Massive Multiple-input And Multiple-output Research Articles

Noise analysis and massive MIMO modelling in VLC for 5G networks using EKF with SCFDM

Visible Light Communications (VLC) is the type of communication, which processes high-speed data transmission using the visible Light Emitting Diodes (LED). The VLC acts as an important supplementary that is used to define the hotspots for heterogeneous networks and plays an important role for 5G networks in wireless communications. However, performance of visible light systems is affected by various noises and Allan variance is used to analyze such noises in 5G networks. The Massive Multiple-Input and Multiple-Output (M-MIMO) technique is used for noise modeling which utilizes the mitigation circuit to find whether the noise is white noise, shot noise, random walk noises or typical noises. The existing Kalman Filter approach failed to attain the required bandwidth and higher spectral efficiency. Therefore, to achieve high data rates, the spectral efficient technologies such as Single Carrier Frequency Division Multiplexing (SCFDM) is performed in the research. The Allan Variance is utilized for analyzing the time-series that extracts the noise features of the data and the major noise is verified and considered by the M-MIMO technique. The present research uses the Extended Kalman Filter (EKF) which determines the observation models and the state transition that does not need linear functions to define the states. The proposed SCFDM was constructed based on the VLC for 5G networks that analyzes in terms of Bit Error Rate (BER) and Signal to Noise Ratio (SNR). The proposed SCFDM obtains a high SNR of 14% for the channels with white LED option when compared to the existing methods.

An iterative detector based on sparse bayesian error recovery for uplink large-scale MIMO systems

In uplink large-scale multiple-input and multiple-output (MIMO) systems, the data detection at the receiver is the major challenge due to the considerable increase in dimensions of MIMO systems. In this paper, a Bayesian strategy is investigated for MIMO systems. The main idea of this algorithm is to improve the performance of a detector by finding the incorrectly detected symbols relying on the sparse property of the estimation error. In the proposed method, the conventional massive MIMO model is converted into a sparse model by inducing a sparse constrain on the symbol error vector obtained from a linear detector (minimum mean square error or zero forcing detectors). Then, by recovering the non-zero entries of error (i.e. the incorrectly detected symbols) via the sparse Bayesian approach, the primary estimate of the information vector is corrected. Numerical results reveal that exploiting the sparse characteristic of the estimation error leads to improvement in detection performance and the proposed Bayesian approach achieves better results than conventional and previous state of the art error recovery methods. Meanwhile, computational complexity of the sparse Bayesian error recovery (SBER) algorithm is the same as that of the linear detectors.

A Scalable and Energy-Efficient IoT System Supported by Cell-Free Massive MIMO

An Internet-of-Things (IoT) system supports a massive number of IoT devices wirelessly. We show how to use cell-free (CF) massive multiple input and multiple output (MIMO) to provide a scalable and energy-efficient IoT system. We employ optimal linear estimation with random pilots to acquire channel state information (CSI) for MIMO precoding and decoding. In the uplink (UL), we employ optimal linear decoder and utilize random matrix (RM) theory to obtain two accurate signal-to-interference plus noise ratio (SINR) approximations involving only large-scale fading coefficients. We derive several max–min type power control algorithms based on both exact SINR expression and RM approximations. Next we consider the power control problem for downlink (DL) transmission. To avoid solving a time-consuming quasiconcave problem that requires repeat tests for the feasibility of a second-order cone programming (SOCP) problem, we develop a neural network (NN) aided power control algorithm that results in 30 times reduction in computation time. This power control algorithm leads to scalable CF Massive MIMO networks in which the amount of computations conducted by each access point (AP) does not depend on the number of network APs. Both UL and DL power control algorithms allow visibly improve the system spectral efficiency (SE) and, more importantly, lead to multifold improvements in energy efficiency (EE), which is crucial for IoT networks.

Performance Analysis of Massive MIMO Relay Systems With Variable-Resolution ADCs/DACs Over Spatially Correlated Channels

The use of low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) is recognized as a practical technique to reduce power consumption and hardware cost for massive multiple-input and multiple-output (MIMO) systems. In this context, by leveraging on the additive quantization noise model (AQNM), this paper investigates the performance of massive MIMO relay systems with a variable-resolution ADC/DAC-based architecture, where each ADC and DAC use arbitrary bits for quantization. To be specific, we first derive the closed-form expression of achievable rate over spatially correlated Rayleigh fading channels by using perfect channel state information (CSI), maximal ratio combining (MRC), and maximal ratio transmission (MRT). Afterwards, we extend the work to the case of imperfect CSI. The analytical expressions reveal some insights of key parameters, i.e., spatial channel correlation, large-scale fading coefficients, estimate errors, and the resolution of ADCs/DACs. Finally, simulation results validate our theoretical analyses and confirm that the achievable rate reduces as the spatial correlation rises. Besides, we provide the condition under which coarse DACs (ADCs) and receive spatial correlation (transmit spatial correlation) dominate the loss of achievable rate. Moreover, provided that the total number of quantization bits is constant, we find that the achievable rate is optimal over spatially correlated channels when all ADC and DAC adopt the same quantization bits from the perspective of statistic.

A Lower Bound for the Coherence Block Length in Mobile Radio Channels

A lower bound for the coherence block (ChB) length in mobile radio channels is derived in this paper. The ChB length, associated with a certain mobile radio channel, is of great practical importance in future wireless systems, mainly those based on massive multiple input and multiple output (M-MIMO) technology. In fact, it is one of the factors that determines the achievable spectral efficiency. Firstly, theoretical aspects regarding the mobile radio channels are summarized, focusing on the rigorous definition of coherence bandwidth (BC) and coherence time (TC) parameters. Secondly, the uncertainty relations developed by B. H. Fleury, involving both BC and TC, are presented. Afterwards, a lower bound for the product BCTC is derived, i.e., the ChB length. The obtained bound is an explicit function of easily measurable parameters, such as the delay spread, mobile speed and carrier frequency. Furthermore, and especially important, this bound is also a function of the degree of coherence with which we define both BC and TC. Finally, an application example that illustrates the practical possibilities of the bound obtained is presented. As a further conclusion, the need to determine what degree of correlation is required to consider mobile channels as effectively flat-fading and stationary is highlighted.

Combined Deep Learning and SOR Detection Technique for High Reliability in Massive MIMO Systems

In this paper, a novel iterative detection technique that combines deep learning (DL) and the approximated algorithm of successive over relaxation (SOR) is proposed to achieve high reliability and reduce the computational complexity. Recently, as the demanded data rates increase, the massive multiple-input and multiple-output (MIMO) system has drawn attention in wireless communication. In massive MIMO, the implementation of traditional detectors for high reliability has become impractical, and the reduction for the complexity of detectors has emerged as a practical implementation challenge. The existing DL-based detection technique of orthogonal approximate message passing network (OAMPNet) can provide high detection performance. However, the computational complexity is too high for the implementation in massive MIMO systems. The proposed detection technique uses SOR algorithm to reduce the computational complexity, and the relaxation parameter of SOR is adaptively determined by a learning algorithm. A non-linear estimator using the DL algorithm is combined with the SOR algorithm to achieve high reliability, and regardless of the size of the MIMO system, only the size of the DL architecture determines the complexity of the non-linear estimator. Simulation results show that the proposed detector outperforms the conventional linear detector based on minimum mean square error (MMSE) and achieves high reliability with lower complexity than OAMPNet in various channel environments with spatial correlation.

Indoor Direct Positioning With Imperfect Massive MIMO Array Using Measured Near-Field Channels

In this article, we focus on indoor direct positioning using a practical massive multiple-input and multiple-output (MIMO) array with imperfections. First, the imperfect array needs to be calibrated in an anechoic chamber from the view of array signal processing. This brings inconveniences due to the large size of massive MIMO systems. To solve this problem, we propose onsite calibration, where a banded calibration matrix, calibrating both phase-gain error and mutual coupling, can be estimated by performing least-squares on the measure near-field channels, i.e., the site survey data. The feasibility of this calibration method can be explained by that the non-line-of-sight (NLOS) channel is able to be modeled using Rayleigh fading; i.e., the sum of the multipath components can be treated as Gaussian noise. Then, to estimate the user position, we propose a new two-stage direct positioning scheme. In the first stage, the coarse position is obtained via global search-based conventional beamforming (CBF) using a global calibration matrix. In the second stage, fine positioning is achieved via local search-based multiple signal classification (MUSIC) using a local calibration matrix. The proposed method is computationally efficient, insensitive to model order selection, free of dense site survey, and robust to partial line-of-sight (LOS) blocking. Finally, the proposed method is verified by practical measurements from a distributed array (DA), uniform linear array (ULA), and uniform rectangular array (URA), and compared with a deep learning positioning-method. The results show that the DA with the proposed method performs the best, achieving sub-centimeter accuracy (typically below 5 mm).

Performance Investigation of NOMA Versus OMA Techniques for mmWave Massive MIMO Communications

The fifth-generation (5G) of cellular technology is currently being deployed over the world. In the next decade of mobile networks, beyond 5G (B5G) cellular networks with the under-development advanced technology enablers are expected to be a fully developed system that could offer tremendous opportunities for both enterprises and society at large. B5G in more ambitious scenarios will be capable to facilitate much-improved performance with the significant upgrade of the key parameters such as massive connectivity, ultra-reliable and low latency (URLL), spectral efficiency (SE) and energy efficiency (EE). Equipping non-orthogonal multiple access (NOMA) with other key drivers will help to explore systems’ applicability to cover a wide variety of applications to forge a path for future networks. NOMA empowers the networks with seamless connectivity and can provide a secure transmission strategy for the industrial internet of things (IIoT) anywhere and anytime. Despite being a promising candidate for B5G networks a comprehensive study that covers operating principles, fundamental features and technological feasibility of NOMA at mmWave massive MIMO communications is not available. To address this, a simulation-based comparative study between NOMA and orthogonal multiple access (OMA) techniques for mmWave massive multiple-input and multiple-output (MIMO) communications is presented with performance discussions and identifying technology gaps. Throughout the paper, aspects of operating principles, fundamental features and technological feasibility of NOMA are discussed. Also, it is demonstrated that NOMA not only has good adaptability but also can outperform other OMA techniques for mmWave massive MIMO communications. Some foreseeable challenges and future directions on applying NOMA to B5G networks are also provided

Physical Layer Spoofing Attack Detection in MmWave Massive MIMO 5G Networks

Identity spoofing attacks pose one of the most serious threats to wireless networks, where the attacker can masquerade as legitimate users by modifying its own identity. Channel-based physical-layer security is a promising technology to counter identity spoofing attacks. Although various channel-based security technologies have been proposed, the study of channel-based spoofing attack detection in 5G networks is largely open. This paper introduces a new channel-based spoofing attack detection scheme based on channel virtual (or called beamspace) representation in millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) 5G networks. The principal components of channel virtual representation (PC-CVR) are extracted as a new channel feature. Compared with traditional channel features, the proposed features can be more sensitive to the location of transmitters and more suitable to mmWave 5G networks. Based on PC-CVR, we offer two detection strategies to achieve the spoofing attack detection tackling static and dynamic radio environments, respectively. For the static radio environment where the channel correlation is stable, Neyman-Pearson (NP) testing-based spoofing attack detection is provided depending on the ${\ell _{2}}$ -norm of PC-CVR. For the dynamic radio environment where the channel correlation is changing, the problem of spoofing attack detection is transformed into a one-class classification problem. To efficiently handle this problem, an online detection framework based on a feedforward neural network with a single hidden layer is presented. Simulation results evaluate and confirm the effectiveness of the proposed detection schemes. For the static radio environment, the detection rate can be improved around 25% with the help of PC-CVR under the NP testing-based detection, and the detection accuracy can reach 99% with the machine learning-based scheme under the dynamic radio environment.

Grouping‐Based Channel Estimation and Tracking for Millimeter Wave Massive MIMO Systems

Although the millimeter wave (mmWave) massive multiple‐input and multiple‐output (MIMO) system can potentially boost the network capacity for future communications, the pilot overhead of the system in practice will greatly increase, which causes a significant decrease in system performance. In this paper, we propose a novel grouping‐based channel estimation and tracking approach to reduce the pilot overhead and computational complexity while improving the estimation accuracy. Specifically, we design a low‐complexity iterative channel estimation and tracking algorithm by fully exploiting the sparsity of mmWave massive MIMO channels, where the signal eigenvectors are estimated and tracked based on the received signals at the base station (BS). With the recovered signal eigenvectors, the celebrated multiple‐signal classification (MUSIC) algorithm can be employed to estimate the direction of arrival (DoA) angles and the path amplitude for the user terminals (UTs). To improve the estimation accuracy and accelerate the tracking speed, we develop a closed‐form solution for updating the step‐size in the proposed iterative algorithm. Furthermore, a grouping method is proposed to reduce the number of sharing pilots in the scenario of multiple UTs to shorten the pilot overhead. The computational complexity of the proposed algorithm is analyzed. Simulation results are provided to verify the effectiveness of the proposed schemes in terms of the estimation accuracy, tracking speed, and overhead reduction.

Mean‐based reciprocity calibration in TDD massive MIMO system

Non-reciprocity limits the potential of time division duplexing (TDD) massive multiple-input and multiple-output (MIMO) systems. Due to the reciprocal channel, there is no need for downlink (DL) pilot transmission implies computational complexity reduction and saving precious time resources. In this study, the DL channel matrix is estimated using the uplink channel matrix by a novel reciprocity calibration technique. Non-reciprocity arises due to the hardware mismatch (HM) across the base station (BS) and the user terminal (UT)/both. Its detrimental effect causes amplitude and phase impairment in the downlink signal transmission. Since a massive MIMO network has a large number of antenna elements across the BS, hence the instantaneous HM coefficient cannot be determined accurately. For solving such a problem, the authors use the statistical response of the hardware unit. Considering a complex Gaussian hardware response across each antenna terminal, the probability density function (PDF) of amplitude and phase mismatch is derived. They also derive the joint PDF of amplitude and phase mismatch, considering the HM. Simulation results address the sensitivity and complexity for evaluating the sum-rate capacity under three linear precoders (matched filter, regularized zero-forcing, and zero-forcing), which validate their effectiveness in this scenario.

Pilot Length and Channel Estimation for Massive MIMO IoT Systems

Massive capacity gain in Massive MIMO (multiple-input and multiple-output) depends on uplink pilots sent by the users to provide up-to-date CSI (channel state information) for closed-loop precision beamforming. To support a huge number of IoT (Internet of Things) devices, a set of orthogonal pilots with possible reuse or random non-orthogonal pilots can be assigned to IoT devices for this purpose. We investigate the performance differences between a simple channel estimation and LMMSE (linear minimum mean square error) channel estimation, combined with orthogonal pilot reuse and random pilot schemes, and the impact of pilot length. We develop simple algorithms to estimate optimal pilot length that can support the most IoT devices simultaneously.

WITHDRAWN: News Development in the 5G Network Era based on Machine Learning and FPGA

ABSTRACT The telecommunications have seen the evolution of the revolution and the three wireless mobile communication standard of technology, namely 3G to 4G and 4G to 5G, General Packet Radio Service (GPRS) to 3G. 5G, the high reliability and high bandwidth (up to 10 Gbps), mobile phone, display, and provides a high-speed downloading and support between devices such as smart homes and smart cars connection. In this previous work to conceivable re-arrangement, Field-Programmable Gate Array (FPGA) is and streams toward the quick ongoing information rates to confirm silicon configuration is developing increasingly more famous in the plan. FPGA additionally works in the rapid as a stage for programming improvement in the pre-silicon climate. The proposed system an FPGA usage of a high-throughput Massive Multiple Input and Multiple Output (MIMO) calculation dependent on the elevated level union. The upcoming network of correspondence, past the current Internet, intend to all Internet exercises. 5G, the postponement of the best compromise in the pioneer of this key, and afterward discharge the expense to a bigger limit and association of the gadget billion eco-framework. The adaptability of the FPGA is, an assortment of circumstances, and you can program an assortment of uses on it. It will have the option to change the information transmission capacity of exchanging between the administrations as per the capacity of the FPGA to reconfigure itself powerfully to the current guidelines. The capacity of the entirety of this FPGA is, driving the main FPGA producers their equipment to manage correspondence advances keep on expanding to put a great deal of cash so as to make it more conceivable.

Physical Layer Authentication for 5G Communications: Opportunities and Road Ahead

Resorting to the exploitation of physical attributes, physical-layer authentication (PLA) is a promising technology to supplement and enhance current cryptography-based security mechanisms in wireless communications. in the fifth-generation (5G) communications, many disruptive technologies spring up, such as millimeter-wave communication (mmWave), massive multiple-input and multiple-output (MiMO) and non-orthogonal- multiple-access (NOMA). PLA schemes in 5G networks are facing challenges while exposed to opportunities at the same time. This article seeks to identify the critical technology gaps as well as the feasible enablers in terms of the unique characteristics of 5G networks. The investigation consists of four hierarchical parts. in the first section, existing PLA schemes are reviewed, and the corresponding challenges are discussed in the next section. in the third section, we investigate potential enablers based on the unique characteristics of 5G networks and provide three corresponding PLA case studies. Open problems and research directions on PLA for 5G and beyond are discussed in the last section, including waveform design, feature learning, mobile users, and terahertz communications.

The Configurable Hybrid Precoding Processor for Bit-Stream-Based mmWave MIMO Systems

Beamforming technology plays an essential role in the promising millimeter wave (mmWave) massive multiple-input and multiple-output (MIMO) communications for fifth generation (5G) new radio system. Specifically, hybrid analog beamforming and digital precoding scheme can be employed to reduce the excessive radio frequency (RF) chains and data converters in the massive MIMO transceiver while still maintaining optimal spectral efficiency. However, traditional hybrid precoding architecture cannot be configured to support different system specifications, such as the number of bit streams or transmit antennas, leading to the limitation to hardware flexibility and efficiency. This article presents a configurable and low-complexity hybrid precoder based on the parallel data-stream processing in respect of system, algorithm, and architecture. The proposed algorithm was designed to avoid signal dependence between data streams so as to realize configurable precoding architecture. The performance and complexity of the proposed algorithm were also simulated and analyzed in detail in this article. Moreover, the hybrid precoding processor chip was designed and implemented based on the proposed algorithm. A novel data-processing flow was designed in the precoder so as to increase the hardware efficiency and configurability. The designed hybrid precoding chip can be configured to support one to four data streams for 16 × 16 mmWave MIMO systems. The designed precoder was implemented by using TSMC 40-nm CMOS technology. The normalized throughput achieved 11.1 M channel-matrices per second at a maximum clock frequency of 300 MHz. The area complexity is 263.5 kGE, and power consumption is 119.1 mW.

Arbitrary Near-Field Spatial Signal Processing

Electromagnetic waves have been widely exploited as signal, carrier, and power for wireless and wireline systems over the electromagnetic spectrum. As the classical approach, parametric interactions among localized or discrete signal processing devices are usually accomplished by electromagnetic fields or waves guided in conductor and/or dielectric bounded structures. These structures feature specific circuit functions in an electronic or photonic manner. Such electromagnetic circuits now face a number of application issues, such as geometrical complexity, power handling capability, and flexible reconfigurability, when they are used in certain large-scale electromagnetic systems, like in a massive multiple-input and multiple-output (MIMO). In this work, we propose and present an unconventional approach to realize a desired interaction or operation among devices. Such an interaction relies on wave or field interferences induced by a set of coherent antenna array over their near-field region where signal processing can be accomplished spatially. Fundamental functions and operation mechanisms of the proposed spatial signal processing approach are discussed and demonstrated in this work. It is worth mentioning that the proposed approach is universally applicable to all physical wave devices and systems, such as acoustic and electromagnetic waves.

Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach

Hybrid beamforming (HB) has emerged as a promising technology to support ultra high transmission capacity and with low complexity for Millimeter Wave (mmWave) multiple-input and multiple-output (MIMO) system. However, the design of digital and analog beamformer is a challenge task with non-convex optimization, especially for the multi-user scenario. Recently, the blooming of deep learning research provides a new vision for the signal processing of communication system. In this work, we propose a deep neural network based HB for the multi-User mmWave massive MIMO system, referred as DNHB. The HB system is formulated as an autoencoder neural network, which is trained in a style of end-to-end self-supervised learning. With the strong representation capability of deep neural network, the proposed DNHB exhibits superior performance than the traditional linear processing methods. According to the simulation results, DNHB outperforms about 2 dB in terms of bit error rate (BER) performance compared with existing methods.

Enhanced development of communication between the network and the end user by eliminating the interference signals inMIMOchannel

AbstractThough the key technology for 5G communication is the massive Multiple input and multiple output (MIMO), it has a major drawback when the user generated inference signals are to be handled by several other users. When these interference signals are used in various users, issues concerning system ability, power management issues and QoS related issues arises with the involvement of MIMO channel. Though internet of things (IoT) making use of MIMO is still under development, the requirements of the IoT is quite different when compared to several other connections. Thus, taking into account the demands, the solutions were framed with the help of the proposed system. One such solution is the Partition Square and Cross‐Processing method that can possess several antennas at the given destination. Based on this solution obtained the data rate can be drastically increased correspondingly reducing the power consumed thus producing the most desirable result with high signal to noise ratio (SNR). Best victory is another such solution that solves the issues relating to power reduction thereby providing the best solution with minimum SNR. These solutions produced by the proposed system still has some disadvantages with respect to the following case: when the number of destination antennas is less than the number of source antennas. This case is considered as most common in the model proposed. For the above‐mentioned case, near optimal solution is produced by both the algorithms. In this article, the transmitters are represented by the source antenna and the receiver is represented by the destination antenna. Thus, with the help of IoT connectivity, major benefits can be bought to the massive MIMO channel.

Channel Estimation in Massive MIMO Systems Based on Generalized Block Adaptive Matching Pursuit Algorithm

Frequency division duplex (FDD) downlink massive multiple-input and multiple-output (MIMO) systems encounter difficulty in acquiring the sparsity and threshold parameter. As a remedy, a time-frequency block sparse channel estimation method based on compressed sensing (CS) is proposed that consists of the generalized block adaptive matching pursuit (gBAMP) algorithm. By using the time-frequency joint block sparsity of massive MIMO systems, the index sets selected in the iterative process of the algorithm are further optimized to improve the stability of the algorithm. Then, in the absence of the threshold parameter, the adaptive iterative stop condition is determined using the residual based on the Frobenius norm (F-norm), and the effectiveness of the method is proved. Simulations show that the algorithm can quickly and accurately estimate the massive MIMO channel information and outperforms its counterparts.

Generalized Space Time Block Coded Spatial Modulation for Open-Loop Massive MIMO Downlink Communication Systems

In this paper, we propose a generalized space-time block coded spatial modulation (GSTBC-SM) scheme for open-loop massive multiple-input and multiple-output (MIMO) downlink communication systems. Specifically, we firstly partition the information bits into multiple groups with each group modulated by the spatial modulation (SM), where the SM symbols are invoked for orthogonal STBC (OSTBC) and quasi-orthogonal STBC (Q-OSTBC) structures. Then, message passing (MP) and block minimum mean square equalization (B-MMSE) detectors are designed for our GSTBC-SM systems, to achieve near-optimal performance with significantly reduced complexity in massive MIMO configurations. Finally, we derive the theoretical average bit error probability (ABEP) of the proposed scheme. The main contribution is that the propose scheme achieves high transmission rate and diversity gain even with small number of radio frequency (RF) chains at the transmitter. Simulation results verify the theoretical derivations and show that the proposed GSTBC-SM scheme provides near 20 dB gain over the conventional GSTBC scheme under massive MIMO configurations.