The Experts below are selected from a list of 13158 Experts worldwide ranked by ideXlab platform
Erik G. Larsson - One of the best experts on this subject based on the ideXlab platform.
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Max-Min Power Control in Downlink Massive MIMO With Distributed Antenna Arrays
IEEE Transactions on Communications, 2021Co-Authors: Noman Akbar, Emil Björnson, Nan Yang, Erik G. LarssonAbstract:In this paper, we investigate optimal downlink power allocation in Massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays (DAAs) under correlated and uncorrelated channel fading. In DAA Massive MIMO, a base station (BS) consists of multiple antenna sub-arrays. Notably, the antenna sub-arrays are deployed in arbitrary locations within a DAA Massive MIMO cell. Consequently, the distance-dependent large-scale propagation coefficients are different from a user to these different antenna sub-arrays, which makes power control a challenging problem. We assume that the network operates in time-division duplex mode, where each BS obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum-ratio transmission in the downlink. We then derive a closed-form signal-to-interference-plus-noise ratio (SINR) expression, where the channels are subject to correlated fading. Based on the SINR expression, we propose a network-wide max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate the performance advantages offered by DAA Massive MIMO. For some specific scenarios, DAA Massive MIMO can improve the average per-user throughput up to 55%. Furthermore, we demonstrate that channel fading covariance is an important factor in determining the performance of DAA Massive MIMO.
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max min power control in downlink Massive MIMO with distributed antenna arrays
IEEE Transactions on Communications, 2020Co-Authors: Noman Akbar, Emil Björnson, Nan Yang, Erik G. LarssonAbstract:In this paper, we investigate optimal downlink power allocation in Massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays (DAAs) under correlated and uncorrelated channel fading. In DAA Massive MIMO, the base station (BS) consists of multiple antenna sub-arrays. Notably, the antenna sub-arrays are deployed in arbitrary locations within a DAA Massive MIMO cell. Consequently, the distance-dependent large-scale propagation coefficients are different from a user to these different antenna sub-arrays, which makes power control a challenging problem. We assume that the network operates in time-division duplex mode, where each BS obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum-ratio transmission in the downlink. We then derive a closed-form signal-to-interference-plus-noise ratio (SINR) expression, where the channels are subject to correlated fading. Based on the SINR expression, we propose a networkwide max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate the performance advantages offered by DAA Massive MIMO. For some specific scenarios, DAA Massive MIMO can improve the average per-user throughput up to 55%. Furthermore, we demonstrate that channel fading covariance is an important factor in determining the performance of DAA Massive MIMO.
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Massive MIMO for internet of things iot connectivity
Physical Communication, 2019Co-Authors: Alexandrusabin Bana, Erik G. Larsson, Elisabeth De Carvalho, Beatriz Soret, Taufik Abrao, Jose Carlos Marinello, Petar PopovskiAbstract:Abstract Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of Massive MIMO is essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying Massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using Massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of Massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: Massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting Massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when Massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of Massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that Massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design.
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Ubiquitous cell-free Massive MIMO communications
EURASIP Journal on Wireless Communications and Networking, 2019Co-Authors: Giovanni Interdonato, Hien Quoc Ngo, Pal Frenger, Emil Björnson, Erik G. LarssonAbstract:Since the first cellular networks were trialled in the 1970s, we have witnessed an incredible wireless revolution. From 1G to 4G, the Massive traffic growth has been managed by a combination of wider bandwidths, refined radio interfaces, and network densification, namely increasing the number of antennas per site. Due its cost-efficiency, the latter has contributed the most. Massive MIMO (multiple-input multiple-output) is a key 5G technology that uses Massive antenna arrays to provide a very high beamforming gain and spatially multiplexing of users and hence increases the spectral and energy efficiency (see references herein). It constitutes a centralized solution to densify a network, and its performance is limited by the inter-cell interference inherent in its cell-centric design. Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive MIMO system implementing coherent user-centric transmission to overcome the inter-cell interference limitation in cellular networks and provide additional macro-diversity. These features, combined with the system scalability inherent in the Massive MIMO design, distinguish ubiquitous cell-free Massive MIMO from prior coordinated distributed wireless systems. In this article, we investigate the enormous potential of this promising technology while addressing practical deployment issues to deal with the increased back/front-hauling overhead deriving from the signal co-processing.
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Massive MIMO for 5g overview and the road ahead
Conference on Information Sciences and Systems, 2017Co-Authors: Erik G. LarssonAbstract:Massive MIMO is the currently most compelling sub-6 GHz wireless access technology for 5G. Since its inception about a decade ago, it has evolved from a wild “academic” idea to one of the most vibrant research topics in the wireless communications community, as well as a main work item in 5G standardization. The concept is to equip base stations with arrays of many antennas that serve many terminals simultaneously, in the same time-frequency resource. The arrays have attractive form factors: in the 2 GHz band, a halfwave-length-spaced rectangular array with 200 dual-polarized elements is about 1.5 × 0.75 meters large. Massive MIMO operates in TDD mode and the downlink beamforming exploits the uplink-downlink reciprocity of radio propagation. Specifically, the base station array uses channel estimates obtained from uplink pilots transmitted by the terminals to learn the channel in both directions. This makes Massive MIMO entirely scalable with respect to the number of base station antennas. Base stations operate autonomously, with no sharing of payload data or channel state information with other cells. In this talk I will discuss the development of Massive MIMO over the last five years, and outline the most important research problems ahead.
Thomas L Marzetta - One of the best experts on this subject based on the ideXlab platform.
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Massive MIMO is a reality what is next five promising research directions for antenna arrays
Digital Signal Processing, 2019Co-Authors: Emil Björnson, Luca Sanguinetti, Henk Wymeersch, Jakob Hoydis, Thomas L MarzettaAbstract:Abstract 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-6 GHz 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.
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Massive MIMO: ten myths and one critical question
IEEE Communications Magazine, 2016Co-Authors: Emil Bjrnson, Emil Björnson, Erik G. Larsson, Thomas L MarzettaAbstract:Wireless communications is one of the most successful technologies in modern years, given that an exponential growth rate in wireless traffic has been sustained for over a century (known as Cooper???s law). This trend will certainly continue, driven by new innovative applications; for example, augmented reality and the Internet of Things. Massive MIMO has been identified as a key technology to handle orders of magnitude more data traffic. Despite the attention it is receiving from the communication community, we have personally witnessed that Massive MIMO is subject to several widespread misunderstandings, as epitomized by following (fictional) abstract: ???The Massive MIMO technology uses a nearly infinite number of high-quality antennas at the base stations. By having at least an order of magnitude more antennas than active terminals, one can exploit asymptotic behaviors that some special kinds of wireless channels have. This technology looks great at first sight, but unfortunately the signal processing complexity is off the charts and the antenna arrays would be so huge that it can only be implemented in millimeter-wave bands.??? These statements are, in fact, completely false. In this overview article, we identify 10 myths and explain why they are not true. We also ask a question that is critical for the practical adoption of the technology and which will require intense future research activities to answer properly. We provide references to key technical papers that support our claims, while a further list of related overview and technical papers can be found at the Massive MIMO Info Point: http://MassiveMIMO. eu
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Massive MIMO: An Introduction
Bell Labs Technical Journal, 2015Co-Authors: Thomas L MarzettaAbstract:Demand for wireless throughput, both mobile and fixed, will always increase. One can anticipate that, in five or ten years, millions of augmented reality users in a large city will want to transmit and receive 3D personal high-definition video more or less continuously, say 100 megabits per second per user in each direction. Massive MIMO-also called Large-Scale Antenna Systems-is a promising candidate technology for meeting this demand. Fifty-fold or greater spectral efficiency improvements over fourth generation (4G) technology are frequently mentioned. A multiplicity of physically small, individually controlled antennas performs aggressive multiplexing/demultiplexing for all active users, utilizing directly measured channel characteristics. Unlike today's Point-to-Point MIMO, by leveraging time-division duplexing (TDD), Massive MIMO is scalable to any desired degree with respect to the number of service antennas. Adding more antennas is always beneficial for increased throughput, reduced radiated power, uniformly great service everywhere in the cell, and greater simplicity in signal processing. Massive MIMO is a brand new technology that has yet to be reduced to practice. Notwithstanding, its principles of operation are well understood, and surprisingly simple to elucidate.
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Cell-Free Massive MIMO: Uniformly great service for everyone
IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, 2015Co-Authors: Hien Quoc Ngo, Alexei Ashikhmin, Erik G. Larsson, Hong Yang, Thomas L MarzettaAbstract:We consider the downlink of Cell-Free Massive MIMO systems, where a very large number of distributed access points (APs) simultaneously serve a much smaller number of users. Each AP uses local channel estimates obtained from received uplink pilots and applies conjugate beamforming to transmit data to the users. We derive a closed-form expression for the achievable rate. This expression enables us to design an optimal max-min power control scheme that gives equal quality of service to all users. We further compare the performance of the Cell-Free Massive MIMO system to that of a conventional small-cell network and show that the throughput of the Cell-Free system is much more concentrated around its median compared to that of the small-cell system. The Cell-Free Massive MIMO system can provide an almost $20-$fold increase in 95%-likely per-user throughput, compared with the small-cell system. Furthermore, Cell-Free systems are more robust to shadow fading correlation than small-cell systems.
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Cell-Free Massive MIMO systems
2015 49th Asilomar Conference on Signals, Systems and Computers, 2015Co-Authors: Elina Nayebi, Alexei Ashikhmin, Thomas L Marzetta, Hong YangAbstract:Cell-Free Massive MIMO systems comprise a large number of distributed, low cost, and low power access point antennas, connected to a network controller. The number of antennas is significantly larger than the number of users. The system is not partitioned into cells and each user is served by all access point antennas simultaneously. In this paper, we define cell-free systems and analyze algorithms for power optimization and linear pre-coding. Compared with the conventional small-cell scheme, Cell-Free Massive MIMO can yield more than ten-fold improvement in terms of 5%-outage rate.
Ove Edfors - One of the best experts on this subject based on the ideXlab platform.
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Decentralized Massive MIMO Processing Exploring Daisy-Chain Architecture and Recursive Algorithms
IEEE Transactions on Signal Processing, 2020Co-Authors: Jesús Rodríguez Sánchez, Fredrik Rusek, Ove Edfors, Muris SarajlićAbstract:Algorithms for Massive MIMO uplink detection and downlink precoding typically rely on a centralized approach, by which baseband data from all antenna modules are routed to a central node in order to be processed. In the case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, said routing becomes a bottleneck since interconnection throughput is limited. This paper presents a fully decentralized architecture and an algorithm for Massive MIMO uplink detection and downlink precoding based on the Coordinate Descent (CD) method, which does not require a central node for these tasks. Through a recursive approach and very low complexity operations, the proposed algorithm provides a good trade-off between performance, interconnection throughput and latency. Further, our proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.
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a vlsi implementation of angular domain Massive MIMO detection
International Symposium on Circuits and Systems, 2019Co-Authors: Mojtaba Mahdavi, Ove Edfors, Viktor Owall, Liang LiuAbstract:This paper presents an angular-domain Massive MIMO detector which exploits sparsity in Massive MIMO channel along with a reconfigurable systolic array architecture to achieve high area efficiency. The underlying idea is to perform signal detection in the angular domain, where the channel matrix can have much lower dimension due the limited number of dominant angles (of arrival and departure) of the wireless signal. Evaluated using the measured Massive MIMO channel, the proposed method results in 40%–70% reduction in processing complexity and memory requirements compared to traditional antenna-domain detection. This complexity reduction enables extensive hardware reuse where all the operations of detection processing are mapped to a condensed reconfigurable systolic array. The angular-domain zero-forcing detector, which supports 128 base station antennas and 16 users is implemented in a 28 nm FD-SOI technology. Synthesis result shows that our design attains a throughput of 510 MSps with an area of 537 kG.
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reciprocity calibration for Massive MIMO proposal modeling and validation
IEEE Transactions on Wireless Communications, 2017Co-Authors: Joao Vieira, Steffen Malkowsky, Fredrik Rusek, Ove Edfors, Fredrik TufvessonAbstract:This paper presents a mutual coupling-based calibration method for time-division-duplex Massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the base station (BS) side by sounding all BS antenna pairs. An expectation-maximization (EM) algorithm is derived, which processes the measured channels in order to estimate calibration coefficients. The EM algorithm outperforms the current state-of-the-art narrow-band calibration schemes in a mean squared error and sum-rate capacity sense. Like its predecessors, the EM algorithm is general in the sense that it is not only suitable to calibrate a co-located Massive MIMO BS, but also very suitable for calibrating multiple BSs in distributed MIMO systems. The proposed method is validated with experimental evidence obtained from a Massive MIMO testbed. In addition, we address the estimated narrow-band calibration coefficients as a stochastic process across frequency, and study the subspace of this process based on measurement data. With the insights of this study, we propose an estimator which exploits the structure of the process in order to reduce the calibration error across frequency. A model for the calibration error is also proposed based on the asymptotic properties of the estimator, and is validated with measurement results.
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a simulation framework for multiple antenna terminals in 5g Massive MIMO systems
IEEE Access, 2017Co-Authors: Erik L. Bengtsson, Steffen Malkowsky, Peter C. Karlsson, Fredrik Rusek, Fredrik Tufvesson, Ove EdforsAbstract:The recent interest in Massive multiple in multiple out (MIMO) has spurred intensive work on Massive MIMO channel modeling in the contemporary literature. However, current models fail to take the characteristics of terminal antennas into account. There is no Massive MIMO channel model available that can be used for the evaluation of the influence of different antenna characteristics at the terminal side. In this paper, we provide a simulation framework that fills this gap. We evaluate the framework with antennas integrated into Sony Xperia handsets operating at 3.7 GHz as this spectrum is identified for the 5G new radio standard by 3rd Generation Partnership Project. The simulation results are compared with the measured terminal performance when communicating with the Lund University’s Massive MIMO testbed under the same loading conditions. Expressions are derived for comparison of the gain obtained from different diversity schemes computed from measured far-field antenna patterns. We conclude that the simulation framework yields the results close to the measured ones and that the framework can be used for antenna evaluation for terminals in a practical precoded Massive MIMO system.
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Massive MIMO Performance Evaluation Based on Measured Propagation Data
IEEE Transactions on Wireless Communications, 2015Co-Authors: Xiang Gao, Fredrik Rusek, Ove Edfors, Fredrik TufvessonAbstract:Massive MIMO, also known as very-large MIMO or large-scale antenna systems, is a new technique that potentially can offer large network capacities in multi-user scenarios. With a Massive MIMO system, we consider the case where a base station equipped with a large number of antenna elements simultaneously serves multiple single-antenna users in the same time-frequency resource. So far, investigations are mostly based on theoretical channels with independent and identically distributed (i.i.d.) complex Gaussian coefficients, i.e., i.i.d. Rayleigh channels. Here, we investigate how Massive MIMO performs in channels measured in real propagation environments. Channel measurements were performed at 2.6 GHz using a virtual uniform linear array (ULA), which has a physically large aperture, and a practical uniform cylindrical array (UCA), which is more compact in size, both having 128 antenna ports. Based on measurement data, we illustrate channel behavior of Massive MIMO in three representative propagation conditions, and evaluate the corresponding performance. The investigation shows that the measured channels, for both array types, allow us to achieve performance close to that in i.i.d. Rayleigh channels. It is concluded that in real propagation environments we have characteristics that can allow for efficient use of Massive MIMO, i.e., the theoretical advantages of this new technology can also be harvested in real channels.
Fredrik Tufvesson - One of the best experts on this subject based on the ideXlab platform.
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Massive MIMO Optimization With Compatible Sets
IEEE Transactions on Wireless Communications, 2019Co-Authors: Emma Fitzgerald, Michal Pioo, Fredrik TufvessonAbstract:Massive multiple-input multiple-output (MIMO) is expected to be a vital component in future 5G systems. As such, there is a need for new modeling in order to investigate the performance of Massive MIMO not only at the physical layer but also higher up the networking stack. In this paper, we present general optimization models for Massive MIMO, based on mixed-integer programming and compatible sets, with both maximum ratio combining and zero-forcing precoding schemes. We then apply our models to the case of joint device scheduling and power control for heterogeneous devices and traffic demands, in contrast to the existing power control schemes that consider only homogeneous users and saturated scenarios. Our results show that substantial benefits, in terms of energy usage, can be achieved without sacrificing throughput and that both the signaling overhead and the complexity of end devices can be reduced by abrogating the need for uplink power control through efficient scheduling.
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reciprocity calibration for Massive MIMO proposal modeling and validation
IEEE Transactions on Wireless Communications, 2017Co-Authors: Joao Vieira, Steffen Malkowsky, Fredrik Rusek, Ove Edfors, Fredrik TufvessonAbstract:This paper presents a mutual coupling-based calibration method for time-division-duplex Massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the base station (BS) side by sounding all BS antenna pairs. An expectation-maximization (EM) algorithm is derived, which processes the measured channels in order to estimate calibration coefficients. The EM algorithm outperforms the current state-of-the-art narrow-band calibration schemes in a mean squared error and sum-rate capacity sense. Like its predecessors, the EM algorithm is general in the sense that it is not only suitable to calibrate a co-located Massive MIMO BS, but also very suitable for calibrating multiple BSs in distributed MIMO systems. The proposed method is validated with experimental evidence obtained from a Massive MIMO testbed. In addition, we address the estimated narrow-band calibration coefficients as a stochastic process across frequency, and study the subspace of this process based on measurement data. With the insights of this study, we propose an estimator which exploits the structure of the process in order to reduce the calibration error across frequency. A model for the calibration error is also proposed based on the asymptotic properties of the estimator, and is validated with measurement results.
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a simulation framework for multiple antenna terminals in 5g Massive MIMO systems
IEEE Access, 2017Co-Authors: Erik L. Bengtsson, Steffen Malkowsky, Peter C. Karlsson, Fredrik Rusek, Fredrik Tufvesson, Ove EdforsAbstract:The recent interest in Massive multiple in multiple out (MIMO) has spurred intensive work on Massive MIMO channel modeling in the contemporary literature. However, current models fail to take the characteristics of terminal antennas into account. There is no Massive MIMO channel model available that can be used for the evaluation of the influence of different antenna characteristics at the terminal side. In this paper, we provide a simulation framework that fills this gap. We evaluate the framework with antennas integrated into Sony Xperia handsets operating at 3.7 GHz as this spectrum is identified for the 5G new radio standard by 3rd Generation Partnership Project. The simulation results are compared with the measured terminal performance when communicating with the Lund University’s Massive MIMO testbed under the same loading conditions. Expressions are derived for comparison of the gain obtained from different diversity schemes computed from measured far-field antenna patterns. We conclude that the simulation framework yields the results close to the measured ones and that the framework can be used for antenna evaluation for terminals in a practical precoded Massive MIMO system.
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Massive MIMO Performance Evaluation Based on Measured Propagation Data
IEEE Transactions on Wireless Communications, 2015Co-Authors: Xiang Gao, Fredrik Rusek, Ove Edfors, Fredrik TufvessonAbstract:Massive MIMO, also known as very-large MIMO or large-scale antenna systems, is a new technique that potentially can offer large network capacities in multi-user scenarios. With a Massive MIMO system, we consider the case where a base station equipped with a large number of antenna elements simultaneously serves multiple single-antenna users in the same time-frequency resource. So far, investigations are mostly based on theoretical channels with independent and identically distributed (i.i.d.) complex Gaussian coefficients, i.e., i.i.d. Rayleigh channels. Here, we investigate how Massive MIMO performs in channels measured in real propagation environments. Channel measurements were performed at 2.6 GHz using a virtual uniform linear array (ULA), which has a physically large aperture, and a practical uniform cylindrical array (UCA), which is more compact in size, both having 128 antenna ports. Based on measurement data, we illustrate channel behavior of Massive MIMO in three representative propagation conditions, and evaluate the corresponding performance. The investigation shows that the measured channels, for both array types, allow us to achieve performance close to that in i.i.d. Rayleigh channels. It is concluded that in real propagation environments we have characteristics that can allow for efficient use of Massive MIMO, i.e., the theoretical advantages of this new technology can also be harvested in real channels.
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Massive MIMO for next generation wireless systems
IEEE Communications Magazine, 2014Co-Authors: Erik G. Larsson, Ove Edfors, Fredrik Tufvesson, Thomas L Marzetta, Erik LarssonAbstract:Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of Massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While Massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the Massive MIMO concept and contemporary research on the topic.
Emil Björnson - One of the best experts on this subject based on the ideXlab platform.
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Max-Min Power Control in Downlink Massive MIMO With Distributed Antenna Arrays
IEEE Transactions on Communications, 2021Co-Authors: Noman Akbar, Emil Björnson, Nan Yang, Erik G. LarssonAbstract:In this paper, we investigate optimal downlink power allocation in Massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays (DAAs) under correlated and uncorrelated channel fading. In DAA Massive MIMO, a base station (BS) consists of multiple antenna sub-arrays. Notably, the antenna sub-arrays are deployed in arbitrary locations within a DAA Massive MIMO cell. Consequently, the distance-dependent large-scale propagation coefficients are different from a user to these different antenna sub-arrays, which makes power control a challenging problem. We assume that the network operates in time-division duplex mode, where each BS obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum-ratio transmission in the downlink. We then derive a closed-form signal-to-interference-plus-noise ratio (SINR) expression, where the channels are subject to correlated fading. Based on the SINR expression, we propose a network-wide max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate the performance advantages offered by DAA Massive MIMO. For some specific scenarios, DAA Massive MIMO can improve the average per-user throughput up to 55%. Furthermore, we demonstrate that channel fading covariance is an important factor in determining the performance of DAA Massive MIMO.
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max min power control in downlink Massive MIMO with distributed antenna arrays
IEEE Transactions on Communications, 2020Co-Authors: Noman Akbar, Emil Björnson, Nan Yang, Erik G. LarssonAbstract:In this paper, we investigate optimal downlink power allocation in Massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays (DAAs) under correlated and uncorrelated channel fading. In DAA Massive MIMO, the base station (BS) consists of multiple antenna sub-arrays. Notably, the antenna sub-arrays are deployed in arbitrary locations within a DAA Massive MIMO cell. Consequently, the distance-dependent large-scale propagation coefficients are different from a user to these different antenna sub-arrays, which makes power control a challenging problem. We assume that the network operates in time-division duplex mode, where each BS obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum-ratio transmission in the downlink. We then derive a closed-form signal-to-interference-plus-noise ratio (SINR) expression, where the channels are subject to correlated fading. Based on the SINR expression, we propose a networkwide max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate the performance advantages offered by DAA Massive MIMO. For some specific scenarios, DAA Massive MIMO can improve the average per-user throughput up to 55%. Furthermore, we demonstrate that channel fading covariance is an important factor in determining the performance of DAA Massive MIMO.
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Massive MIMO is a reality what is next five promising research directions for antenna arrays
Digital Signal Processing, 2019Co-Authors: Emil Björnson, Luca Sanguinetti, Henk Wymeersch, Jakob Hoydis, Thomas L MarzettaAbstract:Abstract 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-6 GHz 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.
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Ubiquitous cell-free Massive MIMO communications
EURASIP Journal on Wireless Communications and Networking, 2019Co-Authors: Giovanni Interdonato, Hien Quoc Ngo, Pal Frenger, Emil Björnson, Erik G. LarssonAbstract:Since the first cellular networks were trialled in the 1970s, we have witnessed an incredible wireless revolution. From 1G to 4G, the Massive traffic growth has been managed by a combination of wider bandwidths, refined radio interfaces, and network densification, namely increasing the number of antennas per site. Due its cost-efficiency, the latter has contributed the most. Massive MIMO (multiple-input multiple-output) is a key 5G technology that uses Massive antenna arrays to provide a very high beamforming gain and spatially multiplexing of users and hence increases the spectral and energy efficiency (see references herein). It constitutes a centralized solution to densify a network, and its performance is limited by the inter-cell interference inherent in its cell-centric design. Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive MIMO system implementing coherent user-centric transmission to overcome the inter-cell interference limitation in cellular networks and provide additional macro-diversity. These features, combined with the system scalability inherent in the Massive MIMO design, distinguish ubiquitous cell-free Massive MIMO from prior coordinated distributed wireless systems. In this article, we investigate the enormous potential of this promising technology while addressing practical deployment issues to deal with the increased back/front-hauling overhead deriving from the signal co-processing.
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what is the benefit of code domain noma in Massive MIMO
Personal Indoor and Mobile Radio Communications, 2019Co-Authors: Luca Sanguinetti, Emil Björnson, Mariagabriella Di BenedettoAbstract:In overloaded Massive MIMO systems, wherein the number K of user equipments (UEs) exceeds the number of base station antennas M, it has recently been shown that non-orthogonal multiple access (NOMA) can increase performance. This paper aims at identifying cases of the classical operating regime K < M, where code-domain NOMA can also improve the spectral efficiency of Massive MIMO. Particular attention is given to use cases in which poor favorable propagation conditions are experienced. Numerical results show that Massive MIMO with planar antenna arrays can benefit from NOMA in practical scenarios where the UEs are spatially close to each other.
