Using a Drone Sounder to Measure Channels forCell-Free Massive MIMO Systems

Thomas Choi∗, Jorge Gomez-Ponce∗†, Colton Bullard∗, Issei Kanno‡, Masaaki Ito‡,Takeo Ohseki‡, Kosuke Yamazaki‡, and Andreas F. Molisch∗∗University of Southern California, Los Angeles, United States

† ESPOL Polytechnic University, Guayaquil, Ecuador‡KDDI Research, Inc., Saitama, Japan

Abstract—Measurements of the propagation channel formthe basis of all realistic system performance evaluations, asfoundation of statistical channel models or to verify ray tracing.This is also true for the analysis of cell-free massive multi-inputmulti-output (CF-mMIMO) systems in real-world environments.However, such experimental data are difficult to obtain, due to thecomplexity and expense of deploying tens or hundreds of channelsounder nodes across the wide area a CF-mMIMO system isexpected to cover, especially when different configurations andnumber of antennas are to be explored. In this paper, weprovide a novel method to measure channels for CF-mMIMOsystems using a channel sounder based on a drone, also knownas a small unmanned aerial vehicle (UAV). Such a method isefficient, flexible, simple, and low-cost, capturing channel datafrom thousands of different access point (AP) locations withinminutes. In addition, we provide sample 3.5 GHz measurementresults analyzing deployment strategies for APs and make thedata open source, so they may be used for various other studies.

Index Terms—Cell-free (distributed) massive MIMO, drone(UAV) channel sounder, air-to-ground (A2G) experiment, opensource channel measurement data, AP deployment strategies

I. INTRODUCTION

A. Motivation

In contrast to the traditional cellular system where thesignal-to-interference-plus-noise ratio (SINR) varies signifi-cantly depending on where the user equipments (UEs) arelocated within a cell, the cell-free massive multi-input multi-output (CF-mMIMO) system can provide an almost uniformquality of services to all UEs by abolishing cell boundariesand distributing many base station (BS) antennas across awide area in forms of access points (APs) [1]–[3]. Whilemany studies analyzed how to scale, optimize, and deploy theCF-mMIMO system in a pragmatic manner, the propagationchannels used in the analyses were either a) synthetic channelsbased on statistical channel models (uncorrelated/correlatedRayleigh/Rician) or b) simulated data based on behaviorsof electromagnetic waves within a selected environment (raytracing), whose accuracy, in particular with respect to angular

This material is supported by KDDI Research, Inc. and the NationalScience Foundation (ECCS-1731694 and ECCS-1923601). The full addressof ESPOL Polytechnic University is Escuela Superior Politecnica del Litoral,ESPOL, Facultad de Ingenierıa en Electricidad y Computacion, Km 30.5 vıaPerimetral, P. O. Box 09-01-5863, Guayaquil, Ecuador. J. Gomez-Ponce ispartially supported by the Foreign Fulbright Ecuador SENESCYT Program.

dispersion, is uncertain. When actual measurements of real-world environments were used, the number of APs was small(see Section I-B).

Nevertheless, the measured channel data between tens tohundreds of APs and multiple UEs distributed across a widearea are needed to accurately model real-world channels forCF-mMIMO systems. This paper presents a novel approach tosuch large-scale channel measurements that drastically reducesefforts and costs while escalating the data volume: using adrone to create a fast-moving virtual array.

B. Related Works

There are several experimental works that study cooperationof multiple BS antennas distributed across a wide area.1 Oneway to measure propagation channels in such systems is touse a “full” real-time system with each antenna having anindividual radio-frequency (RF) chain and transceiver, whilepossibly the APs at different locations are connected throughoptical fiber back-haul [4], [5]. While such measurementsystem would be the closest to the actual deployed system,its main weaknesses are a) difficulty in installing the back-haul network, b) complexity in terms of calibration, synchro-nization, and operation, and c) expense scaling with the largenumber of antennas, and thus transceivers, considered in theCF-mMIMO systems, quickly becoming prohibitive.

An alternative method is to use a single RF chain andRF over fiber modules connected to a RF switch [6], [7].This “switched” real-time system allows easier calibrationand synchronization while many APs are distributed acrossa wide area using the low-loss fiber cables. Yet, its pricestill scales with the number of APs and it is still difficultto make measurements in multiple settings due to challengesof installing and managing many cables and antennas.

The last method is to use a virtual array [8]–[12], whereone antenna (or a co-located antenna array) is used as anAP and another antenna is used as a UE. Such a systemoperates by fixing the location of the UE antenna and movingthe AP antenna to selected AP locations. The UE antennathen moves to the next location and the process repeats. Thismethod is popular, especially in small settings, due to its

1Relevant studies might use the framework of CF-mMIMO, or mightemploy other names, such as distributed MIMO, network MIMO, cooperativemulti-point (CoMP), distributed antenna system (DAS), etc.

arX

iv:2

106.

1527

6v1

[ee

ss.S

P] 2

3 M

ay 2

021

simplicity and low-cost. However, a key bottleneck is the effortin carrying and installing the transceiver emulating the AP tomany different locations. Indeed, all previous measurement-based studies using these three methods involved only a smallnumber of APs. Hence, an efficient, flexible, simple, and low-cost method to measure real-world channels for CF-mMIMOsystems is necessary.

C. Contributions

We describe a new channel measurement method for theCF-mMIMO systems based on a channel sounder flying ona drone, creating a distributed virtual array that can measurechannels from thousands of AP locations in a few minutes.To our knowledge, this is the first channel measurementdedicated to CF-mMIMO analysis with such many possibleAP locations, and the first time a drone is used to measure suchchannels.2 This sounding methodology a) quickly captureschannel data from many APs to a UE, b) accesses any outdoorAP location, c) is easy to calibrate and operate using only asingle RF chain on each AP/UE end, and d) costs little interms of both labor and equipment. A sample 3.5 GHz channelmeasurement campaign is conducted in an outdoor settingat the University of Southern California (USC) campus, andmeasurement results with insights to realizing a CF-mMIMOsystem are given. Finally, we encourage researchers to use thereal-world measurement data for various analyses by makingthe data open source.

II. CHANNEL SOUNDING METHODOLOGY

The channel sounder we use consists of 1) a transmitter(TX) on a drone with a lightweight software-defined radio(SDR) and a single dipole antenna and 2) a receiver (RX)on the ground with a cylindrical antenna array, digitizer, andstorage, which are heavier and bulkier than the TX.3 Weassume the aerial TX serves as an AP and the ground RXserves as a UE4 because a) APs are usually placed higherthan UEs and b) the number of APs is assumed to be largerthan the number of UEs (the TX sounder on a drone can moveto many locations faster than the RX sounder on the ground).

Measurements proceed as follows: in a selected environ-ment, we first decide at which locations to place the APsand UEs. We fix the RX at the location of the first UE andfly the TX along a trajectory that includes locations of allAPs, as shown on Fig. 1. The measured channels from asingle trajectory thus contain the channels between all APs

2While several papers, e.g., [13], explored the possibility of using dronesas aerial APs, there are no investigations using drones to measure channelsfor the CF-mMIMO system.

3The operating frequency is 3.5 GHz, bandwidth is 46 MHz, length of thesounding sequence is 1841, and output power is 28 dBm [14].

4In our specific sounder, the RX consists of a dual-polarized cylindricalantenna array with 128 ports [14]. Since we assume single-antenna UEs, byselecting one port at a time, we can have 128 UE realizations per RX position.Therefore, using one antenna as a UE will not change the methodology givenin this paper. Instead of treating each port as a separate realization, the portscan also be used together for various other evaluation purposes, such asdirection-of-arrival detection, polarization analysis, multi-antenna UE study,small-scale fading statistics, etc., which are not covered in this paper.

Fig. 1. Proposed channel measurement method for a CF-mMIMO system

and a single UE. Because the drone flight can be repeatedin an automated fashion, the TX can fly the same trajectoryrepeatedly. We can thus move the RX to the location ofa different UE and repeat the same trajectory to attain thechannel data between the same APs and the different UE fora multi-user CF-mMIMO system analysis (the reproducibilityof the trajectory will be discussed in Section IV-A).

This measurement method has following advantages:1) Boundless AP locations: The drone, with its small body,

can reach any position at any height conveniently andquickly by using a simple mobile application, as longas the Federal Aviation Administration (FAA) safetyrules are followed.5 Such capability is especially usefulwhen measurements for several different CF-mMIMOsystems are conducted in multiple environments locatedfar apart, since cumbersome installations of AP antennasat multiple rooftops and masts are not necessary.

2) Fast measurement speed and a large dataset: Althoughthis method is technically the same as the virtual arraymethod using one AP antenna and one UE antenna asdescribed in Section I-B, thousands of AP locations canbe swept within several minutes on a drone. In ourmeasurement, the RX captures channel data every 50msand the TX moves at 4m/s. With such measurementspeed, channel data from 1200 AP locations distributedacross 240m range to a UE can be measured per minute.We can either sample some of the spatial points amongthe whole drone trajectory to place a selected numberof APs for a considered CF-mMIMO system, or utilizethe ample size of the dataset to conduct data-hungrystatistical analyses or machine learning applications.

3) Easy to operate and affordable: Setting up and oper-ating the full or the switched sounder mentioned inSection I-B with many AP/UE antennas positioned atmultiple different locations simultaneously is complex,and prone to hardware failures if not properly managed.Additionally, the cost of the RF equipment including thetransceivers, clocks, cables, switches, amplifiers, filters,and antennas is significant, scaling linearly with the

5While a similar method can be created using an automobile with anextendable mast, such a vehicle is more expensive. Furthermore, it can onlyreach AP locations close to a driving/parking lane in a street.

number of APs/UEs. In contrast, measuring channelsusing a drone sounder is simple and cost-efficient,requiring only a single RF chain which does not requireany synchronization due to the virtual array principle.

There are, however, some assumptions and limitations ofthis measurement method which also must be addressed:

1) Channel coherence in a fast fading channel: While allAPs serve multiple UEs simultaneously in an actual CF-mMIMO system, we measure channels between manyAPs and a single UE over several minutes of flight time.Furthermore, measurements for different UEs may spandifferent days. Because the channels for APs/UEs canlie in different coherence blocks, dynamic channel con-ditions such as trucks blocking line-of-sight (LOS) pathcannot be accounted for. To minimize such effects, themeasurements should be performed at times where thenumber of such mobile blockers/scatterers is minimal.

2) Using channels of multiple UEs together: We conductmultiple flights of the drone on the same route tomeasure channels for different UE positions. The dronewill not be at the same location during each flight(error on order of wavelength or more), so analysis thatrequires phase coherence between different UE locationsis challenging. This can be overcome by expanding theRX to operate multiple UE antennas simultaneously,which is easier than operating a larger number of APantennas simultaneously, since RXs are on the ground.

3) Drone limitations and effects: Because the drone haslimited power and weight capacity, the TX payload mustnot be power-hungry or heavy. Performance measureslike the bandwidth, output power, number of antennas,and clock accuracy are traded off for lighter hardwarewith sub-optimal performances [14]. Also, the dronebody and the vibration coming from drone hovering mayalter the antenna gain and pattern [15].

III. MEASUREMENT CAMPAIGN

Using our drone-based sounder that was outlined above anddescribed in more detail in [14], we conducted a CF-mMIMOmeasurement campaign at USC University Park Campus (Fig.2).6 The selected measurement area has a dimension of about400m by 200m. The TX (AP) was flown on a loop trajectoryat 35m (rooftop AP) and 70m (aerial AP [13]) heights. Afterthe trajectory measurements at two heights at a single UElocation, the RX was moved to a new UE location and theTX repeated the same two trajectories. The measurement wasconducted during dawn time to minimize the number of carsand people, and was conducted over three different days (UE1on the first day, UE2 on the second day, and UE3/UE4 on thethird day). The channel transfer function was captured every50ms, and the drone moved with 4m/s (measurement every20cm) for little more than 5 minutes, resulting in over 6000transfer functions covering more than 1200m of AP locationsper drone height per UE.

6The northeast corner of the drone trajectory on Fig. 2 is bent to avoidhitting tall trees in the area.

(a) UE1 (b) UE2

(c) UE3 (d) UE4

Fig. 2. Channel measurement environment at USC: colorbars show channelgains (in dB) between all AP locations (drone positions) along the trajectoryat 35m (inner loop) / 70m height (outer loop) and four different UE locations.

UE1 was placed at a parking lot near the southeast corner ofthe area surrounded by tall buildings at west, north, and east(Fig. 2a), UE2 was near the center of the area away from tallbuildings for most parts except for a building on the east (Fig.2b), and UE3/UE4 were at the northwest corner of the areanear the road (Fig. 2c and Fig. 2d). UE3 and UE4 were placedclose to one another to observe multi-user performance whenthe UEs are spaced close together, as well as differences inchannel characteristic when the UE is placed outside a buildingversus under a protruding roof.

While actual CF-mMIMO systems are likely to have amuch larger number of UEs, this initial study focuses onanalyzing the simple scenario with a small number of UEs inorder to straightforwardly study the feasibility of the uniquechannel sounding method for various CF-mMIMO system

analyses. Extensive measurement campaigns at larger scaleswill be conducted in the future for statistical evaluation ofCF-mMIMO systems.

IV. CELL-FREE MASSIVE MIMO SYSTEM ANALYSIS

A. Channel Gains for Each UEChannel gain is an important parameter as it describes the

quality of the channel between an AP and a UE, and deter-mines other parameters such as signal-to-noise ratio (SNR),SINR, spectral efficiency, etc. We define channel gain betweenAP l and UE k as |hl,k|2 where hl,k is the measured complexchannel between AP l and UE k. Fig. 2 plots channel gainsbetween the AP locations across the trajectories at two heightsand four UE locations, averaged over all measured frequencypoints.7

For UE locations that are surrounded by many buildings(UE1, UE3, and UE4), channel gain is higher (in red) ifthere is a LOS path toward an AP location, but lower (inblue) if there is shadowing from the buildings or if the APlocation is far from the UE location. UE2, which has LOSpaths to many areas due to being away from tall buildings,generally shows higher channel gains across the trajectoriesthan other UEs. Because having a LOS channel to multipleAPs in metropolitan areas with high buildings is difficult, it isbest to spread the APs across many locations, in order forthe UE to have high channel gain to at least one UE, assuggested by the CF-mMIMO idea [2]. In contrast, fewer APsor even one BS may be good enough in rural areas withouttall buildings.

Height of the APs must also be considered when deploy-ing APs for CF-mMIMO systems. If we compare the 35mmeasurement and the 70m measurement, the 70m trajectoriesgenerally have more regions with high channel gains (see,e.g., the east side of UE2 and the northeast side of UE4)because shadowing between APs and UEs surrounded bybuildings can sometimes be eliminated by simply increasingthe height of the AP. While the trajectories we observe areboth beyond 30m height, flying at lower heights is expectedto reduce the regions with high channel gains even further.This qualitatively suggests that the required density of theAPs during the deployment will be strongly correlated withthe heights of the APs.

One important aspect of our sounding method is the repro-ducibility of the measurement. To observe this, we repeatedthe same trajectory measurement twice for UE2 over twodifferent days, and compared the channel gains over the sametrajectories in a following way:

Gl,2,1[dB] = 10log10(

N∑n=1

F∑f=1

|hl,2,n,f,1|2) (1)

Gl,2,2[dB] = 10log10(

N∑n=1

F∑f=1

|hl,2,n,f,2|2) (2)

7On Fig. 2, omni-directional antenna pattern was synthesized for eachUE using multiple ports of the RX antenna array to account for multipathcomponents from all directions.

errRMS =

√∑Ll=1(Gl,2,1[dB]−Gl,2,2[dB])2

L(3)

where hl,2,n,f,d is a channel between AP l and UE 2 at portn of RX antenna array and frequency f for day d, Gl,2,t[dB]is the sum of channel gains between AP l and UE 2 over allfrequency points and antenna ports for day d expressed in dBscale, and errRMS is the RMS error between two Gl,2,d[dB]sover the whole trajectory.

The resulting RMS error was about 2.63dB. While thisvariation is not too big, the comparison of complex channelresulted in much larger variation, which suggests that whilethe magnitude does not vary as much, the phase will varywhen we repeat the same trajectory. Thus, if the exact phaserelationship between different UEs is required, simultaneousmeasurement with multiple UEs is preferable. However, if theUE locations are widely separated and their phase relationshipsare essentially random, the details are not relevant; this is alsothe case for the analysis in Sec. IV.C.

B. Single-User Uplink SNR

We now look at uplink SNR in a single-user case where onlyone UE is served by multiple APs at a given channel resource(time/frequency slot). Among all AP positions, we select agiven number of APs,8 and combine the SNR between the UE9

and the selected APs through maximal-ratio combining (MRC)to get the total SNR at the selected frequency, in our case, 3.5GHz. We consider the measured channel as the ground-truthchannel in our evaluations. While the channel measurementis conducted from the AP (TX) side to the UE side (RX),we can still evaluate the uplink SNR because the channels arereciprocal and independent of whether the operation is uplinkor downlink as long as the responses of RF chains measuredthrough the back-to-back calibration is eliminated from thetotal channel responses of the channel measurement system.

We assume the transmit power from each UE (p) is 10dBmand the noise power at each AP during the uplink (σ2

ul) is-96dBm. Following [3], if there are L single-antenna APswhich are designated for UE k, the resulting uplink SNR forUE k in a cell-free system is:

SNRk =p

σ2ul

L∑l=1

|hl,k|2. (4)

Fig. 3 plots SNR versus number of single-antenna APs, attwo different AP heights for UE1 and UE2, based on themeasured channel data. The number of single-antenna APsvaries by power of 2, going from 2 all the way up to 1024. TheAPs are picked at random among all AP locations, with 10000different AP combinations chosen from the set of measuredlocations. The first observation is that as the number of APsincrease, the mean SNR (shown by the dashed/dashed-dot lines

8The selection of APs can be random or deterministic.9For analysis in Section IV-B and IV-C, we randomly select one antenna

port, accounting for random orientation of the UE patch antenna.

(a) UE1

(b) UE2

Fig. 3. Distribution of UE1/2 uplink SNR for cell-free case per number ofAPs at two different AP heights

- averaged in dB scale) increases and the variation of the SNRvalues decreases for all considered cases. This is because aswe increase the number of APs, the APs would likely covermost parts of the measurement setting, resulting in similarperformance even when we are picking the APs at random.We also see that the variance is smaller when the APs are at70m height than 35m height. This is because the channel gainvaries less along the trajectory at 70m height than 35m height(see Fig. 2).

Another observation is that UE2 generally has better per-formance than UE1. While UE1 only has limited regions withhigh channel gains, UE2 contains many more regions withhigh channel gains. Hence, it is likely that the selected APswill contain at least one path with high channel gain to UE2,while it is not the case for UE1. UE2 also has less variance inSNR than UE1, since the channel gains are more uniformlyhigh in contrast to UE1 where the variance of channel gainsalong the trajectory is very high. SNR values of UE3 and UE4,while omitted, provide similar behaviors in variations as UE1,since the distribution of channel gains is similar as shown inFig. 2.

C. Multi-User Uplink SINR

Now we look at the case when multiple UEs are servedtogether by all APs within the same time/frequency slot.We again use channel data at 3.5 GHz operating frequency.The parameter we look at is the SINR, as the interference

from other UEs may reduce the achievable spectral efficiency.Again, following [3], the SINR is computed as:

SINRk =|vH

k hk|2p

vHk (p

K∑i=1i 6=k

hihHi + σ2

ulIM )vk

≤ phHk (p

K∑i=1i 6=k

hihHi + σ2

ulIM )−1hk

(5)

where M is the number of BS antennas (in our analysis, alsothe number of single-antenna APs), vk is the M×1 precodingvector for UE k, hk is the M × 1 channel vector between UEk and all APs, (·)H is the Hermitian operation, and IM isthe M ×M identity matrix. The upper bound is achieved bycomputing vk via a generalized Rayleigh quotient [16]:

vk = (p

K∑i=1i 6=k

hihHi + σ2

ulIM )−1hk. (6)

The optimal precoding can only be calculated with channelinformation of all APs, which can be impractical in a real-world CF-mMIMO system as it requires the BS to combinethe channel information from all APs and do matrix inversion.In contrast, a simple MRC precoding can be calculated locallyfor each AP by:

vk = h∗k (7)

where (·)∗ is the complex conjugate operation.Fig. 4 shows the comparison between the SNR values and

the SINR values when 64 and 256 APs at 35m height areselected among the trajectory to serve four UEs simultaneouslyunder the two precodings. The first observation is that forUE1, the loss from interference is minimal with the centralizedoptimal precoding, with SNR curve and SINR curve overlap-ping on one another. This is because UE1 has the highestchannel gains toward APs located at the south of the trajectory,while UE2/3/4 all have low channel gains toward the APsat the south, as shown in Fig. 2. Thus, the APs that havehigh weights for reception of UE1 inherently receive littleinterference power from UE2/3/4.

The second observation is that UE2/3/4 all experience someloss from interference, but the loss is less than 1dB for optimalprecoding. The APs toward the east/north/west of the campuscan all receive moderate to high power from UE2/3/4, but theoptimal precoding can suppress the interference. While UE3and UE4 are located close to one another, the overall lossfrom interference is not too large. Finally we observe that UE4generally has relatively smaller channel gains than UE3, dueto being under the protruding roof, except for the northeastcorner of the 70m trajectory where the AP is at LOS onlyfrom UE4.

In contrast to the optimal precoding, the simpler MRCprecoding shows much worse performance, and even UE1

(a) L=64

(b) L=256

Fig. 4. SNR and SINR with varying number of APs for two differentprecodings: the optimal precoding is based on Eq. (6) and the MRC precodingis based on Eq. (7)

experiences greater than 5dB loss in comparison to the SNRcurve at the median while UE4 may experience more than15dB loss. Another thing to note is that UE1 performs betterthan UE2 with MRC precoding because interference powerfor UE2 is considerably higher than UE1. MRC precoding,despite its simplicity, might not be sufficient for the multi-user cases even when the number of APs is 256 as shownon Fig. 4b, so precoding with higher performance than MRC,yet computationally more efficient than optimum precodingmust be developed. This is remarkable insofar as theory forconcentrated massive MIMO says that for the limit of infinitelylarge arrays, MRC becomes optimum.

V. OPEN SOURCE CHANNEL DATA

To encourage researchers to use real-world channel datain verifying their CF-mMIMO analysis (or any other topicswhich can make use of such data), we post our data to [17].The channel data is multi-dimensional, containing complexchannel values between UE and thousands of AP positions at1841 frequency points and 128 antenna ports. Any questionsregarding the data can be emailed to the corresponding authorof this paper.

VI. CONCLUSION AND FUTURE WORK

In this paper, we describe a novel channel sounding methodfor CF-mMIMO systems: using a drone to measure channels atany desired AP location. Such method provides a large datasetin a short period of time and costs little in comparison to

the full setups with many antennas distributed across a widearea. We demonstrate a sample measurement campaign andanalyze parameters such as channel gain, SNR, and SINR, toprovide some insights on realizing CF-mMIMO systems, suchas height of APs, number of APs, and precodings APs mayuse. We also distribute the real-world channel data open sourcefor various other wireless system analysis. In the future, wewill extend the measurement to a larger number of users andenvironments, in order to provide more statistical evaluationsof the CF-mMIMO systems based on real-world data.

REFERENCES

[1] H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson, and T. L. Marzetta,“Cell-free massive mimo versus small cells,” IEEE Transactions onWireless Communications, vol. 16, no. 3, pp. 1834–1850, Jan. 2017.

[2] G. Interdonato, E. Bjornson, H. Q. Ngo, P. Frenger, and E. G. Larsson,“Ubiquitous cell-free massive mimo communications,” EURASIP Jour-nal on Wireless Communications and Networking, vol. 2019, no. 1, p.197, Aug. 2019.

[3] Ozlem Tugfe Demir, E. Bjornson, and L. Sanguinetti, “Foundationsof user-centric cell-free massive mimo,” Foundations and Trends® inSignal Processing, vol. 14, no. 3-4, Jan. 2021.

[4] K. Hiltunen, A. Simonsson, P. Okvist, and B. Halvarsson, “5g trialsystem coverage evaluation utilizing multi-point transmission in 15 ghzfrequency band,” in 2017 Euro. Conf. on Networks and Comm., pp. 1–5.

[5] C. Shepard, R. Doost-Mohammady, J. Ding, R. E. Guerra, and L. Zhong,“ArgosNet: A Multi-Cell Many-Antenna MU-MIMO Platform,” in 201852nd Asilomar Conf. on Signals, Systems, and Comp., pp. 2237–2241.

[6] G. S. D. Gordon, M. J. Crisp, R. V. Penty, and I. H. White, “Experimen-tal Evaluation of Layout Designs for 3 × 3 MIMO-Enabled Radio-Over-Fiber Distributed Antenna Systems,” IEEE Transactions on VehicularTechnology, vol. 63, no. 2, pp. 643–653, Feb 2014.

[7] I. C. Sezgin, T. Eriksson, J. Gustavsson, and C. Fager, “Evaluationof distributed mimo communication using a low-complexity sigma-delta-over-fiber testbed,” in 2019 IEEE MTT-S International MicrowaveSymposium (IMS), pp. 754–757.

[8] M. Alatossava, A. Taparugssanagorn, V. Holappa, and J. Ylitalo, “Mea-surement Based Capacity of Distributed MIMO Antenna System inUrban Microcellular Environment at 5.25 GHz,” in 2008 IEEE VehicularTechnology Conference (VTC Spring), pp. 430–434.

[9] R. Ibernon-Fernandez, J. Molina-Garcia-Pardo, and L. Juan-Llacer,“Comparison Between Measurements and Simulations of Conventionaland Distributed MIMO System,” IEEE Antennas and Wireless Propaga-tion Letters, vol. 7, pp. 546–549, 2008.

[10] V. Jungnickel et al., “Capacity Measurements in a Cooperative MIMONetwork,” IEEE Transactions on Vehicular Technology, vol. 58, no. 5,pp. 2392–2405, Jun 2009.

[11] N. Sheng, J. Zhang, F. Zhang, and L. Tian, “Downlink Performance ofIndoor Distributed Antenna Systems Based on Wideband MIMO Mea-surement at 5.25 GHz,” in 2011 IEEE Vehicular Technology Conference(VTC Fall), pp. 1–5.

[12] T. Choi, P. Luo, A. Ramesh, and A. F. Molisch, “Co-located vsdistributed vs semi-distributed mimo: Measurement-based evaluation,”in 2020 54th Asilomar Conference on Signals, Systems, and Computers.

[13] A. Dhekne, M. Gowda, and R. R. Choudhury, “Extending cell towercoverage through drones,” in Proceedings of the 18th InternationalWorkshop on Mobile Computing Systems and App., 2017, p. 7–12.

[14] J. G. Ponce et al., “Air-to-ground directional channel sounderwith 64-antenna dual-polarized cylindrical array,” arXiv preprintarXiv:2103.09135, 2021.

[15] M. Badi, J. Wensowitch, D. Rajan, and J. Camp, “Experimental evalu-ation of antenna polarization and elevation effects on drone communi-cations,” in 22nd International ACM Conference on Modeling, Analysisand Simulation of Wireless and Mobile Systems, 2019, p. 211–220.

[16] E. Bjornson, J. Hoydis, and L. Sanguinetti, “Massive MIMO Networks:Spectral, Energy, and Hardware Efficiency,” Foundations and Trends®in Signal Processing, vol. 11, no. 3-4, pp. 154–655, Nov. 2017.

[17] USC wireless devices and systems group. [Online]. Available:https://wides.usc.edu