ACCEPTED FROM OPEN CALL Massive MIMO for Next Generation Wireless Systems Erik G. Larsson, ISY, Linkoping University, Sweden Ove Edfors and Fredrik Tufvesson, Lund University, Sweden Thomas L. Marzetta, Bell Labs, Alcatel-Lucent, United States ABSTRACT up on our earlier exposition [1]， with a focus on the developments in the last three years; most Multi-user MIMO offers big advantages over particularly, energy efficiency, exploitation of conventional point-to-point MIMO: it works excess degrees of freedom, time-division duplex with cheap single-antenna terminals, a rich scat- (TDD)calibration, techniques to combat pilot tering environment is not required, and resource contamination, and entirely new channel mea- allocation is simplified because every active ter- surements. minal utilizes all of the time-frequency bins. With massive MIMO, we think of systems However, multi-user MIMO, as originally envi- that use antenna arrays with a few hundred sioned, with roughly equal numbers of service antennas simultaneously serving many tens of antennas and terminals and frequency-division terminals in the same time-frequency resource. duplex operation, is not a scalable technology. The basic premise behind massive MIMO is to Massive MIMO (also known as large-scale reap all the benefits of conventional MIMO, but antenna systems, very large MIMO, hyper on a much greater scale. Overall, massive MIMO MIMO, full-dimension MIMO, and ARGOS) is an enabler for the development of future makes a clean break with current practice broadband (fixed and mobile)networks, which through the use of a large excess of service will be energy-efficient, secure, and robust, and antennas over active terminals and time-division will use the spectrum efficiently. As such, it is an duplex operation. Extra antennas help by focus- enabler for the future digital society infra- ing energy into ever smaller regions of space to structure that will connect the Internet of people bring huge improvements in throughput and and Internet of Things with clouds and other radiated energy efficiency. Other benefits of network infrastructure. Many different configu- massive MIMO include extensive use of inexpen- rations and deployment scenarios for the actual sive low-power components, reduced latency, antenna arrays used by a massive MIMO system simplification of the MAC layer, and robustness can be envisioned (Fig. 1). Each antenna unit against intentional jamming. The anticipated would be small and active, preferably fed via an throughput depends on the propagation environ- optical or electric digital bus. ment providing asymptotically orthogonal chan- Massive MIMO relies on spatial multiplexing, nels to the terminals, but so far experiments which in turn relies on the base station having have not disclosed any limitations in this regard. good enough channel knowledge, on both the While massive MIMO renders many traditional uplink and the downlink. On the uplink, this is research problems irrelevant, it uncovers entirely easy to accomplish by having the terminals send new problems that urgently need attention: the pilots, based on which the base station estimates challenge of making many low-cost low-precision the channel responses to each of the terminals. components that work effectively together, The downlink is more difficult. In conventional acquisition and synchronization for newly joined MIMO systems such as the Long Term Evolu- terminals, the exploitation of extra degrees of tion(LTE)standard, the base station sends out freedom provided by the excess of service anten- pilot waveforms, based on which the terminals nas, reducing internal power consumption to estimate the channel responses, quantize the achieve total energy efficiency reductions, and thus obtained estimates, and feed them back to finding new deployment scenarios. This article the base station. This will not be feasible in mas- presents an overview of the massive MIMO con- sive MIMO systems, at least not when operating cept and contemporary research on the topic. in a high-mobility environment, for two reasons. First, optimal downlink pilots should be mutually GOING LARGE: MASSIVE MIMO orthogonal between the antennas. This means that the amount of time-frequency resources Massive multiple-input multiple-output(MIMO) needed for downlink pilots scales with the num- is an emerging technology that scales up MIMO ber of antennas, so a massive MIMO system by possibly orders of magnitude compared to the would require up to 100 times more such current state of the art. In this article, we follow resources than a conventional system. Second, 186 0163-6804/14/$25.0002014IEEE IEEE Communications Magazine.February 2014
186 0163-6804/14/$25.00 © 2014 IEEE IEEE Communications Magazine • February 2014 GOING LARGE: MASSIVE MIMO Massive multiple-input multiple-output (MIMO) is an emerging technology that scales up MIMO by possibly orders of magnitude compared to the current state of the art. In this article, we follow up on our earlier exposition [1], with a focus on the developments in the last three years; most particularly, energy efficiency, exploitation of excess degrees of freedom, time-division duplex (TDD) calibration, techniques to combat pilot contamination, and entirely new channel measurements. With massive MIMO, we think of systems that use antenna arrays with a few hundred antennas simultaneously serving many tens of terminals in the same time-frequency resource. The basic premise behind massive MIMO is to reap all the benefits of conventional MIMO, but on a much greater scale. Overall, massive MIMO is an enabler for the development of future broadband (fixed and mobile) networks, which will be energy-efficient, secure, and robust, and will use the spectrum efficiently. As such, it is an enabler for the future digital society infrastructure that will connect the Internet of people and Internet of Things with clouds and other network infrastructure. Many different configurations and deployment scenarios for the actual antenna arrays used by a massive MIMO system can be envisioned (Fig. 1). Each antenna unit would be small and active, preferably fed via an optical or electric digital bus. Massive MIMO relies on spatial multiplexing, which in turn relies on the base station having good enough channel knowledge, on both the uplink and the downlink. On the uplink, this is easy to accomplish by having the terminals send pilots, based on which the base station estimates the channel responses to each of the terminals. The downlink is more difficult. In conventional MIMO systems such as the Long Term Evolution (LTE) standard, the base station sends out pilot waveforms, based on which the terminals estimate the channel responses, quantize the thus obtained estimates, and feed them back to the base station. This will not be feasible in massive MIMO systems, at least not when operating in a high-mobility environment, for two reasons. First, optimal downlink pilots should be mutually orthogonal between the antennas. This means that the amount of time-frequency resources needed for downlink pilots scales with the number of antennas, so a massive MIMO system would require up to 100 times more such resources than a conventional system. Second, ABSTRACT 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. ACCEPTED FROM OPEN CALL Erik G. Larsson, ISY, Linköping University, Sweden Ove Edfors and Fredrik Tufvesson, Lund University, Sweden Thomas L. Marzetta, Bell Labs, Alcatel-Lucent, United States Massive MIMO for Next Generation Wireless Systems EDFORS_LAYOUT.qxp_Layout 1/30/14 1:08 PM Page 186
agRachce While the concepts of massive MIMO have ts have been performed [4,5. THE POTENTIAL OF MASSIVE MIMO ease the capacity1 onfigurations and deployment scenarios fo imormercandimaneoi that it ribute MIMO s region in space (Fig2).The underlying 6 d t ly ortho when the hat all w The on in Fig. n an info ndwidt nel sta rmati MRC oise-limited informa z) In about dimension( of the total num ber of bits (sum MR an ing in the "green spen affe ting the o ctions tend to be ov off for t are se one single antenn rving a sing 100 nal (purple inal reg that divi 1g1 ximum ratio nd the scale of the ctiveness atio c .con r6400 IEEE Communications Magazine February 2014 187
IEEE Communications Magazine • February 2014 187 the number of channel responses each terminal must estimate is also proportional to the number of base station antennas. Hence, the uplink resources needed to inform the base station of the channel responses would be up to 100 times larger than in conventional systems. Generally, the solution is to operate in TDD mode, and rely on reciprocity between the uplink and downlink channels, although frequency-division duplext (FDD) operation may be possible in certain cases [2]. While the concepts of massive MIMO have been mostly theoretical so far, stimulating much research particularly in random matrix theory and related mathematics, basic testbeds are becoming available [3], and initial channel measurements have been performed [4, 5]. THE POTENTIAL OF MASSIVE MIMO Massive MIMO technology relies on phasecoherent but computationally very simple processing of signals from all the antennas at the base station. Some specific benefits of a massive MU-MIMO system are: •Massive MIMO can increase the capacity 10 times or more and simultaneously improve the radiated energy efficiency on the order of 100 times. The capacity increase results from the aggressive spatial multiplexing used in massive MIMO. The fundamental principle that makes the dramatic increase in energy efficiency possible is that with a large number of antennas, energy can be focused with extreme sharpness into small regions in space (Fig. 2). The underlying physics is coherent superposition of wavefronts. By appropriately shaping the signals sent out by the antennas, the base station can make sure that all wavefronts collectively emitted by all antennas add up constructively at the locations of the intended terminals, but destructively (randomly) almost everywhere else. Interference between terminals can be suppressed even further by using, for example, zero-forcing (ZF). This, however, may come at the cost of more transmitted power, as illustrated in Fig. 2. More quantitatively, Fig. 3 (from [6]) depicts the fundamental trade-off between energy efficiency in terms of the total number of bits (sum rate) transmitted per Joule per terminal receiving service of energy spent, and spectral efficiency in terms of total number of bits (sum rate) transmitted per unit of radio spectrum consumed. The figure illustrates the relation for the uplink, from the terminals to the base station (the downlink performance is similar). The figure shows the trade-off for three cases: • A reference system with one single antenna serving a single terminal (purple) • A system with 100 antennas serving a single terminal using conventional beamforming (green) • A massive MIMO system with 100 antennas simultaneously serving multiple (about 40 here) terminals (red, using maximum ratio combining, and blue, using ZF). The attractiveness of maximum ratio combining (MRC) compared with ZF is not only its computational simplicity — multiplication of the received signals by the conjugate channel responses — but also that it can be performed in a distributed fashion, independently at each antenna unit. While ZF also works fairly well for a conventional or moderately sized MIMO system, MRC generally does not. The reason that MRC works so well for massive MIMO is that the channel responses associated with different terminals tend to be nearly orthogonal when the number of base station antennas is large. The prediction in Fig. 3 is based on an information-theoretic analysis that takes into account intracell interference, as well as the bandwidth and energy cost of using pilots to acquire channel state information in a high-mobility environment [6]. With the MRC receiver, we operate in the nearly noise-limited regime of information theory. This means providing each terminal with a rate of about 1 b/complex dimension (1 b/s/Hz). In a massive MIMO system, when using MRC and operating in the “green” regime (i.e., scaling down the power as much as possible without seriously affecting the overall spectral efficiency), multiuser interference and effects from hardware imperfections tend to be overwhelmed by the thermal noise. The reason that the overall spectral efficiency still can be 10 times higher than in conventional MIMO is that many tens of terminals are served simultaneously, in the same time-frequency resource. When operating in the 1 b/dimension/terminal regime, there is also some evidence that intersymbol interference can be treated as additional thermal noise [7], hence offering a way of disposing with orthogonal frequency-division multiplexing (OFDM) as a means of combatting intersymbol interference. To understand the scale of the capacity gains massive MIMO offers, consider an array consisting of 6400 omnidirectional antennas (total form factor 6400 × (l/2)2 40 m2) transmitting with a total power of 120 W (i.e., each antenna radiatFigure 1. Some possible antenna configurations and deployment scenarios for a massive MIMO base station. Distributed Cylindrical Rectangular Linear EDFORS_LAYOUT.qxp_Layout 1/30/14 1:08 PM Page 187
range,and th havc broadband 000 homc ltiplexing ficient accura or the uplink)andt om servic e.We use a capacity Mas ssive MIMO can be built with inexpen- which mation error beamforming We use optimal max-min power systems are replaced by hundreds of low-cost 5 10 s15 Area with 400 random scatterers -15 8007 ide bea 10 Area with 400 random scatt ncoming wide bea 800 r (*and four o r users nearby（ IEEE Communications MagazineFebruary 2014
188 IEEE Communications Magazine • February 2014 ing about 20 mW) over a 20 MHz bandwidth in the personal communications services (PCS) band (1900 MHz). The array serves 1000 fixed terminals randomly distributed in a disk of radius 6 km centered on the array, each terminal having an 8 dB gain antenna. The height of the antenna array is 30 m, and the height of the terminals is 5 m. Using the Hata-COST231 model, we find that the path loss is 127 dB at 1 km range, and the range-decay exponent is 3.52. There is also log-normal shadow fading with 8 dB standard deviation. The receivers have a 9 dB noise figure. One-quarter of the time is spent on transmission of uplink pilots for TDD channel estimation, and it is assumed that the channel is substantially constant over intervals of 164 ms in order to estimate the channel gains with sufficient accuracy. Downlink data is transmitted via maximum ratio transmission (MRT) beamforming combined with power control, where the 5 percent of terminals with the worst channels are excluded from service. We use a capacity lower bound from [8] extended to accommodate slow fading, near/far effects and power control, which accounts for receiver noise, channel estimation errors, the overhead of pilot transmission, and the imperfections of MRT beamforming. We use optimal max-min power control, which confers an equal signal-to-interference-plus-noise ratio on each of the 950 terminals and therefore equal throughput. Numerical averaging over random terminal locations and the shadow fading shows that 95 percent of the terminals will receive a throughput of 21.2 Mb/s/terminal. Overall, the array in this example will offer the 1000 terminals a total downlink throughput of 20 Gb/s, resulting in a sum-spectral efficiency of 1000 b/s/Hz. This would be enough, for example, to provide 20 Mb/s broadband service to each of 1000 homes. The max-min power control provides equal service simultaneously to 950 terminals. Other types of power control combined with time-division multiplexing could accommodate heterogeneous traffic demands of a larger set of terminals. The MRC receiver (for the uplink) and its counterpart MRT precoding (for the downlink) are also known as matched filtering (MF) in the literature. • Massive MIMO can be built with inexpensive, low-power components. Massive MIMO is a game changing technology with regard to theory, systems, and implementation. With massive MIMO, expensive ultra-linear 50 W amplifiers used in conventional systems are replaced by hundreds of low-cost Figure 2. Relative field strength around a target terminal in a scattering environment of size 800 l × 800 l when the base station is placed 1600 l to the left. Average field strengths are calculated over 10,000 random placements of 400 scatterers when two different linear precoders are used: a) MRT precoders; b) ZF precoders. Left: pseudo-color plots of average field strengths, with target user positions at the center () and four other users nearby (). Right: average field strengths as surface plots, allowing an alternate view of the spatial focusing. Area with 400 random scatterers ≤ Incoming narrow beam 400 λ 800 λ 800 λ Narrow beam Wide beam Area with 400 random scatterers 100-element λ/2-spaced linear array 1600 λ a) MRT precoding (dB) [dB] -10 400 λ -15 -5 0 5 800 λ ≤ Incoming wide beam 400 λ (dB) [dB] -10 400 λ -15 -5 0 5 800 λ 100-element λ/2-spaced linear array 1600 λ b) ZF precoding -10 ≤ −15 -5 0 5 -10 ≤ 15 -5 0 5 EDFORS_LAYOUT.qxp_Layout 1/30/14 1:08 PM Page 188
a 103 stations today are reduce the conon (r/qy(r/q) 102 10 etemim sngentena 20304050 7080 Spectral efficiency(b//Hz) aea e-friendlysigna rage ratio9 o er ef nalty in terms of incr nt) am Fig.4.With Th signal s Ra r s samp the the spots where tiple paths i ee p em to :0 t is this fad nis possible is that th e ha n chan entl ut affe cting what the ac sm the muliple acces ng. nd of large numbers,the te eases the robustnes tions is ag orldwid ma-made of civilian grid is ay onus efore th nt nt that ant due to the d concerns regard. ng and douar IEEE Communications Magazine February 2014 189
IEEE Communications Magazine • February 2014 189 amplifiers with output power in the milli-Watt range. The contrast to classical array designs, which use few antennas fed from high-power amplifiers, is significant. Several expensive and bulky items, such as large coaxial cables, can be eliminated altogether. (The typical coaxial cables used for tower-mounted base stations today are more than 4 cm in diameter!) Massive MIMO reduces the constraints on accuracy and linearity of each individual amplifier and RF chain. All that matters is their combined action. In a way, massive MIMO relies on the law of large numbers to make sure that noise, fading, and hardware imperfections average out when signals from a large number of antennas are combined in the air. The same property that makes massive MIMO resilient against fading also makes the technology extremely robust to failure of one or a few of the antenna unit(s). A massive MIMO system has a large surplus of degrees of freedom. For example, with 200 antennas serving 20 terminals, 180 degrees of freedom are unused. These degrees of freedom can be used for hardware-friendly signal shaping. In particular, each antenna can transmit signals with very small peak-to-average ratio [9] or even constant envelope [10] at a very modest penalty in terms of increased total radiated power. Such (near-constant) envelope signaling facilitates the use of extremely cheap and powerefficient RF amplifiers. The techniques in [9, 10] must not be confused with conventional beamforming techniques or equal-magnitudeweight beamforming techniques. This distinction is explained in Fig. 4. With (near) constantenvelope multiuser precoding, no beams are formed, and the signals emitted by each antenna are not formed by weighing a symbol. Rather, a wavefield is created such that when this wavefield is sampled at the spots where the terminals are located, the terminals see precisely the signals we want them to see. The fundamental property of the massive MIMO channel that makes this possible is that the channel has a large nullspace: almost anything can be put into this nullspace without affecting what the terminals see. In particular, components can be put into this nullspace that make the transmitted waveforms satisfy the desired envelope constraints. Notwithstanding, the effective channels between the base station and each of the terminals can take any signal constellation as input and do not require the use of phase shift keying (PSK) modulation. The drastically improved energy efficiency enables massive MIMO systems to operate with a total output RF power two orders of magnitude less than with current technology. This matters, because the energy consumption of cellular base stations is a growing concern worldwide. In addition, base stations that consume many orders of magnitude less power could be powered by wind or solar, and hence easily deployed where no electricity grid is available. As a bonus, the total emitted power can be dramatically cut, and therefore the base station will generate substantially less electromagnetic interference. This is important due to the increased concerns regarding electromagnetic exposure. •Massive MIMO enables a significant reduction of latency on the air interface. The performance of wireless communications systems is normally limited by fading. Fading can render the received signal strength very small at certain times. This happens when the signal sent from a base station travels through multiple paths before it reaches the terminal, and the waves resulting from these multiple paths interfere destructively. It is this fading that makes it hard to build low-latency wireless links. If the terminal is trapped in a fading dip, it has to wait until the propagation channel has sufficiently changed until any data can be received. Massive MIMO relies on the law of large numbers and beamforming in order to avoid fading dips, so fading no longer limits latency. •Massive MIMO simplifies the multiple access layer. Due to the law of large numbers, the channel hardens so that frequency domain scheduling no longer pays off. With OFDM, each subcarrier in a massive MIMO system will have substantially the same channel gain. Each terminal can be given the whole bandwidth, which renders most of the physical layer control signaling redundant. •Massive MIMO increases the robustness against both unintended man-made interference and intentional jamming. Intentional jamming of civilian wireless systems is a growing concern and a serious cybersecurity threat that seems to be little known to the public. Simple jammers can be bought off the Internet for a few hundred dollars, and equipment that used to be military-grade can be put together using off-the-shelf software radiobased platforms for a few thousand dollars. Figure 3. Half the power — twice the force (from [6]): Improving uplink spectral efficiency 10 times and simultaneously increasing the radiated power effi- ciency 100 times with massive MIMO technology, using extremely simple signal processing, taking into account the energy and bandwidth costs of obtaining channel state information. Spectral efficiency (b/s/Hz) 0 10 100 10–1 Relative energy efficiency (b /J)/(b/J) 101 102 103 104 20 30 40 50 60 70 80 Single antenna, single terminal 90 100 antennas, single terminal 100 antennas, multiple terminals, MRC processing 100 antennas, multiple terminals, ZF processing EDFORS_LAYOUT.qxp_Layout 1/30/14 1:08 PM Page 189
Combine em UK : ⊕ eioM ing co rasted with p namic range eeeg罗 hguihpied 0 police officers s of mass s is to use multip atch within the chain o ission the nas e to tally dim can be reated as a reference LIMITING FACTORS OF MASSIVE MIMO en the uplink and CHANNEL RECIPROCITY eamfo noidsoc Time-division duplexing operation relies on reduction ing)a PILOT CONTAMINATION pilot diided bythe channcl delay
190 IEEE Communications Magazine • February 2014 Numerous recent incidents, especially in public safety applications, illustrate the magnitude of the problem. During the EU summit in Gothenburg, Sweden, in 2001, demonstrators used a jammer located in a nearby apartment, and during critical phases of riots, the chief commander could not reach any of the 700 police officers engaged [11]. Due to the scarcity of bandwidth, spreading information over frequency just is not feasible, so the only way of improving robustness of wireless communications is to use multiple antennas. Massive MIMO offers many excess degrees of freedom that can be used to cancel signals from intentional jammers. If massive MIMO is implemented using uplink pilots for channel estimation, smart jammers could cause harmful interference with modest transmission power. However, more clever implementations using joint channel estimation and decoding should be able to substantially diminish that problem. LIMITING FACTORS OF MASSIVE MIMO CHANNEL RECIPROCITY Time-division duplexing operation relies on channel reciprocity. There appears to be a reasonable consensus that the propagation channel itself is essentially reciprocal unless the propagation is affected by materials with strange magnetic properties. However, the hardware chains in the base station and terminal transceivers may not be reciprocal between the uplink and the downlink. Calibration of the hardware chains does not seem to constitute a serious problem, and there are calibration-based solutions that have already been tested to some extent in practice [3, 12]. Specifically, [3] treats reciprocity calibration for a 64-antenna system in some detail and claims a successful experimental implementation. Note that calibration of the terminal uplink and downlink chains is not required in order to obtain the full beamforming gains of massive MIMO: if the base station equipment is properly calibrated, the array will indeed transmit a coherent beam to the terminal. (There will still be some mismatch within the receiver chain of the terminal, but this can be handled by transmitting pilots through the beam to the terminal; the overhead for these supplementary pilots is very small.) Absolute calibration within the array is not required. Instead, as proposed in [3], one of the antennas can be treated as a reference, and signals can be traded between the reference antenna and each of the other antennas to derive a compensation factor for that antenna. It may be possible to entirely forgo reciprocity calibration within the array; for example if the maximum phase difference between the uplink and downlink chains were less than 60˚, coherent beamforming would still occur (at least with MRT beamforming), albeit with a possible 3 dB reduction in gain. PILOT CONTAMINATION Ideally, every terminal in a massive MIMO system is assigned an orthogonal uplink pilot sequence. However, the maximum number of orthogonal pilot sequences that can exist is upper-bounded by the duration of the coherence interval divided by the channel delay spread. In Figure 4. Conventional MIMO beamforming contrasted with per-antenna constant envelope transmission in massive MIMO. Left: conventional beamforming, where the signal emitted by each antenna has a large dynamic range. Right: per-antenna constant envelope transmission, where each antenna sends out a signal with a constant envelope. ejφ1 ejφm ejφM α1 Per-antenna varying envelope Combined varying α envelope uk m uK fc u1 αM Linear encoder α = W (H)u Per-antenna constant envelope Combined varying u envelope k uK fc u1 Phase-only encoder φ = f (u,H) EDFORS_LAYOUT.qxp_Layout 1/30/14 1:08 PM Page 190