Argos: Practical Many-Antenna Base StationsClayton Shepard1, Hang Yu1, Narendra Anand1, Li Erran Li2,

Thomas Marzetta2, Richard Yang3, and Lin Zhong11Rice University, Houston, TX 2Bell Labs, Murray Hill, NJ 3Yale University, New Haven, CT

{cws, hang.yu, nanand, lzhong}@rice.edu {erranlli, tlm}@research.bell-labs.com yry@cs.yale.edu

ABSTRACTMulti-user multiple-input multiple-output theory predictsmanyfold capacity gains by leveraging many antennas onwireless base stations to serve multiple clients simultane-ously through multi-user beamforming (MUBF). However,realizing a base station with a large number antennas is non-trivial, and has yet to be achieved in the real-world.We present the design, realization, and evaluation of Ar-

gos, the first reported base station architecture that is ca-pable of serving many terminals simultaneously throughMUBF with a large number of antennas (M � 10). De-signed for extreme flexibility and scalability, Argos exploitshierarchical and modular design principles, properly parti-tions baseband processing, and holistically considers real-time requirements of MUBF. Argos employs a novel, com-pletely distributed, beamforming technique, as well as aninternal calibration procedure to enable implicit beamform-ing with channel estimation cost independent of the numberof base station antennas. We report an Argos prototype with64 antennas and capable of serving 15 clients simultaneously.We experimentally demonstrate that by scaling from 1 to 64antennas the prototype can achieve up to 6.7 fold capacitygains while using a mere 1/64th of the transmission power.

Categories and Subject DescriptorsC.2.1 [Computer-Communication Networks]: NetworkArchitecture and Design—Wireless Communication

KeywordsLarge-scale Antenna Systems (LSAS), Many-Antenna, Mas-sive MIMO, Multi-User MIMO, Beamforming, Conjugate,MRT, Zero-forcing

1. INTRODUCTIONDue to the popularization of smartphones, tablets and

data-hungry applications, mobile data traffic is growing ex-ponentially, with the expectation that it will increase 18-fold within 5 years [7]. In response, wireless operators arescrambling to acquire more spectrum resources and deploymore base stations to increase spatial reuse. However, thereis a fundamental spectrum efficiency limit to existing cel-lular network architectures: they are single-user systems.That is, a base station serves only one terminal in a given

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resource block (i.e., time slot, spectrum channel, or codesequence). Information theory shows that this limit can beovercome through multi-user multiple-input multiple-output(MU-MIMO) [9]; one promising form of MU-MIMO is calledmulti-user beamforming (MUBF). With MUBF, a base sta-tion employs multiple antennas to send independent datastreams to multiple terminals in the same resource block,effectively improving spatial reuse. As theory shows, themore antennas a base station has, the more terminals itcan serve simultaneously, resulting in higher spectral capac-ity. Not surprisingly, the theory community is envisioningMUBF base stations with hundreds of antennas.

However, building a MUBF base station with many an-tennas is non-trivial. Scaling up baseband processing, clockdistribution, transmission synchronization, and channel es-timation raises serious system challenges. As a result, onlytestbeds with a few antennas have been reported in the liter-ature, e.g., [5, 14]. Emerging wireless standards are similarlyrestricted to a small number of antennas and terminals. Thekey question to the proposal of MUBF base stations withmany antennas remains: is it practical at all?

In this work, we answer this question affirmatively withArgos1, a flexible base station architecture that is scalableup to thousands of antennas and able to serve tens of termi-nals simultaneously through MUBF. Using commercial off-the-self radio modules, i.e., the WARP platform [4], we haverealized an Argos prototype with 64 antennas that is capableof serving 15 terminals through zero-forcing and conjugateMUBF. Extensive experimental characterization using thisprototype shows that the spectral capacity increases from12.7 bps/Hz when using a single-antenna to 85 bps/Hz forArgos employing zero-forcing MUBF, and to 38 bps/Hz forArgos employing the less computationally intensive conju-gate MUBF, while using a mere 1/64th of the single-antennatransmission power. We show that the spectral capacitygrows nearly linearly with the number of base station anten-nas and the number of simultaneously served terminals, assuggested by theory. The scale of our prototype and experi-mentation are only limited by the number of WARP boardsthat are available to us. To the best of our knowledge, Ar-gos is the first publicly reported many-antenna MUBF basestation design and realization (M � 10). Our work demon-strates the feasibility of the MUBF theory community’s pro-posal, and presents key design principles for a scalable, flex-ible, and cost-effective realization.

Argos achieves its scalability and flexibility with four noveldesign principles. (i) First, Argos adopts a hierarchicaland modular design. This allows it to scale up easily byincrementally adding modules, e.g., WARP boards in the

1Argos is a giant with 100 eyes in Greek mythology. Thegreat vision of Argos is analogous to the improved capacityof our many-antenna base station.

=

=Constructive interference

Destructive interference

y

x

Figure 1: Aerial view of two antennas, representedby two dark dots, emitting identical sine waves atthe same frequency. The two waves perfectly rein-force each other along the x axis (constructive inter-ference) but completely cancel each other out alongthe y axis (destructive interference). Between thetwo axes the interference gradually varies, produc-ing conical radiation know as a beam pattern.

reported prototype. As Argos scales up it can select theoptimal beamforming algorithm by thoroughly analyzingthe performance factors and data dependencies of variousMUBF techniques. (ii) Second, Argos intelligently parti-tions computation tasks among the different modules in thehierarchy. In the downlink, data to multiple terminals isbroadcast to all antennas. Each antenna locally applies itsbeamforming weights and transmits the combined signal toall terminals simultaneously. In the uplink, I and Q sam-ples from each antenna are combined in upstream modulesalong the hierarchy. (iii) For very large scale operation,Argos leverages a modified version of conjugate beamform-ing that allows localized weight computation at each an-tenna. Specifically, traditional conjugate beamforming re-quires centralized transmission power normalization, whileArgos conducts the normalization locally at each antenna.This modification allows Argos to scale almost indefinitelywith regard to baseband complexity. (iv) Finally, Argosemploys a novel internal calibration procedure that allowsimplicit beamforming across a large number of base stationantennas without explicit channel state information (CSI)estimation, enabling the real-time CSI estimation overheadto become independent of the number of base station anten-nas. Notably, implicit beamforming requires time divisionduplex (TDD) operation, which is a substantial modificationto the frequency division duplex (FDD) systems primarilyused in cellular networks currently.In summary, we make the following contributions to ad-

vance the state of the art of MUBF with many antennas:

• We design and realize Argos, a first-of-its-kind basestation architecture that can scale up to thousands ofantennas serving tens of terminals with either conju-gate or zero-forcing MUBF. We report an Argos pro-totype with 64 antennas simultaneously serving 15 ter-minals;

• Using the Argos prototype, we experimentally demon-strate the real-world feasibility of base stations em-ploying many-antenna MUBF and their capability tosignificantly improve capacity;

• The design of Argos contributes multiple novel tech-

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Figure 2: Multi-user beamforming employs base-band precoding and multiple antennas to send inde-pendent data streams to multiple terminals at thesame time.

niques to address key challenges toward realizing basestations with a large number of antennas, includingclock distribution, transmission synchronization, local-ized weight computation, and channel calibration.

In the rest of this paper, we provide the background inSection 2. We present the design and implementation ofArgos in Sections 3 and 4, respectively. In Section 5 weevaluate the real-world performance of Argos. In Sections 6and 7 we discuss related and future work, respectively, andthen conclude in Section 8.

2. BACKGROUNDWe first provide some background on multi-user beam-

forming (MUBF) and highlight the key benefits and chal-lenges of using a large number of antennas on base stations.

2.1 Beamforming BasicsBeamforming utilizes multiple antennas transmitting at

the same frequency to realize directional transmission. Dueto constructive and destructive interference of signals frommultiple transmission antennas, the signal strength receivedat different directions varies spatially, leading to a beam pat-tern, as shown in Figure 1. This beam pattern can be al-tered by changing the beamforming weights applied to eachantenna, effectively altering the amplitude and phase of thesignal sent from that antenna. Closed-loop beamforming em-ploys CSI to calculate the beamforming weights that maxi-mize the signal strength at intended receivers and minimizethe interference at unintended ones.

2.2 Single and Multi-user BeamformingThere are two major categories of closed-loop beamform-

ing: Single-user beamforming (SUBF) and Multi-user beam-forming (MUBF). SUBF maximizes the signal strength ata single intended receiver by using beamforming weightsthat are the complex conjugate of the CSI, which is alsoknown as maximum ratio transmission [15]. MUBF concur-rently transmits multiple data streams, each to a differentintended receiver as shown in Figure 2. Not surprisingly,information theoretical studies have shown that MUBF canimprove spectral capacity manyfold due to its spatial multi-plexing gain.

There are many baseband techniques to realize MUBF.We focus on linear precoding since other methods are com-putationally infeasible for practical systems. Let s denote

a K × 1 vector representing the data-bearing symbols to Kusers. Linear precoding creates a transmission vector s′ forM antennas, by multiplying the original data vector s by aM × K matrix W: s′ = W · s. Where W consists of thebeamforming weights.In this work, we study two important forms of linear-

precoding for MUBF: conjugate and zero-forcing. Let Hdenote the M ×K channel matrix between the M base sta-tion antennas and K concurrent terminals. Let c denote aconstant chosen to satisfy a transmission power constraint.Conjugate: W = Wconj = c · H∗, where H∗ is the

complex conjugate of H. In other words, conjugate beam-forming simply takes the complex conjugate of each channelcoefficient in H as the beamforming weight, normalized by c.Indeed, it can be viewed as simultaneous single-user beam-forming to K terminals by aggregating the signals intendedfor these terminals. Conjugate MUBF is sub-optimal andmay not perform well with a small M due to inter-terminalinterference. This method has only been recently proposedfor MUBF with a large number of antennas in [17].

Zero-forcing: W = c · Wzf = H∗(HTH∗

)−1. Zero-

forcing beamforming employs the CSI to precode the data-bearing symbols so that they sum to zero, or a ‘null’, atunintended receivers. The effectiveness of zero-forcing hasbeen experimentally demonstrated recently [5] with a smallnumber of antennas (four) and terminals (four). Zero-forcing MUBF can keep inter-terminal interference to zeroif K ≤ M . However, due to the required matrix inversionthe computational overhead quickly becomes infeasible forreal-time applications, as will be discussed in Section 2.4.2.

2.3 Benefits of Many-Antenna MUBFIt is well known in information theory that MUBF with

many antennas provide the following key benefits:First, MUBF can greatly improve spectral capacity

through spatial reuse. Roughly speaking, the spectral ca-pacity gain from MUBF is min(M,K) [9]. A large M allowsthe base station to serve more terminals concurrently andtherefore achieve higher spectral capacity.Second, a very large M allows a more power-efficient and

cost-effective base station. The directional gain from usinga large M can be used to compensate for reduced trans-mission power; that is, a base station can achieve the samecapacity with a much lower total transmission power. Underall conceivable propagation conditions doubling the numberof base station antennas permits the total radiated powerto be reduced by a factor-of-two with no degradation ofperformance. Only when the number of antennas grows solarge that it begins to envelope the terminals or interveningscatterers will this effect cease. Moreover, multi-user beam-forming distributes the total transmission power across Mantennas, leading to a much lower transmission power perantenna. The base station can therefore leverage cheaperpower amplifiers and simpler RF filters. This eliminates theneed for active cooling, further reducing power consumptionand total cost.Finally, since power gains are reciprocal, the preceding

benefit also applies to terminals. Specifically, it allowsbattery-constrained terminals to use much lower transmis-sion power to achieve higher capacity.In Section 5, we will experimentally demonstrate these

benefits using the Argos design.

2.4 Challenges to Many-Antenna MUBFRealizing the key benefits outlined above is, however, non-

trivial. Any implementation of MUBF with many antennasfaces fundamental timing constraints imposed by the coher-ence time of the physical wireless channel. MUBF mustcollect CSI for each terminal then use it to calculate thebeamforming weights within a small fraction of the coher-ence time. Additionally, the computational complexity ofMUBF weight calculation grows with the number of anten-nas, M , and the number of simultaneously served terminals,K. The Argos design has to address both challenges.

2.4.1 CSI EstimationAcquisition of CSI fundamentally limits the capacity of

MUBF with many antennas. MUBF with M antennas toserve K terminals requires CSI between every base stationantenna and terminal, or M ·K channels. Importantly, allM ·K physical channels must be assessed within a periodmuch shorter than the channel coherence time in order to beuseful. The coherence time of a wireless channel depends onhow quickly the terminal and environment move. In cellularsystems this is typically on the order of a few milliseconds,but can drop below 500 μs [1] with vehicular mobility at ornear the terminal. This results in a fundamental tradeoffbetween the time spent collecting CSI, which dictates howmany users can be served simultaneously, and the time al-located to sending beamformed data to those users. Thistradeoff is explored theoretically in [16].

Traditionally, CSI is estimated explicitly. That is, eachbase station antenna broadcasts a pilot to the terminal,where the latter then uses this pilot to estimate its chan-nel to each of the base station antennas. In order for thischannel estimation to be useful, it has to be fed back tothe base station in order to perform downlink beamforming.The reverse of this procedure is then used to find uplink CSI,though feedback is unnecessary for maximum ratio combin-ing at the base station. This method thus requires O(M+K)time to send pilots (one pilot from each base station antennaand terminal) and O(M ·K) estimates that need to be sentback over-the-air (M estimates from K terminals). Thisoverhead is unavoidable in frequency division duplex (FDD)systems, since the physical channel is not reciprocal at dif-ferent frequencies.

In time division duplex (TDD) systems the physical chan-nel is reciprocal, and thus, theoretically, CSI could be esti-mated implicitly. That is, uplink pilots could be used toperform downlink beamforming, reducing channel estima-tion overhead to O(K) and eliminating the required feed-back. This is often called implicit beamforming. However,in practice, the uplink and downlink channels consist of notonly the physical channel, but also the channels introducedby the active RF components in the transmit and receivehardware, as will be further discussed in Section 3.3.

2.4.2 Real-time Beamforming Weight CalculationThe computational complexity of MUBF weight calcula-

tion also grows with the number of base station antennas,M , and the number of terminals, K. For conjugate MUBF,the beam weight computation is trivial. In hardware, tak-ing the complex conjugate of a signal only needs a bit-flipand an adder. Therefore, the delay introduced by weightcalculation is negligible. However, zero-forcing requires thecomputation of a matrix inverse, a calculation with the com-

plexity of O(M ·K2). Moreover, the inverse algorithm hasinternal data-dependencies that limit its ability to be par-allelized. While the incurred latency is acceptable at smallscales, the polynomial time nature of the inverse makes itvery challenging for MUBF systems with a large numberof antennas. For example, we estimate that a single 15 by15 matrix inverse would require approximately 150 μs us-ing a specialized high performance FPGA implementationreported in [8]. 150 μs is already 30% of the 500 μs coher-ence time specified by the LTE channel model. Moreover,in a wideband system such as LTE, this inversion has to beperformed for every 14 subcarriers [17]. Thus, while thesecomputations may be pipelined, the true overall inversiontime incurred will be far greater than 150 μs.Additionally, existing beamforming techniques incur a

high data transmission overhead because the channel esti-mates and beam weights have to be exchanged between acentral controller and each of the antennas. Even usingstate-of-the art hardware, e.g., InfiniBand, such exchangeincurs a sizable latency cost. The fastest InfiniBand bus has1 μs overhead per hop and 40 Gbps transfer rate [3]; it willincur approximately 5 μs delay per subcarrier group in a 15by 15 system. For a 20 MHz bandwidth this amounts to overa 700 μs delay. Zero-forcing cannot avoid this data exchangebecause the inverse calculation requires the full CSI matrix,H. More subtly, even the simplest beamforming algorithm,conjugate, requires full knowledge of H in order to appropri-ately scale the power of the steering weights. In Section 3.4,we present a novel localized conjugate beamforming methodthat eliminates the overhead due to data transfer betweenthe central controller and antennas.

3. DESIGNThe key question we ask in this section is: how do we

design a MUBF base station that can flexibly optimize itsarchitecture over a wide range of M and K? Before pro-ceeding to answer it, we want to highlight its practical in-terest: realistic wireless networks often have large variationsin many of their properties, including the financial budgetfor the base stations, the terminal population within thecoverage, and the data traffic volume from terminals. Tra-ditional base stations can only scale their transmission poweror, equivalently, their cell size, to cope with such variations.In contrast, Argos base stations can also scale the numberof antennas to accommodate various deployment needs.We argue that in order to meet these demands our many-

antenna base station must: (i) be economically affordablewith cost proportional with M , (ii) scale as both M and Kbecome very large, and (iii) select the optimal beamformingtechnique given deployment requirements. We next presenthow our design of Argos accomplishes these attributes.

3.1 ScalabilityThe first question is: can MUBF scale up with M , the

number of base station antennas? MUBF entails three dis-tinct phases: channel estimation, weight calculation, andlinear precoding. We explore the feasibility and design im-plications of these as M scales up.

3.1.1 Channel EstimationExplicit channel estimation does not scale well with M or

K. As discussed in Section 2.4.1, explicit channel estima-tion typically requires M +K pilots to be sent, and M ·K

estimates to be fed back to the base station. This is clearlyan unacceptable overhead for large-scale systems, and sug-gests that Argos must employ TDD reciprocity and implicitbeamforming to reduce this overhead to K pilots and elim-inate the feedback. In order to enable this, however, wemust first overcome the asymmetries introduced by the RFhardware. To accomplish this we devise a novel internalcalibration scheme, which we present in Section 3.3.

3.1.2 Beamforming MethodsUnfortunately, existing beamforming methods are dis-

tinctly unscalable, as they all have centralized data require-ments and typically have polynomial time complexity, asdiscussed in Section 2.4.2.

In light of this, we propose a novel beamforming methodthat allows weights to be computed completely locally, ateach base station radio, as described in Section 3.4. Lever-aging this method allows additional radios to be added with-out requiring additional bandwidth, enabling Argos to easilyscale up to an unprecedented number of base station anten-nas, e.g., 1000s.

However, while this beamforming method performs wellwith a very large number, e.g., 100s, of base station an-tennas serving 10s of terminals simultaneously, it is wellknown to be sub-optimal for smaller scale systems, e.g.,M = 30,K = 10. We demonstrate this empirically in ourresults, Section 5, where we find that zero-forcing results inup to a 4 fold capacity increase over our method. However,this does not account for the data transport and computa-tional overhead of zero-forcing, which becomes prohibitivewith a large number of users or high mobility, as describedin Section 2.4.2. Thus we conclude that in order to scale op-timally Argos must support traditional, centralized beam-forming techniques for smaller scale deployments.

3.1.3 Linear PrecodingLinear precoding requires each antenna to transmit a data

stream that is the linear combination of K data streamswith K beamforming weights. One design option is to ap-ply these weights centrally. Since each antenna transmitsa distinct data stream, this would require the central con-troller to deliver M I and Q sample streams to each of theindividual radios. This approach, obviously, does not scalewell, since it requires the central controller to have an outputbandwidth proportional to M . As M increases to hundredsor even thousands, this becomes exorbitantly expensive andeventually intractable. Thus we conclude that in any effi-cient scalable design, the beamforming weights should beapplied at the radio. This design choice conveniently allowsall of the radios to share a common databus for downlinktransmission. In contrast, for uplink transmission, the radioleverages the same linear precoding to apply K beamform-ing weights to the incoming I and Q samples. Since eachradio has unique weights, this again results in M uniquedata streams (that are K wide)! Fortunately, linear pre-coding requires these streams to simply be added together;conveniently, this can be done anytime two streams merge inthe architecture, thus, again, enabling a constant bandwidthdatabus. Indeed, we see that with careful design decisionslinear precoding can scale up with constant data rate re-quirements. Notably, there is still a need for some form ofcentral controller to demodulate the data once it has been

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Figure 3: Argos architecture: fat tree structure withdaisy-chained leaf nodes.

completely recombined; however this operation is latencyinsensitive, and computationally trivial.Thus we find that, yes, MUBF can scale up with M , but

only with careful design choices and new methods for weightcalculation and channel estimation.

3.2 Architecture and TopologyThe design choices to enable scalability presented above

result in two distinct components: (i) a central controller,which handles modulation and demodulation, and (ii) theM radio front-ends that locally calculate beam weights andapply linear precoding. The immediate question we need toanswer is: how do we interconnect the controller and theradios? On one hand, we can connect all the radios directlyto the controller. This requires the controller to have atleast M ports. Since M can be dynamic and very large, thiswould be a unscalable and inefficient design choice. On theother hand, we can daisy-chain all the radios serially. Whilescalability seems to be maximized, reliability and delay ofthe system are severely compromised.Our solution is to add hierarchies to the base station to

improve flexibility, and simultaneously achieve a balance be-tween scalability, reliability, and delay. But, what type of hi-erarchical structure should we adopt? First we note that de-ploying M separate radios and antennas would be unwieldy,and cost ineffective to manufacture; thus we create our firstlevel hierarchy: a module that contains one or more radiofront-ends. Next, in order to achieve flexible, cost-effective,scaling we allow these modules to be connected serially; en-abling additional modules to be added atomically with lowoverhead. Finally, in order to increase reliability and reduceend-to-end latency, we introduce the Argos hub, which al-lows multiple modules to be connected in parallel. Figure 3depicts the Argos architecture.The Argos base station enables unprecedented scalability

and deployability, while fulfilling performance and cost con-straints. This architecture enables the Argos base station toscale in three directions: by adding more Argos hubs, by in-creasing the length of the module chains, and by increasingthe number of antennas on a module. The hierarchal archi-tecture facilitates deployments of base stations with manyantennas to be flexibly distributed geographically by usinga single link to an Argos hub, as well as deployments of basestations with a small number of antennas where the hub canbe omitted completely, and modules are simply chained to-gether in series. Additionally, if chains become too long to

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jih→it jr

ijh →ir jt

Downlink Channel: jih→ˆ

Uplink Channel: ijh →ˆ

Figure 4: Real channels are not reciprocal due to thedifferences in transmit and receive hardware. Notethat channel reciprocity indicates that within thechannel coherence time the physical channel is re-ciprocal: hi→j = hj→i.

meet latency requirements, Argos hub can simply be addedto parallelize connections and reduce latency.

3.3 Channel CalibrationWe devise a novel, completely internal, calibration proce-

dure to enable implicit beamforming on many-antenna basestations through TDD channel reciprocity in order to collectCSI data in constant time with respect to M .

For an M antenna base station to multi-user beamformto K terminals, the base station must acquire the downlinkchannel state information, ĥm→k, for all m = 1, 2, ...,M andk = 1, 2, ...,K. The key challenge is to estimate the effectivedownlink CSI ĥm→k from the uplink CSI, ĥk→m, acquiredfrom the uplink pilot signals. However, as shown by Fig-ure 4, the uplink and downlink channels are not reciprocaldue to the random phase and amplitude differences in theRF hardware. This is caused by a combination of dynamiceffects from internal clocking structures, such as dividers,multipliers, and PLLs, as well as static effects from manu-facturing deviations. Indeed, we verify that simply resettinga given radio i, or even tuning to a different frequency, ran-domizes the phase effects of ti and ri.

The uplink and downlink channels between any two trans-ceivers is a product of (i) the frequency response of thetransmit hardware, (ii) the physical wireless channel, and(iii) the frequency response of the receive hardware:

ĥi→j = ti · hi→j · rj (1)In order to estimate the reciprocal channel, ĥj→i, we definea calibration coefficient, bi→j , between radios i and j as:

bi→j =ĥi→jĥj→i

=ti · hi→j · rjri · hj→i · tj =

ti · rjri · tj =

1

bj→i(2)

Notably, if both channels are measured within the coherencetime then hj→i = hi→j due to physical channel reciprocity.Clearly, if we know the calibration coefficient between tworadios and one channel estimate, we can find the reciprocalchannel:

ĥi→j = bi→j · ĥj→i or ĥj→i = ĥi→jbi→j

(3)

Now let’s apply this to our scenario where we would like toestimate the downlink CSI from base station antenna m to

terminal k (ĥm→k) from the uplink CSI (ĥk→m). To do thiswe must know the M calibration coefficients between eachbase station antenna and the terminal, that is, all bm→k.These would be impractical to find in a real-system, as esti-mating bm→k requires pilots to be sent between every basestation antenna and terminal pair, as well as feedback fromeach terminal. Moreover, unless the terminal and base sta-tion share clocks, which is impossible in a wireless system,their hardware transmit and receive channels drift relativelyover time, thus requiring this calibration to happen fre-quently. This approach would be counter-productive, sinceestimating bm→k requires downlink pilots, which could beused to directly estimate ĥm→k.

3.3.1 Internal CalibrationWe find that it is possible to internally calibrate the

base station relative to one of it’s antennas, e.g., antenna1. That is, we find all calibration coefficients bm→1 (form = 2, 3, ... M) using Equation 2. Note that these coef-ficients are in fact stable over long periods of time, as weshow in Section 5.4, since all base station antennas shareclocks. We also find that if we know the calibration coeffi-cient between any two radios and a reference radio, then wecan derive the direct calibration coefficient between them:

bi→jbi→y

=

ti·rjri·tjti·ryri·ty

=ty · rjry · tj = by→j (4)

Thus if we know the calibration coefficient between our refer-ence antenna and terminal k, b1→k, we can find the downlinkCSI:

ĥk→m · b1→kb1→m

= ĥk→m · bm→k = ĥm→k (5)

This suggests that full CSI can be found by simply send-ing one pilot from each of the terminals, then just one pilotfrom the base station’s reference antenna! Unfortunately,however, to find b1→k we must feedback the reference an-tenna’s downlink channel estimate, ĥ1→k, from each of thek terminals. This significantly reduces the channel capacity,and quickly becomes infeasible for even a moderate K.

3.3.2 Key Idea: Relative CalibrationOur key idea in solving the calibration problem is that

an absolutely accurate estimation of downlink CSI, ĥm→k,is unnecessary. For all multi-user beamforming techniquesusing linear precoding, it is sufficient for beamforming an-tennas to have a relatively accurate estimation. That is, aslong as each base station antenna’s CSI estimation deviatesfrom the real CSI by the same multiplicative factor, multi-user beamforming will still result in the same beam pattern.To visualize this, refer back to Figure 1; if both antennaswere to experience the same phase offset, the resulting spa-tial beam pattern would remain the same. Thus, we canassume b1→k = 1:

ĥm→k = ĥk→m· b1→kb1→m

⇒ ĥ′m→k = ĥk→mb1→m

= ĥk→m·bm→1(6)

This means that we estimate relative downlink CSI, ĥ′m→k,by using only uplink pilots, without any feedback! To reca-pitulate, the entire CSI collection process involves 4 steps:

1. Find all internal calibration coefficients, b1→m, offline

by sending pilots to and from every base station an-tenna m and reference antenna 1.

2. Send K orthogonal pilots from each terminal and de-termine ĥk→m.

3. Derive all ĥ′m→k from 6.

4. Use ĥ′m→k to calculate the beam weights, then sendthe beamformed data.

Using this process we can efficiently collect full channel stateinformation at the base station by sending only K termi-nal pilots, without any feedback from the terminals. Thisenables us to scale M up without any additional channelestimation overhead, which is a critical feature in order torealize a MUBF system with many antennas.

Note that the measurements of downlink and uplink haveto be done within the channel coherence time in order forhm→1 = h1→m. Since base station antennas do not move,the channel coherence time is much larger than typical basestation to terminal coherence times. However, as we show inSection 4.5, this calibration can easily be done well withineven highly mobile timing constraints; our prototype com-pletes a single antenna pair calibration within 300 μs.

3.4 Decentralized BeamformingIn order to achieve scalable real-time beamforming weight

calculation, Argos employs a novel method that allowsweights to be calculated locally at each antenna, and there-fore avoid the unscalable data-transport overhead requiredby existing beamforming techniques. As discussed in Sec-tion 2.4.2, to perform traditional conjugate beamforming,the weights must be globally normalized so that no basestation radio exceeds its maximum power output. For ex-ample, assuming a maximum radio transmit amplitude of1, and in order to ensure at least one radio transmits atmaximum power:

c =

(max

(K∑

k=1

‖ĥm→k‖))−1

(m = 1, 2, ...M) (7)

where c is the scaling factor used in the beamforming weightcalculation (W = c · H∗). Global power scaling is charac-terized by using a single constant to scale all of the weights.This global scaling is necessary to maintain the ratio be-tween each base station antenna’s weight for a given ter-minal, which ensures per-terminal transmission energy opti-mality, as proven in [15]. However, each base station antennamust know either c (or H) to properly scale its own beam-forming weights. This requires full CSI to be transferredfrom each module to the central controller, nullifying thebenefit from the aforementioned decentralization. To tacklethis, we propose a local power scaling approach that closelyapproximates global normalization.

Argos leverages a key observation that for the different ter-minals in multi-user beamforming, the channels correspond-ing to different terminals are uncorrelated and experienceindependent fading. Therefore, statistically speaking, whenthe number of terminals is large, the actual transmissionpower at each antenna is very similar. Our solution simplynormalizes the total transmission power locally at each base

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station antenna using only the CSI it measures:

cm =

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The conjugate beamforming weights are then scaled via:

W = H∗ · diag(C) (9)Where C is the scaling vector given by Clocal = [c1, c2, ...cM ],from Equation 8; notably the globally scaled conjugate canalso be found in this form, using Cglobal = [c, c, c, c...], fromEquation 7.We have experimentally verified the effectiveness of such

local power scaling and observed that its performance is al-most indistinguishable from the optimal global power scalingmethod (see Section 5), using equal transmit power for bothmethods. Moreover, in real deployments, since local powerscaling ensures that each radio can utilize its full hardwarepower capacity, it can always achieve equal or greater SNRthan global power scaling (since it can send with greatertotal transmit power), as proven in [22, p. 24]. Notably, ifterminals are not approximately equidistant from the basestation, then per-terminal power scaling is required to en-sure fairness (preventing terminals closer to the base stationfrom being allocated all of the transmission power), but thiscan be done at a much coarser time scale (i.e., seconds), thusnot creating additional overhead or affecting performance.

4. IMPLEMENTATIONIn this section we provide a detailed report of our imple-

mentation of Argos, which leverages WARP [4], commer-cially available clock distribution boards, a commodity PC,and an ethernet switch. Figure 5 shows an abstract represen-tation of our implementation. As the first proof-of-conceptprototype, our system includes a central controller, an Ar-gos hub and 16 modules, each with 4 radios. The centralcontroller consists of a single host PC, which uses MATLABto send data, weights, and control commands to the radiomodules. The Argos hub is comprised of a 24-port ether-net switch, a clock distribution board, and a WARP board,which uses its GPIO pins to provide transmission synchro-nization splitting/replication. Due to the limited availabilityof WARP boards, this board also serves as a radio module,however these roles are functionally separate, and in futuregenerations of the Argos prototype they will be physicallyseparated as well. Each radio module is a single WARPboard with 4 radio daughter cards and 4 antennas. Figure 6depicts the real system: the base station includes 16 WARP

boards with 64 antennas that are compactly placed on a cus-tom rack-mount platform. We note that the number of ter-minals supported by each module is fundamentally limitedby its hardware capabilities. In the WARP boards we are us-ing, this bottleneck is the number of multipliers (328 on theVirtex 2 Pro xc2vp70) [26]. We are able to use 240 of thesemultipliers to provide linear precoding for 15 terminals onthe 4 antennas, which requires 60 complex multipliers. Theremaining multipliers are used by other functions, and 4 areunusable due to routing constraints. However, the recentlyreleased Virtex 7 supports up to 3600 multipliers clockedat a rate of 741 MHz; with multiplexing this would enable16,672 complex multiplies per 40 MHz sample (neglectingrouting overhead and other functions that require multipli-ers), which would, obviously, alleviate this bottleneck [25].

To the best of our knowledge, our Argos prototype is thefirst publicly reported many-antenna MUBF system withreal-world feasibility. We next elaborate on our implemen-tation.

4.1 Hardware and Software PlatformWARP is a scalable and programmable wireless platform

for prototyping advanced wireless systems. Each WARPboard allows up to four radio daughter cards to be connectedand therefore can contribute up to four active antennas si-multaneously to Argos. Each radio board includes a Maxim2829 transceiver chip [18], which operates at the 2.4 or 5GHz ISM bands with a 20 MHz bandwidth. WARP conve-niently provides a MATLAB-based framework, WARPLab,which allows MATLAB to control the WARP boards andprocess the transmit and receive data samples. As shownin Figure 5, WARPLab consists of four layers: (i) The un-derlying Simulink model that implements the custom hard-ware for controlling the FPGA board and radio boards; (ii)The Xilinx Platform Studio (XPS) project that integratesand connects all of the hardware components, including theSimulink model, the I/O cores for the serial port, Ethernetport, clocking, etc.; (iii) The C code that runs on the Pow-erPC microprocessor, controls the hardware through mem-ory mapped I/O, and acts as an interface to the Ether-net port; (iv) The MATLAB interface that configures theboards, generates the transmit samples, and processes thereceive samples.

We have extensively customized the WARPLab frame-work to enable hardware MUBF, transmission synchroniza-tion, clock synchronization, and indirect calibration amongbase station antennas. These functionalities are essential tofor Argos to enable MUBF with many antennas.

4.2 Hardware MUBFA straightforward, and much easier approach to realize

MUBF in WARPLab is to implement it in software withinthe MATLAB interface; this, in fact, was our first implemen-tation. In this approach the beamformed baseband signalcan be directly delivered to the WARP boards without theneed of linear-precoding in hardware. However, this methodintroduces major latency between the CSI collection anddata transmission, which increases linearly with the numberof base station antennas, and severely degrades performance.This is a result of the same scaling problem discussed in Sec-tion 3.1. Therefore, we modified the WARPLab hardwareto enable hardware MUBF.

At each base station antenna, applying the beamform-

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ing weights consists of multiplying the baseband symbol in-tended for each terminal, sk, by its corresponding beam-forming weight, wk, and then adding them together: s

′m =∑K

k=1 wk · sk where s′m is the resultant beamformed signaltransmitted by antenna m. Multiplying the signal by a com-plex number is equivalent to rotating the phase and scalingthe amplitude. In hardware, this requires K registers and Kparallel complex multipliers (each complex multiplier needs4 multipliers and 2 adders) in series with 2 K input adders.We store the beamforming weights, wk(k = 1, 2, ...K), inmemory mapped registers. This is important since it en-ables the PowerPC, and in turn, the MATLAB interface todirectly control them.

4.3 Transmission SynchronizationWARPLab has a default function to enable transmis-

sion synchronization between multiple WARP boards. Itis achieved by using the built-in API command ”sendsync()”in the MATLAB interface. However, due to the jitter in-troduced by the ethernet stack, switch, and cables, suchsynchronization can lead to a timing offset on the orderof 20 samples, depending on the ethernet switch and ca-ble lengths, which makes accurate CSI collection and beam-forming impossible.To address this challenge, we employ a WARP board to

distribute the central controller’s transmission synchroniza-tion signal. As part of the Argos hub, this WARP nodeleverages directly connected, registered, GPIO to reliablysend the sync pulse to the radio modules. Notably, to en-sure the modules receive the pulse within 1 clock cycle, thecable lengths should all be within one wavelength, λ. Witha channel bandwidth of 20 MHz, λ is 7.5 meters (40 MHzsampling clock), which is a very easy constraint to meet. Asstated above, this WARP node serves the dual role of syncdistribution and module, thus it “distributes” the sync to it-self with an effective cable length of 0. This means the othercables must be less than 7.5 meters, which is not a problem;in our current setup the length is 2 meters. While each boardmay have a slightly different clock phase, this phase offset isconstant (due to the clock synchronization), and explicitlycompensated for by the beamforming algorithm.We have modified the Simulink model, the XPS project,

and the C code to enable GPIO-based transmission synchro-nization. Specifically, we inserted appropriate gateways andregisters into the Simulink model, re-mapped the GPIO pinsto the appropriate signals in the XPS project, and disabledthe traditional ethernet sync in the C code.

4.4 Clock SynchronizationPrecise inter-board clock synchronization is critical for Ar-

gos, due to its distributed modular architecture. The WARPboard requires two reference clocks: a 20 MHz RF clock anda 40 MHz logic/sampling clock. Both clocks can be eitherforwarded or driven by an external source. In addition, wediscovered that the Maxim 2829 transceiver chip on the ra-dio board can use a 40 MHz clock. Therefore, we were ableto use a single external source to drive the logic clock, thenforward the logic clock to the reference input for the RFclock. This way, inter-board clock synchronization can beachieved in an easily manageable and scalable way.

We leverage a commercial clock distribution evaluationboard designed for LTE, the AD9523/PCBZ, to accomplishthis. The AD9523 provides 18 clock outputs, which we lever-age to drive all of the radio modules. Although we haven’texceeded the capacity of the AD9523, an additional clockdistribution board could be connected (as part of an addi-tional Argos hub), which would provide 17 more outputs.Alternatively, the existing modules can forward their clocksto additional modules, through Argos’ multi-hop extension.

4.5 Indirect CalibrationFor indirect calibration, we need to estimate bm→1 =

tm·r1rm·t1 for each antenna m with respect to the “referenceantenna,” as described in Section 3.3. Due to buffer con-straints, we implement this in a per-module iterative fash-ion. First, the module containing the reference antenna cali-brates internally; that is, the reference antenna sends a pilotwhile other antennas on the module listen, then each of thoseantennas sends a pilot, in turn, while the reference antennalistens. These channel estimates are then reported to thecentral controller so that the reference antenna’s buffer canbe overwritten. Next, the reference antenna sends a pilotsequence while all the antennas on another module listen,then each of those antennas transmits a pilot, in turn, whilethe reference antenna listens. Again, the channel estimatesare reported to the central controller. The process is thenrepeated for each module. The calibration procedure is verylatency sensitive, as the physical channel should not changebetween transmission and reception of pilots for any antennapair. To address this, we implement the calibration locallyon the PowerPC in C code and leverage Argos’ transmissionsynchronization to coordinate the send and receive phases.The resulting calibration happens within 300 μs for each an-tenna pair, which is well within the channel coherence time.

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Figure 7: Environments and the locations of thebase station and terminals for for the reported ex-periments. Note that the base station leverages di-rectional antennas in order to serve one sector. Ter-minals have vertical separation as well, spanning upto three floors.

Another challenge we encountered while performing ourindirect calibration approach is the significant amplitudevariation for the channels between the reference antenna 1and other antennas. This is due to the grid-like configura-tion of our antenna array where different pairs of antennascan have very different antenna spacings. According to ourmeasurement, the SNR difference can be as high as 40 dB,leading to a dilemma for us to properly choose the transmis-sion power for the reference signal. To address this, we iso-late the reference antenna from the others, and place it in aposition so that its horizontal distance to the other antennasare approximately identical. Such placement of the referenceantenna does not affect the calibration performance due toour calibration procedure’s isolation of the radio hardwarechannel from the physical channel.

5. EVALUATIONLeveraging our prototype, we experimentally evaluate the

feasibility of Argos in realistic environments. We have thefollowing impressive observation: compared to using a sin-gle antenna, Argos can improve spectral capacity over 12fold leveraging MUBF with many antennas, using equal to-tal transmission power. With 64 antennas and 15 terminals,the spectral capacity can be boosted from 12.7 bps/Hz to 85bps/Hz (6.7x) for zero-forcing MUBF, and 38 bps/Hz (3x)for conjugate MUBF, while using a mere 1/64th of the totaltransmission power. We find that Argos easily scales from1 to 64 base station antennas serving 1 to 15 terminals, andthat, in general, performance scales linearly with M andK. Finally, we experimentally validate the performance ofour localized conjugate beamforming method, as well as ourinternal calibration procedure.

5.1 Experimental SetupWe employ all 64 antennas at the base station to perform

MUBF to 15 concurrent terminals. We use the 2.4 GHzband with a 625 kHz carrier width to avoid frequency fadingeffects. Since it is relatively easy to move our platform (seeFigure 6), we tested various indoor locations (see Figure 7)in order to collect data from diverse environments. Thereare both LOS and NLOS channels between the base stationand terminals. We repeat our experiments multiple times,

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Figure 8: Cell capacity as the number of base sta-tion antennas (M) increases from 16 to 64, by 4,serving 15 terminals. In order to compensate forthe beamforming gain, total transmission power is1/M , implying average power per-antenna for multi-antenna schemes is 1/M2.

typically collecting over 3000 measurements at each location,to reliably average out performance.

To obtain the cell capacity, we aggregate the Shannoncapacity for each terminal, or CCell =

∑Kk=1 log(1+SINRk)

where SINRk is the measured SINR at terminal k. We letthe base station transmit dummy QPSK-modulated framesto the terminals, which is sufficient to validate the real-worldfeasibility of Argos since MUBF is a physical layer techniquethat is orthogonal to the MAC layer and above.

To accurately measure the terminal SINR, we use theRSSI indicator from the Maxim 2829 transceiver on the ra-dio board to report the received signal strength for eachtransmission, as well as the noise floor after the transmissioncompletes. Since the radio is unable to distinguish signaland interference strength, we slightly stagger the transmis-sion to the intended terminal and that to the unintendedterminals. This way we can separately measure the signalpower and interference power, and acquire the SINR accord-ingly. To make sure the channel remains constant during thetransmissions we conduct our experiments in an ultra-stableenvironment, i.e., late at night, without moving people andwireless traffic.

5.2 Improvement of Cell CapacityThe primary purpose of our experiments is to determine

the capacity improvement of Argos, in order to ultimatelyanswer the practicality of the many-antenna MUBF basestation proposal from the theory community. We reporttwo sets of experiments, which evaluate the scalability withregards to the number of base station antennas, M , and thenumber of terminals, K, respectively.

5.2.1 Scaling up with MIn the first set of experiments, we vary the number of

base station antennas, M , assuming a fixed number of ter-minals, K = 15. Figure 8 shows CCell as a function of Mfor a base station with a single antenna (Single Ant.), SUBF(SUBF ), conjugate MUBF (Conjugate), our modified local-ized conjugate MUBF (Local Conj.), and zero-forcing MUBF(Zero-forcing). In order to compensate for the beamforminggain, total transmission power is scaled by a factor of 1/Mfor all five cases. This enables our experiments to separatethe orthogonality gain of scaling up from the well-known,

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Figure 9: Cell capacity as the number of terminals K increases. Total transmission power is held constantwithin each plot. Left: M = 64; Middle: M = 15; Right: M = 15 with reduced transmission power.

predictable, beamforming gain. We have the following keyobservations:First, when M is much larger than K, both conjugate and

zero-forcing MUBF increase the cell capacity asM scales up,despite reducing the total transmission power proportionallywith M . The beamforming gain from the additional anten-nas compensates for the power reduction, as demonstratedby the flat performance of SUBF, while simultaneously in-creasing the natural orthogonality of the terminals. Thisreduces the inter-terminal interference of conjugate MUBF,and reduces the amount of power wasted to create nulls forzero-forcing MUBF. With M = 64 the improvement for con-jugate and zero-forcing MUBF over a single antenna are 5.7xand 12.7x for equal power, or 3x and 6.7x for 1/64 power,respectively.Second, as M drops to K, i.e., M ≈ K = 15, the per-

formance of zero-forcing drops steeply. This is due to thetightness of the degrees of freedom at the base station; zero-forcing inevitably wastes the majority of transmission powerfor interference cancelation, leading to a much reduced signalpower at the intended terminals. Later, we will show thatwhen M = K this inefficiency can even result in conjugateMUBF out-performing zero-forcing.

5.2.2 Scaling up with KWe next fix M and vary the number of terminals to see

how capacity scales with K. In the experiments reportedby Figures 9 Left and Middle, the total transmission poweris scaled by 1/M , similar to that in Figure 8, and is heldconstant regardless of K. Because the total power is splitamong the terminals, the power per terminal is thereforescaled by 1/K. In the experiment shown by Figure 9 Right,we reduce the transmission power to the minimum WARPsetting in order to demonstrate how the capacity of the threeforms of MUBF are affected by power. We have the followingobservations:First, when M � K, as shown in Figure 9 Left, capac-

ity increases approximately linearly with the number of ter-minals for both conjugate and zero-forcing MUBF; this isattributable to the multiplexing gains from simultaneouslyserving K terminals.Second, conjugate beamforming initially loses capacity as

the number of terminals increases from 1 (SUBF) to 2 dueto the addition of interference from the other terminal, andthus the overwhelming drop in SINR. This loss, however, isquickly compensated for by the multiplexing gains.Third, as K approaches M , the performance of zero-

forcing drops sharply as shown in Figure 9 Middle. This cor-roborates the second observation made in Section 5.2.1. Ad-

ditionally, the performance of conjugate flattens, and evenstarts to decline, as the additional interference from moreterminals causes the average SINR to approach 0 dB.

Finally, when the transmission power is reduced, conju-gate MUBF performs relatively better than zero-forcing, asshown in Figure 9 Right. This is because the performanceof conjugate is inherently limited by interference from otherterminals, while the performance of zero-forcing is insteadlimited by noise, since the interference is explicitly canceled.It is not until the transmission power is reduced to a pointwhere interference has the same magnitude as noise thatthere is a significant effect on the capacity improvement forconjugate.

5.3 Near-optimality of Localized ConjugateIn order to verify the viability of our localized method for

conjugate MUBF, we implement it in Argos and compareit to standard conjugate MUBF with global power scaling.As shown in Figure 10, we see that our local power scalingmethod (Local Conj.) results in a signal power within 1.2 dBof global power scaling (Conjugate), but quickly approachesequivalent power as the number of terminals increases. For afair comparison we ensure that both methods send with thesame transmission power, however in a practical deploymentour method will always transmit equal or more power. Whilelocal power scaling is less efficient for a given transmissionpower, it ensures that each base station radio is being fullyutilized, thus more intelligently adapting to the constraintsof real-world hardware. Furthermore, we see in Figures 8and 9 that the performance difference between global powerscaling (Conjugate) and local power scaling (Local Conj.) isalmost indistinguishable.

5.4 Stability of Indirect CalibrationAs described in the previous section, we implemented a

novel reciprocal calibration method to enable implicit beam-forming and efficient TDD operation. Figure 11 shows thatthis calibration deviates from the mean angle an average ofless than 2.6% (maximum 6.7%), and from the mean am-plitude less than 0.7% (maximum 1.4%), over a period of4 hours. Notably, these measurements were taken duringthe day with normal movement around the base station,indicating the calibration procedure is stable in real-worldenvironments. Angle deviation is calculated by difference inangle from average angle over π, i.e., 2.6% error is equivalentto 0.08 radians. This indicates that our internal calibrationscheme can performed very infrequently, i.e., once a day, andthus has negligible performance overhead.

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Figure 10: Relative signal power between conjugateand our conjugate with local power scaling, sent atthe same transmit power. Local power scaling per-forms within 1.5 dB of global power scaling, and thedifference quickly converges to 0 dB as K increases.

6. RELATED WORKArgos is directly motivated by multi-user beamforming

(MUBF) theory. Recent theoretical works have demon-strated the exciting benefits of large-scale MUBF, or itsgeneral form, massive MIMO [12, 13, 20]. In [17], the re-alization of conjugate beamforming with an infinite numberof antennas in a TDD multi-cell system is discussed. Lever-aging conjugate beamforming for a distributed architectureis proposed in [20], however our paper explicitly addressespower-normalization, and shows that, in a statistical sense,each base station antenna can perform normalization inde-pendently of the others. Single-cell analyses of the perfor-mance of both conjugate and zero-forcing beamforming forfinite numbers of antennas are obtained in [19]. However,these works are theoretical in nature, and do not addressthe system and implementation issues such as decentralizedpower scaling and TDD calibration. Argos is motivated byand built on top of this prior work, and complementarilyaddresses the architectural and system challenges to realizea many-antenna base station in the real-world.Endeavors to push MUBF into practice have been ob-

served recently. State-of-the-art wireless standards in cellu-lar networks and WLAN, such as LTE, WiMAX, and 802.11,have all considered incorporating downlink MUBF in theiremerging releases, e.g., 3GPP release 9 [1] and 802.11ac [2].However, MUBF in these standards is restricted to a muchsmaller scale, e.g., up to eight antennas in 802.11ac. Themost recent research efforts towards practical MUBF [5, 14]are limited to a small number of antennas. Argos, compar-atively, is the most ambitious MUBF prototype, featuring amuch greater number of antennas. As a result, our contri-bution differs from existing works in that we have identifiedand addressed a set of unique challenges regarding practical-ity and scalability. In addition, Argos is programmable andtherefore can be used for any large-scale MU-MIMO tech-nique, although we have focused on MUBF in this paper.There are orthogonal techniques to improve the spatial

reuse of spectrum such as sectorization and small cells.First, sectorization uses multiple antennas to form direc-tional beams, each of which covers a range of directions asa sector. Terminals in different sectors can be served si-multaneously. Therefore, sectorization can be treated as aspecial case of MUBF to physically separate terminals. Sec-ond, small cell techniques, such as femto-cells and pico-cells

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deploy base stations more compactly, with limited coverageto improve spatial reuse. In each sector or small cell, onecan further apply large-scale MUBF to achieve even morespatial reuse and better energy efficiency.

Argos improves spatial reuse efficiency through by em-ploying a large number of antennas on base stations. Onecan also add antennas at the terminal to further improvespectral efficiency and reduce inter-cell interference. Theauthors in [21] reported a system with multiple directionalantennas on mobile terminals where only one antenna is ac-tive at a time to realize uplink directionality. The authorsin [27] studied the feasibility of SUBF on terminals, anddemonstrated its power efficiency and capacity benefit. Ar-gos is completely complementary to these terminal-basedsolutions and provides orthogonal benefits.

A few prior works have offered solutions for efficient chan-nel calibration in TDD systems, e.g., [6, 10, 11, 23, 24]. Allprior solutions require terminal involvement and feedback inthe calibration process, an unacceptable overhead in a large-scale MUBF system. In contrast, the relative calibration inArgos is done internally at the base station without suchoverhead.

7. DISCUSSIONThis paper presents a scalable architecture for many-

antenna base stations, a real-world implementation of thisarchitecture, as well as compelling early experimental re-sults. These results motivate further research in the area ofmany-antenna systems, and raise many practical challengesto be surmounted.

First, the size, cost, and power consumption of a many-antenna base station are significant impediments to adop-tion. However, we believe that advances in manufacturingcombined with the use of specialized hardware in both theanalog and digital domains can overcome these barriers.

While Argos supports multiple precoding techniques, anobvious question this work raises is: which precoding tech-nique is optimal under a given scenario? This is a deceiv-ingly hard question to answer, as there are many variablesto optimize, such as power, fairness, and spectral efficiency,as well as many factors that impact this optimality, such astransmission power, propagation environment, terminal mo-bility, hardware capabilities, number of terminals, and num-ber of base station antennas. Moreover, we conjecture thatit will be advantageous to dynamically select the precodingtechnique, as many of these factors continuously change.

Finally, this many-antenna architecture also presents nu-merous network level challenges, including terminal paging,optimal grouping and scheduling of terminals, and handoverbetween cells. This architecture also raises many opportu-nities for improving total network capacity; for example, aspredicted by [17], the reduced transmit power will signifi-cantly improve inter-cell interference. Addressing these chal-lenges and opportunities is the subject of our future work.

8. CONCLUDING REMARKSWe present the design, realization, and evaluation of Ar-

gos, a base station architecture that can potentially employthousands of antennas to serve tens of terminals simulta-neously through MUBF. In order to enable this unprece-dented scaling in a practical environment Argos employs ahierarchal modular design that facilitates flexible, scalabledeployments while simultaneously constraining latency andproviding fault tolerance. It also features a novel beamform-ing algorithm that is completely decentralized and a new cal-ibration method that allows CSI to be collected in constanttime with regard to the number of base station antennas.Our experimental characterization of an Argos prototype

with 64-antennas clearly shows the practical benefits ofMUBF base stations with many antennas, improving spec-tral and energy efficiency manyfold simultaneously. Ourresults are the first publicly reported evidence that many-antenna MIMO systems can produce significant benefits un-der real-world settings. The scale of our experiments is onlylimited by the number of Argos modules (WARP boards)currently available to us. The architecture of Argos, how-ever, can easily accommodate many times more modules,each with more radios, potentially allowing thousands of an-tennas to serve tens of terminals through MUBF.

AcknowlegementsThis work was supported in part by NSF grants CRI0751173, MRI 0923479, NetSE 101283, MRI 1126478 andCNS 1018292. Clayton Shepard was supported by an ND-SEG fellowship and grant CNS 1018292. We would like tothank Ashutosh Sabharwal, Edward Knightly, Patrick Mur-phy, Reinaldo Valenzuela, Cuong Tran, our reviewers, andour shepherd, Sunghyu Choi, for their input and support.

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