SwarmShare: Mobility-Resilient Spectrum Sharing for Swarm ...

SwarmShare: Mobility-Resilient Spectrum Sharingfor Swarm UAV Networking in the 6 GHz Band

Jiangqi Hu1, Sabarish Krishna Moorthy1, Ankush Harindranath1, Zhangyu Guan1,Nicholas Mastronarde1, Elizabeth Serena Bentley2, and Scott Pudlewski3

1Department of Electrical Engineering, University at Buffalo, Buffalo, NY 14260, USA2Air Force Research Laboratory (AFRL), Rome, NY 13440, USA3Georgia Tech Research Institute (GTRI), Atlanta, GA 30332, USAEmail: {jiangqih, sk382, ankushha, guan, nmastron}@buffalo.edu,[email protected], [email protected]

Abstract—To mitigate the long-term spectrum crunch problem,the FCC recently opened up the 6 GHz frequency band forunlicensed use. However, the existing spectrum sharing strate-gies cannot support the operation of access points in movingvehicles such as cars and UAVs. This is primarily because ofthe directionality-based spectrum sharing among the incumbentsystems in this band and the high mobility of the movingvehicles, which together make it challenging to control the cross-system interference. In this paper we propose SwarmShare, amobility-resilient spectrum sharing framework for swarm UAVnetworking in the 6 GHz band. We first present a mathematicalformulation of the SwarmShare problem, where the objective isto maximize the spectral efficiency of the UAV network by jointlycontrolling the flight and transmission power of the UAVs andtheir association with the ground users, under the interferenceconstraints of the incumbent system. We find that there are noclosed-form mathematical models that can be used characterizethe statistical behaviors of the aggregate interference from theUAVs to the incumbent system. Then we propose a data-driventhree-phase spectrum sharing approach, including Initial PowerEnforcement, Offline-dataset Guided Online Power Adaptation, andReinforcement Learning-based UAV Optimization. We validate theeffectiveness of SwarmShare through an extensive simulationcampaign. Results indicate that, based on SwarmShare, the aggre-gate interference from the UAVs to the incumbent system can beeffectively controlled below the target level without requiring thereal-time cross-system channel state information. The mobilityresilience of SwarmShare is also validated in coexisting networkswith no precise UAV location information.

I. INTRODUCTION

Unmanned aerial vehicles (UAVs) have been envisioned as akey technology for next-generation (i.e., B5G or 6G) wirelessnetworks [1], [2]. Because of their features of fast deployment,high mobility and small size, UAVs have a great potential toenable a wide set of new applications, including UAV-aidedguidance, small cells with flying base stations, emergency

ACKNOWLEDGMENT OF SUPPORT AND DISCLAIMER: (a) Contrac-tor acknowledges Government’s support in the publication of this paper. Thismaterial is based upon work funded in part by AFRL under AFRL ContractNo. # FA8750-20-1-0501 and in part by the NSF under Grant SWIFT-2030157. (b) Any opinions, findings and conclusions or recommendationsexpressed in this material are those of the author(s) and do not necessarilyreflect the views of AFRL.

Distribution A. Approved for public release: Distribution unlimited : AFRL-2021-1038 on March 30, 2021.

wireless networking in the aftermath of disasters, amongothers. The foreseen wide adoption of UAV systems can posea significant burden on the capacity of the underlying wirelessnetworks. In this paper we aim to explore new approaches thatcan enable UAV operations in the 6 GHz band to harvest theaddtitional 1.2 GHz spectrum bandwidth [3].

The primary challenge towards this goal is in the spectrumsharing approaches adopted by the incumbent systems in thisfrequency band. The 6 GHz band consists of four sub-bands,i.e., U-NII-5 (5.925-6.425 GHz), U-NII-6 (6.425-6.525 GHz),U-NII-7 (6.525-6.875 GHz), and U-NII-8 (6.875-7.125 GHz).These bands have been previously occupied by a set of non-government services, including fixed point-to-point services,fixed-satellite service (Earth-to-space), broadcast auxiliary ser-vice and cable television relay service [4]. These incumbentsystems coexist with each other by sharing the spectrum ona directional basis, i.e., they use highly directional antennasto concentrate the signal energy in a particular direction suchthat mutual interference can be effectively mitigated as long astheir antennas are not pointed toward each other. As a result,traditional carrier-sensing-based spectrum sharing as in Wi-Finetworks is non-applicable to extend those wireless systemswith omnidirectional antennas to this frequency band, becauseof the low detectability of the incumbent systems. For thisreason, two operation modes have been proposed by the FCC,i.e., standard-power and low-power modes. The former allowsboth indoor and outdoor operations on the U-NII-5 and U-NII-7 bands with maximum transmission power of 30 dBm. Thelatter focuses on indoor operations in the U-NII-6 and U-NII-8bands with maximum transmission power of 24 dBm.

However, none of the above two modes support UAVoperations in the 6 GHz bands [3], [5]. A major concern iswith the high mobility of the UAV systems, which makes itdifficult to model and control their aggregate interference tothe incumbent systems. The situation gets even worse whenconsidering the altitude-dependent interference range of UAVsand the higher probability of line-of-sight signal propagationat higher altitudes. Additionally, it is also challenging forthe distributed UAVs to control their aggregate interferencecollaboratively by jointly considering their spectrum access

This paper has been accepted for publication on IEEE International Conference on Sensing, Communication and Networking (SECON) 2021

strategies and association to the ground users.To address these challenges, a key step is to understand the

statistical behaviors and effects of the aggregate interferenceexperienced by the incumbent systems, since no real-timecross-system channel state information (CSI) is available. Tothis end, in this paper we focus on a new spectrum sharingscenario in the 6 GHz band called SwarmShare, where a setof UAVs collaboratively provide data streaming services toground users, by sharing the spectrum with the incumbent sys-tems on the 6 GHz band under the cross-system interferenceconstraints. Within this framework, we model and analyzethe aggregate interference from the UAVs to the incumbent,and propose a mobility-resilient stochastic spectrum sharingapproach. The main contributions of this work are as follows:• We first present a mathematical formulation of the

SwarmShare problem, where the objective is to maximizethe spectral efficiency of the wireless UAV networkby jointly controlling the UAVs’ transmission powerand flight trajectory as well as their association to theground users, under the interference constraints of theincumbent system. It is shown that the resulting problemis a mixed integer nonlinear non-convex programming(MINLP) problem.

• We analyze the statistical behavior of the aggregateinterference from the UAVs to the incumbent sys-tem, and find that no existing models can be used tocharacterize the statistical behavior of the interference.With this observation, we propose to solve the aboveMINLP spectrum sharing problem following a data-driven three-phase approach: Initial Power Enforcement,Offline-dataset Guided Online Power Adaptation, andReinforcement Learning-based UAV Optimization.

• We validate the effectiveness of SwarmShare by con-ducting an extensive simulation campaign over UBSim,a newly developed Universal Broadband Simulator forintegrated aerial-ground wireless networks. It is foundthat, with SwarmShare, effective spectrum sharing canbe achieved without real-time cross-system channel stateinformation, and, which is somewhat surprising, evenwith no precise location information of the UAVs.

The rest of the paper is organized as follows. In Section II,we discuss the related works. The system model and problemformulation is presented in Section III. In Section IV, wedescribe the spectrum sharing framework. Performance eval-uation results are discussed in Section V and finally we drawthe main conclusions in Section VI.

II. RELATED WORK

UAV systems have attracted significant research attentionin both academia and industry [1], [6]–[9]. For example, in[1] the authors optimize the achievable rate of UAV-aidedcognitive IoT networks. Wang et al. propose in [6] a dynamichyper-graph coloring approach for spectrum sharing in UAV-assisted networks. In [7], the authors optimize mobile termi-nals’ throughput by jointly controlling UAV trajectory, band-width allocation and user partitioning between the UAV and

ground base stations. In [8], UAV is used as relay to assist D2Dcommunications. [9] studies machine learning based spectrumsharing for UAV-assisted emergency communications. Readersare referred to [10], [11] and references therein for a surveyof the main results in this area.

Spectrum sharing in cognitive radio networks has also beena hot research topic for a long time with a sizable andincreasing body of literature. In [12], the authors aim tomaintain network connectivity in cognitive radio networks bycontrolling the transmission power of sensors. In [13], theauthors maximize the revenue of the newly joined systems incognitive radio networks by controlling the channel access ofnew users. Zhang et al. maximize in [14] the spectral efficiencyin cognitive radio networks based on deep reinforcementlearning. Li et al. propose in [15] to accelerate model freereinforcement learning based on imperfect model knowledgein dynamic spectrum access networks. The authors of [16]propose a cognitive backscatter network to maximize the datarate of the newly joined networks.

Spectrum sharing between directional- and omnidirectional-antenna wireless systems has also been studied in existingliterature. For example, [17] optimizes the performance ofLTE-Unlicensed networks while guaranteeing the performanceof the co-located radar system. The authors of [18] proposeRadChat, a distributed networking protocol for mitigationof interference among frequency modulated continuous waveradars. A cooperative spectrum sharing model is proposed in[19] to mitigate the mutual interference among radar and com-munication system. Please refer to [20], [21] and referencestherein for a good survey of the main results in this field.

Different from the above discussed works, none of whichhave considered the spectrum sharing between UAVs and theincumbent wireless systems in the 6 GHz band, in this paperwe aim to design a new, mobility-resilient spectrum sharingframework to enable wireless UAV networking in this band.

III. SYSTEM MODEL AND PROBLEM FORMULATION

We consider a wireless UAV network coexisting with anincumbent communication pair Tx and Rx by sharing thesame portion of spectrum B in the 6 GHz band. The UAVnetwork consists of a set K of UAVs collaborating with eachother to serve a set M of ground users. The transmissiontime is divided into a set T of consecutive time slots. Ineach time slot t ∈ T , denote the coordinate vector of UAVk ∈ K as codt

k = [xtk, ytk, z

tk]

T, with T being the transposeoperation and xtk, y

tk and ztk representing the x-, y- and z-

axis components, respectively. Similarly, denote respectivelycodTx = [xTx, yTx, zTx]

T, codRx = [xRx, yRx, zRx]T and

codi = [xi, yi, zi]T as the coordinate vectors of incumbent

transmitter Tx, incumbent receiver Rx and ground node i ∈M∪{Tx,Rx}. Denote A = K∪M∪{Tx,Rx} as the set of allthe nodes in the heterogeneous network. The objective of theUAV network is to maximize its own spectral efficiency underthe interference constraints of the incumbent system. Beforepresenting the formal formulation of the spectrum sharing

2


problem, we first describe the considered channel, antenna andthroughput models.

A. Channel Model

We consider both large-scale path-loss and small-scale fad-ing. For path-loss, we consider line-of-sight (LoS) wirelesschannels between the incumbent transmitter Tx and its receiverRx. This is feasible because the incumbent systems are usuallycarefully deployed such that their antennas are well-alignedwithout any obstructions in the link. However, we considernon-line-of-sight (NLoS) links between the incumbent nodesand the ground users of the coexisting networks. For UAVnetwork, we consider as in [22] a probabilistic path-loss modelfor the links between UAVs and ground nodes. Then, the LoSand NLoS path-loss (in dB) between UAV k ∈ K and groundnode i ∈M∪ {Tx,Rx} can be given as, in time slot t ∈ T ,

HLoS,tki = 20 log

(4πdtkif

c

)+ ηLoS, (1)

HNLoS,tki = 20 log

(4πdtkif

c

)+ ηNLoS, (2)

where the first item on the righ-hand side of (1) and (2)represents the free space path-loss with dtki = ||cod

tk−codi||2

being the distance between UAV k and receiver i in time slott, f is the carrier frequency of UAV k, c is the speed of light,and ηLoS and ηNLoS are the additional attenuation factors dueto LoS and NLoS transmissions, respectively. Let Pr(HLoS,t

ki )represent the probability of LoS transmissions in time slot t,then Pr(HLoS,t

ki ) can be expressed as [23],

Pr(HLoS,tki ) = (1 +X exp (−Y [φki −X]))−1, (3)

where X and Y are given environment-dependent constantsand φki = sin−1(ztk/d

tki). Accordingly, the probability of

NLoS transmissions between UAV k ∈ K and receiver i ∈M∪{Tx,Rx} can be given as Pr(HNLoS,t

ki ) = 1−Pr(HLoS,tki ).

Finally, for small-scale fading we consider Rician fading forLoS transmissions and Rayleigh fading for NLoS. Denote Kij

as the Rician factor for the wireless channel between nodesi, j ∈ A, then Kij can be given as Kij = 13−0.03dij for LoStransmissions and 0 for NLoS [24], where dij is the distancebetween the two nodes. Denote the resulting small-scale fadingcoefficient as htij , htij(Kij) for nodes i, j ∈ A.

B. Antenna Model

As described in section I, in this work we consider direc-tional transmissions for the incumbent wireless systems andomnidirectional transmissions for the coexisting UAV network.Specifically, we consider as in [25] bi-sectorized antennamodel to characterize the interference between directionaland omnidirectional antennas. Denote θTx and θRx as thesignal beamwidth of the incumbent transmitter and receiver’santennas, respectively. Let θm ∈ [−π, π] denote the offsetangle of the boresight direction of the Tx’s antenna withrespect to the reference direction for ground user m ∈ M.Here, the reference direction refers to the direction alongwhich the Tx’s antenna would be exactly pointed to user

m. Then the antenna gain of incumbent transmitter Tx withrespect to ground user m ∈ M in time slot t, denoted aswt

mTx, can be written as

wtmTx =

{wmax

Tx , if θm ≤ θTx

wminTx , otherwise

, (4)

where wmaxTx and wmin

Tx represent the maximum and minimumtransmit gains of the incumbent transmitter, respectively. Sim-ilarly, the receive gain of the incumbent receiver Rx withrespect to UAV k ∈ K, denoted as wt

kRx, can be given as

wtkRx =

{wmax

Rx , if θk ≤ θnRx

wminRx , otherwise

, (5)

with wmaxRx and wmin

Rx being the maximum and minimumreceive gains of the incumbent receiver, respectively. Thetransmit and receive gains are set to the maximum values forincumbent transmissions, i.e., wmax

Tx and wmaxRx , respectively.

C. Throughput ModelBased on the above channel and antenna models, the signal-

to-interference-plus-noise ratio (SINR) of the incumbent re-ceiver Rx, denoted as γtRX for time slot t, can be written as

γtRX =pTxw

maxTx wmax

Rx · (htTxRx)2/HLoS

TxRx∑k∈K

ptkwtkRxwk · (htkRx)

2/HtkRx + (σRx)2

(6)

where pTx and ptk represent the transmission power of theincumbent transmitter Tx and UAV k ∈ K in time slot t ∈ T ,respectively; wk denotes the transmit gain of the UAV andis considered to be constant for omnidirectional antennas;and (σRx)

2 is the power of Additive White Gaussian Noise(AWGN) at the incumbent receiver.

The objective of SwarmShare is to guarantee satisfactorySINR for the incumbent system (i.e., γtRX above) by con-trolling the transmission power of the coexisting UAVs. Tothis end, we consider single-home association strategy for theground users of the UAV network, that is in each time slott ∈ T each ground user can be served by at most one UAV.Denote αkm as the association variable, with αkm = 1 ifground user m ∈ M is associated with UAV k ∈ K andαkm = 0 otherwise. Then we have∑

k∈K

αtkm ≤ 1, ∀k ∈ K,m ∈M, t ∈ T (7)

αtkm ∈ {0, 1},∀k ∈ K,m ∈M, t ∈ T (8)

Denote Mtk , {m|m ∈ M, αt

km = 1} as the set of groundusers served by UAV k in time slot t.

We further consider FDMA-based spectrum access amongthe UAVs in K and TDMA for the ground users served by thesame UAV. Then, the SINR of ground user m ∈ M in timeslot t, denoted as γtm = γtm(Ht

mTx) can be expressed as

γtm =ptk(m) · (h

tk(m)m)2/Ht

k(m)m

(pTx/|K|) · wtmTxwm · (htmTx)

2/(HtmTx) + σ2

m

, (9)

where k(m) and wm represent the serving UAV and receivegain of ground user m, respectively; |K| denotes the number

3


of UAVs in K; Htk(m)m ∈ {H

NLoS,tk(m)m , H

LoS,tk(m)m} is the path-

loss from UAV k(m) to ground user m in time slot t withHNLoS,t

k(m)m and HLoS,tk(m)m defined in Section III-A; and σ2

m is thepower of the AWGN noise at ground user m. Notice in (9) thatonly 1

|K| of the incumbent transmitter’s power (i.e., pTx/|K|)is considered for each UAV and its associated ground usersbecause of the UAVs’ FDMA-based spectrum access. It isworth pointing out that we consider FDMA- and TDMA-based spectrum access for the UAV networks because we wantto focus this work on the interference control between theUAV and the incumbent systems. The resulting cross-systemspectrum sharing scheme can also be extended to other moreadvanced spectrum access schemes for UAVs [26], [27].

Finally, the capacity achievable by user m in time slot t,denoted as Ct

k(m)m, can be expressed as

Ctk(m)m =

B

|K||Mtk|[Pr(HNLoS,t

k(m)m

)log2

(1 + γtm(HNLoS

k(m)m))

+ Pr(HLoS,t

k(m)m

)log2

(1 + γtm(HLoS

k(m)m))], (10)

where Pr(·) is the probability of LoS and NLoS transmissionsdefined in Section III-A and γtm(·) is the SINR of ground userm defined in (9).

D. Problem Formulation

Define P = (ptk)t∈Tk∈K as the transmission power vector of

the UAVs, A = (αtkm)t∈Tk∈K,m∈M as the UAV-user association

vector, and Q = (codtk)

t∈Tk∈K as the UAV location vector.

Then the objective of the SwarmShare control problem isto maximize the aggregate capacity of the UAV network byjointing controlling the transmission power of the UAVs andtheir flight trajectory as well as association with the groundusers, while meeting the cross-system interference constraints,as formulated as

MaximizeP, A, Q

1

|T |∑t∈T

∑m∈M

Ctk(m)m (11)

Subject to : 0 ≤ ptk ≤ pmax,∀k ∈ K, t ∈ T , (12)Association Constraints (7), (8) (13)

1

|T |∑t∈T

I(γtRx ≤ γthRx) ≤ PrmaxRx︸︷︷︸

Cross−system Interference Constraint

(14)

where Ctk(m)m is defined in (10), pmax is the maximum

transmission power of each UAV, I(·) is the indication functiontaking value of 1 if the condition holds and 0 otherwise, andγthRx and Prmax

Rx denote threshold SINR and the maximumtolerable SINR outage probability of the incumbent system.

Estimated OutageProbability

Power Enforcement

Coefficient 𝜂𝑡

Target

Outage

Probability

Initial Power

Enforcement

Distance and Angle

Calculation

Offline-Dataset Guided

Online Power Adaptation

Offline DatasetGeneration

Model-based Feature Extraction

Data-Driven Calibration

UAV Network

Power and Tolerable

SINROutage

Probability

Location &

Orientation

Information

Automated Frequency Controller (AFC)Incumbent System

𝐜𝐨𝐝Tx, 𝐜𝐨𝐝Rx,𝐚𝐧𝐠𝐈𝐧𝐜

𝑝𝑇𝑥, PrRxmax

Initial PowerCalculation

Power Control

Principles

Online UAV

Throughput

Optimization

RL-guided

Exploration and

Exploitation

RL-based UAV

OptimizationUAV Locations

Transmission

Power

Initialization

Incumbent System

Location &Orientation

Power CalibrationCoefficient

Fig. 1: SwarmShare Spectrum Sharing Framework.

IV. SPECTRUM COEXISTENCE DESIGN

The SwarmShare problem formulated in (11)-(14) is amixed integer nonlinear nonconvex programming (MINLP)problem, because of the binary UAV-user association variablesαtkm and the underlying complicated mathematical expressions

in (11) and (14). Moreover, to solve the problem directly itrequires to know the real-time channel state information (CSI)between the UAV network and the incumbent system, whichis however unavailable, as discussed in Section I, because ofthe low-detectability of the directional incumbent signals.

To address the above challenges, in this work we consideran AFC (Automated Frequency Controller)-assisted spectrumsharing. AFC has been adopted for spectrum sharing in the TVwhitespace band as well as the 6 GHz band by determiningcertain exclusion zones nearby the incumbent systems [28],[29]. Our work differs from this with our objective to enableexclusion-zone-free hence more flexible spectrum sharing, andstudy the statistical behavior of the aggregate interferencefrom the UAV networks to the incumbent system, whilekeeping the cross-system signaling at a minimum level. Thediagram of the proposed spectrum coexistence framework isillustrated in Fig. 1, where there are three major components,i.e., Initial Power Enforcement, Offline-dataset Guided OnlinePower Adaptation, and Reinforcement Learning-based UAVOptimization.

A. Initial Power Enforcement

The objective of this phase is to determine, following aset of Power Control Principles, a rough transmission powerfor each of the UAVs. In this work, we consider three basicprinciples to accommodate the effects of the UAVs’ flightaltitude and their locations on the interference to the incumbentsystem, while more sophisticated principles can be incorpo-rated in the future. These principles are i) UAVs that are closerto the incumbent receiver should transmit at lower power; (ii)with the same distance to the incumbent receiver, UAVs flyinghigher should transmit at lower power; and (iii) with the samedistance and altitude, UAVs with smaller angles relative to theboresight axis of the incumbent receivers’ directional antennashould transmit at lower power. Particularly, the rationale ofthe second principle is that, with the hybrid LoS/NLoS channelmodel described in Section III-A, it is more likely for a UAVto establish LoS links to the incumbent receiver when flyinghigher and hence cause more interference. Similarly, for thethird principle, based on the directional antenna model de-scribed in Section III-B, a UAV will cause higher interferencewhen more aligned with the incumbent receiver’s antenna.

In SwarmShare, an initial power enforcement coefficient,denoted as Enf(codt

k, codRx,angInc), will be calculated foreach UAV k ∈ K in time slot t ∈ T based on the above threeprinciples. This is accomplished using three Sigmoid-familyfunctions Sig1(·), Sig2(·) and Sig3(·), as follows:

4


Enf(codtk, codRx,angRx) =

Sig1

(leuc(cod

tk, codRx)

ltheuc

)︸︷︷︸

Principle 1

·Sig2(h(codt

k) + h(codRx)

lthhgh

)︸︷︷︸

Principle 2

· Sig3(lrad(cod

tk, codRx,angInc)

)︸︷︷︸

Principle 3

, (15)

where leuc(·, ·) represents the Euclidean distance betweenUAV k and the incumbent receiver given their coordinates;h(·) represents their height, and lrad(·, ·, ·) ∈ [0, π] is theangle (in radians) of UAV k with respect to the boresightaxis of the incumbent receiver antenna; finally, ltheuc andlthhgh in equation (15) are respectively threshold distance andheight beyond which Sig1(·) and Sig2(·) become nearlyconstant. It is worth pointing out that, since a standard sigmoidfunction is a differentiable, monotonically increasing, realfunction taking values in [0, 1], we design Sig1(·), Sig2(·)and Sig3(·) by scaling, shifting and reversing the standardsigmoid function to consider the effects of UAV location,flight altitude and relative angle to the incumbent receiver.For example, Sig1(x) = 1

1+e−3(x/70−2) has been adopted forprinciple 1 in this work, while Sig2(·) and Sig3(·) can bedefined similarly. With the obtained power enforcement coef-ficient Enf(codt

k, codRx,angInc), each UAV’s power can beinitialized as, in time slot t ∈ T ,

pinik = pmaxEnf(codtk, codRx,angInc), ∀k ∈ K, (16)

where pmax is the maximum transmission power of each UAV.

B. Offline-dataset Guided Online Power Adaptation

Recall in Section III that our goal is to enable UAVoperations in the 6 GHz band while meeting the cross-systeminterference constraint (14). In SwarmShare, this is accom-plished by fine tuning the above obtained initial transmissionpowers for the UAVs following a three-step approach, asdescribed as follows.

1) Model-based Feature Extraction: In this step, we firstextract the network features that can be used later in Data-Driven Calibration, rather than using directly the raw networktopology information such as UAV location vector (codt

k)t∈Tk∈K.

This is important to mitigate the curse of dimensionalityproblem [30] especially with large number of UAVs. InSwarmShare, we select the power adaptation coefficient, de-noted as ηt for time slot t, as the network feature. Then, giventhe above obtained initial transmission power pinik for UAVk ∈ K, a new transmission power ptk can be calculated as

ptk = pinik ηt (17)and interference constraint (14) can be rewritten as

1

|T |∑t∈T

I(γtRx(ηt) ≤ γthRx) ≤ Prmax

Rx , (18)

where γtRx(ηt) is the SINR of the incumbent receiver de-

fined in (6) by substituting (17) into (6). Consider ergodicstochastic process for the aggregate interference and denoteProb

(γtRx(η

t) ≤ γthRx

)as the SINR outage probability in time

slot t ∈ T , then the left-hand side of (18) can be equivalentlyrepresented as

Prob(γtRx(η

t) ≤ γthRx

)(19)

= Prob

(P sigRx

P itfRx

≤ γthRx

)(20)

=

∫ +∞

0

∫ +∞

PsigRxγthRx

pdfP sigRx

(psig)︸︷︷︸NoncentralChi−squareDistribution

· pdfP itfRx(pitf)︸︷︷︸

GammaDistribution

dpitfdpsig, (21)

where P sigRx and P itf

Rx are the numerator and denominator of(6), respectively; Rayleigh distribution has been consideredfor the small-scale fading and hence noncentral chi-squaredistribution [31] for the receive power of the incumbentsignals; and finally as in [32], [33] Gamma distribution isconsidered for the aggregate interference power. The detailsof mathematical equations are omitted for the distributionsdue to space limitations. It is worth pointing out that, asshown later in Section V, the aggregate interference of UAVsdoes not follow any existing statistical distributions. In thiswork, we consider Gamma distribution in (21) because wewant to obtain a rough estimation of the power adaptationcoefficient ηt, which will be further calibrated based on offlinedataset. Notice that given the maximum tolerable SINR outageprobability Prmax

Rx in (18), the maximum ηt can be determinedefficiently by bisection search, since the left-hand side of (18),which is equivalent to (21), is a monotonically increasingfunction of the UAVs’ transmission power hence ηt.

2) Offline-Dataset Generation: Given the above obtainednetwork feature ηt, and each UAV’s transmission power ptkcan be updated according to (17). Since the power adaptationmay be inaccurate because of the inaccuracy of the Gammadistribution-based interference model in (21), we further cali-brate the power control for UAVs with the assistance of offlinemeasurements. Specifically, given the transmission power vec-tor (ptk)k∈K, the corresponding SINR outage probability of theincumbent system can be obtained by offline simulations. Byvarying the number of UAVs, their locations as well as themaximum tolerable SINR outage probability in the simula-tions, we are able to obtain an SINR outage probability vector.Denote Prmax

Rx = (PrmaxRx ) as the vector of the maximum toler-

able outage probability, and accordingly denote the simulatedoutage probability vector as Pr

max

Rx (η) = (Prmax

Rx (ηt)) withPr

max

Rx (ηt) being the SINR outage probability given networkmetric ηt and η = (ηt) the network feature vector.

3) Data-Driven Calibration: Finally, a mapping betweenPrmax

Rx and Prmax

Rx (η) can be established through functionapproximation, e.g., based on linear regression [34], echo statelearning [35] or deep neural networks [14]. In this work wefind that it is enough to approximate the mapping based on lin-ear regression. Denote the mapping as Prmax

Rx = f(Prmax

Rx (ηt).Then, given Pr

max

Rx , the value of PrmaxRx and the corresponding

network feature ηt can be obtained at network run time andfurther used for UAV power control based on (17).

5


C. Reinforcement Learning-based UAV Optimization

As illustrated in Fig. 1, the above obtained ηt will be broad-cast to the UAVs, which will then calculate their transmissionpower based on (17). Meanwhile, the UAVs will update theirflight and association strategies to serve their users with higherspectral efficiency. To this end, we consider as in [25] shortest-distance-based association strategy. Then, in each time slott ∈ T the association variables αk,m defined in Section IIIcan be determined as

αk,m =

{1, if k = argmink‖codt

k − codm‖2

0, otherwise. (22)

Further divide the whole network area into a set of threedimensional rectangles. In each time slot, each UAV is allowedto either move to one of its adjacent rectangles or stay in thecurrent. For each of the candidate rectangles, the UAV will firstcalculate the achievable capacity given the transmission powercalculated in Sections IV-A and IV-B and the set of groundusers it serves. Finally, as in [36], reinforcement learningwith ε-greedy search is adopted to guide the exploitation andexploration during the UAV’s flight control. The details of thelearing algorithm are omitted due to space limitations.

D. Complexity Analysis

The most time-consuming operation in the above pro-cedure is offline-dataset guided online power adaptation inSection IV-B. In Section IV-B1, we need to determine thenetwork feature ηt given the UAV network’s topology and themaximum tolerable SINR outage probability. Since the SINRoutage probability is a monotonically increasing function of ηt,the value of ηt can be obtained efficiently based on bisectionsearch. The dataset generation and regression in Section IV-B2can be conducted offline, and in Section IV-B3 Prmax

Rx can bedetermined online by solving a linear problem.

Regarding communication overhead, as illustrated in Fig. 1,in Section IV-A the AFC needs to collect one-time location andorientation information of the incumbent system and broadcastthe collected information to the UAVs. If the incumbent systemdoes not move frequently (which is usually the case, e.g.,fixed point-to-point applications), the resulting communicationoverhead can be neglected. The AFC also needs to collectperiodically the UAVs’ locations and broadcast the updatedpower adaptation coefficient ηt to the UAVs. Since it is enough

to represent these information in 16 bytes (three float numbersfor location and one for the power adaptation coefficient,and each float number takes 4 bytes), the resulting broadcastoverhead is low as well. Moreover, we will show later inSection V that the UAVs do not need to report their locations tothe AFC in real-time, without obviously increasing the SINRoutage probability of the incumbent system. This will furtherreduce the communication overhead.

V. PERFORMANCE EVALUATIONIn this section we validate the effectiveness of the

SwarmShare framework described in Sections III and IV. Weconsider a network area of 500×500×50 m3, with 50 groundusers randomly located in the network and the number ofUAVs varying from 3 to 24. The incumbent transmitter andreceiver are deployed with coordinates of (200, 200, 10) and(250, 250, 10), respectively. The center frequency of the sharedspectrum is set to 6 GHz with total bandwidth of 10 MHz. Themaximum transmission power of the incumbent transmitterand the UAVs are set to 1 W and 0.25 W, respectively.For the bisectorized antenna model desribed in Section III,the maximum and minimum gains are set to 1 and 0.5,respectively. The power density of the AWGN is set to -174 dBm/Hz. The probability of LoS and NLoS links are setto 0.7 and 0.3, respectively. The threshold parameters ltheuc andlthhgh in (15) are set to 70 m and 30 m, respectively. Next, beforediscussing the interference control results, we first determinethe threshold angle for the directional antenna model describedin Section III-B and validate the effectiveness of the data-driven calibration scheme proposed in Section IV-B.

Threshold Angle Measurement. We first determine thethreshold angle for the directional antenna model describedin Section III-B by conducting a set of experimental mea-surements. A snapshot of the testbed is shown in Fig. 2(a),where the transmitter is a USRP B210 software radio withomnidirectional antenna, the receiver is another USRP B210with Tupavco TP542 antenna, and the baseband signal pro-cessing is conducted based on GNU Radio on a Dell Latitude7400 laptop. Tupavco TP542 is a directional Wi-Fi antennaoperating in frequency range up to 5.8 GHz (very close to the6 GHz band) with antenna gain of 13 dBi. We measure thereceived power by varying the relative of the transmitter withrespect to the boresight direction of the directional antenna(as illustrated in Fig. 2 (a)) and the transmission distance

Transmitter (USRP B210)

Receiver(USRP B210 &

Tupavco TP542)

Host (Dell Latitude 7400 )

Protractor

Boresight Direction

0 Degrees 30 Degrees

60 Degrees 90 Degrees 120 Degrees

(a) (b)Fig. 2: (a) Snapshot of the testbed setup for threshold angle measurement; (b) Examples of the measurement results.

6


0 1 2 3 4Aggregrate Interference Value (Watt) 1e 9

0

2

4

6

8

Prob

abilit

y De

nsity

1e8

mean = 1.398641e-09variance=5.71581e-19

GaussianInverse GaussianGammaInverse Gamma

0.0 0.5 1.0 1.5 2.0 2.5Aggregrate Interference Value (Watt) 1e 9

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Prob

abilit

y De

nsity

1e9

mean = 1.088118e-09variance=1.88978e-19


0.050 0.075 0.100 0.125 0.150 0.175 0.200 0.225 0.250Calculated SINR Outage Probability

0.00

0.02

0.04

0.06

0.08

0.10

Sim

ulat

ed S

INR

Outa

ge P

roba

bilit

y

6 UAVs, Predicted6 UAVs, Simulated12 UAVs, Predicted12 UAVs, Simulated18 UAVs, Predicted18 UAVs, Simulated

(a) (b) (c)Fig. 3: Aggregate interference pdf with (a) 10 and (b) 20 UAVs; (c) Validation of data-driven predication of the SINR outage probabilityfor the incumbent system.

0 1 2 3 4Aggregrate Interference Value (Watt) 1e 9

0

2

4

6

8

Prob

abilit

y De

nsity

1e8

mean = 1.398641e-09variance=5.71581e-19


0.0 0.5 1.0 1.5 2.0 2.5Aggregrate Interference Value (Watt) 1e 9

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Prob

abilit

y De

nsity

1e9

mean = 1.088118e-09variance=1.88978e-19


0.050 0.075 0.100 0.125 0.150 0.175 0.200 0.225 0.250Network Feature

0.00

0.02

0.04

0.06

0.08

0.10

SINR

Out

age

Prob

abilit

y

6 UAVs, Predicted6 UAVs, Simulated12 UAVs, Predicted12 UAVs, Simulated18 UAVs, Predicted18 UAVs, Simulated

(a) (b) (c)

Fig. 4: Aggregate interference pdf with (a) 10 and (b) 20 UAVs; (c) Validation of data-driven predication of the SINR outage probabilityfor the incumbent system.

and relative angle is established based on XXX aggregationmethod [REF]. Based on the regression results, we set 30degree as the threshold angle for the bisectorized antennamodel Section III-B, which corresponds to the 3 dB angleof the Tupavco TP542 antenna.

Data-Driven Interference Prediction. Given above ob-tained threshold angle, we further characterize the statisticalbehavior of the aggregate interference from the coexistingUAVs to the incumbent receiver. To this end, we conduct aset of simulations over UBSim, a newly developed even-drivensimulator for integrated aerial-ground networks. Details of thesimulator are omitted due to space limitations. The resultsare reported in Figs. 4(a) and (b) with XX and XX UAVs,respectively. We fit the collected interference values withfour distributions, including Gaussian [REF], Inverse Gaussian[REF], Gamma [REF] and Inverse Gamma [REF], and foundthat the power of the aggregate interference does not followany of these distributions. This is actually our initial motivationto design SwarmShare based on a data-driven approach.

Fig. 4(c) reports the results of the data-driven predictionof the SINR outage probability against the network featuredefined in Section IV-B, i.e., Prmax

Rx in (17). We can findthat the predicated SINR outage probability matches wellthe simulated. For example, in the case of 6 UAVs, theprediction accuracy ranges between XX% and XX% with anaverage of XX%. The average prediction accuracy are XX%and XX% for 12 and 18 UAVs, respectively. This verifiedthe effectiveness of the data-driven approach for aggregationinterference characterization.

Case Study. Figure 5 shows an example of the powercontrol results based on SwarmShare. To make it easier to gainsome insights visually, in this example all the XX UAVs aredeployed uniformlly along 4 circles with different altitudes andradiuses. Note that the power allocation has been determinedby jointly considering the three basic principles describedin Section IV-A. From the figure it can be seen that lowertransmission powers have been allocated to UAVs along thelower circles. It can be seen that, because of the shorterdistances from the incumbent receiver, lower transmission

Fig. 5: Case study of power control based on SwarmShareRemove “Power Unit: Watt”; Change Drone to UAV.

power has been allocated to the UAVs of the first circle fromthe bottom, e.g., XXX mW for UAV 1 (i.e., D1[1] in Fig. 5)against XX for UAV XX and XX for UAV XX along thesecond and third circles, respectively. Moreover, along thesame circle UAVs more aligned with the incumbent has beenallocated lower transmission powers, e.g., XX for UAV XXvs XX for UAV XX along the second circle.

It is worth pointing out that UAVs along the fourthε for RL method is set to 0.98. The target PER probability

threshold is set to 0.05. each rectangle is with 50×50×10 m3

In Fig.6, Fig.?? and Fig.7, we plot instantaneous throughputof the incumbent receiver and the UAV network with differentnumber of UAVs in both static and dynamic scenarios. Asshown in the three figures, our proposed interference controlscheme works very well in both static and dynamic scenarios,that is the UAV can work in 6 GHz band with limitedinterference to incumbent users. In static scenario, we fixedthe channel type, LoS and NLoS, between UAV and othernodes and run simulator for 1000 times to get the throughputfor both UAV network and incumbent network. In dynamicscenarios, we adopt both ε-greedy search and random selectionto optimize UAV trajectory and generate the channel typebetween UAV and nodes based on the probability.

7

Fig. 4: Case study of power control based on SwarmShare. D#1[#2]:#1 is the UAV index, and #2 denotes the transmission power of theUAV in mW.

from 1 to 3 meters. Examples of the measurement resultsare given in Fig. 2(b) with transmission range of 1 m andrelative angles varying from 0 to 120 degrees at step of30 degrees. The mapping between the received power andrelative angle is established based on logarithmic regressionmethod [37]. Based on the regression results, we set 30 degreeas the threshold angle for the bisectorized antenna modelSection III-B, which corresponds to the 3 dB angle of theTupavco TP542 antenna.

Data-Driven Interference Prediction. Given the aboveobtained threshold angle, we further characterize the statisticalbehavior of the aggregate interference from the coexistingUAVs to the incumbent receiver. To this end, we conduct aset of simulations over UBSim, a newly developed UniversalBroadband Simulator for integrated aerial-ground networking.Details of the simulator are omitted due to space limitations.The results are reported in Figs. 3(a) and (b) with 10 and20 UAVs, respectively. We fit as in [38], [39] the collectedinterference values using four distributions, including Gaus-sian, Inverse Gaussian, Gamma and Inverse Gamma, and findthat the power of the aggregate interference does not followany of these distributions. This is actually our motivation todesign SwarmShare based on a data-driven approach. Fig. 3(c)reports the results of the data-driven prediction of the SINRoutage probability. We can find that the predicted SINR outageprobability matches well the simulated.

Case Study. Figure 4 shows an example of the powercontrol results based on SwarmShare. To visualize the effectsof the power control principles described in Section IV-A,in this example all the 24 UAVs are deployed uniformllyalong 4 circles with different altitudes and radiuses. Fromthe figure it can be seen that lower transmission powers havebeen allocated to UAVs along the lower circles. Also, becauseof the shorter distances from the incumbent receiver, lowertransmission power has been allocated to the UAVs of the firstcircle from the bottom, e.g., 1.0102 mW for UAV 1 (i.e., D1[1]in Fig. 4) against 16.7896 mW for UAV 7 and 39.6234 mWfor UAV 13 along the second and third circles, respectively.Moreover, along the same circle UAVs more aligned with theincumbent receiver have been allocated lower transmissionpowers, e.g., 8.3948 mW for UAV 10 vs 39.6234 mW for UAV9 along the second circle. Finally, we notice in this examplethat all the UAVs along the fourth (i.e., the highest) circle havebeen allocated zero transmission power because no users areassociated with them based on the shortest-distance associationstrategy described in Section IV-C. This also conforms to thethird power control principle, i.e., with the same distance andrelative angle, higher altitudes result in lower transmissionpower because of higher probability of LoS transmissions.It is worth pointing out that the power allocation results aredetermined by jointly considering the three basic principlesdescribed in Section IV-A. In the following experiments, wewill further evaluate the effectiveness of SwarmShare on thecross-system interference control.

In Figs. 5 and 6, we plot the instantaneous capacity achiev-able by the incumbent system and the UAV networks with dif-ferent numbers of UAVs. In Fig. 5(a), we consider 6 hoveringUAVs, and the maximum tolerable SINR outage probabilityis set to 0.05 for the incumbent system. The achievablecapacity is plotted for 1000 time slots. Results indicate that theinterference constraint of the incumbent system can be verywell fulfilled, with SINR outage probability of 0.032. Similarresults can be obtained with 12 and 18 hovering UAVs inFigs. 5(b) and (c), with the SINR outage probabilities of 0.029and 0.021, respectively.

Figure 6 shows the corresponding results with movingUAVs. In this experiment, the network area is divided into

7


0 200 400 600 800 1000

Time(s)

20

30

40

50

60

70

80

Inst

anta

neou

s ca

paci

ty (

Mbp

s)

Incumbent NetworkUAV NetworkThreshold

(a) 6 UAVs, Hovering

0 200 400 600 800 1000

Time(s)

20

30

40

50

60

70

80

90

Inst

anta

neou

s ca

paci

ty (

Mbp

s)


(b) 12 UAVs, Hovering

0 200 400 600 800 1000

Time(s)

10

20

30

40

50

60

70

80

90

100

Inst

anta

neou

s ca

paci

ty (

Mbp

s)


(c) 18 UAVs, Hovering

Fig. 5: Instantaneous capacity of the incumbent system and the UAV network with hovering UAVs. The violation probabilitiesare (a) 0.032, (b) 0.029 and (c) 0.021, respectively.

0 200 400 600 800 1000

Time(s)

20

40

60

80

100

120

140

Inst

anta

neou

s ca

paci

ty (

Mbp

s)


(a) 6 UAVs, Moving

0 200 400 600 800 1000

Time(s)

10

20

30

40

50

60

70

80

90

100

110

Inst

anta

neou

s ca

paci

ty (

Mbp

s)Incumbent NetworkUAV NetworkThreshold

(b) 12 UAVs, Moving

0 200 400 600 800 1000

Time(s)

20

30

40

50

60

70

80

90

100

110

Inst

anta

neou

s ca

paci

ty (

Mbp

s)


(c) 18 UAVs, Moving

Fig. 6: Instantaneous capacity of the incumbent system and the UAV network with moving UAVs. The violation probabilities are (a) 0.023,(b) 0.019 and (c) 0.018, respectively.

3 6 9 12 15Number of Drones

40

50

60

70

80

90

100

110

Ave

rage

Cap

acity

(M

bps)

Moving UAV, Random Movement, UAV NetworkMoving UAV, Random Movement, Incumbent RXMoving UAV, RL(Exploitation Probability 0.98), UAV NetworkMoving UAV, RL(Exploitation Probability 0.98), Incumbent RXHovering UAV, UAV NetworkHovering UAV, Incumbent RX

Fig. 7: Average capacity of the incumbent system and UAV networkswith moving UAVs.

a set of three-dimension rectangles each of 50× 50× 10 m3.The trajectory of the UAVs are controlled as described inSection IV-C, with exploitation probability of 0.98. The sameas in Fig. 5, the incumbent system’s interference constraintscan be satisfied in all the tested cases, with SINR outageprobability of 0.023, 0.019 and 0.018 for 6, 12 and 18UAVs, respectively, all below the maximum tolerable outageprobability 0.05. This verifies the effectiveness of SwarmSharein cross-system interference control.

Average Results. In Fig.7 we report the average capacityachievable by the incumbent system and the UAV networkwith the number of UAVs varying from 3 to 15 at step of3. Three UAV motility patterns are considered: i) randommovement; ii) reinforcement learning controlled movementwith exploitation probability of 0.98; and iii) hovering UAVs.The resutls are obtained by averaging over 50000 time slotsfor each mobility pattern. It can be seen that, as expected,

obvious capacity gain can be achieved by the UAV networkwith all the above three mobility patterns by deploying moreUAVs. For example, for hovering UAVs, the average capacityincreases from around 60 Mbps with 3 UAVs to 80 Mbpswith 15 UAVs. The capacity is further increased to around100 Mbps with RL-controlled UAV movement. Particularly,we find that there is no obvious degradation in the capacity ofthe incumbent system when there are 6 or more coexistingUAVs. The average capacity of the incumbent system canbe further increased with less UAVs, e.g., 3, because of thereduced cross-system interference.

In previous experiments (i.e., Fig. 5) the UAVs report theirlocations to the AFC in every time slot. In this experiment,we investigate the mobility resilience of SwarmShare forspectrum sharing in the presence of inaccurate UAV locations.The SINR outage probability results are reported in Fig. 8,where two mobility patterns are considered for the UAVs,i.e., random movement and RL-guided movement, and themaximum tolerable SINR outage probability is set to 0.05 forthe incumbent system. The location reporting period is variedfrom 10 time slots to 60. It can be seen that the SINR outageprobability of the incumbent system increases monotonicallywith the location reporting period if the UAVs move in anuncontrolled manner, i.e., completely randomly. For example,the outage probability is around 0.07 when the reporting periodis 10 time slots and can be up to 0.2 for 60 time slots.In the case of controlled UAV movement, the SINR outage

8


10 20 30 40 50 60UAV Location Reporting Period (Time Slot)

0

0.05

0.1

0.15

0.2

0.25

SIN

R O

utag

e P

roba

bilit

y6 UAVs, RL(Exploitation Probability 0.98)12 UAVs, RL(Exploitation Probability 0.98)18 UAVs, RL(Exploitation Probability 0.98)6 UAVs, Random Movement12 UAVs, Random Movement18 UAVs, Random Movement

Fig. 8: SINR outage probability vs UAV location reporting period.

probability is barely affected by the location reporting periodand always below the maximum tolerable. This is becausethe UAVs will stick with their current best locations at ahigh probability (0.98 in this experiment) while exploring newlocations at a low probability (0.02). As a result, the topologyof the UAV network and hence the statistical behavior of theiraggregate interference to the incumbent system changes onlyslowly. Therefore, with controlled UAV movement, effectiveinterference control can be achieved with SwarmShare inmobile scenarios even with inaccurate UAV locations, e.g.,because of the temporary loss of the connections to the AFC.

VI. CONCLUSIONS

In this paper, we proposed a new framework calledSwarmShare to enable spectrum sharing between the incum-bent systems and the coexisting UAV networks in the 6 GHzband. We validated the effectiveness of the framework throughan extensive simulation campaign. SwarmShare is shown to bemobility-resilient and hence is suitable for the operations ofmoving vehicles such as cars and UAVs on this newly openedspectrum band without requiring pre-defined exclusion zones.It is also found that the aggregate interference of the UAVsdoes not follow any existing distributions. In future workwe will develop new theoretical models to characterize theaggregate interference of the UAVs and further validate theeffectiveness of SwarmShare over the UAV testing facilitiesbeing developed in our lab.

REFERENCES

[1] Z. Chu, W. Hao, P. Xiao, and J. Shi, “UAV Assisted Spectrum Shar-ing Ultra-Reliable and Low-Latency Communications,” in Proc. IEEEGlobal Communications Conference (GLOBECOM), Waikoloa, USA,Dec. 2019.

[2] B. Shang, L. Liu, R. M. Rao, V. Marojevic, and J. H. Reed, “3D Spec-trum Sharing for Hybrid D2D and UAV Networks,” IEEE Transactionson Communications, vol. 68, no. 9, pp. 5375–5389, May 2020.

[3] Unlicensed Use of the 6 GHz Band. 85 FR 31390.[4] G. Naik, J. M. Park, J. Ashdown, and W. Lehr, “Next Generation Wi-

Fi and 5G NR-U in the 6 GHz Bands: Opportunities and Challenges,”IEEE Access, vol. 8, pp. 153 027–153 056, Aug. 2020.

[5] V. Sathya, M. I. Rochman, M. Ghosh, and S. Roy, “StandardizationAdvances for Cellular and Wi-Fi Coexistence in the Unlicensed 5 and6 GHz Bands,” GetMobile, vol. 24, no. 1, pp. 5–15, Mar. 2020.

[6] B. Wang, Y. Sun, Z. Sun, L. D. Nguyen, and T. Q. Duong, “UAV-Assisted Emergency Communications in Social IoT: A Dynamic Hy-pergraph Coloring Approach,” IEEE Internet of Things Journal, vol. 7,no. 8, pp. 7663–7677, Apr. 2020.

[7] J. Lyu, Y. Zeng, and R. Zhang, “Spectrum Sharing and Cyclical MultipleAccess in UAV-Aided Cellular Offloading,” in Proc. IEEE GlobalCommunications Conference (GLOBECOM), Singapore, Jan. 2018.

[8] H. Wang, J. Wang, G. Ding, J. Chen, Y. Li, and Z. Han, “SpectrumSharing Planning for Full-Duplex UAV Relaying Systems With Un-derlaid D2D Communications,” IEEE Journal on Selected Areas inCommunications, vol. 36, no. 9, pp. 1986–1999, Aug. 2018.

[9] T. Q. Duong, L. D. Nguyen, H. D. Tuan, and L. Hanzo, “Learning-AidedRealtime Performance Optimisation of Cognitive UAV-Assisted DisasterCommunication,” in Proc. of IEEE Global Communications Conference(GLOBECOM), Waikoloa, USA, Feb. 2019.

[10] A. Fotouhi, H. Qiang, M. Ding, M. Hassan, L. G. Giordano, A. Garcia-Rodriguez, and J. Yuan, “Survey on UAV Cellular Communications:Practical Aspects, Standardization Advancements, Regulation, and Se-curity Challenges,” IEEE Communications Surveys Tutorials, vol. 21,no. 4, pp. 3417–3442, Mar. 2019.

[11] L. Zhang, H. Zhao, S. Hou, Z. Zhao, H. Xu, X. Wu, Q. Wu, andR. Zhang, “A Survey on 5G Millimeter Wave Communications for UAV-Assisted Wireless Networks,” IEEE Access, vol. 7, pp. 117 460–117 504,Jul. 2019.

[12] M. Zareei, C. Vargas-Rosales, R. V. Hernndez, and E. Azpilicueta,“Efficient Transmission Power Control for Energy-harvesting CognitiveRadio Sensor Network,” in Proc. IEEE 30th International Symposiumon Personal, Indoor and Mobile Radio Communications (PIMRC Work-shops), Istanbul, Turkey, Sept. 2019.

[13] Jie Xiang, Yan Zhang, and Tor Skeie, “Joint Admission and Power Con-trol for Cognitive Radio Cellular Networks,” in Proc. 11th IEEE Singa-pore International Conference on Communication Systems, Guangzhou,China, Jan. 2008, pp. 1519–1523.

[14] H. Zhang, N. Yang, W. Huangfu, K. Long, and V. C. M. Leung, “PowerControl Based on Deep Reinforcement Learning for Spectrum Sharing,”IEEE Transactions on Wireless Communications, vol. 19, no. 6, pp.4209–4219, Jun. 2020.

[15] L. Li, L. Liu, J. Bai, H. H. Chang, H. Chen, J. D. Ashdown, J. Zhang,and Y. Yi, “Accelerating Model-Free Reinforcement Learning WithImperfect Model Knowledge in Dynamic Spectrum Access,” IEEEInternet of Things Journal, vol. 7, no. 8, pp. 7517–7528, Aug. 2020.

[16] H. Guo, R. Long, and Y. Liang, “Cognitive Backscatter Network: ASpectrum Sharing Paradigm for Passive IoT,” IEEE Wireless Communi-cations Letters, vol. 8, no. 5, pp. 1423–1426, Oct. 2019.

[17] M. Labib, A. F. Martone, V. Marojevic, J. H. Reed, and A. I. Zaghloul,“A Stochastic Optimization Approach for Spectrum Sharing of Radarand LTE Systems,” IEEE Access, vol. 7, pp. 60 814–60 826, Apr. 2019.

[18] C. Aydogdu, M. F. Keskin, N. Garcia, H. Wymeersch, and D. W.Bliss, “RadChat: Spectrum Sharing for Automotive Radar InterferenceMitigation,” IEEE Transactions on Intelligent Transportation Systems,vol. 22, no. 1, pp. 416–429, Jan. 2021.

[19] A. F. Martone, K. A. Gallagher, and K. D. Sherbondy, “Joint Radar andCommunication System Optimization for Spectrum Sharing,” in Proc.IEEE Radar Conference (RadarConf), Boston, USA, Apr. 2019.

[20] D. Tarek, A. Benslimane, M. Darwish, and A. M. Kotb, “Survey onSpectrum Sharing/Allocation for Cognitive Radio Networks Internet ofThings,” Egyptian Informatics Journal, vol. 21, no. 4, pp. 231 – 239,Mar. 2020.

[21] M. L. Attiah, A. A. M. Isa, Z. Zakaria, M. Abdulhameed, M. K. Mohsen,and I. Ali, “A Survey of mmWave User Association Mechanismsand Spectrum Sharing Approaches: An Overview, Open Issues andChallenges, Future Research Trends,” Wireless Networks, vol. 26, no. 4,pp. 2487–2514, May 2020.

[22] J. Ren, Y. He, G. Huang, G. Yu, Y. Cai, and Z. Zhang, “An edge-computing based architecture for mobile augmented reality,” IEEENetwork, vol. 33, no. 4, pp. 162–169, Jan. 2019.

[23] J. Cui, Y. Liu, and A. Nallanathan, “Multi-Agent ReinforcementLearning-Based Resource Allocation for UAV Networks,” IEEE Trans-actions on Wireless Communications, vol. 19, no. 2, pp. 729–743, Aug.2020.

[24] “IEE Colloquium on ‘Role of Site Shielding in Prediction Models for Ur-ban Radiowave Propagation’ (Digest No.1994/231),” in IEE Colloquiumon Role of Site Shielding in Prediction Models for Urban RadiowavePropagation (Digest No. 1994/231), Aug. 1994.

[25] S. K. Moorthy and Z. Guan, “FlyTera: Echo State Learning for JointAccess and Flight Control in THz-enabled Drone Networks,” in Proc.of IEEE International Conference on Sensing, Communication andNetworking (SECON), Como, Italy, June 2020.

[26] L. Bertizzolo, E. Demirors, Z. Guan, and T. Melodia, “CoBeam:Beamforming-based Spectrum Sharing With Zero Cross-TechnologySignaling for 5G Wireless Networks,” in Proc. of IEEE Interna-

9


tional Conference on Computer Communications (INFOCOM), Toronto,Canada, July 2020.

[27] Y. Li, H. Zhang, K. Long, S. Choi, and A. Nallanathan, “ResourceAllocation for Optimizing Energy Efficiency in NOMA-based Fog UAVWireless Networks,” IEEE Network, vol. 34, no. 2, pp. 158–163, Sept.2020.

[28] “Unlicensed Use of the 6 GHz Band,” Notice of Proposed RulemakingET Docket No. 18-295; GN Docket No. 17-183, Oct. 2018.

[29] “Automated Frequency Coordination: An Established Tool for ModernSpectrum Management,” DSA, Mar. 2019.

[30] N. Vervliet, O. Debals, L. Sorber, and L. De Lathauwer, “Breaking theCurse of Dimensionality Using Decompositions of Incomplete Tensors:Tensor-Based Scientific Computing in Big Data Analysis,” IEEE SignalProcessing Magazine, vol. 31, no. 5, pp. 71–79, Aug. 2014.

[31] K. T. Hemachandra and N. C. Beaulieu, “Novel Representations forthe Equicorrelated Multivariate Non-Central Chi-Square Distribution andApplications to MIMO Systems in Correlated Rician Fading,” IEEETransactions on Communications, vol. 59, no. 9, pp. 2349–2354, 2011.

[32] S. Kusaladharma and C. Tellambura, “Aggregate Interference Analysisfor Underlay Cognitive Radio Networks,” IEEE Wireless Communica-tions Letters, vol. 1, no. 6, pp. 641–644, Sept. 2012.

[33] A. A. Khuwaja, Y. Chen, and G. Zheng, “Effect of User Mobility and

Channel Fading on the Outage Performance of UAV Communications,”IEEE Wireless Communications Letters, vol. 9, no. 3, pp. 367–370, Nov.2020.

[34] A. F. Schmidt and C. Finan, “Linear Regression and the NormalityAssumption,” Journal of clinical epidemiology, vol. 98, pp. 146–151,Jun. 2018.

[35] S. Krishna Moorthy and Z. Guan, “Beam Learning in MmWave/THz-band Drone Networks Under In-Flight Mobility Uncertainties,” IEEETransactions on Mobile Computing, accepted for publication, 2021.

[36] S. A. Al-Ahmed, M. Z. Shakir, and S. A. R. Zaidi, “Optimal 3DUAV Base Station Placement by Considering Autonomous VoverageHole Detection, Wireless Backhaul and User Demand,” Journal ofCommunications and Networks, vol. 22, no. 6, pp. 467–475, Dec. 2020.

[37] D. W. Hosmer Jr, S. Lemeshow, and R. X. Sturdivant, Applied LogisticRegression. John Wiley & Sons, 2013, vol. 398.

[38] Z. Guan, T. Melodia, and G. Scutari, “To Transmit or Not to Transmit?Distributed Queueing Games for Infrastructureless Wireless Networks,”IEEE/ACM Transactions on Networking, vol. 24, no. 2, pp. 1153–1166,Apr. 2016.

[39] R. K. Ganti and M. Haenggi, “Interference in Ad Hoc Networks withGeneral Motion-Invariant Node Distributions,” in Proc. IEEE Interna-tional Symposium on Information Theory, Toronto, Canada, July 2008.

10


SwarmShare: Mobility-Resilient Spectrum Sharing for Swarm ...

Documents

Transcript of SwarmShare: Mobility-Resilient Spectrum Sharing for Swarm ...