Massive Antenna Research Articles

A Comparison Between Tone-Based and Code-Based Cell Search Schemes for Multipath Division Multiple Access

Cell search procedure is an essential and critical process in an early stage when the user equipment (UE) is powered on. It mainly comprises symbol and frame timing synchronization, frequency offset compensation, and base station (BS) identification. Among the current exiting 3G, 4G and 5G mobile networks, the UE completes the initial cell search based on different code sequences. In this paper, we investigate and compare two kinds of cell search methodology. Tone-based and code-based methods are explored for massive antenna systems. The detailed description and analysis are offered for two approaches. Simulation results indicate that the tone-based cell search not only possesses stable performance with respect to path numbers but also outperforms the code-based one in general channel realizations in terms of cell search error probability. The results suggest that the tone-based cell search could be used for 5G communication systems.

Array Orientation Adjustments Subject to Optimal Direct Position Determination Performance

Arrays deployed in antenna-array-based direct position determination (DPD) systems are conventionally placed parallel to each other with invariant orientations, which may be inappropriate. This paper proposes a novel array orientation adjustment method to achieve optimal DPD performance, in which we seek optimal array orientations that maximize the trace of the Fisher information matrix (FIM) and then minimize localization uncertainty. The optimization problem is unconstrained, multivariable, and can be converted into multiple tractable single-variable optimization problems. An exact solution can be obtained by a straightforward but computationally tedious one-dimensional exhaustive search. To reduce the computational load, we then derive an asymptotic solution in cases where the arrays consist of massive antennas. Numerical examples demonstrate the effectiveness and feasibility of the proposed method in resolution and precision enhancement, especially in a low signal-to-noise ratio (SNR).

Spectrum sensing optimization in uncalibrated massive antennas systems

In this paper, we propose a spectrum sensing (SS) design for massive MIMO (M-MIMO) antennas system based on decision threshold optimization given a fixed subset of antennas and sample size able to minimize the total error probability subject to target detection and false alarm probabilities. This formulation results in a nonlinear optimization problem (NLP). Empirical expressions based on the central limit theorem (CLT) with estimated sample-mean parameter values are deployed to find the optimal decision threshold. Two operational modes are considered: (i) spectral-efficient (SE) mode, i.e., low number of samples combined with a high number of antennas, implying in a reduced sensing time and increased transmission rate; (ii) energy-efficient (EE) mode, meaning reduced number of sensing antenna and massive number of processing samples, while the remaining antennas are in sleep mode, saving energy. To solve the NLP, an algorithm based on the sequential quadratic programming (SQP) is proposed, being able to attain convergence in a few iterations. Moreover, to deal with uncalibrated antennas, more elaborated detectors are evoked, including the Hadamard detector (HD), volume detector (VD) and the covariance detector (CAV). Such detector parameters are optimized in the sense of maximizing the SS task subject to uncalibrated massive antennas effects.

Clustering and power optimization in mmWave massive MIMO–NOMA systems

Integrating massive multiple input multiple output (MIMO) architecture and non-orthogonal multiple access (NOMA) technology is deemed as a promising solution to the gridlock of ever increasing demand in data rate via extending communication frequency to millimeter wave (mmWave) band and improving system spectrum efficiency. In this paper, we consider the downlink operation of MIMO–NOMA systems, where the base station (BS) is equipped with massive antenna array that uses hybrid precoding to reduce the number of required radio-frequency (RF) chains without performance loss with less hardware cost. A clustering strategy and a joint power allocation scheme of such systems are proposed to maximize the sum-rate. Specifically, we adopt the concept of chordal distance in the clustering strategy, i.e., the selection of cluster head users and assignment of other users in each beam. Finally, we consider a power allocation scheme for each user that takes care of inter-beam and intra-beam interference. Upon the formulation of the power allocation problem while considering all relevant constraints, it is transformed to a dual problem via the Lagrange duality theorem, and then the optimal power allocation is derived by the optimal dual value. Simulation results show that the proposed scheme can achieve better performance in terms of spectrum efficiency compared to the existing methods, and the mmWave-based massive MIMO–NOMA system outperforms the MIMO–OMA system.

Multisegment Mapping Network for Massive MIMO Detection

The massive multiple-input multiple-output (MIMO) technology is one of the core technologies of 5G, which can significantly improve spectral efficiency. Because of the large number of massive MIMO antennas, the computational complexity of detection has increased significantly, which poses a significant challenge to traditional detection algorithms. However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem. This paper proposes a deep neural network for massive MIMO detection, named Multisegment Mapping Network (MsNet). MsNet is obtained by optimizing the prior detection networks that are termed as DetNet and ScNet. MsNet further simplifies the sparse connection structure and reduces network complexity, which also changes the coefficients of the residual structure in the network into trainable variables. In addition, this paper designs an activation function to improve the performance of massive MIMO detection in high-order modulation scenarios. The simulation results show that MsNet has better symbol error rate (SER) performance and both computational complexity and the number of training parameters are significantly reduced.

A Different Shaped Radiating Element Wide Band Multi-Band Massive MIMO Antenna for 5G/WLAN applications with Enhanced Correlation Coefficient

In this paper, a different shaped 8-element multiband multiple input multiple output (MIMO) massive antenna is investigated for 5G and various applications. It can be operated on various wireless communication application such as WLAN (5.15GHz to 5.30GHz and 5.73GHz to 5.85GHs) and 5G band & C-band (4 to 8GHz) for satellite communication with wideband. The 8-element Massive MIMO antenna is designed for 6GHz. This antenna is working on multiband with wideband bandwidth. The defected ground structure (DGS) systems are used for enhancement of return loss, isolation, total active reflection coefficients (TARC), correlation coefficients (CC) and envelope correlation coefficients (ECC). The rectangular shaped DGS is used in the ground plane for improvement of various parameter of antenna. This antenna provides return loss (RL) and isolation is less than -10dB.

Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems With Resolution-Adaptive ADCs

In this letter, we investigate the uplink of a reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multi-user system. In the considered system, however, problems with hardware cost and power consumption arise when massive antenna arrays coupled with power-demanding analog-to-digital converters (ADCs) are employed. To account for practical hardware complexity, we consider that the access point (AP) is equipped with resolution-adaptive analog-to-digital converters (RADCs). We maximize the achievable rate under hardware constraints by jointly optimizing the ADC quantization bits, the RIS phase shifts, and the beam selection matrix. The formulated problem is non-convex. To efficiently tackle this problem, a block coordinated descent (BCD)-based algorithm is proposed. Simulations demonstrate that an RIS can mitigate the hardware loss due to the use of RADCs, and that the proposed BCD-based algorithm outperforms state-of-the-art algorithms.

Massive Wireless Energy Transfer With Statistical CSI Beamforming

Wireless energy transfer (WET) is a promising solution to enable massive machine-type communications (mMTC) with low-complexity and low-powered wireless devices. Given the energy restrictions of the devices, instant channel state information at the transmitter (CSIT) is not expected to be available in practical WET-enabled mMTC. However, because it is common that the terminals appear spatially clustered, some degree of spatial correlation between their channels to the base station (BS) is expected to occur. The paper considers a massive antenna array at the BS for WET that only has access to i) the first and second order statistics of the Rician channel component of the multiple-input multiple-output (MIMO) channel and also to ii) the line-of-sight MIMO component. The optimal precoding scheme that maximizes the total energy available to the single-antenna devices is derived considering a continuous alphabet for the precoders, permitting any modulated or deterministic waveform. This may lead to some devices in the clusters being assigned a low fraction of the total available power in the cluster, creating a rather uneven situation among them. Consequently, a fairness criterion is introduced, imposing a minimum amount of power allocated to the terminals. A piece-wise linear harvesting circuit is considered at the terminals, with both saturation and a minimum sensitivity, and a constrained version of the precoder is also proposed by solving a non-linear programming problem. A paramount benefit of the constrained precoder is the encompassment of fairness in the power allocation to the different clusters. Moreover, given the polynomial complexity increase of the proposed unconstrained precoder, and the observed linear gain of the system's available sum-power with an increasing number of antennas at the ULA, the use of massive antenna arrays is desirable.

Hybrid precoding algorithm for massive MIMO system with imperfect channel knowledge

Millimeter wave MIMO system offers orders of magnitude high data rate due to the excess bandwidth available then conventional MIMO system. But, at the cost of excessive free space losses that highly influence the transmission at very high millimeter wave frequencies. The resulting small Wavelength at the millimeter wave frequency can overcome the path losses using beam forming gain offered by the massive antennas which is known as pre-coding. In millimeter wave massive MIMO system, hybrid pre-coding is the technique that combines analog pre-coding and digital pre-coding to reduce the requirement of high number of RF chains and thus energy consumption. Hybrid beam forming uses number of RF chains equal to the number of simulations users being served rather than using separate RF chains in the case of digital pre-coding. In designing analog and digital pre-coders, large number of calculations that uses complex mathematical process such as block diagonalization, SVD for channel matrix inversion required for the computation of pre-coder weight vector computation. In this paper, we propose low complexity hybrid pre-coding algorithm using simple linear approximation algorithms for matrix inversion operations. We first used the simple inversion algorithm in the computation of analog beam forming matrix without any SVD or direct inversion of matrices. Then, the same is being used in digital pre-coding matrix. In the proposed algorithm, there is no need for the complicated operations as SVD as in traditional pre coding algorithms.

A simplified genetic algorithm in multi-beam combination for mmWave communication system

In mmWave communication system we propose a simplified Genetic Algorithm (GA) in multiple-beam combination, which helps to overcome problems such as grating-lobe in common beam-forming algorithms, and it can get a better Signal Noise Ratio (SNR) performance at User Equipment (UE). The simplified GA can select optimal antenna cells from massive antenna array with a lower computational complexity and higher power-efficiency. Theoretical analysis and software simulation show that the method can support more UEs with a higher power efficiency, reduce the bit error rate and computational complexity.

Dimensionality reduction of interference subspace for multiuser massive MIMO null-space expansion

This paper proposes a dimensionality reduction of interference subspace to improve the interference suppression capability of null-space expansion (NSE) for multiuser massive multiple-input-multiple-output (MIMO) systems. The fundamental concept of the NSE is to nullify the interference subspace generated by the combined multiple past channel state information (CSI) vectors. It can suppress inter-user interference (IUI) leaked due to outdated CSI in a mobility environment. It is enabled by plentiful degrees of freedom (DoF) brought by massive antenna elements the base station has, and hence NSE can outperform existing channel prediction based precoding approaches. However, additive noise-induced CSI estimation error limits its effectiveness; expanded dimension of null-space cannot be appropriately spanned to interference subspace. The key idea of our new proposal is to extract principal interference subspace channel. Null-space can be appropriately expanded with reduced its dimension, and remaining DoF can be assigned to beamforming. It results in improving overall signal-to-interference-plus-noise power ratio (SINR) performance which is verified via numerical simulation.

Harmonic Inversion Based Posterirori Minimization in HPA for 5G Communication Framework

The Number of Android Mobile (Voice, Mobile Data, Internet) users getting increased year by year, But there is a major scarcity of operating frequency for the users. Even though many spectral efficient techniques like OFDMA and NOMA are introduced to manage the spectral scarcity. In 5G communication, various frameworks play a vital role in peer-to-peer communication and cloud-based network frameworks. It required errorless signal transmission to achieve the goal of a high-quality signal. The harmonic cancellation or reduction process is the critical task in the 5G communication application. Post error detection and correction is a highly complex process to reduce harmonic distortions. The real time interference problems due to harmonics still exist. With the introduction of 5G Mobile Communication there is an exponential increase of radiations from communication equipment (BTS, Mobiles) due to massive MIMO antennas, mixers, H-PA (High-Power-Amplifier). To reduce the above effects, a modified V-LMS linearizer has been introduced instead of the conventional non-linear filter methods (N-LMS, RLS). V-LMS not only estimates the gain error (priori) of the HPA, but also introduces very low additive error (posteriori). Look up table (LUT) is used to distribute the digital data to the arithmetic units of V-LMS. High speed operation can able to reduce the complexity of the overall system. Multilevel 2D-LUT can perform much faster in proposed V-LMS algorithm. Digital data process through LUT is used to perform accurate error estimation and it reduce the adaptive error in the proposed V-LMS. The proposed has been executed through the 64-QAM non-linear modulated signal with amplifier operating at 1.89–2.32 GHz.

Greener Physical Layer Technologies for 6G Mobile Communications

Energy efficiency (EE) has been regarded as an essential design metric in 5G mobile communications. However, the massive antenna numbers, larger bandwidths, and higher density of base stations have been severely deteriorating the power consumption of 5G networks. In this article, the fundamental EE-spectrum efficiency (SE) joint design issues are thoughtfully considered. The relationships between EE and SE in single-input single-output (SISO), multiple-input multiple-output, and hybrid beamforming structures are first reviewed, where the increase of SE (EE) will unfortunately bring a reduction of EE (SE). To resolve this contradiction, a non-orthogonal multiple access (NOMA) scheme with energy-efficient user scheduling and resource allocation is investigated, which promises a simultaneous increase of EE and SE. Furthermore, a precoded waveform overlapping multiplexing scheme is presented, with significant improvements in EE and SE over the traditional Nyquist-criterion-based SISO systems. The waveform overlapping multiple access scheme is further proposed, which promises large performance improvements over the NOMA schemes discussed in 5G New Radio. The application of the waveform overlapping scheme in multiple-antenna systems is also investigated. Finally, the challenges of the waveform overlapping scheme and several other research topics are discussed. For sustainable development of the wireless communications industry, greener design in physical layer technologies should be second to none in the future 6G networks and beyond.

A novel self-healing model using precoding & big-data based approach for 5G networks

The study of self organized networks is considered a key element in developing the new generation of cellular networks. Mobile service providers are adopting new technologies such as small cells and precoding to deploy their networks according to 5G standards. As these type of technologies gain more importance and popularity, network self organization become a more essential function in operating the network resources. Self organized networks have been classified into three main categorizes: self optimization, self configuration, and self healing. The first two categories showed further development and research interest when compared to self-healing. Due to the high density nature of small cells, using massive antennas in the network, and their susceptibility to failures for various reasons, the demand for a clear self-healing process and architecture is deemed urgent. In this paper the current self-healing process in addition to the fault tolerance aspects for the future 5G are studied and a new process model for organizing the self-healing process is proposed. The new process model is meant to map the different functionalities needed to perform a successful self healing process. The model with the proposed descriptive network architecture aims to identify the different functions within the self-healing process model. In order to test the operation ability of the model, a new big data aspect is added to the network architecture to aid in analyzing the huge amount of data needed to efficiently perform the self-healing process. Results show that the proposed precoding technique in conjunction with the machine learning algorithm based on a decision tree model that uses empirical data collected from the network can identify the status of cells (healthy, congested or failing) and suitable self-healing procedures can be triggered to recover the cell accordingly.

Massive multi‐in multi‐out multiuser multiplexing based on transmission mode 3

SummaryMultiuser multi‐in multi‐out (MU‐MIMO) technology using the fifth generation mobile network (5G) massive antenna can greatly increase the existing network capacity and help address problems related to large increases in network traffic. In this study, massive MIMO multiuser space division multiplexing based on transmission mode 3 (TM3) is realized by dividing massive MIMO antenna into virtual beam and fan section. Then the performance evaluations on the MU‐MIMO system in practical existing networks were conducted. The results showed that this technology can realize three times the amount of spatial multiplexing while still maintaining good mobility performance in a no‐load network. In addition, the capacity of the cell can be increased by the massive MIMO system in an existing network for a large user population (for a space division multiplexing factor of approximately 1.83). Test results are used to prove the effectiveness of the frequency‐division duplex massive MIMO multiuser multiplexing based on TM3 to increase the capacity of an existing network.

5G NR mmWave indoor coverage with massive antenna system

In this paper, we introduce a novel mmWave access architecture, called mmWave over cable (mmWoC), for achieving effective indoor coverage, which is characterized by using an analog modulated relay link to transport the outdoor mmWave signalsto the indoors. To enable an effective mapping of radio signals onthe cable sub-carriers, we introduce non-configurable air-to-cable(NC-A2C) scheduler that is characterized by its low control com-plexity and hardware requirement. We will discuss the merits ofthe proposed mmWoC access architecture and the NC-A2C sched-uler, which are further validated via extensive simulations.

Impact of Pilot Allocation Strategies on Outage in Wireless Energy Transfer Using Massive Antenna Arrays

We investigate the viability of wireless energy transfer (WET) to multiple sensors using massive number of base station (BS) antennas based on estimates obtained through different uplink pilot signaling strategies, namely, orthogonal and shared. For the aforementioned strategies, we derive novel upper bounds on probability of outage in WET referred to as the probability that any sensor node fails to harvest a certain minimum amount of energy E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">u</sub> that it needs to send uplink pilots plus the energy E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> that it needs to process its main tasks. We show how number of BS antennas scales with the number and position of sensor nodes, array transmit energy, channel estimation errors and nature of pilot signaling strategy employed. We prove when the sensor nodes are all equidistant relative to the BS, shared strategy gives identical outage probability as obtained through orthogonal strategy. However, when they are placed at different distances, shared strategy gives poorer outage performance than orthogonal strategy. To address this, we propose a simple location-dependent clustering and hybrid pilot assignment algorithm and also derive the corresponding probability of outage in WET. The proposed strategy elucidates an interesting trade-off between the outage performance that can be obtained and resources that must be spent on channel estimation.

Hybrid Beamforming for Massive MIMO Over-the-Air Computation

Over-the-air computation (AirComp) has been recognized as a promising technique in Internet-of-Things (IoT) networks for fast data aggregation from a large number of wireless devices. However, the computation accuracy of AirComp highly depends on the devices with the worst channels condition, which degrades severely when the number of devices becomes large. To address this issue, we exploit the massive multiple-input multiple-output (MIMO) with hybrid beamforming, in order to enhance the computational accuracy of AirComp in a cost-effective manner. In particular, we consider the scenario with a large number of multi-antenna devices simultaneously sending data to an access point (AP) equipped with massive antennas for functional computation over the air. Under this setup, we jointly optimize the transmit digital beamforming at the wireless devices and the receive hybrid beamforming at the AP, with the objective of minimizing the computational mean-squared error (MSE) subject to the individual transmit power constraints at the wireless devices. To solve the non-convex hybrid beamforming design optimization problem, we propose an alternating-optimization-based approach, in which the transmit digital beamforming and the receive analog and digital beamforming are optimized in an alternating manner. In particular, we propose two computationally efficient algorithms to handle the challenging receive analog beamforming problem, by exploiting the techniques of successive convex approximation (SCA) and coordinate descent (CD), respectively. It is shown that for the special case with a fully-digital receiver at the AP, the achieved MSE of the massive MIMO AirComp system is inversely proportional to the number of receive antennas. Furthermore, numerical results show that the proposed hybrid beamforming design substantially enhances the computation MSE performance as compared to other benchmark schemes, while the SCA-based algorithm performs closely to the performance upper bound achieved by the fully-digital beamforming.

A 3D Tractable Model for UAV-Enabled Cellular Networks With Multiple Antennas

This paper aims to propose a three-dimensional (3D) point process that can be employed to generally deploy unmanned aerial vehicles (UAVs) in a large-scale 3D cellular network and to tractably analyze the fundamental network-wide performances of the network. The proposed 3D point process is devised based on a 2D marked Poisson point process in which each point and its random mark uniquely correspond to the projection and the altitude of each point in the 3D point process, respectively. We study some of the important statistical properties of the proposed 3D point process and shed light on some crucial insights into them that facilitate the analyses of a UAV-enabled cellular network wherein all UAVs equipped with multiple antennas are deployed by the proposed 3D point process to serve as aerial base stations. The salient features of the proposed 3D point process lie in its suitability in practical 3D channel modeling and tractability in analysis. The downlink coverages of the UAV-enabled cellular network are found and their closed-form results for some special cases are also derived. Most importantly, their fundamental limits achieved by cell-free massive antenna array are characterized when coordinating all the UAVs to jointly perform non-coherent downlink transmission. These key findings and observations are numerically validated in this paper.

Reconfigurable Intelligent Surface Aided Multi-User Communications: State-of-the-Art Techniques and Open Issues

Reconfigurable intelligent surface (RIS)-aided communication is considered as an exciting research topic in academic and industrial communities since it provides an emerging affordable solution to achieve high quality and secure next-generation wireless systems. Especially, the deployment of RIS in multi-user wireless networks promises to reduce system hardware costs, signal processing complexity, as well as energy consumption due to small size, lightweight and ability to actively shape the wireless propagation environment. Further, by realizing a cost-effective radio environment, RIS-aided communication can be implemented to be an appealing technology for future integration with other emerging wireless applications and communication systems. Despite the positive appeal, RISs face new challenges that hinder integrating efficiently into wireless networks, such as network secrecy performance and system sum-rates, as well as achieving efficient deployment design in highly dynamic and time-varying wireless environments. To this end, we overview recent state-of-the-art techniques to address the above issues faced in the integration of RISs with various emerging multi-user communication techniques, such as Unmanned Aerial Vehicles (UAVs), Non-Orthogonal Multiple Access (NOMA), Millimeter Wave (mmWave) and Terahertz (THz) communications, Physical Layer Security (PLS), massive antennas, and Simultaneous Wireless Information and Power Transfer (SWIPT). Finally, we highlight promising future research directions of RIS-aided communication in Cell-Free Massive Multiple-Input-Multiple-Output (MIMO) systems, Rate-Splitting Multiple Access (RSMA), Light Fidelity (LiFi), and Cognitive Radio (CR) systems.