiuser interference (MUI) cancellation through beamforming network, after which the multiuser OFDM uplink model can be decomposed into a set of parallel single user models. Then, carrier frequency offset (CFO) estimation for each user can be performed individually. The beamforming network is further optimized to minimize the mean square error (MSE) of the CFO estimation. The provided numerical results validate the proposed studies.

This Letter investigates the transmission method for downlink full-dimension multiple-input multiple-output systems. To balance the performance and the amount of channel information acquired at base station, a dual-layer beamforming method is proposed, which exploits the statistical as well as effective channel state information (CSI) of a small fraction of users. User scheduling and grouping algorithms utilising only statistical CSI are also proposed. Simulations verify that the proposed transmission method can achieve high throughput with much less CSI, and can flexibly balance the requirement of CSI and performance.

Noncoherent detection schemes are an appealing and low-complexity alternative in multi-user massive MIMO uplink systems compared to classical coherent detection algorithms, since no actual channel knowledge is required at the receiver. For noncoherent multi-user detection to function, the induced power at the base station is utilized to separate the different users. However, spatial separation is impossible when the users are located in the far-field of the receiving antenna array. Consequently, noncoherent detection fails in such scenarios. To this end, beamspace techniques can be applied, focusing the energy of the incident wave to a smaller subset of the receive antennas and enabling again the noncoherent detection scheme. This paper analyzes the beamspace capabilities of a dielectric lens and an analog beamforming network applied at the receiver. Furthermore, a sub-array architecture is proposed, relaxing the design requirements for practical implementation. It is shown that noncoherent detection in combination with beamspace techniques performs comparably to channel-estimation-based detection. In addition, the sub-array architecture revealed a significant performance enhancement accompanied by a reduced user separability., acceptedVersion

In this paper, a method of power control is proposed in massive multiple-input multiple-output systems on the downlink transmission in order to maximize the spectral efficiency, where a base station with a large number of antennas serves the users equipped with a single antenna. Given the total power budget at the base station, power is allocated to all pilots symbols and various data symbols among the users. Experimental results indicate that the proposed power control method is superior to the most of existing schemes in terms of the spectral efficiency.

A massive MIMO system uses very large number of base station antennas in order to give service to many users. Pilot contamination because of inter-cell interference is suffered by a massive multiple input multiple output system. A smart pilot assignment (SPA) strategy has been utilised in this paper, so that the performance of user terminals can be increased that are suffering from pilot contamination severely. The inter-cell interference of each pilot sequence is firstly measured by the base station. This interference is caused by the users having same pilot sequence in the adjacent cells. Contrary to conventional strategy which assigns the pilot sequence randomly to the users, the SPA strategy does the assignment of pilot sequences to the user terminals having the worst channel quality with the smallest inter-cell interference, so that the performance can be improved. In this paper, simulations have been done for the verification of the performance gain of the proposed SPA scheme in massive MIMO systems.

Reconfigurable intelligent surface (RIS) technology has recently gained a lot of attention since it promises to enhance the spectral and energy efficiency of future wireless communication systems. In this paper, we propose several approaches to optimize the beamformers at the base station (BS) and the phase shifts at the RIS for an RIS-assisted multiple-input multiple-output (MIMO) system. By comparing the minimum signal power, minimum signal to interference plus noise ratio (SINR) and rate achieved by the proposed maximum ratio transmission (MRT), zero-forcing (ZF) and max min SINR based optimizations, we conclude that the MRT based optimization can achieve the highest minimum signal power among other approaches, whereas the max min SINR optimization can achieve the highest minimum SINR and minimum rate. In addition, the effect of quantized RIS-phase shifts is analyzed, showing that almost optimal performance can be achieved with relatively high phase resolution.

In this article, the improvement in the performance of massive multiple input multiple output (mMIMO) systems has been shown using successive interference cancellation (SIC) at the base station (BS). The pilot contamination (PC) due to pilot reuse in multiple cells sharing the same frequency band results in the interference that worsens as the number of cells increases. The SIC at the BS reduces the impact of PC when compared with standalone decoding using maximal ratio combining (MRC). The performance in terms of sum rate (SR) capacity has been obtained when the number of users has been varied. The MRC with mMIMO systems exhibits poor performance as the SR reduces with increase in the number of users while the MRCSIC scheme improves the SR since the symbol of a user which is being decoded is not considered in the decoding of next user symbol. The paper also shows the variation in the SR with increase in the number of BS antennas for MRC and MRCSIC schemes. The simulation results have shown that the MRC-SIC scheme outperforms the standalone MRC scheme.

In this paper, the downlink transmission of a multi-cell massive multiple input multiple output (MIMO) system is studied, in which constant envelope precoding (CEP) is utilized for the precoding operation. The single-cell realization of CEP is presented in the literature in case exact CSI is at hand. In this work we aim to extend this structure to a multi-cell scenario with noisy CSI available to the base stations (BS). To this end, first of all, a fully cooperative cellular structure is presented, and then, an effort is made to mitigate the overhead by proposing a limited cooperation setting where the amount of the exchanged CSI among the cells is significantly decreased. Finally, by suggesting a distributed realization of the system, the overhead is diminished to its lowest value. Furthermore, it is attempted to analyze the system performance in case of having a noisy CSI and to compensate for this effect in the proposed schemes. Numerical results demonstrate that by reduction of the overhead in the limited cooperation and the distributed structures compared to the fully cooperative one, the performance faces merely a negligible drop, which compared to the throughput gain, seems entirely worth the cost.

A dual-polarized antenna array with novel baffles is proposed for base station application. To meet the challenge of the decoupling in MIMO systems, the baffles are implemented in a $2 \times 2$ MIMO antenna array to efficiently suppress the mutual couplings among antenna elements. As a result, the proposed antenna array operates in 3.2-3.8 GHz with low mutual coupling characteristic. In the proposed array, the mutual coupling coefficients between antenna ports are lower than -18 dB over the operating band. With the advantages of low coupling characteristics and simple structure, the designed antenna array is a good candidate for base station antenna in sub-6 GHz band.

This paper provides an algorithm that aims to address the problem of Pilot Contamination (PC) within Massive MIMO systems. The algorithm is mainly derived from the asymptotic regime, where Base Stations (BSs) are equipped with a large number of antennas. Regarding the fact that the available pilot resources are limited and many users should be assigned with pilots, the reuse of the same pilots within different cells is essential. Accordingly, the pattern where pilots are reused within different cells is considered herein. Specifically, the large scale fading coefficients (LSFs) are exploited to show that the PC upon users depends on the distance between users that employ the same pilots. Hence, instead of optimizing the manner where pilots are assigned to users, the proposed algorithm assigns pilots to users of different cells based on their LSF coefficients and through the exploitation of a simple matrix arrangement. Simulation results show that the problem of PC is widely reduced.

This work studies secure transmission in intelligent reflecting surfaces (IRS)-assisted MIMO systems when an active eavesdropper is available in the network. We consider a scenario in which the eavesdropper performs an active pilot attack to contaminate the channel estimation at the base station. Invoking the method of secure regularized zero forcing, we develop an algorithm that designs beamforming vectors, as well as phase-shifts at the IRS, such that the active attacker is blinded. Our numerical investigations confirm that the proposed algorithm can suppress the active eavesdropper effectively, as long as legitimate and malicious terminals are statistically distinguishable., Accepted for Presentation in Asilomar Conference 2020. 8 pages and 2 figures

In this paper, we propose a novel dynamic user admission (UA) scheme to mitigate pilot contamination and reduce pilots reassignment complexity in multi-cell massive MIMO systems. Consider a dynamic user admission scenario, where some active users may leave the networks after pilot assignment, and the base stations (BSs) can admit new users. To improve the fairness of users, the harmonic asymptotic SINR utility function is adopted to measure pilot contamination during the UA. By exploiting the supermodular function, the UA problem is formulated as a supermodular minimization problem, which is typically NP-hard. Then, to handle this challenging issue, we propose a greedy algorithm by minimizing the supermodular UA function, and obtain a suboptimal UA scheme without reassigning the pilots for all users. Furthermore, we present a theoretical guarantee for general supermodular minimization problems via the greedy searching. Simulation results demonstrate the proposed UA scheme significantly outperforms other existing UA schemes, and performs closely to the optimal UA scheme.

In this paper, the scaling laws of scheduling gain and the feasibility of user scheduling for uplink massive multiple input&ndash, multiple output (MIMO) systems are investigated by analyzing the second moment of mutual information. We consider two well-known linear receivers of matched filter (MF) and zero-forcing (ZF). The exact distribution of the signal-to-interference-plus-noise ratio (SINR) and its moment-generating function are first obtained, and the approximated variance of the mutual information for a user is derived as a closed form with a function of the number of antennas. The achievable scheduling gain under the optimal user scheduler is then derived using the Gaussianity of the sum rate. From the analyses and simulation results, it is found that the scheduling gain for the MF receiver increases with the number of base station (BS) antennas, while that for the ZF receiver decreases as the number of BS antennas increases, for most cases (except some impractical scenarios). Therefore, it is verified that user scheduling is still beneficial for the MF receiver while random user selection is sufficient for the ZF receiver in massive MIMO systems.

International audience; In this paper, we consider the uplink of a railway communication system with onboard mobile relays. Each on-board relay communicates with two cooperating base stations at the ends of the section along which the train is moving. Each carriage is equipped with two distinct antenna arrays. We design transmit coding schemes distributed between the antenna arrays and we apply receive macrodiversity. Simulations show that the proposed schemes enable to maintain transmission quality and throughput as the carriage moves along the section.

To fully achieve the spectral and energy efficiency intended in large-scale antenna systems, acquiring the Channel State Information (CSI) is inevitable at the base station (BS) side. Due to the massive array at the BS side and large number of required pilots accordingly, acquisition of the channel can be challenging when the system is implemented in frequency-division duplex (FDD) protocol. In this paper, we introduce a novel compressive sensing (CS) algorithm which takes the advantages of correlation between the received and transmitted signals into account for an iterative estimation. For this purpose, we use the intersection among paired users and then select those who minimize the residual norm while the number of non-zero elements are also minimum. Simulation results indicates that the proposed algorithm outperforms other existing solutions and is able to approach the performance bound.

Providing higher data rate is a momentous goal for wireless communications systems, while interference is an important obstacle to reach this purpose. To cope with this problem, interference alignment (IA) has been proposed. In this paper, we propose two rank minimization methods to enhance the performance of IA in the presence of uncoordinated interference, i.e. , interference that cannot be properly aligned with the rest of the network and thus is a crucial issue. In this scenario, perfect and imperfect channel state information (CSI) cases are considered. Our proposed approaches employ the $l_{2}$ and the Schatten- $p$ norms to approximate the rank function, due to its non-convexity. Also, we propose a new convex relaxation to expand the feasible set of our optimization problem, providing lower rank solutions compared to other IA methods from the literature. In addition, we propose a modified weighted-sum method to deal with interference in the relay-aided MIMO interference channel, which employs a set of weighting parameters in order to find more solutions.

Traditional network MIMO systems with joint data processing usually require a large amount of backhaul data and a central processing station with very high processing power. In order to address this problem, the capacity of access network with network-enabled MIMO systems with distributed data processing is investigated in this paper. A study of network throughput for a system with interference mitigation and base station cooperation techniques is given. We also propose a distributed iterative decoding network structure as an alternative of joint detection/decoding scheme with network-enabled MIMO configurations, where iterative signal processing is performed at network level. Simulation results show that, with the proposed schemes, the complexity of data processing required to compute the detection solution is restricted and the complexity is distributed across access base stations, which results in a significant reduction in complexity of signal processing in hub base stations.

In this paper, we propose a novel design of calibration matrix for time-division duplex multiuser multiple-input multiple-output with maximal ratio transmit beamforming, which maximizes the achievable sum rate in the presence of reciprocity mismatch induced by radio-frequency (RF) gain responses at base station side. We show that two conventional designs are equivalent to the asymptotic expressions of proposed design when signal-to-noise ratio (SNR) tends to infinity or zero, respectively, since the proposed calibration matrix behaves as a decorrelator that rejects RF gain mismatch for the former SNR region, or as a matched filter maximizing the power of useful signal for the latter. The robust version against the channel estimation error is also investigated. Simulation results illustrate the effectiveness of our proposed design.

In this paper, we propose a fractional pilot reuse (FPR) scheme to mitigate pilot contamination in massive multiple-input multiple-output (MIMO) system. In FPR, users are classified into the cell-center and the cell-edge ones according to their signal-to-interference-plus-noise ratios (SINRs) at the receiver side. Then, the cell-edge users with low SINRs in different cells use different pilots or orthogonal pilots to avoid strong interference from adjacent cells while the cell-center users with high SINRs in different cells reuse the same pilots to improve the pilot efficiency. In order to identify the cell-center users and the cell-edge ones, we first approximate the SINR as a function of the distance between the user and the base station (BS) and then find the relationship between the network throughput and the distance threshold. The optimal distance threshold is found to maximize the network throughput. The analytical and simulation results demonstrate advantages of the proposed scheme compared with the conventional schemes.

Massive multiple-input multiple-output (MIMO) is considered as a promising technique in wireless communication systems, which contains cellular base stations equipped with a very large number of antennas to serve multiple users. However, the performance of massive MIMO is limited by the impact of pilot contamination due to inter-cell interference (ICI). In conventional massive MIMO systems, pilot sequences are randomly assigned to users without any further consideration. In this paper, a vertex graph coloring-based pilot assignment (VGC-PA) algorithm is proposed in conjunction with the existing post-processing discrete Fourier transform (DFT) filtering channel estimation to mitigate the ICI between users with the same pilot sequence in the channel estimation process to improve the capacity of the whole system. Specifically, we propose a metric to measure the potential ICI strength between any two users in the system based on their angle of arrival, correlation, and distances to construct an ICI graph, where each user is regarded as a node. This ICI graph denotes the potential ICI strength relationship among all users in the system. Then, we propose a VGC-PA algorithm to mitigate the ICI relationship between users with the same pilot sequences by assigning different pilots to connected users with high ICI metric based on some criteria. After the pilot assignment process, we apply the existing post-processing DFT filtering in the channel estimation process. The simulation results show the significant improvement of our proposed VGC-PA algorithm compared with existing methods.

In this paper, we propose rate-splitting (RS) multiple access to mitigate the effects of quantization noise (QN) inherent in low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). We consider the downlink (DL) of a multiuser massive multiple-input multiple-output (MIMO) system where the base station (BS) is equipped with low-resolution ADCs/DACs. The BS employs the RS scheme for data transmission. Under imperfect channel state information (CSI), we characterize the spectral efficiency (SE) and energy efficiency (EE) by deriving the asymptotic signal-to-interference-and-noise ratio (SINR). For 1-bit resolution, the QN is very high, and the RS scheme shows no rate gain over the non-RS scheme. As the ADC/DAC resolution increases (i.e., 2–3 bits), the RS scheme achieves higher SE in the high signal-to-noise ratio (SNR) regime compared to that of the non-RS scheme. For a 3-bit resolution, the number of antennas can be reduced by 27% in the RS scheme to achieve the same SE as the non-RS scheme. Low-resolution DACs degrades the system performance more than low-resolution ADCs. Hence, it is preferable to equip the system with low-resolution ADCs than low-resolution DACs. The system achieves the best SE/EE tradeoff for 4-bit resolution ADCs/DACs.

A dual-polarized base station (BS) antenna is proposed that has a spiral isolator, a T-shaped matching stub, and a metal cube. The antenna is composed of a metallic cube, micro-strip feed line board, metallic feeding cylinders, spiral isolator, isolator connecting pins, micro-strip patch, and T-shaped matching stubs. The measured frequency bandwidth is 898–970 MHz at ports-1 and 2. The measured isolation is 40 dB over the operating frequency band and the measured antenna peak gain is 7.5 dBi.

The pilot contamination problem in multi-cell multi-user massive MIMO systems is addressed in this paper. The overall performance of massive MIMO system is limited to the accuracy of the channel-state information (CSI) estimation at base station (BS). In time-division duplex systems, BS attains CSI using uplink-training sequence (TS) sent by users. The pilot contamination problem caused by using non-orthogonal TSs is the main challenge in this method. In this paper, a novel channel estimation method in the presence of pilot contamination is presented. The proposed method exploits sparse nature of the channel impulse response (CIR). Due to the pilot contamination and noise, the estimated CIR is not sparse anymore and consists of the desired and interference taps. To distinguish the desired taps from interferences, first, the initial estimation of the channel frequency response and equivalent CIR is achieved by taking EVD of received data covariance matrix. Finally, the initial estimated CIR will be modified using compressed-sensing methods. Simulation results indicate that the proposed approach achieves much better performance in terms of BER and sum-rate in comparison with the conventional methods.

This work concerns wireless cellular networks applying time division duplexing (TDD) massive multiple-input multiple-output (MIMO) technology. Such systems suffer from pilot contamination during channel estimation, due to the shortage of orthogonal pilot sequences. This paper presents a solution based on pilot sequence hopping, which provides a randomization of the pilot contamination. It is shown that such randomized contamination can be significantly suppressed through appropriate filtering. The resulting channel estimation scheme requires no inter-cell coordination, which is a strong advantage for practical implementations. Comparisons with conventional estimation methods show that the MSE can be lowered as much as an order of magnitude at low mobility. Achievable uplink and downlink rates are increased by 42 and 46%, respectively, in a system with 128 antennas at the base station.

Due to the difficulty in collecting accurate circuit power consumption parameters in practical communication systems, this letter investigates energy efficiency (EE) optimization with no knowledge of the circuit power consumption in massive MIMO systems. First, the influence of the imperfect estimation of circuit power consumption on the EE optimization is analyzed. It reveals that the EE optimization is insensitive to the imperfect circuit power consumption provided that the estimation error is not too large. Accordingly, we propose a simple EE optimization method for massive MIMO systems, in which the base station is unaware of the circuit power consumption parameters. Through the proposed scheme, the EE loss due to imperfect circuit power consumption parameters asymptotically tends to zero as the number of transmit antennas increases without bound. Simulation results validate that the proposed method can provide an EE performance very close to the optimal scheme with accurate circuit power consumption parameters.

In mobile communication, the signal that a base station received cannot be regarded as a point source anymore because of the multipath propagation. A distributed source model is more suitable for the realistic scenarios. In this paper, an approach of direction-of-arrival (DOA) estimation is proposed for coherently distributed (CD) sources consisting of circular and noncircular signals in a massive or large-scale multiple-input multiple-output system. The noncircular characteristic can improve the performance of DOA estimation. However, most algorithms are not suitable for DOA estimation where circular and noncircular signals coexist. Based on the analysis of constraint conditions satisfied by the steering vector of mixed signals, a multiple signal classification (MUSIC) like approach is proposed for the DOA estimation of CD sources consisting of circular and noncircular signals. On the conditions of low signal-to-noise ratio, small snapshot numbers, and large source numbers, the pseudopeaking may appear. This phenomenon has been analyzed, and a modified MUSIC-like approach is proposed. Simulation results demonstrate that both two proposed algorithms can estimate more sources than the number of sensors. The estimation performances of the two proposed algorithms outperform the traditional MUSIC algorithm.

Massive multiple-input, multiple-output (MIMO) is seen as an enabling technology to fulfill dramatic improvements in spectral efficiency for fifth-generation (5G) deployment in 2020. For massive MIMO systems, the learning loop from early-stage prototype design to final-stage performance validation is expected to be slow and ineffective. There is a strong need to evaluate massive MIMO base station (BS) performance with over-the-air (OTA) methods. Until now, such OTA solutions have not been discussed for massive MIMO BS systems.