Large-scale Antenna Systems Research Articles

A Variable Fractional Delay Filter Based True-Time-Delay Array Architecture for Wideband Beamforming

True-time delay (TTD) units are critical in Wideband Large-Scale Antenna Systems (LSASs) for resolving beam squint and frequency selective fading. In this paper, we propose a TTD array architecture for wideband beamforming that addresses the challenges posed by LSASs by utilizing variable fractional delay (VFD) filters and time delay estimations (TDEs). This study introduces a convex optimization problem to develop a novel method for designing VFD filters that can achieve high precision time delay resolution while maintaining an acceptable frequency response peak error. Using these VFD filters, we present a TDE approach with Newton-Raphson iteration update that corrects the difference in arrival time between sensors. Due to the implementation of VFD filters and fast TDE modules, the new architecture can complete beam steering and tracking in 10 ms with MATLAB simulation. Furthermore, an example was designed to demonstrate that the proposed architecture is capable of producing precise beam pointing while maintaining the bit error rate (BER) that is extremely close to the theoretical curve over the entire bandwidth.

Optimal linear array orientation design for 3D direct position determination via semi-Definite relaxation

This paper presents an optimization strategy for array orientations in a three-dimensional (3D) direct position determination (DPD) system. Specifically, we consider a scenario in which uniform linear arrays (ULAs) are used to locate emitters, and we seek to optimize the array orientations in terms of localization accuracy. The E-optimality criterion, which minimizes the spectral norm of the Cramér-Rao lower bound (CRLB), is exploited to formulate this problem. As the objective function is non-convex with bilinear structures, we leverage semi-definite relaxation (SDR) to transform it into a convex semi-definite programming (SDP) problem by substituting the bilinear terms with a matrix variable. In addition, we tackle the array orientation configuration problem in the context of multi-emitter scenarios and develop an SDR solution for large-scale antenna array systems. Simulation results validate that the ULAs with array orientations designed by the proposed SDR-based method have near-optimal localization performance.

РАЗРАБОТКА НИЗКОРАНГОВЫХ ПРОСТРАНСТВЕННЫХ ВЕКТОРОВ ПРЕДВАРИТЕЛЬНОГО КОДИРОВАНИЯ ВО ВРЕМЕННОЙ ОБЛАСТИ

In this paper we consider the alternative way of development of spatial beamforming processing algorithm in time-domain under low-rank Massive-MIMO systems. It is well-known, that the telecommunication systems, which based on spatial multiplexing technology achieve following advantages: increase the spectral efficiency in many times due to spatial channel multiplexing gain, the beamforming technology implementation (effective energy distribution in the space). The organization of spatial filtering (the beamforming vector processing) – one of the most significant tools in MIMO-OFDM multiplexing. In this paper, we consider a new spatial filtering processing approach (precoding vectors calculation), which is a significant tool in large-scale antenna systems, due to the fact of smart processing of the radio narrow-beam implementation increases the efficiency of the communication system many times over. Nowadays, the spatial filter design algorithms with low computational complexity have attracted considerable attention from researchers seeking to improve existing MIMO-OFDM wireless communication systems. In the paper we developed basic algorithms for calculating precoding vectors in the frequency domain and proposed the time-domain precoder approach with aim to achieve the complexity reduction gain. The proposed method was tested on the geometric statistical simulation channel model QuAdRiGa according to the multipath channel scenario "3GPP-3D Uma-NLoS". We provide the assessment of whole presented methods and the evaluation range of discrepancies was revealed in the form of pictures of the correlation and integral metrics. The analysis of integral cumulative distribution function of simulation results shows the relation gap between accuracy of correlation metric and losses of beamforming processing recalculation approximately -0.5 dB for 1st layer. In conclusion of paper was revealed the fact that proposed SVD factorization for each impulse response did not take into account the beams and eigenvectors distribution over the whole frequency bandwidth

5G wireless communication systems: Performance analysis and optimization using massive MIMO technology

Fifth-Generation (5G) cellular networks target substantial gains over 4G networks to meet the increasing data rate, capacity and low-latency demands of emerging mobile applications. This research investigates the potential of largescale antenna system architectures named Massive Multiple-Input Multiple-Output (MIMO) to enhance the spectral efficiency and reliability of 5G new radio (NR) wireless access. Comprehensive system-level modeling and simulations quantify the throughput, capacity and transmit power savings realizable under various realistic propagation environments. Results demonstrate significant performance gains over contemporary network configurations, supporting Massive MIMO as a key enabler on the road to 5G.

A Polynomial Inversion-Based Fast Time-Delay Estimation Method for Wideband Large-Scale Antenna Systems

This paper proposes a new fast time-delay estimation (TDE) method based on polynomial inversion which addresses the challenges arising from the requirements for high-precision, low-computational-complexity synchronization error estimation in wideband large-scale antenna systems (LSASs). In this work, we use the convex parabolic extreme point equation as the timing error detector (TED), and develop a polynomial inversion-based function to characterize the one-to-one mapping relationship between true time delay (TTD) and TED estimates using the least square (LS) method, then obtain the time-delay difference with a high accuracy and high computational efficiency. The results of the performance analysis indicate that the Mean Square Error (MSE) of the proposed algorithm is less than 1 dB away from the Cramer–Rao lower bound (CRLB), which is produced in this paper, while adding a few multipliers compared to the convex parabolic interpolation method. Finally, a further example illustrates that the proposed algorithm can achieve a synchronization error of less than 5 ps between channels based on the NI PXI broadband multichannel acquisition platform.

FBMC Receiver Design and Analysis for Medium and Large Scale Antenna Systems

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.

A Novel Synchronization Method in Terahertz Large‐Scale Antenna Array System

We focus on the problems of the accurate time delay estimation, the design of training pilots, and hybrid matrix optimization within the large-scale antenna array Terahertz (THz) broadband communication system. In contrast to the existing researches based on narrow-band arrays, we hereby shed light on the time delay estimation of broadband arrays. In THz broadband communication systems, the data symbol duration is relatively short when comparing with the dimension of the antenna array. In large-scale antenna systems, signals received in each antenna are no longer different phase-shifted copies of the same symbol, but completely different symbols in which occasion traditional narrow-band structure is no longer suitable. Based on the above conclusion, firstly, we put forward a system model based on large-scale antenna arrays and Time delay line (TDL) structure. Secondly, we deduce the Cramer-Rao lower bound (CRLB) of the time delay estimation, and present a time delay estimation algorithm that could reach the CRLB. Thirdly, by minimizing the CRLB, we address the design of the training pilot and optimized TDL structure under the condition of constant envelope training pilot and modulus TDL structure. Finally, we disclose the numerical simulation results. According to the simulation results, the aforementioned method is workable in reaching the CRLB, the TDL structure can significantly surpass that of the traditional model, and the optimal pilot design method outperforms the pseudo-random pilot structure.

Identification of active users for grant-free massive connectivity in large scale antenna systems

Conventional grant-based random access scheme is inappropriate to massive Internet of Things (IoT) connectivity since massive devices results in large number of collisions. This is unacceptable for the low latency requirement in 5 G and future networks. It is also not possible to assign orthogonal pilot sequences to all users to perform user activity detection (UAD) due to the massive number of devices and limited channel coherence time. In this paper, a novel grant-free (GF) UAD scheme is proposed with extremely low complexity and latency in an IoT network with a massive number of users. We exploit multiple antennas at the base station (BS) to produce spatial filtering by a fixed beamforming network (FBN), there then the inter-beam interference can be mitigated. Moreover, intra-beam interference is removed in temporal domain by orthogonal multiple access (OMA) technology. Joint UAD and multiuser detection (MUD) is realized by a bank of spatial-temporal matched filters at BS. The proposed method is efficient and the complexity is much less than the existing compressed sensing (CS)-based GF non-orthogonal multiple access (GFNOMA) algorithms. Performances of the proposed method is extensively analyzed in terms of the successful activity detection rate (SADR) as well as the Receiver operating characteristic (ROC) based on Neyman-Pearson (NP) decision rule. Numerical results demonstrate that it is comparable to the recently proposed iterative Maximum Likelihood (ML) algorithm, yet the computation load of the proposed scheme is extensively reduced.

Deep Learning Based Channel Extrapolation for Large-Scale Antenna Systems: Opportunities, Challenges and Solutions

With the depletion of spectrum, wireless communication systems turn to exploit large antenna arrays to achieve the degree of freedom in the space domain, such as millimeter wave massive multi-input multi-output (MIMO), reconfigurable intelligent surface assisted communications and cell-free massive MIMO. In these systems, how to acquire accurate channel state information is difficult and becomes a bottleneck of the communication links. In this article, we introduce the concept of channel extrapolation that relies on a small portion of channel parameters to infer the remaining channel parameters. Since the substance of channel extrapolation is a mapping from one parameter subspace to another, we can resort to deep learning (DL), a powerful learning architecture, to approximate such a mapping function. Specifically, we first analyze the requirements, conditions and challenges for channel extrapolation. Then, we present three typical extrapolations over the antenna dimension, the frequency dimension, and the physical terminal, respectively. We also illustrate their respective principles, design challenges and DL strategies. It will be seen that channel extrapolation could greatly reduce the transmission overhead and subsequently enhance the performance gains compared with the traditional strategies. In the end, we provide several potential research directions on channel extrapolation for future intelligent communication systems.

A New Optimization Technique in Massive MIMO and LSAS using Hybrid Architecture and Channel Estimation Algorithm for 5G Networks

Massive Multiple Input Multiple Output (m-MIMO) or Large Scale Antenna System (LSAS) is the latest version of cellular network technology with the purpose to send data from the base station to different users. The huge determined expansion in the mobile equipment and data rate requirement has created the rigid necessity for future wireless communication networks. The m-MIMO envisages a significant increase in the capacity but at the verge of excessive hardware complication. In this paper, we put forward a less complex precoding scheme based on the hybrid architecture to achieve a performance higher than the conventional baseband Minimum Mean Square Estimation (MMSE) precoding. In this paper, we propose a precoding schema namely amplitude and phase MMSE (APMMSE) to attain the performance of traditional MMSE precoding. The informed greedy best-first search has offered to estimate the baseband precoding matrix. It formulates the amplitude of the baseband precoder. Radio Frequency Precoder modifies the phase and provides an optimal signal-to-interference -noise ratio SINR and thus improves spectral efficiency of hybrid architecture.

Blind Diagnosis for Millimeter-Wave Large-Scale Antenna Systems

Millimeter-wave (mmWave) communication systems rely on large-scale antenna arrays to combat large path-loss at mmWave band. Due to hardware characteristics and deployment environments, mmWave large-scale antenna systems are vulnerable to antenna element blockages and failures, which necessitate diagnostic techniques to locate faulty antenna elements for calibration purposes. Current diagnostic techniques require full or partial knowledge of channel state information (CSI), which can be challenging to acquire in the presence of antenna failures. In this letter, we propose a blind diagnostic technique to identify faulty antenna elements in mmWave large-scale antenna systems, which does not require any CSI knowledge. By jointly exploiting the sparsity of mmWave channel and failure pattern, we first formulate the diagnosis problem as a joint sparse recovery problem. Then, the atomic norm is introduced to induce the sparsity of mmWave channel over continuous Fourier dictionary. An efficient algorithm based on alternating direction method of multipliers (ADMM) is proposed to solve the formulated problem. Finally, the performance of the proposed technique is evaluated through numerical simulations.

Mixed Near-Field and Far-Field Source Localization Based on Exact Spatial Propagation Geometry

The large-scale multiple-input-multiple-output (MIMO), also known as massive MIMO, is one of the key techniques for the fifth-generation (5 G) mobile communications. Due to large-scale antenna systems equipped at the basestations, the user-basestation distance in massive MIMO systems may be within the so-called Rayleigh distance. This would cause challenges in developing algorithms for user localization, because both near-field (NF) and far-field (FF) sources (users) may coexist. To solve this problem, most of existing algorithms are based on a simplified source-sensor spatial model, where the sensor-magnitude is assumed to be equal and the spatial phase is approximated by the Taylor polynomial. In contrast, a new algorithm based on the exact spatial geometry is developed, where no model simplification is made. The new algorithm is termed as MIxed Localization using the Exact model (MILE) in that it sets up a unified (non-approximation) model framework to the problem under consideration, and solves this problem in a mathematically quite simple manner. In fact, the MILE has the following three important advantages: (1) it is not restricted to exploit equally spaced arrays, (2) it can accommodate any arbitrary propagation loss, and (3) it does not suffer the model mismatch caused performance loss. All these advantages are not offered by current state-of-the-art techniques. The matlab codes for replication of the results in this study are available at: https://github.com/jinhesjtu/MILE.git .

Analysis of confidential large-scale antenna systems

The physical-layer (PHY) security has been increasingly studied as a rising approach to enhance the security of wireless systems in addition to the extensively applied cryptographic strategy. This manuscript considers the issue of PHY security behavior in the downlink of large scale arrays. The adopted scheme is based on a maximum ratio precoder for the data signal and artificial noise that can be forced in the null-space of the legitimate streaming links to upset the co-ordinated eavesdroppers. We focus on the quantitative investigation of the average secrecy capacity in terms of illegal users’ number, transmit antenna number, and allocated power fraction to the legal user. Also, we address the feature of the average secrecy capacity in case of channel knowledge imperfection in some detail. We notice that increasing illegal users’ number and deviation of channel information degrades the security performance while increasing Alice’s transmitting antennas can significantly compensate for this performance deterioration. Besides, power management between information and AN plays a vital role in the secrecy to be guaranteed with the aforementioned circumstances.

Interference Subspace Alignment-Based Precoding Design for Multi-Cell Multi-User Systems

Interference alignment (IA) in the spatial domain has been recognized as an important technique to handle the interference in dense network. However, traditional IA is flawed especially for large scale antenna systems. With challenges brought by hybrid precoding, IA cannot be perfectly achieved with limited radio frequency (RF) chains. In this article, we adopt the thought of interference subspace alignment (ISA) and provide full digital and hybrid precoding schemes for multi-cell multi-user systems under an interfering broadcast channel model. For full digital precoding, we derive the requirement for transmitting antennas to achieve ISA. A closed-form ISA solution space is further developed, where the interference subspace of each user is no longer constrained. Then, a projection-based ISA algorithm is proposed for full digital precoding. The projection from the selfish design to the ISA solution space strengthens the desired signal while achieving ISA. For hybrid precoding, we formulate a vector approximation problem to minimize interference misalignment. A novel generalized gradient projection algorithm is proposed to design each RF chain. To approximately achieve ISA, two algorithms are proposed with different approximation principles. For low SNR, a specific vector in the ISA solution space is approximated to combat additive noise, while for high SNR, the whole ISA solution space is approximated to suppress interference. The proposed schemes are further extended to imperfect CSI cases for more practical scenarios. Simulation results highlight that the proposed full digital and hybrid precoding schemes can effectively handle the inter-cell and intra-cell interference and outperform the existing schemes.

MUSIC-Based Pilot Decontamination and Channel Estimation in Multiuser Massive MIMO System

This paper addresses the problem of channel estimation in a multiuser multi-cell wireless communications system in which the base station (BS) is equipped with a very large number of antennas (also referred to as “massive multiple-input multiple-output (MIMO)”). We consider a time-division duplexing (TDD) scheme, in which reciprocity between the uplink and downlink channels can be assumed. Channel estimation is essential for downlink beamforming in massive MIMO, nevertheless, the pilot contamination effect hinders accurate channel estimation, which leads to overall performance degradation. Benefitted from the asymptotic orthogonality between signal and interference subspaces for non-overlapping angle-of arrivals (AOAs) in the large-scale antenna system, we propose a multiple signals classification (MUSIC) based channel estimation algorithm during the uplink transmission. Analytical and numerical results verify complete pilot decontamination and the effectiveness of the proposed channel estimation algorithm in the multiuser multi-cell massive MIMO system.

Constant Envelope MIMO-OFDM Precoding for Low Complexity Large-Scale Antenna Array Systems

Herein, we consider constant envelope precoding in a multiple-input multiple-output orthogonal frequency division multiplexing system (CE MIMO-OFDM) for frequency selective channels. In CE precoding the signals for each transmit antenna are designed to have constant amplitude regardless of the channel realization and the information symbols that must be conveyed to the users. This facilitates the use of power-efficient components, such as phase shifters (PS) and nonlinear power amplifiers, which are key for the feasibility of large-scale antenna array systems because of their low cost and power consumption. The CE precoding problem is firstly formulated as a least-squares problem with a unit modulus constraint and solved using an algorithm based on coordinate descent. The large number of optimization variables in the case of the MIMO-OFDM system motivates the search for a more computationally efficient solution. To tackle this, we reformulate the CE precoding design into an unconstrained nonlinear least-squares problem, which is solved efficiently using the Gauss-Newton algorithm. Simulation results underline the efficiency of the proposed solutions and show that they outperform state of the art techniques.

Energy Efficient Linear and Non-Linear Precoders for Massive MIMO Systems

The term Massive MIMO means, Massive multiple input multiple output also known as (large-scale antenna system, very large MIMO). Massive Multiple-Input-MultipleOutput (MIMO) is the major key technique for the future Fifth Generation (5G) of mobile wireless communication network due to its characteristics, elements and advantages. Massive MIMO will be comprised of five major elements; antennas, electronic components, network architectures, protocols and signal processing. We realize that precoding technique is a processing technique that utilizes Channel State Information Technique (CSIT) by operating on the signals before transmitting them. This technique varies base on the type of CSIT and performance criterion. Precoding technique is the last digital processing block at the transmitting side. In this paper, linear and non-linear Precoding technique was reviewed and we proposed two techniques under each that is Minimum Mean Square Error (MMSE), Block Diagonalization (BD), Tomlinson-Harashima (TH) and Dirty paper coding (DPC). Four Precoding techniques: MMSE, BD, DPC and TH were used in the studies to power consumption, energy efficiency and area throughput for single-cell and multi-cell scenarios. In comparing the proposed techniques, in terms of energy efficiency and area throughput, reuse factor (Reuse 4) performs better than other techniques when there is an imperfect CSI is used

Detecting active eavesdropper in large-scale antenna systems over Rician fading channels

Physical layer security is efficient in improving the security of transmission in wireless communications. It is effective in relaxing the complexity of the required cryptographic technologies. In this paper, we propose two detection schemes of an active eavesdropper in the physical layer of wireless communications. The system consists of a multi-antenna base station, a single-antenna legitimate user, and a single-antenna active eavesdropper. The large number of antennas at the base station is arranged as a planar array. It is assumed that the system is time division duplex (TDD) and the channels are of Rician distribution. The proposed two detection schemes are used to detect the eavesdropper by the base station and legitimate user, respectively. In both methods, the statistical characteristics of the received signals are analyzed and two test-statistics are constructed to detect the eavesdropper. The performances of the two methods are also analyzed. Simulation results show that both approaches are effective in detecting the active eavesdropper.

Oversampling Based Analog Beamforming for Initial Access With a Large Number of Receive Antennas

Beamforming can provide huge array gains in large-scale antenna systems under the condition that the channel state information (CSI) has been obtained by training signals. During the initial access, no CSI is available at the receiver because the communication link between the transmitter and the receiver has not been established. This paper considers the problem of obtaining the beamforming gain for receivers with the analog beamforming architecture in the initial access, assuming that a fixed beam pattern is employed. We present the necessary and sufficient condition to obtain the receive beamforming gain in the absence of CSI, and then derive the upper bound of the receive beamforming gain for different antenna spacings. It is shown that oversampling in space is needed at the receiver to obtain a non-trivial beamforming gain without CSI, since the total received signal power is proportional to the number of receive antennas. We also design the beamformer that can achieve the upper bound. Numerical results demonstrate the effectiveness of the proposed beamformer in the scenarios of downlink synchronization and random access in millimeter-wave systems.

Large-Scale Antenna Systems and Massive Machine Type Communications

In this paper, we identify issues and possible solutions in the key area of large-scale antenna systems, also know as Massive Multiple Input Multiple Output (MIMO) systems. Additionally, we propose the use of Massive MIMO technology as a means to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), the capacity of the MTC network and, in turn, that of the whole system, can be increased by grouping Machine-Type Communication (MTC) devices into clusters and letting each cluster share the same time-frequency physical resource blocks. We study the feasibility of applying sub-optimal linear detection to the problem of detecting a large number of MTC devices sharing the same time-frequency resources at the uplink of a base station (BS) equipped with a large number of antennas, M. In our study, we derive the achievable lower-bound rates for the studied sub-optimal linear detectors and show that the transmitted power of each MTC device can be reduced as M increases, which is a very important result for power-constrained MTC devices running on batteries. Our simulation results suggest that, as M is made progressively larger, the performance of sub-optimal linear detection methods approach the matched filter bound, also known as perfect interference-cancellation bound.