《MIMO多进多出多天线》课程教学资源（文献资料）6G——Massive MIMO is a Reality—What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO is a Reality-What is Next? Five Promising Research Directions for Antenna Arrays Emil Bjornson,Luca Sanguinetti,Henk Wymeersch,Jakob Hoydis,Thomas L.Marzetta 64 fully a reality.Bas Antenna il8eant 6 required and the limitation due【 directio Digital signal iewed processing (b)Beamforming to one point in space ith fyd ream 1:Beamforming from an antenna array can be usedt of the s the r angular or (b bea un.The coming wide- scale deployment of BS the might have no dominant directivity.The radiation pattems in this figure were computed using eight tial pro ing are omnipre antenna uniform linear arrays. tion applic ns,such as low-power ach ing and ning.We ou dominant directivity as shown in Fig.1(b).Both are commonl referred to if ae ape .Si-di is strictly speaking only created in the former case.In addition he aray an t desed to sen the p ions,po ing. er [1]by I.INTRODUCTIO exploration,as While an individual nta has a f n antenna arrays are capa th ing on pat This is traditionally illustrated as the formation of spatial as maximum ratio (MR).zero-forcing (ZF).and minimum beams in one (or a few)distinct angular directions,as shown eror(MMSE)processing were already know in Fig.I(a) Fays are a O C many 0 ng thi that controls be used to focus a signal at an iple antenna communications are still treating MR ZE and arbitrary point in space which.in a rich multi-path propa MMSE as the state-of-the-art methods.With that in mind,one what has the research community been doing he ams so th 30 letails.E ard very on itsuniqu d it is E.Bjon initial concept and a successful commercial solution.Let us E k at the development of multi-use r MIMO. of Pisa.Dip use an taly (lu i.it) stems that at the same time-f esource.In a paper from 1987 of Tech logy.413 s-be-ab Bell 5)Winters described that one can use antenna arrays to uplink signal from different users 61.Suproc how con

1 Massive MIMO is a Reality—What is Next? Five Promising Research Directions for Antenna Arrays Emil Björnson, Luca Sanguinetti, Henk Wymeersch, Jakob Hoydis, Thomas L. Marzetta Abstract—Massive MIMO (multiple-input multiple-output) is no longer a “wild” or “promising” concept for future cellular networks—in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies—once viewed prohibitively com￾plicated and costly—is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication ap￾plications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO. Index Terms—Massive MIMO, future directions, communica￾tions, positioning and radar, machine learning. I. INTRODUCTION While an individual antenna has a fixed radiation pattern, antenna arrays are capable of changing their radiation patterns over time and frequency, for both transmission and reception. This is traditionally illustrated as the formation of spatial beams in one (or a few) distinct angular directions, as shown in Fig. 1(a), but antenna arrays are also capable of many other types of spatial filtering. For example, the signal processing that controls the array can be used to focus a signal at an arbitrary point in space which, in a rich multi-path propa￾gation environment, corresponds to emitting a superposition of many angular beams so that the radiated pattern has no E. Björnson is with the Department of Electrical Engineering (ISY), Linköping University, 58183 Linköping, Sweden (emil.bjornson@liu.se). He was supported by ELLIIT and CENIIT. L. Sanguinetti is with the University of Pisa, Dipartimento di Ingegneria dell’Informazione, 56122 Pisa, Italy (luca.sanguinetti@unipi.it). H. Wymeersch is with the Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden (henkw@chalmers.se). J. Hoydis is with Nokia Bell Labs, Paris-Saclay, 91620 Nozay, France (jakob.hoydis@nokia-bell-labs.com). T. L. Marzetta is with the Department of Electrical and Computer Engi￾neering, New York University, Tandon School of Engineering, Brooklyn, NY (tom.marzetta@nyu.edu). (a) Beamforming in one angular direction (b) Beamforming to one point in space Antenna array Digital signal processing Fig. 1: Beamforming from an antenna array can be used to (a) focus the radiated signal in one angular direction or (b) focus the signal at one particular point in space, in which case the radiated signal might have no dominant directivity. The radiation patterns in this figure were computed using eight￾antenna uniform linear arrays. dominant directivity, as shown in Fig. 1(b). Both examples are commonly referred to as beamforming, even if a “beam” is strictly speaking only created in the former case. In addition, the array can be used to sense the propagation environment, for example, to detect anomalies or moving objects. Many different applications for antenna/sensor arrays have been conceived over the years. The 1988 overview paper [1] by Van Veen and Buckley outlined radar, sonar, communications, imaging, geophysical exploration, astrophysical exploration, and biomedical applications that were identified in the 70s and 80s. The signal processing methods that are nowadays known as maximum ratio (MR), zero-forcing (ZF), and minimum￾mean square error (MMSE) processing were already known at that time, but under different names. When writing this article—30 years later—the recent textbooks [2]–[4] on mul￾tiple antenna communications are still treating MR, ZF, and MMSE as the state-of-the-art methods. With that in mind, one might wonder: what has the research community been doing the past 30 years? The devil is in the details. Every application has its unique characteristics and it is hard to bridge the divide between an initial concept and a successful commercial solution. Let us take a closer look at the development of multi-user MIMO, by which we refer to communication systems that use an￾tenna arrays at the BSs to spatially multiplex several users at the same time-frequency resource. In a paper from 1987 [5], Winters described that one can use antenna arrays to discriminate between uplink signals from different users by spatial processing, called receive combining. A few years later [6], Swales et al. described how antenna arrays can be also arXiv:1902.07678v2 [eess.SP] 12 Jun 2019

MI eo meeme mue deplovment can make the total number of multiplexed 2 tepboymea development was largely driven b tion theoretic breakthroughs for multi-user MIMO wer made in the 00s[10]12].The early papers considere perect channe. ation(CS）and its to rovide guidance on how to deal with the imperfec n in (al MIMO can radiate multiple signals (indicated by different colors)that are focused at their respective receivers.as shown 3)Mos in (b). ing in such systems [13].Beamforming based on angle used to spatially multiplex users in the downlink,in which of-arrival (AoA)estimation and quantization codeb case the spatial processing is called transmit precoding. n roughly the“"ighl clas commu but the accuracy was insufficient to control inter-user interference. ly"into the rage area of the transmitter,an antenna can focu 4)The number of antennas was fairly small (which the is insuffi ective at the receiver,without chng ZF or MMSE processing.even with perfect CSI. sin T These factors created a negative attitude against the multi co sequence is that less signal power is ob ed at othe places and one can,therefo ocusother signalstow the user MIMO technology.which has partially remained until ology has changed dramatically during oints in spa ing mu he radiated power.but if M ning will Massive MIMO is a Realiry of th yto the was in ted that Mand are jointly increased 3).That is why is be resolved by equipping the BSs with very large numbers of the preferable opera ting regime multi-user MIMO. ntennas and utilizing time-division duplex (IDD)operation MIM Olpeemieeas th uplink user can have any numbe of antennas and channel conditions Extensive multi-user MIMO field trials were carried out in Each antenna can be built us ive handse the90s[☑and ArrayC Japa the mu lown (SDMA)did not become a commercial successin the 9 nd finds th s deeply rooted in info 0r00s rties of imperfect CSl into acc nt [21.[31. impact on a few key factors The signal processing complexity is manageable f dedi 1)In a time when circuit-switched low-rate voice commu ircuits are designed [16].[17 odebooks which only work well for ngular sparsity and calibrated array structures metrie.Since muli-user MIMO was rathe complicated ,A large numoe of antennas (M 6)leads to an un ind expensive to implem t in the 90s,i was simply fadin ss agal Ss in sma ent to more B multi-user MIMo technology Since classical BSs rely ence even with imperfect CSIif A solid theory for Massive MIMO in block-fading channel on orthogonal time-frequency scheduling.each BS could has been developed in recent years,thanks to the contributions earchers in academia and industry. -user ching the

2 (a) (b) Fig. 2: A classical BS radiates one signal uniformly into its coverage area, as shown in (a). A BS capable of multi-user MIMO can radiate multiple signals (indicated by different colors) that are focused at their respective receivers, as shown in (b). used to spatially multiplex users in the downlink, in which case the spatial processing is called transmit precoding. The difference between classical communication systems and multi-user MIMO can be seen by comparing Fig. 2(a) and Fig. 2(b). Instead of radiating one signal “uniformly” into the coverage area of the transmitter, an antenna array can focus the same signal at its intended receiver. If M antennas are used, an M times stronger signal can (ideally) be achieved at the receiver, without changing the radiated power. The consequence is that less signal power is observed at other places and one can, therefore, focus other signals towards other points in space without causing much interference between transmissions. If K users are spatially multiplexed in the downlink, each user might be allocated only 1/K of the total radiated power, but if M ≥ K, the beamforming will still make the received signal M/K > 1 times stronger than in the classical system. Hence, the overall spectral efficiency [b/s/Hz] of the system grows proportionally to the number of users if M and K are jointly increased [3]. That is why M  K is the preferable operating regime for multi-user MIMO. Note that the word “multiple” in the MIMO acronym refers to the multiple antennas at the BS and the multiple users, while each user can have any number of antennas. Extensive multi-user MIMO field trials were carried out in the 90s [7] and ArrayComm deployed commercial products in Japan [8, Example 10.1]. However, the multi-user MIMO technology (then known as spatial division multiple access (SDMA) [9]) did not become a commercial success in the 90s or 00s. There are many contributing factors and their relative impact is debatable, but we will mention a few key factors: 1) In a time when circuit-switched low-rate voice commu￾nication was the dominant service, it was not the spectral efficiency but the capability of multiplexing a certain number of users per km2 that was the key performance metric. Since multi-user MIMO was rather complicated and expensive to implement in the 90s, it was simply more cost-efficient to deploy more BSs using classical hardware than to invest in the new and rather untested multi-user MIMO technology. Since classical BSs rely on orthogonal time-frequency scheduling, each BS could multiplex a much smaller number of users than a BS supporting multi-user MIMO. Nevertheless, a denser deployment can make the total number of multiplexed users per km2 the same as with a less dense multi-user MIMO deployment. 2) The technological development was largely driven by heuristics and experiments since the first major informa￾tion theoretic breakthroughs for multi-user MIMO were made in the 00s [10]–[12]. The early papers considered perfect channel state information (CSI) and it took many more years for the information theory literature to provide guidance on how to deal with the imperfect CSI that occurs in any practical communication system. 3) Most telecom operators had frequency-division duplex (FDD) licenses at the time and it is hard to acquire downlink CSI that is sufficiently accurate for beamform￾ing in such systems [13]. Beamforming based on angle￾of-arrival (AoA) estimation and quantization codebooks were considered to send beams in roughly the “right” way. This worked rather well for single-user systems, but the accuracy was insufficient to control inter-user interference. 4) The number of antennas was fairly small (M ≈ 8) which is insufficient to achieve the spatial resolution that is necessary for effective interference suppression, using ZF or MMSE processing, even with perfect CSI. These factors created a negative attitude against the multi￾user MIMO technology, which has partially remained until today, even if the technology has changed dramatically during the last decade. A. Massive MIMO is a Reality To address the shortcomings of conventional multi-user MIMO, the Massive MIMO concept was introduced in [14]. It is now well accepted that many of the previous challenges can be resolved by equipping the BSs with very large numbers of antennas and utilizing time-division duplex (TDD) operation and the uplink-downlink channel reciprocity to achieve a com￾munication protocol that supports arbitrary antenna numbers and channel conditions: • Each antenna can be built using inexpensive handset￾grade hardware components [15], which keeps the cost down. • The communication design is deeply rooted in informa￾tion theory and finds the right operating regime by taking the properties of imperfect CSI into account [2], [3]. • The signal processing complexity is manageable if dedi￾cated circuits are designed [16], [17]. • There is no need to rely on AoA estimation or quantiza￾tion codebooks, which only work well for channels with angular sparsity and calibrated array structures. • A large number of antennas (M ≥ 64) leads to an un￾precedented spatial resolution, robustness against small￾scale fading, and the ability to spatially suppress interfer￾ence even with imperfect CSI if M  K. A solid theory for Massive MIMO in block-fading channels has been developed in recent years, thanks to the contributions of many researchers in academia and industry. Some of the key research directions that are now approaching the finish lines

3 8an in Fig.3.There is no massive difference in size since the many low-gain antennas in Massive MIMO must be compared s are 1351 Thi 1410mn two-dimensional configuration also makes the array 988m 15 20MHz up o ch) signaling using QPSK,16-QAM.64-QAM,and 256-OAM The maximum ra 187mm 70 mm for the array 520mm 154mm AAII an Nokia Airscale are two competing product lines.Many tele (a) Fig.3:Comparison of the form factors of(a)the Eri on AIR 6468.64-antenna array:(b)the Kathrein 80010621 antenna nc ding the used the Ma 5 GH (a of their current commercial shipm has either 320r6 since 64 low antennas in (a)are compared with one high- antennas [37].This demonstrates that Massive MIMO is nov gain antenna in(b). 'too complicated and expensive to implement"has finally been disproved. eie18[211 eray effici Since there standardize which signal pro ing me e BS.he maturity of th s underlined networks can change over time.The first Massive MIMO the two as adve on top cover th products are (proba in the aforem inue unde ning and not have enough simultaneously active users to benefit much that the ba ar rstood and from spatial multiplexing.Instead,the telecom operators are n nas been manly obser for impr ng the performance at th [13].often rooted in the negative pastex nces with multi- edg ed in ary pol I) nts in sd firs user MIMO.For example.Massive MIMO has been accu However when the antenna arrays have been deploved the sive to impe d by a softwa 909 the Ma hybrid analog-digital array implementations and complicated nlicated me od m are needed methods are not needed until the number of simultaneo d their rafhic demands urpass the limits of th infeasible to impleme icularly in mmwave bands but orks hav also at sub-6 GHz band t is this correct? improved theirs ectral efficiency in similar ways In 2014.the tes at Lund University sh ed that ully dig "only"a maior enei 331 1m2018 the FCO suppor ssive MIMO p ducts.including I likely be turned into a commercial product in the nex Erics AIR +0 134].This has ante years Hence,even if the first 5G P ducts for mm Wav and dou and is de for 4G ITE so it i ch the matte

3 988 mm 520 mm 187 mm 70 mm 1410 mm 154 mm (a) (b) Fig. 3: Comparison of the form factors of (a) the Ericsson AIR 6468, 64-antenna array; (b) the Kathrein 80010621 antenna panel with 16 dBi directivity. Both arrays support the 2.5 GHz band. The 64 antennas in (a) have varying polarization, in￾dicated by two colors. There is no massive difference in size since 64 low-gain antennas in (a) are compared with one high￾gain antenna in (b). are the spectral efficiency analysis [18]–[21], system design for high energy efficiency [22]–[24], pilot contamination and decontamination [25]–[29], and power optimization [30]–[32]. The maturity of the research on Massive MIMO is underlined by the two recent textbooks on the topic that cover the fundamentals [2] as well as advanced topics [3]. The research in the aforementioned directions can certainly continue under more practical modeling assumptions, but the main point is that the basics are well understood and noncontroversial. Nevertheless, Massive MIMO has been (and still is) met with skepticism and many misconceptions have flourished [13], often rooted in the negative past experiences with multi￾user MIMO. For example, Massive MIMO has been accused of being too complicated and expensive to implement, as if the transceiver hardware technology had not evolved since the 90s. This belief also spurred large investments into “less expensive” hybrid analog-digital array implementations and complicated beam-searching and beam-tracking algorithms that are needed to operate such arrays. The premise for this development is the belief that fully digital transceiver chains are practically infeasible to implement—particularly in mmWave bands but also at sub-6 GHz bands—but is this correct? In 2014, the real-time testbed at Lund University showed that Massive MIMO with 100 fully digital transceiver chains can be implemented using off-the-shelf hardware, requiring “only” a major engineering effort [33]. In 2018, the FCC approved the first line of Massive MIMO products, including the Ericsson AIR 6468 [34]. This product has 64 antennas connected to 64 fully digital transceiver chains in both uplink and downlink, and it is designed for 4G LTE, so it is even a pre-5G product. The AIR 6468 can be used in either the 2.5 GHz or 3.5 GHz band. Compared to a conventional fixed￾beam sector antenna designed for the same bands, the Massive MIMO array is wider but has a smaller height, as illustrated in Fig. 3. There is no massive difference in size since the many low-gain antennas in Massive MIMO must be compared with one high-gain antenna. The 64 antennas are deployed on 4 rows, each containing 8 dual-polarized antennas [35]. This two-dimensional configuration also makes the array compact compared to the large one-dimensional uniform linear arrays that are commonly considered in the academic literature. The AIR 6468 can aggregate up to three carriers (15- 20 MHz each), supports reciprocity-based beamforming, and signaling using QPSK, 16-QAM, 64-QAM, and 256-QAM. The maximum radiated power is 1.875 W per antenna, which corresponds to 120 W in total for the array. The Ericsson AIR 6468 is not unique: Huawei AAU and Nokia Airscale are two competing product lines. Many tele￾com operators started to deploy this type of array in 2018, including the US operator Sprint that even used the Massive MIMO term in its marketing towards the end users [36]. Huawei reported at the Mobile World Congress 2019 that 95% of their current commercial shipments has either 32 or 64 antennas [37]. This demonstrates that Massive MIMO is now a reality for cellular networks operating in conventional sub- 6 GHz bands. Hence, the previous claim of Massive MIMO being “too complicated and expensive to implement” has finally been disproved. Since there is no need to standardize which signal pro￾cessing methods will be used for beamforming and chan￾nel estimation at the BS, the solutions implemented in real networks can change over time. The first Massive MIMO products are (probably) using fairly simple signal processing methods, such as MR for beamforming and least-squares for channel estimation. The reason is that most current cells do not have enough simultaneously active users to benefit much from spatial multiplexing. Instead, the telecom operators are mainly observing a need for improving the performance at the cell edge, so basic beamforming to arbitrary points in space, as illustrated in Fig. 1(b), is the feature that is implemented first. However, when the antenna arrays have been deployed, the spectral efficiency can be improved by a software update that switches to more advanced methods from the Massive MIMO literature [3]. A gradual refinement makes practical sense: less complicated methods are easier to implement, more advanced methods are not needed until the number of simultaneously active users and their traffic demands surpass the limits of the simpler methods, and then the more advanced methods can be sold as feature upgrades. Note that 3G and 4G networks have improved their spectral efficiency in similar ways. At mmWave frequencies, the first experimental verification of fully digital antenna arrays was presented in 2018. NEC has developed a 24-antenna uniform linear array that supports digital beamforming in the 28 GHz band [38]. This prototype will likely be turned into a commercial product in the next few years. Hence, even if the first 5G products for mmWave communications rely on analog or hybrid implementations to quickly reach the market, it is only a matter of time before

digital solutions prevail and become the most cost and ener (a)Compact co-located Massive MIMO arrays efficient implementations,thanks to the fast development in ly go in opposite direction? B.What is Nert? The development of Massive MIMO ommunication tech in the hands of the 0 number of communication. tion alg signal proce and optimiza oms have Modeling simplifications that have beer demia (e.g.block- ding channels with stochasti e fading or determinist with ang e4ncaa0g chance to try out the existing algorithms. arrays,as shown in (a).The e users are in t it is hard to nent is actually nee fomm of a signal beam.To deploy arrays with very man antennas.we can instead create ELAAs where the antennas are distributed over a large area and hidden into other constructior under practical.hardware-related and regulatory constraints ements. At the same time,it is important to initiate more forward- the new signal beams. might If 5G becomes a commercial success,massive digitally a set of users.There practical limits be d in the ind rooftop locations,fo provide spatial multiplexing over wide areas.while new BSs operating in the mmWave bands will be deployed indoors and at the street leve to provide local area The to an the spatial multiplexi atial r waveforms that give constructive interference at particula in the points in space and resolving the hine details of received where large one-dimensional arrays are often considered in a two-dimensional world.In many practical deployment scena ments evolve in the future? s are mainly separable in the nonzonta The remainder of this ar icle will consider five forward- small The sear tions tha aim at us sing antenna arrays existing 64-antenna products have only eight antennas pe exciting ho ontal row massive is tha sector site that ce mul interesting research challenges. the norm in c II.DIRECTION 1:EXTREMELY LARGE APERTURE ARRAYS han a few hundred antennas per site and to obtain a truly The antenna separation in an array is of the order of the ive spatial horizontal domain.we nee velength A and the users are located in the fa -field of the BS array.Ih are two clas mptions in tead of gath hering all the antenas ina single to be reasonablebut be revised gong forward.The a substantially lareer area and made invisible b grows monotonically them into existing construction elements.Fig.4(b)exemplifie antennas 28]. we can expect dreds or thousands of antennas are used te ing me dual-polaned one

4 digital solutions prevail and become the most cost and energy efficient implementations, thanks to the fast development in semiconductor technology. This should come as no surprise— in the time when digitalization is embraced in every part of the society, why would cellular technology suddenly go in the opposite direction? B. What is Next? The development of Massive MIMO communication tech￾nology is now in the hands of the product departments of companies such as Ericsson, Huawei, Nokia, etc. A large number of communication, signal processing, and optimiza￾tion algorithms have been developed over the years and it remains to be seen which ones will work well in practice. Modeling simplifications that have been made in academia (e.g., block-fading channels with stochastic small-scale fading or deterministic channel models with angular sparsity) might prevent a straightforward transfer from theory to practical implementation. Before the product developers have had the chance to try out the existing algorithms, it is hard to tell what further algorithmic development is actually needed. The MIMO research community should certainly support the prod￾uct developers in their efforts to implement existing algorithms under practical, hardware-related and regulatory constraints. At the same time, it is important to initiate more forward￾looking research that considers new applications of antenna arrays that might become the foundation for beyond 5G networks. If 5G becomes a commercial success, massive digitally controllable antenna arrays will be deployed “everywhere”. Conventional sites operating in the sub-6 GHz band will be equipped with arrays of 64 or more antennas (per sector) to provide spatial multiplexing over wide areas, while new BSs operating in the mmWave bands will be deployed indoors and at the street level to provide local area coverage. The network equipment that controls these antennas has access to an unprecedented spatial resolution in terms of emitting waveforms that give constructive interference at particular points in space and resolving the fine details of received waveforms. What else can we use this spatial resolution for, beyond mobile broadband applications, and how will the antenna deployments evolve in the future? The remainder of this article will consider five forward￾looking research directions that aim at using antenna arrays for new non-communication applications and deployment con￾cepts that open up new exciting possibilities but also pose interesting research challenges. II. DIRECTION 1: EXTREMELY LARGE APERTURE ARRAYS The antenna separation in an array is of the order of the wavelength λ and the users are located in the far-field of the BS array. These are two classical assumptions in the array processing and wireless communication literature that used to be reasonable but need to be revised going forward. The spectral efficiency of Massive MIMO grows monotonically with the number of antennas [28]. Thus, we can expect a future where hundreds or thousands of antennas are used to (a) Compact co-located Massive MIMO arrays (b) Extremely large aperture array Fig. 4: The first deployments of Massive MIMO use compact arrays, as shown in (a). The users are in the far-field of the array and, thus, the transmission to a LoS user takes the form of a signal beam. To deploy arrays with very many antennas, we can instead create ELAAs where the antennas are distributed over a large area and hidden into other construction elements, for example, windows as in (b). The user might be in the near-field of the array and then LoS users will not observe signal beams. serve a set of users. There are, however, practical limits to how many antennas can be deployed at conventional towers and rooftop locations, for example, determined by the array dimensions allowed by the site owner, the weight, and the wind load. The rather compact 64-antenna product shown in Fig. 3(a) has already been deployed and we will likely see de￾ployments of somewhat larger arrays at some locations as well. Nevertheless, the spatial multiplexing capability of these two￾dimensional planar arrays in our three-dimensional world is far from what has been demonstrated in the academic literature, where large one-dimensional arrays are often considered in a two-dimensional world. In many practical deployment scenar￾ios, the user channels are mainly separable in the horizontal domain [35] since the variations in elevation angle between different users and scattering objects are relatively small. The existing 64-antenna products have only eight antennas per horizontal row—how massive is that? Since multi-sector sites are the norm in cellular networks, co-location of three or more compact arrays that point in different directions are also likely to happen, as illustrated in Fig. 4(a). However, to deploy more than a few hundred antennas per site and to obtain a truly massive spatial resolution in the horizontal domain, we need new antenna deployment strategies. Instead of gathering all the antennas in a single box, which will be visible and heavy, the antennas can be distributed over a substantially larger area and made invisible by integrating them into existing construction elements. Fig. 4(b) exemplifies a setup where the antennas are deployed next to each window in a tall building. If one dual-polarized antenna is hidden in

each corner of the window,there are 1512 antennas in this Research direction mely large apert山reay MIMO [55 A M056 compact arrays illustrated in Fig.3:the antenna separation is at the order of meters,which is much larger than the wavelength lolographic Massive MIM (be ing in fron de s62 exa mple of an ELAA is when the antennas are distributed nt wa so that each user is essentially ce66 TL: alled Cell-fre eMIM0401-42. TABLE I:The proposed research directions 1 and 2 collect many other research topics as special cases concent has its roots in papers on distributed mimo from th early 00s [43].[44]andc oordinated multipoint from the early Impo tly,the spat an bt th so it is generally beneficial to beamforming at a particular point in space using we ons but are nhase-shifted to add con ution the 48 491 at the target point.Due to the different directivity.the signal ually decays when leaving target poin We use the flaa terminology to iointly des ibe a family gra of arch topics that have previously beer considered sepa- n a s d the tar with radius S 541.The rate owing c that features signal amplification is typically of this size in the BS an nas that are jointly and coherently serving many ear-field while it can be much larger in the far-field,implying distrbuted users. belia list cial cases s is provided in Table A.Vision quence of using elaas is that the radiative near The grand vision of ELAAs is to provide order -of field stretches many kil away magnitude hig oughput in wireless networks com y be in the ary spatial channel ies [391.[501.In the and the distribut ed ante field,the signal that reaches the array from a user s well and inc the appro by a superpos of pla ach in th don (UDN ith an Ao 661.167]that alsc resolve not only the Aoa of a wave but als but each antenna box is then an auton us BS that service has traveled (e.g.the spatial depth)by exploiting the spherical n exclus e set of users.It is known that the throughpu shape of t It is a that rences to visihle to a suhset of the in the this barrier as the number of antennas grows large [281.[29 blocked to the other antennas 51].Hence.channel mo deling [691-1711.at least in theory.The ultimate goal of ELAAs i is substantially o deploy so ma erently ope rating antennas that all the ore p when using EL MAs and ramel hard ilar to to a per- out since there are many antenna vith similar channel gain hannel without any propagation loss [53].[72].In additio exp I the rom ELA epa ate o enhancing mobile broadb services,which c the h grea in 53 which of an unprecedented number of machine-t is known favorable propagation in t ive MIMO devices [3].Anothe quenc rans The mas production of smartphones has tured adva om at dis atly the nid deve ent of Sim

5 each corner of the window, there are 1512 antennas in this example. Suppose the adjacent windows are 3 m apart, then the array spans an area of 24 m × 60 m. This is an Extremely Large Aperture Array (ELAA) [39] compared to the conventional compact arrays illustrated in Fig. 3; the antenna separation is at the order of meters, which is much larger than the wavelength (being in the range from one decimeter down to a few millimeters in the frequency ranges considered in 5G). Another example of an ELAA is when the antennas are distributed over a large geographical area so that each user is essentially surrounded by BS antennas, rather than the conventional case of each BS being surrounded by users. This has recently been called Cell-free Massive MIMO [40]–[42], but the basic concept has its roots in papers on distributed MIMO from the early 00s [43], [44] and coordinated multipoint from the early 10s [45]–[47]. Importantly, the spatial resolution of an array is not determined by the number of antennas but the array’s aperture, so it is generally beneficial to spread out antennas, even if this also gives rise to spatial aliasing phenomena where signals coming from widely different directions cannot be separated [3]. Non-uniform array geometries can provide better spatial resolution than uniform geometries [48], [49]. We use the ELAA terminology to jointly describe a family of research topics that have previously been considered sepa￾rately but all comply the following definition. Definition 1: An ELAA consists of hundreds of distributed BS antennas that are jointly and coherently serving many distributed users. A list of different special cases is provided in Table I. We believe that these special cases can, to a large extent, be jointly analyzed under the ELAA umbrella in the future. A consequence of using ELAAs is that the radiative near- field stretches many kilometers away from the array. Hence, the users will typically be in the near-field of the array, instead of the far-field as is traditionally the case, leading to non￾stationary spatial channel properties [39], [50]. In the far- field, the signal that reaches the array from a user is well approximated by a superposition of plane waves, each being determined by two parameters: a channel gain and an AoA. In contrast, an array with an extremely large aperture can resolve not only the AoA of a wave but also the distance it has traveled (e.g., the spatial depth) by exploiting the spherical shape of the wave and/or the channel gain differences to the antennas. It is also possible that some wave components are only visible to a subset of the antennas in the array and blocked to the other antennas [51]. Hence, channel modeling is substantially harder when using ELAAs and involves many more parameters. While conventional Massive MIMO benefits from channel hardening, where the small-scale fading average out since there are many antennas with similar channel gains, we cannot expect the same from ELAAs since well separated antennas have large gain differences [52]. On the other hand, the great spatial resolution will likely make the channels to different users nearly orthogonal [50], [52], [53], which is known as favorable propagation in the Massive MIMO literature [3]. Another consequence is that the transmission from the array cannot be illustrated as a beam, but rather as strong coherent signal amplification at distinct points in space, Research direction Special cases Extremely large aperture array Cell-free Massive MIMO [40] Coordinated multipoint [47] Very large aperture Massive MIMO [55] Distributed MIMO [56] Radio stripes [57] Network MIMO [58] Holographic Massive MIMO Holographic RF system [59] Holographic beamforming [60] Large intelligent surface [61] Reconfigurable reflectarrays [62] Intelligent walls [63] Software-controlled metasurfaces [64] Intelligent reflecting surface [65] TABLE I: The proposed research directions 1 and 2 collect many other research topics as special cases. as illustrated in Fig. 4(b), while the antennas’ signal compo￾nents add non-coherently at most other places. When aiming the beamforming at a particular point in space using well separated antennas, the signal components arrive from widely different directions but are phase-shifted to add constructively at the target point. Due to the different directivity, the signal amplification gradually decays when leaving the target point, but irrespective of the antenna configuration it is always strong in a sphere around the target with radius λ/8 [54]. The volume that features signal amplification is typically of this size in the near-field while it can be much larger in the far-field, implying that closely located users can be spatially multiplexed with less mutual interference when using an ELAA. A. Vision The grand vision of ELAAs is to provide orders-of￾magnitude higher area throughput in wireless networks com￾pared to what Massive MIMO with compact arrays can practi￾cally deliver. The keys to reaching this goal are the even larger number of antennas and the distributed antenna deployment that reduces the average propagation loss and increases the spatial resolution (particularly, in the horizontal domain). There is a competing concept of ultra-dense networks (UDN) [66], [67] that also relies on distributed antenna deployment, but each antenna box is then an autonomous BS that services its own exclusive set of users. It is known that the throughput of UDNs is fundamentally limited by inter-cell interference [68]. Cooperation between the distributed antennas can break this barrier as the number of antennas grows large [28], [29], [69]–[71], at least in theory. The ultimate goal of ELAAs is to deploy so many coherently operating antennas that all the users have mutually orthogonal channels, leading to a per-user throughput similar to that of an additive white Gaussian noise channel without any propagation loss [53], [72]. In addition to enhancing mobile broadband services, which constitute the majority of the wireless traffic [73], the great spatial resolution of an ELAA can also be exploited for spatial multiplexing of an unprecedented number of machine-type communication devices. The mass production of smartphones has turned advanced antennas and transceiver equipment into a commodity. Simi￾larly, the rapid development of integrated circuits (following