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.

/pdf/massive-mimo-for-next-generation-wireless-systems-1eydagg3js.pdf

Massive MIMO for next generation wireless systems

It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

/pdf/an-overview-of-limited-feedback-in-wireless-communication-1njp2bb1kz.pdf

An overview of limited feedback in wireless communication systems

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs), which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max–min power control algorithms. Max–min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly fivefold improvement in 95%-likely per-user throughput over the small-cell scheme, and tenfold improvement when shadow fading is correlated.

/pdf/cell-free-massive-mimo-versus-small-cells-49asq7q87u.pdf

Cell-Free Massive MIMO Versus Small Cells

A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a very large number of distributed access points (APs)which simultaneously serve a much smaller number of users over the same time/frequency resources based on directly measured channel characteristics. The APs and users have only one antenna each. The APs acquire channel state information through time-division duplex operation and the reception of uplink pilot signals transmitted by the users. The APs perform multiplexing/de-multiplexing through conjugate beamforming on the downlink and matched filtering on the uplink. Closed-form expressions for individual user uplink and downlink throughputs lead to max-min power control algorithms. Max-min power control ensures uniformly good service throughout the area of coverage. A pilot assignment algorithm helps to mitigate the effects of pilot contamination, but power control is far more important in that regard. 
Cell-Free Massive MIMO has considerably improved performance with respect to a conventional small-cell scheme, whereby each user is served by a dedicated AP, in terms of both 95%-likely per-user throughput and immunity to shadow fading spatial correlation. Under uncorrelated shadow fading conditions, the cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user throughput over the small-cell scheme, and 10-fold improvement when shadow fading is correlated.

Cell-Free Massive MIMO versus Small Cells

Blending theoretical results with practical applications, this book provides an introduction to random matrix theory and shows how it can be used to tackle a variety of problems in wireless communications The Stieltjes transform method, free probability theory, combinatoric approaches, deterministic equivalents and spectral analysis methods for statistical inference are all covered from a unique engineering perspective Detailed mathematical derivations are presented throughout, with thorough explanation of the key results and all fundamental lemmas required for the reader to derive similar calculus on their own These core theoretical concepts are then applied to a wide range of real-world problems in signal processing and wireless communications, including performance analysis of CDMA, MIMO and multi-cell networks, as well as signal detection and estimation in cognitive radio networks The rigorous yet intuitive style helps demonstrate to students and researchers alike how to choose the correct approach for obtaining mathematically accurate results

Random Matrix Methods for Wireless Communications

Channel state information at the transmitter (CSIT) can greatly improve the capacity of a wireless MIMO communication system. In a time division duplex (TDD) system CSIT can be obtained by exploiting the reciprocity of the wireless channel. This however requires calibration of the radio frequency (RF) chains of the receiver and the transmitter, which are in general not reciprocal. In this paper we investigate different methods for relative calibration in the presence of frequency offsets between transmitter and receiver. We show results of theses calibration methods with real two-directional channel measurements, which were performed using the Eure-com MIMO Openair Sounder (EMOS). We demonstrate that in a single-user MIMO channel and for low signal-to-noise (SNR) ratios, the relative calibration method can increase the capacity close to the theoretical limit.

/pdf/relative-channel-reciprocity-calibration-in-mimo-tdd-systems-2legr4lknn.pdf

Relative channel reciprocity calibration in MIMO/TDD systems

A relatively recent idea of extending the benefits of MIMO systems to multiuser scenarios seems promising in the context of achieving high data rates envisioned for future cellular standards after 3G (3rd Generation). Although substantial research has been done on the theoretical front, recent focus is on making Multiuser Multiple-Input Multiple-Output (MUMIMO) practically realizable. This paper presents an overview of the different MU-MIMO schemes included/being studied in 3GPP standardization from LTE (long-term evolution) to LTE Advanced. MU-MIMO system concepts and implementation aspects have been studied here. Various low-complexity receiver architectures are investigated, and their performance assessed through link-level simulations. Appealing performance offered by low-complexity interference aware (IA) receivers is notably emphasized. Furthermore, system level simulations for LTE Release 8 are provided. Interestingly, it is shown that MU-MIMO only offers marginal performance gains with respect to single-user MIMO. This arises from the limited MU-MIMO features included in Release 8 and calls for improved schemes for the upcoming releases.

/pdf/mu-mimo-in-lte-systems-1ce2yo18nm.pdf

MU-MIMO in LTE Systems

We propose and parameterize an empirical model of the outdoor-to-indoor and indoor-to-indoor distributed (cooperative) radio channel, using experimental data in the 2.4-GHz band. In addition to the well-known physical effects of path loss, shadowing, and fading, we include several new aspects in our model that are specific to multiuser distributed channels: 1) correlated shadowing between different point-to-point links, which has a strong impact on cooperative system performance; 2) different types of indoor node mobility with respect to the transmitter and/or receiver nodes, implying a distinction between static and dynamic shadowing motivated by the measurement data; and 3) a small-scale fading distribution that captures more severe fading than that given by the Rayleigh distribution.

/pdf/experimental-characterization-and-modeling-of-outdoor-to-4dj08n4g8i.pdf

Experimental Characterization and Modeling of Outdoor-to-Indoor and Indoor-to-Indoor Distributed Channels

In multi-user multiple-input multiple-output (MU-MIMO) systems, spatial multiplexing can be employed to increase the throughput without the need for multiple antennas and expensive signal processing at the user equipments. In theory, MU-MIMO is also more immune to most of propagation limitations plaguing single-user MIMO (SU-MIMO) systems, such as channel rank loss or antenna correlation. However, in this paper we show that this is not always true. We compare the capacity and the correlation of measured MU-MIMO channels for both outdoor and indoor scenarios. The measurement data has been acquired using Eurecompsilas MIMO openair sounder (EMOS). The EMOS can perform real-time MIMO channel measurements synchronously over multiple users. The results show that in most scenarios MU-MIMO provides a higher throughput than SU-MIMO also in the measured channels. However, in outdoor scenarios with a line of sight, the capacity drops significantly when the users are close together, due to high correlation at the transmitter side of the channel. In such a case, the performance of SU-MIMO and MU-MIMO is comparable.

/pdf/correlation-and-capacity-of-measured-multi-user-mimo-3fisnruy0g.pdf

Correlation and capacity of measured multi-user MIMO channels

Ultra-reliable low-latency communication (URLLC) has been introduced in 5G new radio for new applications that have strict reliability and latency requirements such as augmented/virtual reality, industrial automation and autonomous vehicles. The first full set of the physical layer design of 5G release, Release 15, was finalized in December 2017. It provided a foundation for URLLC with new features such as flexible sub-carrier spacing, a sub-slot-based transmission scheme, new channel quality indicator, new modulation and coding scheme tables, and configured-grant transmission with automatic repetitions. The second 5G release, Release 16, was finalized in December 2019 and allows achieving improved metrics for latency and reliability to support new use cases of URLLC. A number of new features such as enhanced physical downlink (DL) control channel monitoring capability, new DL control information format, sub-slot physical uplink (UL) control channel transmission, sub-slot-based physical UL shared channel repetition, enhanced mobile broadband and URLLC inter-user-equipment multiplexing with cancellation indication and enhanced power control were standardized. This article provides a detailed overview of the URLLC features from 5G Release 15 to Release 16 by describing how these features allow meeting URLLC target requirements in 5G networks. The ongoing Release 17 targets further enhanced URLLC operation by improving mechanisms such as feedback, intra-user-equipment multiplexing and prioritization of traffic with different priority, support of time synchronization and new quality of service related parameters. In addition, a fundamental feature targeted in URLLC Release 17 is to enable URLLC operation over shared unlicensed spectrum. The potential directions of URLLC research in unlicensed spectrum in Release 17 are presented to serve as a bridge from URLLC in licensed spectrum in Release 16 to URLLC in unlicensed spectrum in Release 17.

/pdf/an-overview-of-physical-layer-design-for-ultra-reliable-low-kky5lxqs2p.pdf

Florian Kaltenberger

Papers

Relative channel reciprocity calibration in MIMO/TDD systems

MU-MIMO in LTE Systems

Experimental Characterization and Modeling of Outdoor-to-Indoor and Indoor-to-Indoor Distributed Channels

Correlation and capacity of measured multi-user MIMO channels

An Overview of Physical Layer Design for Ultra-Reliable Low-Latency Communications in 3GPP Releases 15, 16, and 17