Showing papers by "Thomas L. Marzetta published in 2015"

Massive MIMO: An Introduction

Abstract: 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.

Cell-Free Massive MIMO: Uniformly great service for everyone

Abstract: 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 smallcell 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 smallcell systems.

Cell-Free Massive MIMO systems

Abstract: 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.

Cell-Free Massive MIMO: Uniformly Great Service For Everyone

Abstract: 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.

Massive MIMO: 10 Myths and One Grand Question.

Abstract: 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 internet-of-things. Massive MIMO (multiple-input multiple-output) 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." The "properties" above are, in fact, completely false. In this article, we identify 10 myths and explain why they are not true. We also ask a grand question that will require intense future research activities to answer properly.

Energy Efficient Design of Massive MIMO: How Many Antennas?

Abstract: We provide explicit formulas for the optimal number of antennas per base station to maximize the cell total energy efficiency of a power- controlled multi-cell Massive MIMO. Furthermore, we show that equipping the same number of antennas in each base station results in virtually no loss in energy efficiency due to the flatness of energy efficiency function. Conjugate beamforming compares very competitively with zero-forcing and in fact, due to the loss in effective array gain and the additional computational cost in zero-forcing, conjugate beamforming can achieve better energy efficiency when per user throughput demand is moderate. In single cell case, we provide explicit algebraic formulas for the optimal number of antennas and optimal radiated power when a target per user throughput is given.

Performance of pilot reuse in multi-cell Massive MIMO

Abstract: Pilot reuse in multiple cell Massive MIMO induces coherent inter-cell interference that does not disappear with large numbers of antennas. A simple way to mitigate pilot contamination is to use a pilot reuse factor that is greater than one, which surrounds the home cell by one or more rings of non-contaminating cells. The penalty for this measure is that training consumes an increasing fraction of the slot. We perform comprehensive multiple cell semi-analytical simulations for various scenarios in order to determine the optimum pilot reuse factor. We find that in dense urban deployments, the relatively small cell size combined with low user mobility dictates pilot reuse seven. Conversely in suburban deployments pilot reuse three is optimal. Our conclusions are at variance with others who advocate pilot reuse one, which we attribute to a great extent to the fact that our performance criterion is 95% likely per user throughput rather than sum throughput.

Pilot assignment in cell free massive MIMO wireless systems

Abstract: Systems and methods for assigning pilot sequences include randomly assigning pilot sequences to a population of access terminals. A first pilot sequence assigned to a worst performing access terminal of the population of access terminals is updated to a different pilot sequence that improves performance of the worst performing access terminal. A second pilot sequence assigned to a best performing access terminal of the population of access terminals is updated to another pilot sequence that reduces performance of the best performing access terminal.

Wireless communication with suppression of inter-cell interference in large-scale antenna systems

Abstract: In exemplary LSAS (large-scale antenna system) networks, uplink signals are processed to compensate for pilot contamination. Slow-fading coefficients are generated for terminals in the wireless network, and postcoding matrices are generated based on the slow- fading coefficients and terminal transmit power levels. Uplink signals are received from the terminals, and M-dimensional postcoding is performed to generate estimated uplink signals from the received uplink signals, where M is the number of antennas at a base station of the wireless network. The postcoding matrices are applied to the estimated uplink signals to compensate for pilot contamination. The improved technique used to generate postcoding matrices depends on whether the M-dimensional postcoding involves matched filtering or zero forcing. Postcoding matrices generated using improved techniques work better than those generated independent of the terminal transmit power levels by inverting slow-fading coefficient matrices for LSAS networks having intermediate numbers (>10 and <1000) of antennas per base station.

On existence of power controls for Massive MIMO

Abstract: For a general multicell Massive MIMO network, we obtain easily verifiable conditions for the existence of power controls that meet given down-link and up-link per access terminal SINR requirements. Surprisingly, some loosely constructed sufficient conditions that guarantee the existence of power controls become necessary in the single cell case, whose “max-min” SINR problems are then easily solved.

Quantized beamforming in Massive MIMO

Abstract: We investigate the effects of quantized beamforming on the capacity of a Massive MIMO by deriving explicit performance formulas. Our approach is applicable to a large class of quantizations, including PSK, QAM and polar quantizations. We show that with any degree of quantization, the beamforming gain grows linearly with the number of transmit antennas. Our results have important implications for the practical design and implementation of Massive MIMO as they indicate quantitatively the feasibility of highly economical A/D and D/A converters with very low resolutions.

Methods and apparatuses for configuring number of antennas for massive mimo communication

Abstract: A central node of a Massive Multiple-Input-Multiple-Output (MIMO) system includes a processor and a transceiver. The processor is configured to determine a number of active antennas to be used to serve users in at least one cell of the Massive MIMO system based on wireless network parameters for the Massive MIMO system. The transceiver is configured to transmit the determined number of active antennas to a Massive MIMO base station in the at least one cell.

Method and apparatus of wireless communication using tdd transmissions

Abstract: A method is provided, in which a base station allocates pilot signals to mobile terminals in a cell, obtains CSI from uplink pilot signals transmitted by mobile terminals, uses the CSI to precode messages, and transmits the messages in conformance with a TDD protocol. The CSI is obtained by comparing the pilot signal received from each mobile terminal to a known pilot signal associated with that mobile terminal. The known pilot signals are associated with respective mobile terminals according to a pilot signal reuse pattern in which adjacent cells are allocated mutually orthogonal reuse groups of mutually orthogonal pilot signals, and mobile terminals within a given cell are limited to transmitting only pilot signals allocated to that cell.