Massive Antenna Arrays Research Articles

An Approach for Data Rate Maximization and Interference Mitigation in Massive MIMO Communication Systems Using SRPWGC-PD Algorithm

Increasing usage of wireless communication systems and demand for higher data speed is appealing newer generations of cellular communication systems to provide higher data rate along with a reliable system, i.e. system which is free from interference. 5G Communication systems are believed to provide at least ten times betterment in area throughput, i.e. data rate by increasing the spectral efficiency. Massive MIMO (multiple-input multiple-output) is a key 5G technology that uses massive antenna arrays to provide a very high beamforming gain and spatial multiplexing of users and hence increases the spectral and energy efficiency. However, although Massive MIMO suppresses intra-cell interference and uncorrelated noise to a significant amount, it cannot mitigate the Pilot Contamination (PC) which is caused by reusing the same set of pilot signals in adjacent cells. In this paper, a new Pilot Decontamination (PD) technique, named as Softly Reused Pilot Weighted Graph Coloring-based Pilot Decontamination (SRPWGC-PD) has been introduced. In the proposed scheme, a scheme named soft pilot reusing is applied to separate the users in cells into two (inner and outer) zones, and then outer-zone users are allocated with orthogonal pilots; whereas a special pilot allocation scheme named weighted graph coloring algorithm is applied to the inner zone users to make generated PC as less as possible. Simulation and numerical analysis provide an insight that the proposed scheme outperforms in most of the performance indices, when compared to the existing schemes reported in the literature.

Fundamental limits of single anchor-based cooperative localization in millimeter wave systems

Thanks to massive antenna arrays in millimeter wave communications, a much higher resolution can be achieved for the estimation of angle of arrival. By taking full advantage of beamforming capability and high angular resolution in millimeter wave systems, a single anchor is sufficient to obtain a good position estimation for agents by combining angle of arrival and time of arrival. In this paper, a single-anchor based cooperative localization is proposed for millimeter wave systems. Fundamental limits of cooperative localization with a single anchor is investigated, where all nodes are equipped with massive antenna arrays. Bounds of time-based range and position estimation are derived by Crámer-Rao bound in general multipath channels. For the single-anchor based cooperative localization, the structure of the fisher information matrix is investigated and the relationship between ranging accuracy and positioning accuracy is theoretically clarified. Numerical results demonstrate a consistency with theory behind the proposed millimeter wave’s cooperative localization network.

Crowd-Based Cognitive Perception of the Physical World: Towards the Internet of Senses.

This paper introduces a possible architecture and discusses the research directions for the realization of the Cognitive Perceptual Internet (CPI), which is enabled by the convergence of wired and wireless communications, traditional sensor networks, mobile crowd-sensing, and machine learning techniques. The CPI concept stems from the fact that mobile devices, such as smartphones and wearables, are becoming an outstanding mean for zero-effort world-sensing and digitalization thanks to their pervasive diffusion and the increasing number of embedded sensors. Data collected by such devices provide unprecedented insights into the physical world that can be inferred through cognitive processes, thus originating a digital sixth sense. In this paper, we describe how the Internet can behave like a sensing brain, thus evolving into the Internet of Senses, with network-based cognitive perception and action capabilities built upon mobile crowd-sensing mechanisms. The new concept of hyper-map is envisioned as an efficient geo-referenced repository of knowledge about the physical world. Such knowledge is acquired and augmented through heterogeneous sensors, multi-user cooperation and distributed learning mechanisms. Furthermore, we indicate the possibility to accommodate proactive sensors, in addition to common reactive sensors such as cameras, antennas, thermometers and inertial measurement units, by exploiting massive antenna arrays at millimeter-waves to enhance mobile terminals perception capabilities as well as the range of new applications. Finally, we distillate some insights about the challenges arising in the realization of the CPI, corroborated by preliminary results, and we depict a futuristic scenario where the proposed Internet of Senses becomes true.

AoA-based channel estimation for massive MIMO OFDM communication systems on high speed rails

Channel estimation is a well-known challenge for wireless orthogonal frequency division multiplexing (OFDM) communication systems with massive antennas on high speed rails (HSRs). This paper investigates this problem and design two practicable uplink and downlink channel estimators for orthogonal frequency division multiplexing (OFDM) communication systems with massive antenna arrays at base station on HSRs. Specifically, we first use pilots to estimate the initial angle of arrival (AoA) and channel gain information of each uplink path through discrete Fourier transform (DFT), and then refine the estimates via the angle rotation technique and suggested pilot design. Based on the uplink angel estimation, we design a new downlink channel estimator for frequency division duplexing (FDD) systems. Additionally, we derive the Cramer-Rao lower bounds (CRLBs) of the AoA and channel gain estimates. Finally, numerical results are provided to corroborate our proposed studies.

Retroreflective Transceiver Array Using a Novel Calibration Method Based on Optimum Phase Searching

The retrodirective or retroreflective method has been used as a radio-frequency beamforming method for massive antenna array, for wireless communication systems, radars, and microwave power transfer (MPT) systems. In particular, the retroreflective method is suitable for optimum beamforming for both near- and far-field zones. Each transmitter (Tx) and receiver (Rx) circuit in the Tx array has its own phase offset, which should be calibrated for optimum retroreflective operation. This article presents a simple and effective phase offset calibration method for the retroreflective beamforming arrays using a one-by-one basis optimum phase searching method with a receiver at a reference point. To verify the proposed calibration method for a massive retroreflective beamforming array, a 5.2 GHz shortand mid-range (≤4 m) MPT system, including a Tx array based on 8 × 8 antennas and an Rx using six antennas (five rectennas for power reception, and 1 additional for pilot transmission) for charging mobile handsets was designed and implemented. Optimal retroreflective beamforming from the Tx array, which was calibrated using the proposed method, was experimentally verified for elevation angles of within ±45 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> for both xz- and yz-planes. The implemented Tx and Rx circuits achieved a received dc power of about 100 mW at a distance of 4 m.

MmWave Doubly-Massive-MIMO Communications Enhanced With an Intelligent Reflecting Surface: Asymptotic Analysis

As a means to control wireless propagation environments, the use of emerging and novel intelligent reflecting surfaces (IRS) is envisioned to enhance and broaden many applications in future wireless networks. This paper is concerned with an IRS-assisted millimeter-wave (mmWave) system in which the IRS consists of multiple subsurfaces, each having the same number of passive reflecting elements, whereas both the transmitter and receiver are equipped with massive antenna arrays. Under the scenario of having very large numbers of antennas at both transmit and receive ends, the achievable rate of the system is derived. Furthermore, with the objective of maximizing the achievable rate, the paper presents optimal solutions of power allocation, precoding/combining, and IRS's phase shifts. Then it is shown that when the number of reflecting elements at each subsurface is very large, the number of favorable and controllable propagation paths provided by the IRS is simply equal to the number of subsurfaces while the received signal-to-noise ratio corresponding to each of the favorable paths increases quadratically with the number of reflecting elements. The problem of minimizing the transmit power subject to the rate constraint is also analyzed for the scenario without direct paths in the pure LOS propagation. In addition, the asymptotic analysis is extended to the multiuser scenario. Finally, numerical results are provided to corroborate the obtained analysis.

Downlink Transmission of Multicell Distributed Massive MIMO With Pilot Contamination Under Rician Fading

In this paper, we investigate the spectral efficiency (SE) of a multicell downlink (DL) distributed massive MIMO (DM-MIMO) system with pilot contamination operating over Rician fading channels in which each remote access unit (RAU) is equipped with a large number of distributed massive antenna arrays, while each user has a single antenna. In contrast to many previous works about DM-MIMO systems, the channel between users and the RAUs antennas in the same cell is modeled to be Rician fading, which is general for the 5G scenarios like Internet of Things. We explore maximum-ratio transmission (MRT) and line-of-sight (LOS) component-based equal-gain transmission (EGT) under imperfect channel state information. The tractable, but accurate closed-form expressions for the lower bounds of the achievable rate are derived for the MRT and the LOS component-based EGT over Rician fading channels in the DM-MIMO systems. Based on the obtained closed-form expressions, various power scaling laws concerning DL data transmit power and pilot transmit power are analyzed in detail. Numerical results are used to corroborate that these approximations are asymptotically tight, but accurate for systems. They also show that employing the LOS component-based EGT processing is more preferable than the MRT processing for DM-MIMO systems in conditions having a large number of RAU antennas and stronger LOS scenarios. Finally, the simulation results further show that when the number of all antennas for RAUs is fixed, the better SE performance can be obtained with more RAUs.

MmWave UAV Networks With Multi-Cell Association: Performance Limit and Optimization

This paper aims to exploit the fundamental limits on the downlink coverage and spatial throughput performances of a cellular network comprised of a tier of unmanned aerial vehicle (UAV) base stations (BSs) using the millimeter wave (mmWave) band and a tier of ground BSs using the ultra high frequency (UHF) band. To reduce handover signaling overhead, the ground BSs take charge of control signaling delivery whereas the UAVs are in charge of payload data transmission so that users need to be simultaneously associated with a ground BS and a UAV in this network with a control-data plane-split architecture. We first propose a three-dimensional (3D) location distribution model of the UAVs using stochastic geometry which is able to generally characterize the positions of the UAVs in the sky. Using this 3D distribution model of UAVs, two performance metrics, i.e., multi-cell coverage probability and volume spectral efficiency, are proposed. Their explicit low-complexity expressions are derived and their upper limits are found when each of the UAVs and ground BSs is equipped with a massive antenna array. We further show that the multi-cell coverage probability and the volume spectral efficiency can be maximized by optimally deploying and positioning the UAVs in the sky and thereby their fundamental maximal limits are found. These important analytical findings are validated by numerical simulations.

Maximum likelihood approach to DoA estimation using lens antenna array

Massive antenna array has been proposed to improve the spectral efficiency and link reliability in wireless communication systems. However, using large antenna arrays incurs additional cost in terms of signal processing and hardware complexity. The electromagnetic (EM) lens-focusing antennas are introduced as a promising technique to reduce the hardware complexity and cost. On the other hand, determining the location of users in terms of their direction-of-arrival (DoA) using these lens array becomes of great interest for different 5G services. This paper addresses the issue of DoA estimation by adopting lens antenna array (LNA). We firstly derive an expression for the received signal with the adoption of LNA, and then a maximum likelihood (ML) estimator for the DoA has been obtained. Depending on the ability of the lens array to focus the signal power on a subset of antennas as a function of DoA. We propose using the antenna selection (AS) technology to select an antenna subset aiming to reduce the number of radio frequency (RF) chains and accordingly reducing the hardware cost. The simulation results show the the capability of the proposed method to avoid the phase ambiguity problem and provide high accurate DoA estimation of signals.

On the Performance Gain of NOMA Over OMA in Uplink Communication Systems

In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink cellular communication systems. A base station equipped with a single-antenna, with multiple antennas, and with massive antenna arrays is considered both in single-cell and multi-cell deployments. In particular, in single-antenna systems, we identify two types of gains brought about by NOMA: 1) a large-scale near-far gain arising from the distance discrepancy between the base station and users; 2) a small-scale fading gain originating from the multipath channel fading. Furthermore, we reveal that the large-scale near-far gain increases with the normalized cell size, while the small-scale fading gain is a constant, given by $\gamma$ = 0.57721 nat/s/Hz, in Rayleigh fading channels. When extending single-antenna NOMA to $M$-antenna NOMA, we prove that both the large-scale near-far gain and small-scale fading gain achieved by single-antenna NOMA can be increased by a factor of $M$ for a large number of users. Moreover, given a massive antenna array at the base station and considering a fixed ratio between the number of antennas, $M$, and the number of users, $K$, the ESG of NOMA over OMA increases linearly with both $M$ and $K$. We then further extend the analysis to a multi-cell scenario. Compared to the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more severe inter-cell interference due to the non-orthogonal transmissions. Besides, we unveil that a large cell size is always beneficial to the ergodic sum-rate performance of NOMA in both single-cell and multi-cell systems. Numerical results verify the accuracy of the analytical results derived and confirm the insights revealed about the ESG of NOMA over OMA in different scenarios.

Diagnosis of Calibration State for Massive Antenna Array via Deep Learning

Time division duplex mode provides an attracting advantage called channel reciprocity to avoid excessive downlink (DL) pilot overhead, where the DL channel state information can be obtained from the uplink pilot. Unfortunately, such an ideal reciprocity would be broken easily because of hardware glitches from the transmit or receive radio frequency branches. In this letter, we develop a novel learning-based calibration state diagnosis network, where the linear pilot matrix and the non-linear reconstruction mapping decoder are learned jointly through training measure samples. With the assist of proposed network, a learning-based diagnosis procedure is designed to pick out the antennas that need to be calibrated. Experimental results demonstrate that both the normalized mean square error and accuracy are improved compared with the existing compressive sensing-based method.

Wireless Information and Power Transfer in Relay-Assisted Downlink Massive MIMO

The feasibility of utilizing a relay-assisted downlink massive multiple-input multiple-output (MIMO) to boost the performance of simultaneous wireless information and power transfer (SWIPT) is investigated. The key idea is to simultaneously transmit power and information toward the direct/relayed-users by leveraging the excess degrees-of-freedom offered by massive antenna arrays at the base-station (BS). The performance limits are established by deriving the harvested energies, achievable rates, and energy-rate trade-offs in the presence of imperfectly estimated/partial channel state information (CSI). These performance metrics are derived for a generalized SWIPT model, which facilitates analysis for both time-switching (TS) and power-splitting (PS) protocols. Two linear precoders based on zero-forcing and maximal ratio transmission are used at the massive MIMO BS, and a practically viable non-linear energy harvesting model is adopted for the direct/relayed-users. Joint detrimental implications of imperfect CSI at the BS, limited/statistical CSI at the users, and non-linear characteristics of energy harvesters are analytically quantified. In order to jointly guarantee user-fairness in terms of both energy and rate, a max-min optimal transmit power control policy is proposed. Then, the max-min fairness optimal energy-rate trade-offs are quantified for non-linear massive MIMO SWIPT. Our results reveal that the proposed relay-assisted downlink massive MIMO can be exploited to boost the achievable energy-rate trade-off by effectively reducing the end-to-end path-loss via shorter hop distances rendered by intermediate amplify-and-forward relays.

Performance Evaluation of Mimo System with Massive Antenna Arrays

In the ground of digital word, wireless communication system has widespread functions. The demand for wireless communication is growing day by day. All users want better quality service in scope of wireless communication. Due to high bit error rate (BER), low signal to noise ratio (SNR), limited bandwidth and high standard deviation (SD) of phase error conventional wireless communiqué systems like SISO, SIMO and MISO flops to gather the mounting demand of users. Therefore a new technique MIMO system is realized whose performance is evaluated by parameters BER, SNR and SD of Phase Error. Latest approach for increasing the performance of virtual MIMO communications within peers by ill-treatment situation with giant antenna arrays is developed. MIMO provides low BER and low SD of Phase Error with increased SNR, which forms a good feature for wireless communication system. In this paper, performance of MIMO is assessed under Nakagami-m and Rayleigh fading channels. For a wireless system BER and SD of phase error should be minimum, which is achieved in MIMO system.

Multipair Virtual Full Duplex Relaying with Massive MIMO

This paper considers a massive multiple-input multiple-output (MIMO) aided multipair relay network with virtual full-duplex relaying, where multiple single-antenna sources transmit signals to single-antenna destinations via two massive MIMO relays. The two half-duplex relay nodes work in virtual full-duplex relaying mode that transmit and receive signals in turn. We investigate the sum-rate performance of multipair virtual full-duplex relaying with simple maximum ratio combination and maximum ratio transmission at the relays, and derive closed-form expressions for the asymptotic signal-to-interference-plus-noise ratios when the numbers of relay antennas go infinite. We also compare the sum-rate performance of virtual full-duplex relaying with real full-duplex and conventional half-duplex relaying schemes. Both analytical and numerical results show that virtual full-duplex relaying with massive antenna arrays can achieve the same performance as real full-duplex relaying in symmetric multipair networks, and significantly outperform its half-duplex counterpart.

LI Cancellation and Power Allocation for Multipair FD Relay Systems With Massive Antenna Arrays

Massive antenna arrays are capable of canceling out the loop interference (LI) at the relay station in multipair full-duplex (FD) relay networks even without LI channel knowledge if the number of antennas is allowed to grow without a bound. For large but finite number of antennas, however, channel estimation-based LI cancelation is required. In this letter, we propose a pilot protocol for LI channel estimation by exploiting the channel coherence time difference between static and moving transceivers in a multipair FD relay system. To maximize the end-to-end achievable rate, we also design a novel power allocation scheme to adjust the transmit power of each link at the relay. The analytical and numerical results show that the proposed novel pilot protocol and power allocation scheme jointly improve spectral and energy efficiency significantly with realistic coherence time differences.

Quasi-optics-inspired low-profile endfire antenna element

In this paper, the design, operational principle and experimental validation of an endfire antenna element that is inspired by the energy-focusing characteristics of graded-index optical fibre are presented. The antenna operates with a bandwidth of 1.5 GHz centred around 14 GHz. It has a gain of around 5.5 dBi along the band of interest and a good pattern stability over the band. The antenna derives its unique nature from the arrangement of the arc-shaped parasitics, that couple onto the antenna´s driver dipole. An electromagnetic refractive index retrieval mechanism is used to guide the placement of the parasitics to enhance the gain. In addition to the design principle, a parametric study of the main parameters and their influence on the antenna behaviour is presented. The antenna is a potential candidate for use in multi-user massive MIMO antenna arrays for 5G communications where space is premium and in antenna array applications where a low-profile antenna element with a high gain is a necessity.

Massive MIMO is a reality—What is next?: Five promising research directions for antenna arrays

Massive MIMO (multiple-input multiple-output) is no longer a “wild” or “promising” concept for future cellular networks—in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies—once viewed prohibitively complicated and costly—is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6GHz and mmWave frequencies.In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.

Pursuance of mm-Level Accuracy: Ranging and Positioning in mmWave Systems

This paper aims at investigating the feasibility of ranging and positioning in millimeter (mm)-level accuracy by adopting millimeter-wave (mmWave) and three-dimensional massive antenna arrays. The agent to be localized is equipped with massive uniform rectangular array (URA) and multiple anchors are considered to pursue a higher accuracy, where a far-field environment is assumed for phased massive URA. By considering both a static scenario where the agent with an unknown coordinate is stationary and a dynamic scenario where the agent is moving, the fundamental limits of time-based ranging and positioning are derived by Cr $\acute{\text {a}}$ mer–Rao bound in a multipath model with and without a priori channel knowledge associated with mmWave transmission and massive URA. The relationship between the fundamental bound of range estimation and that of position estimation is theoretically clarified and the lower bound of position dilution of precision is derived. By investigating both models, the effect of the agent's movement to ranging and positioning accuracy is observed. Numerical results show that the proposed localization network achieves a precise mm-level accuracy for ranging and positioning.

A C-V-BLAST spread spectrum massive MIMO NOMA scheme for 5G systems with channel imperfections

In this paper, a spread spectrum non orthogonal multiple access (NOMA) scheme with massive multiple input multiple output (MIMO) deployment is investigated. The proposed scheme utilizes a low complexity Vertical Bell Laboratories Layered Space Time (C-V-BLAST) receiver in an uplink setting with distributed single-antenna users and a co-located base-station with massive antenna array system. We study the performance of the proposed scheme under a variety of channel imperfections including imperfect channel estimation, channel correlation, and mutual coupling among antenna elements. Analytical evaluation of the system performance under these conditions is presented in terms of the group error probability and validated through extensive simulations. It is shown that the proposed spread spectrum NOMA scheme provides significant improvements in performance compared to conventional V-BLAST system in case of a large MIMO deployment under channel imperfections.

A survey on 5G massive MIMO localization

Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different spatial signatures of users. This same property can also be harnessed to determine the locations of those users. In order to perform massive MIMO localization, refined channel estimation routines and localization methods have been developed. This paper provides a brief overview of this emerging field.