Massive Mimo

Abstract—In this paper, an equivalent ‘virtual ’ SIMO (Single Input Multiple Output) radar representation of the MIMO radar system is established which enables the analysis of the full MIMO system geometry. Analytical expressions are derived to describe the key geometrical properties of the virtual array’s manifold, which fully characterises the entire transmit-receive system geometry. A new family of subarrayed collinear MIMO array structures is presented for obtaining uniform linear virtual arrays. It is shown that this reduces the required radar platform size by up to a half, compared to popular existing methods, without reducing virtual array aperture.

In this paper, a novel approach for physical layer security is implemented using massive cylindrical antenna arrays. Part of the arrays are used for transmitting a signal reliably from source to destination using highly directive beams. At the same time, the remaining part can be used to mitigate eavesdropping by transmitting a jamming signal through another directive beam towards the eavesdropper. In addition, cylindrical arrays can mitigate the impact of malicious jamming by placing a null in the array pattern at the destination in the direction of the jammer. Results show that the proposed approach can lead to good jamming mitigation and high secrecy capacity in both 2D and 3D, even when there are errors in determining the location of the eavesdropper.

To achieve the best performance in terms of accuracy and complexity of massive multiple-input multiple-output (Massive MIMO) in wireless communication systems, hybrid beamforming (HBF) is a promising technique that provides high data rate multiplexing gains and enhances the spectral efficiency (SE) of the system. In this paper, a novel received signal strength indicator (RSSI) method is proposed to design an HBF for Massive MIMO BF via multitasking deep learning (DL) that minimizes the reliance on the channel state information (CSI) feedback. The trade-off between the enhancement SE of the system and the deep neural networks (DNNs) performance is optimized, and the results reveal that the proposed novel DL techniques achieve predicted spectral efficiencies with accuracy of 99.23% and 95.64% for Deep-HBF and Deep-AFP, respectively. The processing times for Deep-HBF and Deep-AFP are 709.2914 sec and 1425.864 sec, respectively. Notably, Deep-AFP exhibits a higher range of computational complexity compared to Deep-HBF. It is worth mentioning that the proposed techniques utilize the same DNN architecture.

As 4G technology served as a foundation, the emergence of 5G is now underway, ushering in a new era of connectivity. With the growing demand for seamless internet experiences, particularly in the face of escalating internet subscribers, the networking domain must evolve. Developing nations strive to align with this dynamic landscape, necessitating an upgrade that integrates internet of things (IoT), natural language processing (NLP), and artificial intelligence (AI) with network infrastructure. By enhancing networking sys teams with lower latency and improved scalability, 5G addresses congestion issues. This is achieved by coupling mm-wave technology with the 5G framework through the 3rd Generation Partnership Project, significantly amplifying channel bandwidth. However, the comprehensive analysis underscores challenges in 5G implementation, encompass ing aspects like distance, orientation, non-line-of-sight conditions, protocol utilization, and server positioning. Drawing from a dataset provided by the University of Minnesota that illuminates the limitations of 5G implementation in select US cities, this paper extends its focus to densely populated regions in developing countries of the sub-continent. Employ ing a machine learning approach, the paper delves into the constraints of 5G deployment in such areas.

In this article, we show that Pilot contamination problem can be seen as a source separation problem using time, frequency, and space domains. Our method capitalizes on a nonunitary joint diagonalization of spatial quadratic time-frequency (STFD) signal representation to identify the desired and interfering users. We first compute the noise subspace from the STFD matrices selected appropriately. Secondly, we use the noise subspace obtained to estimate the Elevation (El) and the Azimuth (Az) angles for which the MUSIC cost function is maximized. Numerical simulations are provided to illustrate the effectiveness and the behavior of the proposed approach.

This article provides an overview of the fifth generation of cellular communications (5G) and beyond. It presents the transmission techniques of current 5G communications and those expected of future developments, namely a brief study of non-orthogonal multiple access (NOMA) using the single carrier with frequency domain equalization (SC-FDE) block transmission technique, evidencing its added value in terms of spectral efficiency. An introduction to the sixth generation of cellular communications (6G) is also provided. The insertion of 5G and 6G within the Fourth Industrial Revolution framework (also known as Industry 4.0) is also dealt with. Consisting of a change in paradigm, when compared to previous generations, 5G supports a myriad of new services based on the Internet of things (IoT) and on vehicle-to-vehicle (V2V) communications, supporting technologies such as autonomous driving, smart cities, and remote surgery. The new services provided by 5G are supported by new techniques, such as millimeter waves (mm-wave), in addition to traditional microwave communication, and by massive multiple-input multiple-output (m-MIMO) technology. These techniques were not employed in the fourth generation of cellular communications (4G). While 5G plays an important role in the initial implementation of the Fourth Industrial Revolution, 6G will address a number of new services such as virtual reality (VR), augmented reality (AR), holographic services, the advanced Internet of things (IoT), AI-infused applications, wireless brain-computer interaction (BCI), and mobility at higher speeds. The current research on systems beyond 5G indicates that these applications shall be supported by new MIMO techniques and make use of terahertz (THz) bands.

By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can greatly increase spectral and energy efficiency over traditional MIMO systems. However, increasing the number of antennas at the base station (BS) makes the uplink joint channel estimation and data detection (JED) challenging in massive MIMO systems. In this paper, we consider the JED problem for massive SIMO (single input multiple output) wireless systems, which is a special case of wireless systems with large antenna arrays. We propose exact Generalized Likelihood Ratio Test (GLRT) optimal JED algorithms with low expected complexity, for both constantmodulus and nonconstant-modulus constellations. We show that, despite the large number of unknown channel coefficients, the expected computational complexity of these algorithms is polynomial in channel coherence time (T) and the number of receive antennas (N), even when the number of receive antennas grows polynomially in the channel coherence time (N = O(T 11) suffices to guarantee an expected computational complexity cubic in T and linear in N). Simulation results show that the GLRT-optimal JED algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity.

Massive MIMO will improve the performance of future 5G systems in terms of data rate and spectral efficiency, while accommodating a large number of users. Furthermore, it allows for 3D beamforming in order to provide more degrees of freedom and increase the number of high-throughput users. Massive MIMO is expected to provide more advantages compared to other solutions in terms of energy and spectral efficiency. This will be achieved by focusing the radiation towards the direction of the intended users, thus implementing simultaneous transmission to many users while keeping interference low. It can boost the capacity compared to a conventional antenna solution, resulting in a spectral efficiency up to 50 times greater than that provided by actual 4G technology. However, to take full advantage of this technology and to overcome the challenges of implementation in a real environment, a complicated radio system is required. The purpose of this work is to present the MIMO technology evol...

This paper proposes a Bayesian approach for angle-based hybrid beamforming and tracking that is robust to uncertain or erroneous direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Because the resolution of the phase shifters is finite and typically adjustable through a digital control, the DOA can be modeled as a discrete random variable with a prior distribution defined over a discrete set of candidate DOAs, and the variance of this distribution can be introduced to describe the level of uncertainty. The estimation problem of DOA is thereby formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. To alleviate the computational complexity and cost, we present a motion trajectory-constrained a priori probability approximation method. It suggests that within a specific spatial region, a directional estimate can be close to true DOA with a high probability and sufficient to ensure trustworthiness. We show that the proposed approach has the advantage of robustness to uncertain DOA, and the beam tracking problem can be solved by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art benchmarks.

Energy efficient spatial modulation-aided uplink and downlink designs for future millimeterwave (mmWave) large-scale multiple-input multipleoutput (MIMO) systems are considered. Two novel uplink transceivers are proposed with the aim of considerably reducing the energy consumption at the user terminal, while achieving high spectral efficiency both in uplink and downlink transmissions. System performance is investigated using both stochastic and deterministic channels emulating real world urban scenarios.

Recent developments in engineering and computer sciences have heightened the need for digital image enhancement. Most of the previously reported works, however, focused on image enhancement using classical methods like mathematical transformations and spatial and frequency-domain methods. Hence, recently, there has been an increasing interest in using nature-inspired optimization techniques for image processing purposes. The water cycle algorithm (WCA) is one of the nature-inspired algorithms (NIAs) that have gotten much attention in optimizing real-world engineering problems due to its appealing performance. However, to the best of the author's knowledge, little research has been undertaken on the WCA's imageenhancing capacity. Thus, this work is intended to offer a modified histogram equalization (HE) approach using WCA to enhance the contrast of an image and maintain its brightness. Besides, the proposed WCA-based image enhancement technique was compared to linear contrast stretching (LCS), HE and its modified versions, particle swarm optimization (PSO), and accelerated particle swarm optimization (APSO). In addition to the objective function fitness, 11 full reference (FR) image quality assessment (IQA) metrics were employed to evaluate image quality and compare performance. Experimental results showed that the suggested image enhancement technique exhibited better performance than others in enhancing dark grayscale images in terms of objective function fitness and perceptual visual IQA metrics like multi-scale structural similarity (MS-SSIM), information-weighted structural similarity (IW-SSIM), information-weighted mean squared error (IW-MSE), and information-weighted peak signal-to-noise ratio (IW-PSNR). The proposed method also demonstrated a faster convergence time to an optimum solution. INDEX TERMS Grayscale image, histogram equalization, image edge detection, image enhancement, image quality assessment metrics, nature-inspired algorithms, water cycle algorithm.

In this paper, we study physical layer security for the downlink of cellular networks, where the confidential messages transmitted to each mobile user can be eavesdropped by both (i) the other users in the same cell and (ii) the users in the other cells. The locations of base stations and mobile users are modeled as two independent two-dimensional Poisson point processes. Using the proposed model, we analyze the secrecy rates achievable by regularized channel inversion (RCI) precoding by performing a large-system analysis that combines tools from stochastic geometry and random matrix theory. We obtain approximations for the probability of secrecy outage and the mean secrecy rate, and characterize regimes where RCI precoding achieves a non-zero secrecy rate. We find that unlike isolated cells, the secrecy rate in a cellular network does not grow monotonically with the transmit power, and the network tends to be in secrecy outage if the transmit power grows unbounded. Furthermore, we ...

The goal of next-generation wireless systems is to support many users by achieving higher data rates and reduced latency. Multiple input and multiple output systems (MIMO) are utilized in order to achieve high data rates. Multiple antennas are employed by Massive MIMO systems in both the transmitter along with the receiver. A signal processing method known as beamforming is used on several transmitting and receiving stations in order to deliver and receive multiple messages at once. To increase spectral efficiency, hybrid beamforming using a uniform rectangular antenna array is used in this research work. The results of hybrid beamforming using different numbers of antennas are compared with those of fully digital beamforming. A comparison between signal-to-noise (SNR) and bit error rate (BER) is performed. Comparing hybrid beamforming to fully digital beamforming, simulation findings show that hybrid beamforming reduces computing complexity due to reduced number of RF links. Also observed that the spectral efficiency rises with an increase in the quantity of transmitting antennas.

The massive MIMO systems are the more popular field in the present era for the 5G wireless communication system. The MIMO system is a demanding research topic for the last four decades. This topic is under implementation and observation from the last few years. These systems have many advantages and many research sub-areas but this paper investigates the modified model of the massive MIMO receiver system. The traditional receiver system model of massive MIMO system reduces the channel noise using a linear filter in the receive combiner bank (RCB) but the proposed model removes the channel noise before the RCB using an adaptive filter bank (AFB). The AFB is the combination of LMS adaptive filters. The analysis parameters are channel noise, signal-to-noise ratio (SNR) and bit-error-rate (BER) using the hybrid precoder and combiner computation algorithms – Quantized Sparse Hybrid Beamforming (QSHB) and Hybrid Beamforming Peak Search (HBPS). Therefore, the proposed massive MIMO system model gives the less channel noise in the received signal, higher SNR and lower BER as compared to the traditional massive MIMO receiver systems.

This paper performs a new comparison between distributed and centralized processing implementations of user-centric distributed massive multiple-input multiple-output (D-mMIMO) systems, also known as cell-free mMIMO. The impacts of processing capacity limitations and inter-central processing unit (CPU) coordination are considered to perform a fairer comparison. The analysis includes two radio units (RUs) selection methods, varying levels of inter-CPU coordination, and different processing capacity restrictions. Simulation results indicate that processing capacity limitations affect spectral efficiency (SE) more in centralized implementations than in distributed ones. Moreover, centralized implementations may require more inter-CPU coordination to improve SE.

We consider a decentralized multi-sensor estimation problem where sensor nodes observe noisy versions of a correlated random source vector. The sensors amplify and forward their observations over a fading coherent multiple access channel (MAC) to a fusion center (FC). The FC is equipped with a large array of antennas and adopts a minimum mean-square error (MMSE) approach for estimating the source. We optimize the amplification factor (or equivalently transmission power) at each sensor node in two different scenarios: a) with the objective of total power minimization subject to mean square error (MSE) of source estimation constraint, and b) with the objective of minimizing MSE subject to total power constraint. For this purpose, based on the well-known favorable propagation condition (when) achieved in massive multiple-input multiple-output (MIMO), we apply an asymptotic approximation on the MSE and use convex optimization techniques to solve for the optimal sensor power allocation in a) and b). In a), we show that the total power consumption at the sensors decays as , replicating the power savings obtained in massive MIMO mobile communications literature. We also show several extensions of the aforementioned scenarios to the cases where sensor-to-FC fading channels are correlated, and channel coefficients are subject to estimation error. Through numerical studies, we also illustrate the superiority of the proposed optimal power allocation methods over uniform power allocation.

Receive spatial modulation (RSM) schemes are promising for massive multiple-input-multiple-output (MIMO) systems at millimeter wave (mmWave) bands because they require reduced complexity and low consumption hardware at the user terminal and exploit the receive spatial dimension to attain high spectral efficiency. To the best of our knowledge, these schemes have been developed for narrowband transmission. In this paper, we adapt RSM schemes for outdoor wideband mmWave massive MIMO systems. We consider the downlink of a single user system operating with single carrier RSM and design a low complexity time-domain finite impulse response pre-equalizer to combat the intersymbol interference caused by the wideband transmission, assuming imperfect channel knowledge. We show that receive antenna selection (RAS) is necessary for attaining high spectral efficiency and we suggest fast and efficient RAS algorithm. Simulation results show that the proposed RSM scheme achieves comparable spectral efficiency to the fully digital orthogonal frequency division multiplexing MIMO system with superior energy efficiency.

Receive spatial modulation (RSM) schemes enable simple and energy efficient multiple-input-multiple-output (MIMO) transceivers and yet attain high spectral efficiency, which renders them promising schemes for millimeter wave (mmWave) communication in massive MIMO systems. When these schemes are designed to include zero forcing (ZF) precoders, performance can be impaired in the presence of highly spatially correlated channels. Extending these schemes for minimum mean square error (MMSE) precoding is not trivial due to the hardware constraints of the energy efficient user terminal architecture. In this paper, we adapt the MMSE precoder to the low complexity RSM architecture and develop detection methods for the spatial and modulation symbols. The proposed MMSE RSM scheme with total and per-antenna power constraints have been compared with ZF RSM in terms of average and outage mutual information by simulations showing superior gain for mmWave channels.

The purpose of this study is to investigate the integration of ethical Artificial Intelligence (AI) with Internet of Things (IoT) and corporate systems, with a particular focus on the significant functions that responsible AI plays in the development of environmentally responsible business practices. The synthesis of research places an emphasis on the integration of AI technology with robust ethical standards, principles of corporate social responsibility, and concerns for the environment. This integration enhances both the operational efficiency of the organization as well as the overall sustainable business philosophy. The evaluation places a strong emphasis on the significant part that public administrations play in the process of building ethical governance frameworks for AI, which ensure that AI is in accordance with social and environmental objectives. In addition to this, it studies the potential for AI, the IoT, and big data to work together to solve challenges related to sustainability. According to the findings of the study, it is essential to make continuous improvements to AI systems in order to guarantee their scientific growth while also taking into consideration the concerns of society and the environment. It
has been recommended that in the future, research should place a higher priority on performing empirical validations and adopting these integrative technologies in specific businesses. It would be beneficial to bridge the gap between theoretical notions and practical implementations, which would ultimately result in a business climate that is more environmentally sensitive and socially conscientious.

Hybrid precoding is a significant procedure for decreasing the hardware complexity and power usage in massive multiple-input multiple-output (MIMO) systems. However, the effectiveness of hybrid precoding is highly dependent on precise channel state info and designing of the beamforming matrix. In recent years, deep learning-based approaches have emerged as a promising solution to address these challenges. This research focuses on improving the performance of massive MIMO systems. However, several methods have been introduced to develop the hybrid precoding model, but these models suffer from several issues such as complexity, interference and quantization error. Currently, deep learning-based methods have gained huge attention in this domain where these methods learn from the data and try to overcome the challenges. Here, a deep learning-based model is presented where our main aim is to develop a hybrid precoder along with the deep learning-based optimal power allocation model. Therefore, the proposed model overcomes the issue of hybrid precoding and power distribution resulting in improving the overall performance of massive MIMO systems on the parameters such as spectral efficiency (SE) and the sum rate.

Improved quality of service and excellent signal coverage is the hallmark of any installed wireless mobile infrastructure. As technology evolve, the need to rely on seamless mobile services become crucial. For this reason, this paper investigates the performance of a wireless mobile network cluster of 27 base stations using call quality and drive test approach while proposing continual system optimization as the network system becomes more robust. The Key Performance Indicators (KPIs) were compared before and after base station tuning and results showed that the optimization approach resulted in better performance.

In this paper we address the problem of channel state information guessing algorithms in the context of massive MIMO systems. We illustrate the problem of pilot contamination in the case where several cells share the same spectrum bandwidth and we calculate the capacity of the system for Maximum Ratio Combining receiver. The theoretical expressions we obtained are the most accurate ones in the open literature of the field.

5G is a promising technology that will support high connectivity and device to device communication. It also promises to improve the existing technologies and will support them. Existing LTE-A utilize centralized communication scheme where all the authentication mechanisms need to go through the base station. This centralized authentication mechanism may generate authentication and key management overhead as well as computational complexity, thus not in line with the 5G requirements. On the other hand, distributed communication scheme lacks hop by hop authentication, thus, it is challenging to share the initial security credentials within the relay stations at multi-hop. Secondly, distributed communication scheme required decode and forward relays, a partial intelligent relays that can act as a semi base stations. Such relays are known as non-transparent relays. However, inclusion of such intelligent relays can leads towards a ROGUE RELAY STATION (RRS) attacks, which consequently ge...

nergy savings are now a mandatory demand of most aspects of information and communications [1]. Today, scientists are asked to create not only powerful communication systems but systems that consume less energy. This request has a significant impact on the design of new wireless communication systems [2]. It also requires improvement in existing ones. Mobile ad-hoc networks born in the 1970s are a type of wireless network that allows us to exchange data in a very convenient way. Although limited in power and capability, mobile ad-hoc networks have proven themselves as convenient infrastructure-free communication tools. Supporting mobility, allowing better connectivity and performance when moving between different networks, they promise an important contribution to the future evolution of the Internet [3]. Recently, cloud computing has been widely recognized by both industry and academia. Advances in cloud computing have made it possible to provide infrastructure, platform, and software as services for users from any computer/device with an Internet connection. Leveraging cloud computing in mobile ad-hoc networks gives birth to rich applications [4]. In particular, for future fifth generation (5G) mobile ad-hoc networks, the study of cloud-assisted service computing can be a hotter topic than ever before. The combination of cloud computing with mobile ad-hoc networks in 5G obviously creates new powerful networks by giving additional advantages, significantly reduced computing, and longer lasting mobility in the network, respectively. These advantages have facilitated the quick growth of richer applications and services, both of which require the removal of heavy

Having lower quantization resolution, has been introduced in the literature, as a solution to reduce the power consumption of massive MIMO and millimeter wave MIMO systems. In this paper, we analyze bit error rate (BER) performance of quantized uplink massive MIMO employing a few-bit resolution ADCs. Considering Zero-Forcing (ZF) detection, we derive a closed-form quantized signal-to-interference-plus-noise ratio (SINR) to achieve an analytical BER approximation for coarse quantized M-QAM massive MIMO systems, by using a linear quantization model. The proposed expression is a function of quantization resolution in bits. We further numerically investigate the effects of different quantization levels, from 1-bit to 4-bits, on the BER of three modulation types of QPSK, 16-QAM, and 64-QAM. Uniform and non-uniform quantizers are employed in our simulation. Monte Carlo simulation results reveal that our approximate formula gives a tight upper bound for the BER performance of b-bit resolution quantized systems using non-uniform quantizers, whereas the use of uniform quantizers cause a lower performance for the same systems. We also found a small BER performance degradation in coarse quantized systems, for example 2-3 bits QPSK and 3-4 bits 16-QAM, compared to the full-precision (unquantized) case. However, this performance degradation can be compensated by increasing the number of antennas at the BS.

Some more practical channels that model the networks in a real environment is the multi-path communication channels. In order to investigate these communications channels. This work addressed Channel Estimation (CE) in the Uplink (UL) phase for a multi-cell multi-user massive multipleinput multiple-output (M-MIMO) system that studies multi-path communication between each user and its serving Base Station (BS). We suppose that the network operates under Time-Division Duplex (TDD) protocol. We studied and analyzed the multi-path channels and their benefit over CE since it presents a more realistic channel that displays a real propagation circumstance. on the flip side, we evaluated the CE quality using ideal MinimumMean Square Error (MMSE). This latter relies on an impractical property that can be explicated since the MMSE estimator considers foreknowledge on Large-Scale Fading (LSF) coefficients of interfering users. Thus, the suggested estimator is introduced to overcome this issue,...

• Develop a Simplified Precoding system for the downlink of Massive MIMO using mm-Waves • Nevertheless MRC and EGC generate interference. We consider an interference canceller. • Block transmission techniques, with appropriate cyclic prefixes and employing FDE techniques, have been shown to be suitable for high data rate transmission over severely time-dispersive channels. • SC-FDE signals presents lower PAPR than OFDM signals.

The Internet of Things is a rapidly emerging technology (IoT). Thanks to numerous sensors, billions of smart objects (referred to as "Things") may acquire data about themselves and their environment. So they may regulate and monitor industrial services or increase commercial services and activities. However, the Internet of Things is now more vulnerable than ever. Machine Learning (ML) has advanced significantly, bringing up new research avenues to solve current and future IoT challenges. On the other hand, machine learning is an effective method for identifying peril in intelligent devices and networks. Following a thorough literature review on Machine Learning methods and the necessity of IoT security, this study will assess numerous ML algorithms for threat detection and the various security methods, which are associated with Machine Learning techniques.

We consider channel estimation for an uplink massive multiple-input multiple-output (MIMO) system where the base station (BS) uses an array with low-resolution (1-2 bit) analog-to-digital converters and a spatial Sigma-Delta (Σ∆) architecture to shape the quantization noise away from users in some angular sector. We develop a linear minimum mean squared error (LMMSE) channel estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. We also analyze the uplink achievable rate with maximal ratio combining (MRC), zero-forcing (ZF) and LMMSE receivers and provide a lower bound for the achievable rate with the MRC receiver. Numerical results show superior channel estimation and sum spectral efficiency performance using the Σ∆ architecture compared to conventional 1-or 2-bit quantized massive MIMO systems.

In this paper, a joint spatio–radio frequency resource allocation and hybrid beamforming scheme for the massive multiple-input multiple-output (MIMO) systems is proposed. We consider limited feedback two-stage hybrid beamformimg for decomposing the precoding matrix at the base-station. To reduce the channel state information (CSI) feedback of massive MIMO, we utilize the channel covariance-based RF precoding and beam selection. This beam selection process minimizes the inter-group interference. The regularized block diagonalization can mitigate the inter-group interference, but requires substantial overhead feedback. We use channel covariance-based eigenmodes and discrete Fourier transforms (DFT) to reduce the feedback overhead and design a simplified analog precoder. The columns of the analog beamforming matrix are selected based on the users’ grouping performed by the K-mean unsupervised machine learning algorithm. The digital precoder is designed with joint optimization of intra-...

The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multipleoutput (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energyefficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers' structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain.

Despite the inevitable presence of transceiver impairments, most prior work on multiple-input multiple-output (MIMO) wireless systems assumes perfect transceiver hardware which is unrealistic in practice. In this direction, motivated by the increasing interest in MIMO relay systems due to their improved spectral efficiency and coverage, this paper investigates the impact of residual hardware impairments on the ergodic capacity of dual-hop (DH) amplify-and-forward (AF) MIMO relay systems. Specifically, a thorough characterization of the ergodic channel capacity of DH AF relay systems in the presence of hardware impairments is presented herein for both the finite and large antenna regimes by employing results from finite-dimensional and large random matrix theory, respectively. Regarding the former setting, we derive the exact ergodic capacity as well as closed-form expressions for tight upper and lower bounds. Furthermore, we provide an insightful study for the low signalto-noise ratio (SNR) regimes. Next, the application of the free probability (FP) theory allows us to study the effects of the hardware impairments in future 5G deployments including a large number of antennas. While these results are obtained for the large system limit, simulations show that the asymptotic results are quite precise even for conventional system dimensions.

Delayed channel state information at the transmitter (CSIT) due to time variation of the channel, coming from the users' relative movement with regard to the BS antennas, is an inevitable degrading performance factor in practical systems. Despite its importance, little attention has been paid to the literature of multi-cellular multiple-input massive multiple-output (MIMO) system by investigating only the maximal ratio combining (MRC) receiver and the maximum ratio transmission (MRT) precoder. Hence, the contribution of this work is designated by the performance analysis/comparison of/with more sophisticated linear techniques, i.e., a minimum-mean-square-error (MMSE) detector for the uplink and a regularized zero-forcing (RZF) precoder for the downlink are assessed. In particular, we derive the deterministic equivalents of the signal-to-interference-plus-noise ratios (SINRs), which capture the effect of delayed CSIT, and make the use of lengthy Monte Carlo simulations unnecessary. Furthermore, prediction of the current CSIT after applying a Wiener filter allows to evaluate the mitigation capabilities of MMSE and RZF. Numerical results depict that the proposed achievable SINRs (MMSE/RZF) are more efficient than simpler solutions (MRC/MRT) in delayed CSIT conditions, and yield a higher prediction at no special computational cost due to their deterministic nature. Nevertheless, it is shown that massive MIMO are preferable even in time-varying channel conditions. Index Terms-Massive MIMO, delayed CSIT, channel estimation, channel prediction, linear precoding, linear detection. I. INTRODUCTION Multiuser MIMO (MU-MIMO), applied to next generation systems (e.g., 802.16m [3], LTE-Advanced [4]), is one of the core technologies promising to provide a remarkable increase in data rates. Such systems include several co-channel users communicating with a base station (BS) equipped with multiple antennas. However, the technological transition to 5G systems is expected to demand a thousand-fold higher capacity. Massive MIMO, where the BS includes a very large number of antennas, have emerged as one of the most promising technologies towards this direction because more degrees of freedom and increased power efficiency are achieved by simplifying multiuser processing, reducing transmit power, as

This paper investigates the achievable sum-rate of massive multiple-input multiple-output (MIMO) systems in the presence of channel aging. For the uplink, by assuming that the base station (BS) deploys maximum ratio combining (MRC) or zero-forcing (ZF) receivers, we present tight closed-form lower bounds on the achievable sum-rate for both receivers with aged channel state information (CSI). In addition, the benefit of implementing channel prediction methods on the sum-rate is examined, and closed-form sum rate lower bounds are derived. Moreover, the impact of channel aging and channel prediction on the power scaling law is characterized. Extension to the downlink scenario and multi-cell scenario are also considered. It is found that, for a system with/without channel prediction, the transmit power of each user can be scaled down at most by 1/ √ M (where M is the number of BS antennas), which indicates that aged CSI does not degrade the power scaling law, and channel prediction does not enhance the power scaling law; instead, these phenomena affect the achievable sum-rate by degrading or enhancing the effective signal to interference and noise ratio, respectively.

We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Sigma-Delta (ΣΔ) sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the ΣΔ approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the ΣΔ array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the ΣΔ approach for both MRC and zero-forcing receivers.

Massive multiple-input multiple-output (MIMO) systems with one-bit analog-to-digital converters (ADCs) suffer from substantial channel estimation error caused by the coarse quantization which results in spectral efficiency (SE) loss. In this paper, we investigate the effectiveness of so-called mixed-ADC architectures to overcome this issue. We study to what extent one can combat the SE loss by exploiting just N M pairs of high-resolution ADCs, where M denotes the number of BS antennas. Unlike previous work on this problem, we incorporate both high-resolution and one-bit observations in the channel estimation algorithm, and we use a more accurate model for the quantization noise based on the Bussgang decomposition.

We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Sigma-Delta (ΣΔ) sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the ΣΔ approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the ΣΔ array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the ΣΔ approach for both MRC and zero-forcing receivers.

The one-bit spatial Sigma-Delta concept has recently been proposed as an approach for achieving low distortion and low power consumption for massive multi-input multi-output systems. The approach exploits users located in known angular sectors or spatial oversampling to shape the quantization noise away from desired directions of arrival. While reducing the antenna spacing alleviates the adverse impact of quantization noise, it can potentially deteriorate the performance of the massive array due to excessive mutual coupling. In this paper, we analyze the impact of mutual coupling on the uplink spectral efficiency of a spatial one-bit Sigma-Delta massive MIMO architecture, and compare the resulting performance degradation to standard onebit quantization as well as the ideal case with infinite precision. Our simulations show that the one-bit Sigma-Delta array is particularly advantageous in space-constrained scenarios, can still provide significant gains even in the presence of mutual coupling when the antennas are closely spaced.

We study the spectral efficiency (SE) of a mixed-ADC massive MIMO system in which K single-antenna users communicate with a base station (BS) equipped with M antennas connected to N high-resolution ADCs and M − N one-bit ADCs. This architecture has been proposed as an approach for realizing massive MIMO systems with reasonable power consumption. First, we investigate the effectiveness of mixed-ADC architectures in overcoming the channel estimation error caused by coarse quantization. For the channel estimation phase, we study to what extent one can combat the SE loss by exploiting just N M pairs of high-resolution ADCs. We extend the roundrobin training scheme for mixed-ADC systems to include both high-resolution and one-bit quantized observations. Then, we analyze the impact of the resulting channel estimation error in the data detection phase. We consider random high-resolution ADC assignment and also analyze a simple antenna selection scheme to increase the SE. Analytical expressions are derived for the SE for maximum ratio combining (MRC) and numerical results are presented for zero-forcing (ZF) detection. Performance comparisons are made against systems with uniform ADC resolution and against mixed-ADC systems without round-robin training to illustrate under what conditions each approach provides the greatest benefit.

We consider uplink transmission of a massive multiuser multiple-input multiple-output (MU-MIMO) system in the presence of a smart jammer. The jammer aims to degrade the sum spectral efficiency of the legitimate system by attacking both the training and data transmission phases. First, we derive a closed-form expression for the sum spectral efficiency by taking into account the presence of a smart jammer. Then, we determine how a jammer with a given energy budget should attack the training and data transmission phases to induce the maximum loss to the sum spectral efficiency. Numerical results illustrate the impact of optimal jamming specifically in the large limit of the number of base station (BS) antennas.

The spectral efficiency of three-dimensional (3D) massive MIMO is impaired due to the spatial correlation (SC) between closed-spaced elements of antenna arrays (AAs). This paper investigates the impacts of arbitrary Q-power variations of the Power Cosine distribution for a precise characterization of the SC. This is done by presenting mathematical expressions characterizing the SC of cylindrical antenna array (CAA) regarding Student's-t, Von Mises and Cosine distributions, for the direction of arrival (DoA) of the electromagnetic waves impinging on the receiving antenna. Unlike the Student's-t and Von Mises distribution, our outcome recorded huge mismatch between theoretical and simulation correlation coefficients for arbitrary Q-power values above ten (10) which also reduced the downlink channel capacity. The practical relevance is that even though higher Q-power values are the key parameters for system performance, meticulous selection of arbitrary Q-power values above ten (10) is essential to minimize errors regarding channel modelling.

Massive MIMO will improve the performance of future 5G systems in terms of data rate and spectral efficiency, while accommodating a large number of users. Furthermore, it allows for 3D beamforming in order to provide more degrees of freedom and increase the number of high-throughput users. Massive MIMO is expected to provide more advantages compared to other solutions in terms of energy and spectral efficiency. This will be achieved by focusing the radiation towards the direction of the intended users, thus implementing simultaneous transmission to many users while keeping interference low. It can boost the capacity compared to a conventional antenna solution, resulting in a spectral efficiency up to 50 times greater than that provided by actual 4G technology. However, to take full advantage of this technology and to overcome the challenges of implementation in a real environment, a complicated radio system is required. The purpose of this work is to present the MIMO technology evol...

In this paper, we investigate the suitability of single carrier frequency domain processing (SC-FDP) as the downlink transmission scheme in a massive multiple input multiple output (MIMO) system. By deriving the sum-rate of the SC-FDP massive MIMO system theoretically, we show that this method obtains a sum-rate similar to that of orthogonal frequency division multiplexing (OFDM) massive MIMO. We also derive the theoretical sum-rate of both SC-FDP and OFDM in a nonsynchronized massive MIMO scenario and show that the former is significantly larger than the latter. Moreover, we theoretically analyze the sum-rate of both systems in the presence of power amplifier non-linearity. All the sum-rates are derived for both zero forcing (ZF) and matched filter (MF) precoding schemes. The results show that the effect of power amplifier non-linearity on the sum-rate of both systems is similar when the number of users is large. We also compare SC-FDP with OFDM from the peak to average power ratio (PAPR) and complexity viewpoints. Although the PAPR of SC-FDP signals is lower than that of OFDM signals, for MIMO systems this difference decreases as we increase the number of users, which means that the techniques can have similar PAPR in massive MIMO systems. The overall complexities are similar for SC-FDP and OFDM. Due to the mentioned facts, we can conclude that SC-FDP is a promising transmission scheme for the downlink of the massive MIMO system in the presence of carrier frequency offset (CFO) and power amplifier non-linearities.