Marzetta Massive10x or 100x more wireless throughput using many antennas. Masayoshi Son at Softbank has just deployed the first commercial Massive MIMO systems. 100 systems, each with 128 antennas, are being deployed in Japan. The early results show 6-10X capacity improvements. Some of the world's best engineers - Henry Samueli, Andrea Goldsmith, Arogyaswami Paulraj, Vint Cerf - expect ultimately MIMO will yield a 50X improvement. (That's not guaranteed, of course.)

Ericsson announced they'd be shipping 64 antenna systems in 2017. Huawei, ZTE, and Facebook have demonstrated 96-128 antenna systems. Each transmitting antenna sends a separate signal. The receivers mostly have two or four antennas. Each signal bounces off walls and other obstacles in a slightly different way, allowing the receivers to separate them. 

Paulraj invented MIMO in 1993 and nearly twenty years ago predicted a 100X improvement in capacity at very little power increase. Many of us have seen a preview of what's possible from WiFi 802.11ac. That uses 4 antennas and triples performance under the right conditions. Add more antennas and enough processing power and almost anything may be achievable. 

 I believe Tom Marzetta of Bell Labs coined the name "massive MIMO" for the use of 16 or more antennas. I've collected some of the abstracts from Marzetta, a good place to begin your research.

 In 2015, I wrote: Standards in WiFi and LTE already cover up to 8 antennas, although few if any networks using more than 4 have been deployed. The calculations involved challenge today's most advanced chips. ZTE has a test unit with 128 antennas. Tower has a chip for 256. pCell publically demonstrated a unit of 35 antennas. 

Verizon, Nokia and pCell are going into field tests with a great deal of optimism. "Massive MIMO" is proven in the lab and few doubt it will work. In November 2015, Verizon and Nokia startled the wireless world by announcing they are moving ahead. Until then, few expected practical results until 5G in 2020-2022. (Update, 2016 pCwll has not yet delivered on its claims.)

The biggest obstacle is canceling interference. The calculations needed are far beyond current small computers. pCell needed 3 workstations for a simplified demonstration in a single room with no barriers. Far more processing power will be needed to reach moving phones, walls and windows. Henry Samueli at Broadcom is working on a chip for more than 50 transmitters. ZTE claims a prototype.

Separating the signals from so many transmitters is also a challenge. Receivers can't tell the signals apart if they have clear line of sight to the transmitters unless the receivers are meters across. As Paulraj discovered in 1993, if the signals bounce off walls, etc, the receiver can separate them even if they are very close when sent. This was in conflict with previous belief. It wasn't until Paul founded Iospan and produced working units that most people were convinced. MIMO soon became standard in WiFi and WiMAX. LTE includes MIMO up to 8 antennas. (The LTE standards are substantially derived from Paul's WiMAX work.)

Beamforming refers to a process of focusing the radio waves so that each receiver is served with a maximum. The technique is decades old but continually being refined. More here

At Marconi webinars I chaired, four distinguished engineers were comfortable predicting 50x better wireless performance. "100x better will be a little harder," one guessed, "but may be possible." I believe that's based on technologies proven in the labs, including M-MIMO, beamforming and interference cancellation. Twenty years ago, Stanford Professor Paulraj predicted MIMO could scale to 100x and even 1000x better performance. Almost no one believed him.

I need to write up a better explanation for less technical readers. Suggestions welcome.

Meanwhile, below are the conference summary and two abstracts from a 2014 IEEE event and the November 2015 Wikipedia article on MIMO. I will create a separate article for Massive MIMO.

Massive MIMO Communications

Recently, large-scale or massive multiple-input multiple-output (MIMO) techniques have been proposed to dramatically improve the performance of wireless networks. Massive MIMO base stations are equipped with a large number of antennas, co-located or distributed around the main base station site, so as to mitigate the effects of noise, fading, and multi-user interference. The key enabler lies in the asymptotic orthogonality properties of the large-dimensional random vectors associated with the massive MIMO channels, and in the large array gains that massive MIMO arrays offer. Additional promises of large-scale antenna systems include (i) lending themselves to low-complexity precoding/detection, (ii) savings in terms of radiated RF energy per transmitted information bit, (iii) enabling hardware-friendly waveform shaping, and (iv) reduced sensitivity to distortions stemming from hardware non-linearities and imperfections. Yet, challenges abound regarding both fundamental performance analysis as well as implementation of massive MIMO systems in real-life scenarios. To foster understanding within this hot topic, original contributions are sought in the following areas:

  • Channel estimation for massive MIMO systems

  • Low-complexity precoding and decoding design for massive MIMO systems

  • Energy-efficient signal processing for massive MIMO systems

  • Impact and mitigation of hardware impairments in massive MIMO systems

  • Resource allocation schemes for massive MIMO systems

  • Distributed large-scale MIMO systems

  • Network optimization for massive MIMO systems, deployment scenarios

  • Experiments and measurements for massive MIMO channels

  • Efficient feedback methods for FDD deployments

 

An Introduction to Massive MIMO

Thomas Marzetta, Bell Labs, Alcatel-Lucent

Friday, 9:15-10:15. Conference 2

Abstract: Massive MIMO, widely considered a promising candidate for the 5G physical layer, makes a clean break with current wireless practice by upsetting the traditional parity between base station antennas and user antennas, and serving every active user in all of the time/frequency resources with aggressive multiplexing based on measured propagation channels. The most elementary multiplexing signal processing can yield order-of-magnitude or better spectral efficiency gains over LTE, power control ensures that users at the edges of the cell experience the same high throughput that users near the base station enjoy, any number of antennas can be employed to advantage with no tightening of array tolerances, and radiated power is reduced in proportion to the number of antennas, which in turn, can translate into hundred-fold gains in total energy efficiency. Massive MIMO arrays need not have large form factors to be extraordinarily effective. Massive MIMO systems are likely to be simpler to operate than LTE systems.

The role of long-term antenna statistics in massive MIMO networks

Giuseppe Caire, Alexander von Humboldt Professor, Technical University of Berlin, Germany

Friday, 10:15–11:15. Conference 2

Abstract: Massive MIMO (aka very large antenna systems) is a paradigm for which the number of antennas at the infrastructure nodes (base stations) is much larger than the number of data streams transmitted (in the downlink) or received (in the uplink) using spatial multiplexing. Thanks to this large dimensionality expansion, a number of very favorable properties emerge such as (near) optimality of very simple per-cell signal processing, simplified scheduling and improved rate stability due to the channel hardening effect and energy efficiency due to the large beamforming gain.

While massive MIMO has been initially studied for i.i.d. (isotropic) fading, in this talk we review a stream of recent work focused on exploiting spatial correlation properties of the channel vectors, due to the fact that large massive MIMO base stations often ‘‘see’’ groups of users and small cells in lower tiers under a small angular spread. We refer to such properties as ‘‘long-term antenna statistics’’, since the second-order statistics of the channel vectors are typically slowly varying in comparison with the small scale fading. Exploiting the knowledge of the channel correlation enables a number of interesting improvements over standard massive MIMO. These include: 1) the ability of eliminating pilot contamination through a multi-cell coordinated pilot scheme; 2) very efficient hybrid digital-analog beamforming implementation; 3) joint space division and multiplexing, with reduced dimension effective channel estimation; 4) inter-cell and inter-tier interference coordination schemes, based on the notion of ‘‘spatial blanking’’ and scheduling compatible beams. In addition, we shall also discuss the application of these schemes to the channel models typical of mmwave communications.

 

MIMO

From Wikipedia, the free encyclopedia
 
 
This article is about MIMO in wireless communication. For other uses, see MIMO (disambiguation).
 
MIMO exploits multipath propagation to multiply link capacity

In radiomultiple-input and multiple-output, or MIMO(pronounced as "my-moh" or "me-moh"), is a method for multiplying the capacity of a radio link using multiple transmit and receive antennas to exploit multipath propagation.[1]MIMO has become an essential element of wireless communication standards including IEEE 802.11n (Wi-Fi),IEEE 802.11ac (Wi-Fi), HSPA+ (3G), WiMAX (4G), and Long Term Evolution (4G). More recently, MIMO has been applied to power-line communication for 3-wire installations as part of ITU G.hn standard and HomePlug AV2 specification.[2][3]

At one time in wireless the term “MIMO” referred to the mainly theoretical use of multiple antennas at both the transmitter and the receiver. In modern usage, “MIMO” specifically refers to a practical technique for sending and receiving more than one data signal on the same radio channel at the same time via multipath propagation. MIMO is fundamentally different from smart antenna techniques developed to enhance the performance of a single data signal, such as beamforming anddiversity.

 

 

History of MIMO[edit source | edit]

Early research[edit source | edit]

MIMO is often traced back to 1970s research papers concerning multi-channel digital transmission systems and interference (crosstalk) between wire pairs in a cable bundle: AR Kaye and DA George (1970),[4] Branderburg and Wyner (1974),[5] and W. van Etten (1975, 1976).[6] Although these are not examples of exploiting multipath propagation to send multiple information streams, some of the mathematical techniques for dealing with mutual interference proved useful to MIMO development. In the mid-1980s Jack Salz at Bell Laboratories took this research a step further, investigating multi-user systems operating over “mutually cross-coupled linear networks with additive noise sources” such as time-division multiplexing and dually-polarized radio systems.[7]

Methods were developed to improve the performance of cellular radio networks and enable more aggressive frequency reuse in the early 1990s. Space-division multiple access (SDMA) uses directional or smart antennas to communicate on the same frequency with users in different locations within range of the same base station. An SDMA system was proposed by Richard Roy and Björn Ottersten, researchers at ArrayComm, in 1991. Their US patent (No. 5515378 issued in 1996[8]) describes a method for increasing capacity using "an array of receiving antennas at the base station" with a "plurality of remote users." Arogyaswami Paulrajand Thomas Kailath proposed an SDMA-based inverse multiplexing technique in 1993. Their US patent (No. 5,345,599 issued in 1994[9]) described a method of broadcasting at high data rates by splitting a high-rate signal "into several low-rate signals" to be transmitted from “spatially separated transmitters” and recovered by the receive antenna array based on differences in “directions-of-arrival.” However, neither patent contemplated the use of co-located antennas at both ends of a radio link in order to exploit multipath propagation.

Invention[edit source | edit]

In an April 1996 paper and subsequent patent, Greg Raleigh proposed that natural multipath propagation can be exploited to transmit multiple, independent information streams using co-located antennas and multi-dimensional signal processing.[10] The paper also identified practical solutions for modulation (MIMO-OFDM), coding, synchronization, and channel estimation. Later that year (September 1996) Gerard J. Foschini submitted a paper that also suggested it is possible to multiply the capacity of a wireless link using what the author described as “layered space-time architecture.”[11]

Greg Raleigh, V. K. Jones, and Michael Pollack founded Clarity Wireless in 1996, and built and field-tested a prototype MIMO system.[12] Cisco Systems acquired Clarity Wireless in 1998.[13] Bell Labs built a laboratory prototype demonstrating its V-BLAST (Vertical-Bell Laboratories Layered Space-Time) technology in 1998.[14] Arogyaswami Paulraj founded Iospan Wireless in late 1998 to develop MIMO-OFDM products. Iospan was acquired by Intel in 2003.[15] V-BLAST was never commercialized, and neither Clarity Wireless nor Iospan Wireless shipped MIMO-OFDM products before being acquired.[16]

Standards and commercialization[edit source | edit]

MIMO technology has been standardized for wireless LANs3G mobile phone networks, and4G mobile phone networks and is now in widespread commercial use. Greg Raleigh and V. K. Jones founded Airgo Networks in 2001 to develop MIMO-OFDM chipsets for wireless LANs. The Institute of Electrical and Electronics Engineers (IEEE) created a task group in late 2003 to develop a wireless LAN standard delivering at least 100 Mbit/s of user data throughput. There were two major competing proposals: TGn Sync was backed by companies including Intel and Philips, and WWiSE was supported by companies including Airgo Networks,Broadcom, and Texas Instruments. Both groups agreed that the 802.11n standard would be based on MIMO-OFDM with 20 MHz and 40 MHz channel options.[17] TGn Sync, WWiSE, and a third proposal (MITMOT, backed by Motorola and Mitsubishi) were merged to create what was called the Joint Proposal.[18] In 2004, Airgo became the first company to ship MIMO-OFDM products.[19] Qualcomm acquired Airgo Networks in late 2006.[20] The final 802.11n standard supported speeds up to 600 Mbit/s (using four simultaneous data streams) and was published in late 2009.[21]

Surendra Babu Mandava and Arogyaswami Paulraj founded Beceem Communications in 2004 to produce MIMO-OFDM chipsets for WiMAX. The company was acquired by Broadcom in 2010.[22] WiMAX was developed as an alternative to cellular standards, is based on the802.16e standard, and uses MIMO-OFDM to deliver speeds up to 138 Mbit/s. The more advanced 802.16m standard enables download speeds up to 1 Gbit/s.[23] A nationwide WiMAX network was built in the United States by Clearwire, a subsidiary of Sprint-Nextel, covering 130 million points of presence (PoP) by mid-2012.[24] Sprint subsequently announced plans to deploy LTE (the cellular 4G standard) covering 31 cities by mid-2013[25]and to shut down its WiMAX network by the end of 2015.[26]

The first 4G cellular standard was proposed by NTT DoCoMo in 2004.[27] Long term evolution (LTE) is based on MIMO-OFDM and continues to be developed by the 3rd Generation Partnership Project (3GPP). LTE specifies downlink rates up to 300 Mbit/s, uplink rates up to 75 Mbit/s, and quality of service parameters such as low latency.[28] LTE Advanced adds support for picocells, femtocells, and multi-carrier channels up to 100 MHz wide. LTE has been embraced by both GSM/UMTS and CDMA operators.[29]

The first LTE services were launched in Oslo and Stockholm by TeliaSonera in 2009.[30]Deployment is most advanced in the United States, where all four Tier 1 operators have or are constructing nationwide LTE networks. There are currently more than 360 LTE networks in 123 countries operational with approximately 373 million connections (devices).[31]

Functions of MIMO[edit source | edit]

MIMO can be sub-divided into three main categories, precodingspatial multiplexing or SM, and diversity coding.

Precoding is multi-stream beamforming, in the narrowest definition. In more general terms, it is considered to be all spatial processing that occurs at the transmitter. In (single-stream) beamforming, the same signal is emitted from each of the transmit antennas with appropriate phase and gain weighting such that the signal power is maximized at the receiver input. The benefits of beamforming are to increase the received signal gain - by making signals emitted from different antennas add up constructively - and to reduce the multipath fading effect. Inline-of-sight propagation, beamforming results in a well-defined directional pattern. However, conventional beams are not a good analogy in cellular networks, which are mainly characterized by multipath propagation. When the receiver has multiple antennas, the transmit beamforming cannot simultaneously maximize the signal level at all of the receive antennas, and precoding with multiple streams is often beneficial. Note that precoding requires knowledge of channel state information (CSI) at the transmitter and the receiver.

Spatial multiplexing requires MIMO antenna configuration. In spatial multiplexing, a high-rate signal is split into multiple lower-rate streams and each stream is transmitted from a different transmit antenna in the same frequency channel. If these signals arrive at the receiver antenna array with sufficiently different spatial signatures and the receiver has accurate CSI, it can separate these streams into (almost) parallel channels. Spatial multiplexing is a very powerful technique for increasing channel capacity at higher signal-to-noise ratios (SNR). The maximum number of spatial streams is limited by the lesser of the number of antennas at the transmitter or receiver. Spatial multiplexing can be used without CSI at the transmitter, but can be combined with precoding if CSI is available. Spatial multiplexing can also be used for simultaneous transmission to multiple receivers, known as space-division multiple access ormulti-user MIMO, in which case CSI is required at the transmitter.[32] The scheduling of receivers with different spatial signatures allows good separability.

Diversity coding techniques are used when there is no channel knowledge at the transmitter. In diversity methods, a single stream (unlike multiple streams in spatial multiplexing) is transmitted, but the signal is coded using techniques called space-time coding. The signal is emitted from each of the transmit antennas with full or near orthogonal coding. Diversity coding exploits the independent fading in the multiple antenna links to enhance signal diversity. Because there is no channel knowledge, there is no beamforming or array gain from diversity coding. Diversity coding can be combined with spatial multiplexing when some channel knowledge is available at the transmitter.

Forms of MIMO[edit source | edit]

 
Example of an antenna forLTE with 2 port antenna diversity

Multi-antenna types[edit source | edit]

Multi-antenna MIMO (or Single user MIMO) technology has been developed and implemented in some standards, e.g., 802.11n products.

  • SISO/SIMO/MISO are special cases of MIMO
    • Multiple-input and single-output (MISO) is a special case when the receiver has a single antenna.
    • Single-input and multiple-output (SIMO) is a special case when the transmitter has a single antenna.
    • Single-input single-output (SISO) is a conventional radio system where neither transmitter nor receiver has multiple antennae.
  • Principal single-user MIMO techniques
  • Some limitations
    • The physical antenna spacing is selected to be large; multiple wavelengths at the base station. The antenna separation at the receiver is heavily space-constrained in handsets, though advanced antenna design and algorithm techniques are under discussion. Refer to: multi-user MIMO

Multi-user types[edit source | edit]

Main article: Multi-user MIMO

Recently, results of research on multi-user MIMO technology have been emerging. While full multi-user MIMO (or network MIMO) can have a higher potential, practically, the research on (partial) multi-user MIMO (or multi-user and multi-antenna MIMO) technology is more active.

  • Multi-user MIMO (MU-MIMO)
    • In recent 3GPP and WiMAX standards, MU-MIMO is being treated as one of the candidate technologies adoptable in the specification by a number of companies, including Samsung, Intel, Qualcomm, Ericsson, TI, Huawei, Philips, Alcatel-Lucent, and Freescale. For these and other firms active in the mobile hardware market, MU-MIMO is more feasible for low-complexity cell phones with a small number of reception antennas, whereas single-user SU-MIMO's higher per-user throughput is better suited to more complex user devices with more antennas.
    • Enhanced multiuser MIMO: 1) Employs advanced decoding techniques, 2) Employs advanced precoding techniques
    • SDMA represents either space-division multiple access or super-division multiple access where super emphasises that orthogonal division such as frequency and time division is not used but non-orthogonal approaches such as superposition coding are used.
  • Cooperative MIMO (CO-MIMO)
    • Uses distributed antennas which belong to other users.
  • Macrodiversity MIMO
    • A form of space diversity scheme which uses multiple transmit or receive base stations for communicating coherently with single or multiple users which are possibly distributed in the coverage area, in the same time and frequency resource.[33][34][35]
    • The transmitters are far apart in contrast to traditional microdiversity MIMO schemes such as single-user MIMO. In a multi-user macrodiversity MIMO scenario, users may also be far apart. Therefore, every constituent link in the virtual MIMO link has distinct average link SNR. This difference is mainly due to the different long-term channel impairments such as path loss and shadow fading which are experienced by different links.
    • Macrodiversity MIMO schemes pose unprecedented theoretical and practical challenges. Among many theoretical challenges, perhaps the most fundamental challenge is to understand how the different average link SNRs affect the overall system capacity and individual user performance in fading environments.[36]
  • MIMO Routing
    • Routing a cluster by a cluster in each hop, where the number of nodes in each cluster is larger or equal to one. MIMO routing is different from conventional (SISO) routing since conventional routing protocols route node-by-node in each hop.[37]
  • Massive MIMO is a technology where the number of terminals is much less than the number of base station (mobile station) antennas.[38] In a rich scattering environment, the full advantages of the massive MIMO system can be exploited using simple beamforming strategies such as maximum ratio transmission (MRT) or zero forcing (ZF). To achieve these benefits of massive MIMO, accurate CSI must be available perfectly. However, in practice, the channel between the transmitter and receiver is estimated from orthogonal pilot sequences which are limited by the coherence time of the channel. Most importantly, in a multicell setup, the reuse of pilot sequences of several co-channel cells will create pilot contamination. When there is pilot contamination, the performance of massive MIMO degrades quite drastically. To alleviate the effect of pilot contamination, the work of[39]proposes a simple pilot assignment and channel estimation method from limited training sequences.

Applications of MIMO[edit source | edit]

Spatial multiplexing techniques make the receivers very complex, and therefore they are typically combined with Orthogonal frequency-division multiplexing (OFDM) or with Orthogonal Frequency Division Multiple Access (OFDMA) modulation, where the problems created by a multi-path channel are handled efficiently. The IEEE 802.16e standard incorporates MIMO-OFDMA. The IEEE 802.11n standard, released in October 2009, recommends MIMO-OFDM.

MIMO is also planned to be used in Mobile radio telephone standards such as recent 3GPPand 3GPP2. In 3GPP, High-Speed Packet Access plus (HSPA+) and Long Term Evolution (LTE) standards take MIMO into account. Moreover, to fully support cellular environments, MIMO research consortia including IST-MASCOT propose to develop advanced MIMO techniques, e.g., multi-user MIMO (MU-MIMO).

MIMO technology can be used in non-wireless communications systems. One example is the home networking standard ITU-T G.9963, which defines a powerline communications system that uses MIMO techniques to transmit multiple signals over multiple AC wires (phase, neutral and ground).[2]

Mathematical description[edit source | edit]

 
MIMO channel model

In MIMO systems, a transmitter sends multiple streams by multiple transmit antennas. The transmit streams go through a matrix channel which consists of all \scriptstyle N_t N_r paths between the \scriptstyle N_t transmit antennas at the transmitter and \scriptstyle N_r receive antennas at the receiver. Then, the receiver gets the received signalvectors by the multiple receive antennas and decodes the received signal vectors into the original information. A narrowband flat fading MIMO system is modelled as [40][41]

\mathbf{y} = \mathbf{H}\mathbf{x} + \mathbf{n}

where \scriptstyle\mathbf{y} and \scriptstyle\mathbf{x} are the receive and transmit vectors, respectively, and \scriptstyle\mathbf{H} and \scriptstyle\mathbf{n} are the channel matrix and the noise vector, respectively.

Referring to information theory, the ergodic channel capacity of MIMO systems where both the transmitter and the receiver have perfect instantaneous channel state information is[42]

C_\mathrm{perfect-CSI} = E\left[\max_{\mathbf{Q}; \, \mbox{tr}(\mathbf{Q}) \leq 1} \log_2 \det\left(\mathbf{I} + \rho \mathbf{H}\mathbf{Q}\mathbf{H}^{H}\right)\right] = E\left[\log_2 \det\left(\mathbf{I} + \rho \mathbf{D}\mathbf{S} \mathbf{D} \right)\right]

where \scriptstyle ()^H denotes Hermitian transpose and \scriptstyle\rho is the ratio between transmit power and noise power (i.e., transmit SNR). The optimal signal covariance \scriptstyle \mathbf{Q}=\mathbf{VSV}^H is achieved throughsingular value decomposition of the channel matrix \scriptstyle\mathbf{UDV}^H \,=\, \mathbf{H} and an optimal diagonal power allocation matrix \scriptstyle \mathbf{S}=\textrm{diag}(s_1,\ldots,s_{\min(N_t, N_r)},0,\ldots,0). The optimal power allocation is achieved throughwaterfilling,[43] that is

s_i = \left(\mu - \frac{1}{\rho d_i^2} \right)^+, \quad \textrm{for} \,\, i=1,\ldots,\min(N_t, N_r),

where \scriptstyle d_1,\ldots,d_{\min(N_t, N_r)} are the diagonal elements of \scriptstyle \mathbf{D}\scriptstyle (\cdot)^+ is zero if its argument is negative, and \mu is selected such that \scriptstyle s_1+\ldots+s_{\min(N_t, N_r)}=N_t.

If the transmitter has only statistical channel state information, then the ergodic channel capacity will decrease as the signal covariance \scriptstyle \mathbf{Q} can only be optimized in terms of the average mutual information as[42]

C_\mathrm{statistical-CSI} = \max_{\mathbf{Q}} E\left[\log_2 \det\left(\mathbf{I} + \rho \mathbf{H}\mathbf{Q}\mathbf{H}^{H}\right)\right].

The spatial correlation of the channel has a strong impact on the ergodic channel capacitywith statistical information.

If the transmitter has no channel state information it can select the signal covariance \scriptstyle \mathbf{Q} to maximize channel capacity under worst-case statistics, which means \scriptstyle \mathbf{Q}=1/N_t \mathbf{I} and accordingly

C_\mathrm{no-CSI} = E\left[\log_2 \det\left(\mathbf{I} + \frac{\rho}{N_t}\mathbf{H}\mathbf{H}^{H}\right)\right].

Depending on the statistical properties of the channel, the ergodic capacity is no greater than \scriptstyle\min(N_t, N_r) times larger than that of a SISO system.

MIMO testing[edit source | edit]

MIMO signal testing focuses first on the transmitter/receiver system. The random phases of the sub-carrier signals can produce instantaneous power levels that cause the amplifier to compress, momentarily causing distortion and ultimately symbol errors. Signals with a highPAR (peak-to-average ratio) can cause amplifiers to compress unpredictably during transmission. OFDM signals are very dynamic and compression problems can be hard to detect because of their noise-like nature.[44]

Knowing the quality of the signal channel is also critical. A channel emulator can simulate how a device performs at the cell edge, can add noise or can simulate what the channel looks like at speed. To fully qualify the performance of a receiver, a calibrated transmitter, such as avector signal generator (VSG), and channel emulator can be used to test the receiver under a variety of different conditions. Conversely, the transmitter's performance under a number of different conditions can be verified using a channel emulator and a calibrated receiver, such as a vector signal analyzer (VSA).

Understanding the channel allows for manipulation of the phase and amplitude of each transmitter in order to form a beam. To correctly form a beam, the transmitter needs to understand the characteristics of the channel. This process is called channel sounding orchannel estimation. A known signal is sent to the mobile device that enables it to build a picture of the channel environment. The mobile device sends back the channel characteristics to the transmitter. The transmitter can then apply the correct phase and amplitude adjustments to form a beam directed at the mobile device. This is called a closed-loop MIMO system. For beamforming, it is required to adjust the phases and amplitude of each transmitter. In a beamformer optimized for spatial diversity or spatial multiplexing, each antenna element simultaneously transmits a weighted combination of two data symbols.[45]

MIMO literature[edit source | edit]

Principal researches[edit source | edit]

Papers by Gerard J. Foschini and Michael J. Gans,[46] Foschini[47] and Emre Telatar[48] have shown that the channel capacity (a theoretical upper bound on system throughput) for a MIMO system is increased as the number of antennas is increased, proportional to the smaller of the number of transmit antennas and the number of receive antennas. This is known as the multiplexing gain and this basic finding in information theory is what led to a spurt of research in this area. Despite the simple propagation models used in the aforementioned seminal works, the multiplexing gain is a fundamental property that can be proved under almost any physical channel propagation model and with practical hardware that is prone to transceiver impairments.[49]

A textbook by A. Paulraj, R. Nabar and D. Gore has published an introduction to this area.[50]There are many other principal textbooks available as well.[51][52][53] Mobile Experts has published a research report which predicts the use of MIMO technology in 500 million PCs, tablets, and smartphones by 2016.

Diversity-multiplexing tradeoff (DMT)[edit source | edit]

There exists a fundamental tradeoff between transmit diversity and spatial multiplexing gains in a MIMO system (Zheng and Tse, 2003).[54] In particular, achieving high spatial multiplexing gains is of profound importance in modern wireless systems.[55]

Other applications[edit source | edit]

Given the nature of MIMO, it is not limited to wireless communication. It can be used for wire line communication as well. For example, a new type of DSL technology (gigabit DSL) has been proposed based on binder MIMO channels.

Sampling theory in MIMO systems[edit source | edit]

An important question which attracts the attention of engineers and mathematicians is how to use the multi-output signals at the receiver to recover the multi-input signals at the transmitter. In Shang, Sun and Zhou (2007), sufficient and necessary conditions are established to guarantee the complete recovery of the multi-input signals.[56]

 

MIMO

From Wikipedia, the free encyclopedia
 
 
This article is about MIMO in wireless communication. For other uses, see MIMO (disambiguation).
 
MIMO exploits multipath propagation to multiply link capacity

In radiomultiple-input and multiple-output, or MIMO(pronounced as "my-moh" or "me-moh"), is a method for multiplying the capacity of a radio link using multiple transmit and receive antennas to exploit multipath propagation.[1]MIMO has become an essential element of wireless communication standards including IEEE 802.11n (Wi-Fi),IEEE 802.11ac (Wi-Fi), HSPA+ (3G), WiMAX (4G), and Long Term Evolution (4G). More recently, MIMO has been applied to power-line communication for 3-wire installations as part of ITU G.hn standard and HomePlug AV2 specification.[2][3]

At one time in wireless the term “MIMO” referred to the mainly theoretical use of multiple antennas at both the transmitter and the receiver. In modern usage, “MIMO” specifically refers to a practical technique for sending and receiving more than one data signal on the same radio channel at the same time via multipath propagation. MIMO is fundamentally different from smart antenna techniques developed to enhance the performance of a single data signal, such as beamforming anddiversity.

 

 

History of MIMO[edit source | edit]

Early research[edit source | edit]

MIMO is often traced back to 1970s research papers concerning multi-channel digital transmission systems and interference (crosstalk) between wire pairs in a cable bundle: AR Kaye and DA George (1970),[4] Branderburg and Wyner (1974),[5] and W. van Etten (1975, 1976).[6] Although these are not examples of exploiting multipath propagation to send multiple information streams, some of the mathematical techniques for dealing with mutual interference proved useful to MIMO development. In the mid-1980s Jack Salz at Bell Laboratories took this research a step further, investigating multi-user systems operating over “mutually cross-coupled linear networks with additive noise sources” such as time-division multiplexing and dually-polarized radio systems.[7]

Methods were developed to improve the performance of cellular radio networks and enable more aggressive frequency reuse in the early 1990s. Space-division multiple access (SDMA) uses directional or smart antennas to communicate on the same frequency with users in different locations within range of the same base station. An SDMA system was proposed by Richard Roy and Björn Ottersten, researchers at ArrayComm, in 1991. Their US patent (No. 5515378 issued in 1996[8]) describes a method for increasing capacity using "an array of receiving antennas at the base station" with a "plurality of remote users." Arogyaswami Paulrajand Thomas Kailath proposed an SDMA-based inverse multiplexing technique in 1993. Their US patent (No. 5,345,599 issued in 1994[9]) described a method of broadcasting at high data rates by splitting a high-rate signal "into several low-rate signals" to be transmitted from “spatially separated transmitters” and recovered by the receive antenna array based on differences in “directions-of-arrival.” However, neither patent contemplated the use of co-located antennas at both ends of a radio link in order to exploit multipath propagation.

Invention[edit source | edit]

In an April 1996 paper and subsequent patent, Greg Raleigh proposed that natural multipath propagation can be exploited to transmit multiple, independent information streams using co-located antennas and multi-dimensional signal processing.[10] The paper also identified practical solutions for modulation (MIMO-OFDM), coding, synchronization, and channel estimation. Later that year (September 1996) Gerard J. Foschini submitted a paper that also suggested it is possible to multiply the capacity of a wireless link using what the author described as “layered space-time architecture.”[11]

Greg Raleigh, V. K. Jones, and Michael Pollack founded Clarity Wireless in 1996, and built and field-tested a prototype MIMO system.[12] Cisco Systems acquired Clarity Wireless in 1998.[13] Bell Labs built a laboratory prototype demonstrating its V-BLAST (Vertical-Bell Laboratories Layered Space-Time) technology in 1998.[14] Arogyaswami Paulraj founded Iospan Wireless in late 1998 to develop MIMO-OFDM products. Iospan was acquired by Intel in 2003.[15] V-BLAST was never commercialized, and neither Clarity Wireless nor Iospan Wireless shipped MIMO-OFDM products before being acquired.[16]

Standards and commercialization[edit source | edit]

MIMO technology has been standardized for wireless LANs3G mobile phone networks, and4G mobile phone networks and is now in widespread commercial use. Greg Raleigh and V. K. Jones founded Airgo Networks in 2001 to develop MIMO-OFDM chipsets for wireless LANs. The Institute of Electrical and Electronics Engineers (IEEE) created a task group in late 2003 to develop a wireless LAN standard delivering at least 100 Mbit/s of user data throughput. There were two major competing proposals: TGn Sync was backed by companies including Intel and Philips, and WWiSE was supported by companies including Airgo Networks,Broadcom, and Texas Instruments. Both groups agreed that the 802.11n standard would be based on MIMO-OFDM with 20 MHz and 40 MHz channel options.[17] TGn Sync, WWiSE, and a third proposal (MITMOT, backed by Motorola and Mitsubishi) were merged to create what was called the Joint Proposal.[18] In 2004, Airgo became the first company to ship MIMO-OFDM products.[19] Qualcomm acquired Airgo Networks in late 2006.[20] The final 802.11n standard supported speeds up to 600 Mbit/s (using four simultaneous data streams) and was published in late 2009.[21]

Surendra Babu Mandava and Arogyaswami Paulraj founded Beceem Communications in 2004 to produce MIMO-OFDM chipsets for WiMAX. The company was acquired by Broadcom in 2010.[22] WiMAX was developed as an alternative to cellular standards, is based on the802.16e standard, and uses MIMO-OFDM to deliver speeds up to 138 Mbit/s. The more advanced 802.16m standard enables download speeds up to 1 Gbit/s.[23] A nationwide WiMAX network was built in the United States by Clearwire, a subsidiary of Sprint-Nextel, covering 130 million points of presence (PoP) by mid-2012.[24] Sprint subsequently announced plans to deploy LTE (the cellular 4G standard) covering 31 cities by mid-2013[25]and to shut down its WiMAX network by the end of 2015.[26]

The first 4G cellular standard was proposed by NTT DoCoMo in 2004.[27] Long term evolution (LTE) is based on MIMO-OFDM and continues to be developed by the 3rd Generation Partnership Project (3GPP). LTE specifies downlink rates up to 300 Mbit/s, uplink rates up to 75 Mbit/s, and quality of service parameters such as low latency.[28] LTE Advanced adds support for picocells, femtocells, and multi-carrier channels up to 100 MHz wide. LTE has been embraced by both GSM/UMTS and CDMA operators.[29]

The first LTE services were launched in Oslo and Stockholm by TeliaSonera in 2009.[30]Deployment is most advanced in the United States, where all four Tier 1 operators have or are constructing nationwide LTE networks. There are currently more than 360 LTE networks in 123 countries operational with approximately 373 million connections (devices).[31]

Functions of MIMO[edit source | edit]

MIMO can be sub-divided into three main categories, precodingspatial multiplexing or SM, and diversity coding.

Precoding is multi-stream beamforming, in the narrowest definition. In more general terms, it is considered to be all spatial processing that occurs at the transmitter. In (single-stream) beamforming, the same signal is emitted from each of the transmit antennas with appropriate phase and gain weighting such that the signal power is maximized at the receiver input. The benefits of beamforming are to increase the received signal gain - by making signals emitted from different antennas add up constructively - and to reduce the multipath fading effect. Inline-of-sight propagation, beamforming results in a well-defined directional pattern. However, conventional beams are not a good analogy in cellular networks, which are mainly characterized by multipath propagation. When the receiver has multiple antennas, the transmit beamforming cannot simultaneously maximize the signal level at all of the receive antennas, and precoding with multiple streams is often beneficial. Note that precoding requires knowledge of channel state information (CSI) at the transmitter and the receiver.

Spatial multiplexing requires MIMO antenna configuration. In spatial multiplexing, a high-rate signal is split into multiple lower-rate streams and each stream is transmitted from a different transmit antenna in the same frequency channel. If these signals arrive at the receiver antenna array with sufficiently different spatial signatures and the receiver has accurate CSI, it can separate these streams into (almost) parallel channels. Spatial multiplexing is a very powerful technique for increasing channel capacity at higher signal-to-noise ratios (SNR). The maximum number of spatial streams is limited by the lesser of the number of antennas at the transmitter or receiver. Spatial multiplexing can be used without CSI at the transmitter, but can be combined with precoding if CSI is available. Spatial multiplexing can also be used for simultaneous transmission to multiple receivers, known as space-division multiple access ormulti-user MIMO, in which case CSI is required at the transmitter.[32] The scheduling of receivers with different spatial signatures allows good separability.

Diversity coding techniques are used when there is no channel knowledge at the transmitter. In diversity methods, a single stream (unlike multiple streams in spatial multiplexing) is transmitted, but the signal is coded using techniques called space-time coding. The signal is emitted from each of the transmit antennas with full or near orthogonal coding. Diversity coding exploits the independent fading in the multiple antenna links to enhance signal diversity. Because there is no channel knowledge, there is no beamforming or array gain from diversity coding. Diversity coding can be combined with spatial multiplexing when some channel knowledge is available at the transmitter.

Forms of MIMO[edit source | edit]

 
Example of an antenna forLTE with 2 port antenna diversity

Multi-antenna types[edit source | edit]

Multi-antenna MIMO (or Single user MIMO) technology has been developed and implemented in some standards, e.g., 802.11n products.

  • SISO/SIMO/MISO are special cases of MIMO
    • Multiple-input and single-output (MISO) is a special case when the receiver has a single antenna.
    • Single-input and multiple-output (SIMO) is a special case when the transmitter has a single antenna.
    • Single-input single-output (SISO) is a conventional radio system where neither transmitter nor receiver has multiple antennae.
  • Principal single-user MIMO techniques
  • Some limitations
    • The physical antenna spacing is selected to be large; multiple wavelengths at the base station. The antenna separation at the receiver is heavily space-constrained in handsets, though advanced antenna design and algorithm techniques are under discussion. Refer to: multi-user MIMO

Multi-user types[edit source | edit]

Main article: Multi-user MIMO

Recently, results of research on multi-user MIMO technology have been emerging. While full multi-user MIMO (or network MIMO) can have a higher potential, practically, the research on (partial) multi-user MIMO (or multi-user and multi-antenna MIMO) technology is more active.

  • Multi-user MIMO (MU-MIMO)
    • In recent 3GPP and WiMAX standards, MU-MIMO is being treated as one of the candidate technologies adoptable in the specification by a number of companies, including Samsung, Intel, Qualcomm, Ericsson, TI, Huawei, Philips, Alcatel-Lucent, and Freescale. For these and other firms active in the mobile hardware market, MU-MIMO is more feasible for low-complexity cell phones with a small number of reception antennas, whereas single-user SU-MIMO's higher per-user throughput is better suited to more complex user devices with more antennas.
    • Enhanced multiuser MIMO: 1) Employs advanced decoding techniques, 2) Employs advanced precoding techniques
    • SDMA represents either space-division multiple access or super-division multiple access where super emphasises that orthogonal division such as frequency and time division is not used but non-orthogonal approaches such as superposition coding are used.
  • Cooperative MIMO (CO-MIMO)
    • Uses distributed antennas which belong to other users.
  • Macrodiversity MIMO
    • A form of space diversity scheme which uses multiple transmit or receive base stations for communicating coherently with single or multiple users which are possibly distributed in the coverage area, in the same time and frequency resource.[33][34][35]
    • The transmitters are far apart in contrast to traditional microdiversity MIMO schemes such as single-user MIMO. In a multi-user macrodiversity MIMO scenario, users may also be far apart. Therefore, every constituent link in the virtual MIMO link has distinct average link SNR. This difference is mainly due to the different long-term channel impairments such as path loss and shadow fading which are experienced by different links.
    • Macrodiversity MIMO schemes pose unprecedented theoretical and practical challenges. Among many theoretical challenges, perhaps the most fundamental challenge is to understand how the different average link SNRs affect the overall system capacity and individual user performance in fading environments.[36]
  • MIMO Routing
    • Routing a cluster by a cluster in each hop, where the number of nodes in each cluster is larger or equal to one. MIMO routing is different from conventional (SISO) routing since conventional routing protocols route node-by-node in each hop.[37]
  • Massive MIMO is a technology where the number of terminals is much less than the number of base station (mobile station) antennas.[38] In a rich scattering environment, the full advantages of the massive MIMO system can be exploited using simple beamforming strategies such as maximum ratio transmission (MRT) or zero forcing (ZF). To achieve these benefits of massive MIMO, accurate CSI must be available perfectly. However, in practice, the channel between the transmitter and receiver is estimated from orthogonal pilot sequences which are limited by the coherence time of the channel. Most importantly, in a multicell setup, the reuse of pilot sequences of several co-channel cells will create pilot contamination. When there is pilot contamination, the performance of massive MIMO degrades quite drastically. To alleviate the effect of pilot contamination, the work of[39]proposes a simple pilot assignment and channel estimation method from limited training sequences.

Applications of MIMO[edit source | edit]

Spatial multiplexing techniques make the receivers very complex, and therefore they are typically combined with Orthogonal frequency-division multiplexing (OFDM) or with Orthogonal Frequency Division Multiple Access (OFDMA) modulation, where the problems created by a multi-path channel are handled efficiently. The IEEE 802.16e standard incorporates MIMO-OFDMA. The IEEE 802.11n standard, released in October 2009, recommends MIMO-OFDM.

MIMO is also planned to be used in Mobile radio telephone standards such as recent 3GPPand 3GPP2. In 3GPP, High-Speed Packet Access plus (HSPA+) and Long Term Evolution (LTE) standards take MIMO into account. Moreover, to fully support cellular environments, MIMO research consortia including IST-MASCOT propose to develop advanced MIMO techniques, e.g., multi-user MIMO (MU-MIMO).

MIMO technology can be used in non-wireless communications systems. One example is the home networking standard ITU-T G.9963, which defines a powerline communications system that uses MIMO techniques to transmit multiple signals over multiple AC wires (phase, neutral and ground).[2]

Mathematical description[edit source | edit]

 
MIMO channel model

In MIMO systems, a transmitter sends multiple streams by multiple transmit antennas. The transmit streams go through a matrix channel which consists of all \scriptstyle N_t N_r paths between the \scriptstyle N_t transmit antennas at the transmitter and \scriptstyle N_r receive antennas at the receiver. Then, the receiver gets the received signalvectors by the multiple receive antennas and decodes the received signal vectors into the original information. A narrowband flat fading MIMO system is modelled as [40][41]

\mathbf{y} = \mathbf{H}\mathbf{x} + \mathbf{n}

where \scriptstyle\mathbf{y} and \scriptstyle\mathbf{x} are the receive and transmit vectors, respectively, and \scriptstyle\mathbf{H} and \scriptstyle\mathbf{n} are the channel matrix and the noise vector, respectively.

Referring to information theory, the ergodic channel capacity of MIMO systems where both the transmitter and the receiver have perfect instantaneous channel state information is[42]

C_\mathrm{perfect-CSI} = E\left[\max_{\mathbf{Q}; \, \mbox{tr}(\mathbf{Q}) \leq 1} \log_2 \det\left(\mathbf{I} + \rho \mathbf{H}\mathbf{Q}\mathbf{H}^{H}\right)\right] = E\left[\log_2 \det\left(\mathbf{I} + \rho \mathbf{D}\mathbf{S} \mathbf{D} \right)\right]

where \scriptstyle ()^H denotes Hermitian transpose and \scriptstyle\rho is the ratio between transmit power and noise power (i.e., transmit SNR). The optimal signal covariance \scriptstyle \mathbf{Q}=\mathbf{VSV}^H is achieved throughsingular value decomposition of the channel matrix \scriptstyle\mathbf{UDV}^H \,=\, \mathbf{H} and an optimal diagonal power allocation matrix \scriptstyle \mathbf{S}=\textrm{diag}(s_1,\ldots,s_{\min(N_t, N_r)},0,\ldots,0). The optimal power allocation is achieved throughwaterfilling,[43] that is

s_i = \left(\mu - \frac{1}{\rho d_i^2} \right)^+, \quad \textrm{for} \,\, i=1,\ldots,\min(N_t, N_r),

where \scriptstyle d_1,\ldots,d_{\min(N_t, N_r)} are the diagonal elements of \scriptstyle \mathbf{D}\scriptstyle (\cdot)^+ is zero if its argument is negative, and \mu is selected such that \scriptstyle s_1+\ldots+s_{\min(N_t, N_r)}=N_t.

If the transmitter has only statistical channel state information, then the ergodic channel capacity will decrease as the signal covariance \scriptstyle \mathbf{Q} can only be optimized in terms of the average mutual information as[42]

C_\mathrm{statistical-CSI} = \max_{\mathbf{Q}} E\left[\log_2 \det\left(\mathbf{I} + \rho \mathbf{H}\mathbf{Q}\mathbf{H}^{H}\right)\right].

The spatial correlation of the channel has a strong impact on the ergodic channel capacitywith statistical information.

If the transmitter has no channel state information it can select the signal covariance \scriptstyle \mathbf{Q} to maximize channel capacity under worst-case statistics, which means \scriptstyle \mathbf{Q}=1/N_t \mathbf{I} and accordingly

C_\mathrm{no-CSI} = E\left[\log_2 \det\left(\mathbf{I} + \frac{\rho}{N_t}\mathbf{H}\mathbf{H}^{H}\right)\right].

Depending on the statistical properties of the channel, the ergodic capacity is no greater than \scriptstyle\min(N_t, N_r) times larger than that of a SISO system.

MIMO testing[edit source | edit]

MIMO signal testing focuses first on the transmitter/receiver system. The random phases of the sub-carrier signals can produce instantaneous power levels that cause the amplifier to compress, momentarily causing distortion and ultimately symbol errors. Signals with a highPAR (peak-to-average ratio) can cause amplifiers to compress unpredictably during transmission. OFDM signals are very dynamic and compression problems can be hard to detect because of their noise-like nature.[44]

Knowing the quality of the signal channel is also critical. A channel emulator can simulate how a device performs at the cell edge, can add noise or can simulate what the channel looks like at speed. To fully qualify the performance of a receiver, a calibrated transmitter, such as avector signal generator (VSG), and channel emulator can be used to test the receiver under a variety of different conditions. Conversely, the transmitter's performance under a number of different conditions can be verified using a channel emulator and a calibrated receiver, such as a vector signal analyzer (VSA).

Understanding the channel allows for manipulation of the phase and amplitude of each transmitter in order to form a beam. To correctly form a beam, the transmitter needs to understand the characteristics of the channel. This process is called channel sounding orchannel estimation. A known signal is sent to the mobile device that enables it to build a picture of the channel environment. The mobile device sends back the channel characteristics to the transmitter. The transmitter can then apply the correct phase and amplitude adjustments to form a beam directed at the mobile device. This is called a closed-loop MIMO system. For beamforming, it is required to adjust the phases and amplitude of each transmitter. In a beamformer optimized for spatial diversity or spatial multiplexing, each antenna element simultaneously transmits a weighted combination of two data symbols.[45]

MIMO literature[edit source | edit]

Principal researches[edit source | edit]

Papers by Gerard J. Foschini and Michael J. Gans,[46] Foschini[47] and Emre Telatar[48] have shown that the channel capacity (a theoretical upper bound on system throughput) for a MIMO system is increased as the number of antennas is increased, proportional to the smaller of the number of transmit antennas and the number of receive antennas. This is known as the multiplexing gain and this basic finding in information theory is what led to a spurt of research in this area. Despite the simple propagation models used in the aforementioned seminal works, the multiplexing gain is a fundamental property that can be proved under almost any physical channel propagation model and with practical hardware that is prone to transceiver impairments.[49]

A textbook by A. Paulraj, R. Nabar and D. Gore has published an introduction to this area.[50]There are many other principal textbooks available as well.[51][52][53] Mobile Experts has published a research report which predicts the use of MIMO technology in 500 million PCs, tablets, and smartphones by 2016.

Diversity-multiplexing tradeoff (DMT)[edit source | edit]

There exists a fundamental tradeoff between transmit diversity and spatial multiplexing gains in a MIMO system (Zheng and Tse, 2003).[54] In particular, achieving high spatial multiplexing gains is of profound importance in modern wireless systems.[55]

Other applications[edit source | edit]

Given the nature of MIMO, it is not limited to wireless communication. It can be used for wire line communication as well. For example, a new type of DSL technology (gigabit DSL) has been proposed based on binder MIMO channels.

Sampling theory in MIMO systems[edit source | edit]

An important question which attracts the attention of engineers and mathematicians is how to use the multi-output signals at the receiver to recover the multi-input signals at the transmitter. In Shang, Sun and Zhou (2007), sufficient and necessary conditions are established to guarantee the complete recovery of the multi-input signals.[56]

 

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