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A Machine Learning-based Framework forOptimizing the Operation of Future Networks

Claudio Fiandrino, Member, IEEE, Chaoyun Zhang, Paul Patras, Senior Member, IEEE,Albert Banchs, Senior Member, IEEE, and Joerg Widmer, Fellow, IEEE

Abstract—The fifth generation of mobile networks (5G)and beyond are not only sophisticated and difficult tomanage, but must also satisfy a wide range of stringentperformance requirements and adapt quickly to changesin traffic and network state. Advances in machine learningand parallel computing underpin new powerful tools thathave the potential to tackle these complex challenges.In this paper, we develop a general machine learning-based framework that leverages artificial intelligence toforecast future traffic demands and characterize trafficfeatures. This enables to exploit such traffic insights toimprove the performance of critical network control mech-anisms, such as load balancing, routing, and scheduling.In contrast to prior works that design problem-specificmachine learning algorithms, our generic approach can beapplied to different network functions, allowing to re-useexisting control mechanisms with minimal modifications.We explain how our framework can orchestrate ML toimprove two different network mechanisms. Further, weundertake validation by implementing one of these, i.e.,mobile backhaul routing, using data collected by a majorEuropean operator and demonstrating a 3× reduction ofthe packet delay, compared to traditional approaches.

Index Terms—Machine learning, deep learning, mobilenetworks, 5G systems, network optimization.

I. INTRODUCTION

Recent advances in machine learning (ML) en-able optimization at levels of complexity that werepreviously unaffordable. This has led to dramaticperformance improvements, fostering the use of MLalgorithms like neural networks across a wide rangeof fields.

Harnessing ML to enhance the performance ofwireless networks started with 5G and will be essen-tial to promote zero-touch configuration and man-agement, thereby enabling self-configuration andself-optimization envisioned for 6G networks [1].Wireless network operation depends on many vari-ables that are not always known at the time when de-cisions need to be taken and which cannot be easily

C. Fiandrino, A. Banchs and J. Widmer are with IMDEA NetworksInstitute, Madrid, Spain. E-mail: {firstname.lastname}@imdea.org.

C. Zhang and P. Patras are with the University of Edinburgh, Edin-burgh, UK. E-mail: chaoyun.zhang@ed.ac.uk, ppatras@inf.ed.ac.uk.

forecast or inferred. Furthermore, wireless networksare increasingly complex and heterogeneous, as theycomprise many different radio access technologiesand modules that mutually interact, need to sat-isfy diverse evolving requirements, and have toadapt quickly to changes. This renders the problemof real-time performance optimization of wirelesssystems prohibitive for traditional techniques. Incontrast, the ability of ML tools to handle verycomplex systems makes them suitable for managinghighly dynamic wireless networks and take moreintelligent decisions, e.g., based on predicted futuretraffic patterns [2].

Stemming from these observations, this paperproposes a modular ML-based wireless networkoptimization framework, which enables plug-and-play integration of machine intelligence into new,as well as existing, network functions. Specifically,we leverage ML to (i) forecast future traffic volumeand (ii) characterize traffic features. We then feedthis information into network control mechanismsto improve their performance. The advantage of ourapproach is two-fold: (i) it is sufficiently general andallows to instantiate ML pipelines across differentnetwork elements and functions, thus being compli-ant with the recent ITU-T Y.3172 recommendationfor integrating ML in future networks [3], and (ii) itpermits to retrofit ML to legacy architectures andreuse existing network control mechanisms withminimal or no modifications.

Previous works embed ML into the design ofspecific algorithms, focusing on network functionsincluding (i) mobility management, resource man-agement and orchestration, and service provision-ing [4]; (ii) detection and channel estimation inmassive MIMO systems [5]; (iii) routing [6]; and(iv) resource scaling of Virtual Network Functions(VNF) [7]. Their main drawback is precisely thatthey are mechanism-specific, i.e., each network con-trol mechanism requires a purpose-built ML ap-proach, and cannot be easily reused.

In contrast, we use ML to make accurate traffic

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predictions that can be straightforwardly used asinput to well-established algorithms and decisionmodules. Traffic forecasting and characterization us-ing ML has received significant research interest [8],[9]. Yet, previous work largely focuses on trafficanalysis to optimize specific network operations,e.g. routing (see [10] for a survey of ML techniquesapplied to SDN), or VNF resource scaling [7],while our approach relies on traffic analytics toimprove the performance of generic network controlmechanisms.

In addition, we incorporate an ML orchestratorthat is responsible for managing and monitoring re-sources, but also for deriving suitable configurationsfor training ML models. We expect that instantiatingan ML pipeline for a specific function with the aidof our framework will bear similar costs to thoseincurred by purpose-built ML algorithms serving thesame purpose. The one-off signalling cost associatedwith the orchestration of a function in our approachis a small price to pay for the flexibility it enables.

To demonstrate the feasibility and performancegains achievable with our framework, we showhow to orchestrate two ML pipelines, i.e., traffic-driven VNF scaling and routing in mobile backhaulnetworks. We practically evaluate the latter use case.By feeding a state-of-the-art routing scheme withcity-scale forecasts of future traffic consumption,obtained with a deep learning structure, our frame-work attains 3× reductions in packet delay.

II. ML-BASED 5G NETWORK OPTIMIZATION

We propose an ML-based framework for net-work optimization and explain how to incorporateit within 5G networks.

A. ML-based Framework

Fig. 1 illustrates the building blocks of our frame-work, which comprises (i) the ML orchestrator,(ii) modules to measure mobile network traffic,(iii) ML algorithms that process this data, and(iv) modules performing specific network optimiza-tions based on the output of the ML algorithms.

According to the specific network function tooptimize, the orchestrator defines in the form oftemplate the set of collector nodes, the durationand the aggregation level of traffic measurements,and ML pipeline-specific parameters, such as num-ber of epochs, layers, and possibly a custom loss

MeasurementPoints:

Base stations,UEs,

Edge data center,etc.

Extraction ofInput Parameters:

Packet Length,Interarrival Times,

Flow Duration,etc.

Data

TrafficClassification

TrafficForecasting

Decision Modules:

Scheduling,Routing,

Orchestration,Load Balancing,

etc.

Requirements

Traffic ParametersErrorFeedback

ML Algorithms

ML Orchestrator

Fig. 1. Building blocks of the proposed framework. ML algorithmsused to characterize and forecast traffic based on measurements andflow metadata. Knowledge extracted is fed to modules implementingnetwork functions.

function as in [11]. Different functions require dif-ferent inputs; for example, while routing requiresto monitor traffic from a set of base stations todecide optimal routes, scaling computational re-sources of VNFs executing core services requiresto monitor control traffic from the same set ofbase stations. The orchestrator thus coordinates theinstantiation of an ML pipeline accordingly, alongwith the mechanisms to update the decisions foreach network function (e.g., by interacting with theVNF orchestrator), and ensures sufficient computingcapacity is provisioned to train ML models in eithera centralized or a distributed manner.

Gathering measurements requires direct access toflow information available at, e.g., Software DefinedNetworking (SDN) switches or base stations. Ratherthan defining a limited set of input features, themeasurement modules extract sequences of pack-ets of each flow, along with their lengths, inter-arrival times, direction (uplink/downlink), and pos-sibly even parts of the payload. The key advantageof working with such comprehensive data as input isthat abstract features can be extracted automaticallyduring training, instead of relying on a restricted andmanually identified set. Indeed, feature engineeringis costly and may lead to poor performance [12].Further, new use cases may require different fea-tures. Thus, our approach is future proof.

Our framework is general enough to allow fordifferent learning techniques. We focus on deeplearning (DL) because (i) DL algorithms scalebetter than ML approaches as the volume of datagrows, (ii) in network settings where inferencesmust be made based on a large number of inputparameters, DL produces highly accurate outputs,and (iii) advances in parallel computing enable totrain complex neural networks rapidly and to apply

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Outputs

Predicted traffic consumption

...

eNodeB #1

eNodeB #n

Spatio-temporal Neural Network

+ +

Encoder Decoder

ConvLSTMs

(Extract spatio-temporal features)

Stacks of 3D convolutional layers

(Extract spatio-temporal features)

Fusion

(Ensembling

system)

Fusion

(Ensembling

system) Fully-connected layers

(Features decoding)

...

Inputs

Mobile traffic measurements

Inputs Spatio-temporal Neural Network Outputs

Traffic Measures Traffic Prediction

eNodeB # n

eNodeB # 1

FusionFeature

DecodingConvLSTMs

Stacked 3D-CNNs

Fig. 2. Example of a deep learning pipeline for mobile trafficforecasting adapted from [13]. City level measurements are fed intostacks of 3D-CNNs and ConvLSTMs, which extract spatio-temporalfeatures to predict future traffic demands at eNodeB level.

them in different settings without re-training.We are especially interested in DL structures that

can identify distinct types of flows within largeaggregates, in order to (i) accommodate specificrequirements, such as latency and reliability (trafficclassification), and (ii) predict essential characteris-tics of future network traffic, such as average andpeak data rates, level of burstiness, etc. (traffic fore-casting). Depending on the target task, different DLstructures can be employed [12]. Auto-encoders areparticularly effective in traffic classification basedon TCP traffic flow information. Structures typicallyused for image segmentation (e.g., ConvolutionalNeural Networks – CNNs) are also effective inclassification. Optimization of network functionslike scheduling and load balancing depends on theability to accurately classify traffic (see Section III).

Traffic forecasting depends significantly ontemporal features. Long-Short Term Memories(LSTMs) work well with time series. Similarly,the convolution operation can be extended to thetemporal dimension to construct a 3D-CNN, therebyextracting spatio-temporal features that are char-acteristic to mobile traffic [13]. Fig. 2 illustratesa deep learning pipeline tailored to mobile trafficforecasting. City level traffic measurements are fedinto stacks of such 3D-CNNs and ConvLSTMs toextract spatio-temporal features within traffic snap-shots that a group of fully connected layers uses tomake future traffic predictions per eNB.

Finally, our decision modules (Fig. 1) are basedon existing algorithms which only need to be up-dated to take as input the predictions made bythe DL algorithms. Thus, with our framework thebasic operation of these algorithms remains unmod-ified. This provides much greater control over theiroperation, unlike fully ML-based solutions whosedecisions are made directly by ML algorithms.

Edge Cloud

CoreFunctions

Edge Cloud

C-RAN

RAT-1

RAT-2

UE

Optimization Core

Optimization RAN

Scheduling

RoutingLoad Balancing

src

sink

c

pre-processing srccdeep learning

output parameters

Fig. 3. Integration of our framework in a 5G architecture. Workflowexecution shown by highlighting measurement points and functionsbenefiting from ML.

B. 5G Architecture Integration

The design of our framework adheres to theITU-T guidelines [3]. Although it is tailored specif-ically for (beyond) 5G networks, the solution isbackward-compatible with 4G networks (exceptwith limited range of network mechanism that canbe optimized). Our framework can instantiate mea-surement modules in the core, backhaul/fronthaul,and RAN, while exploiting standard interfaces toextract measurements from the network equipment.

Fig. 3 shows a realization of our architecture.The sources (src) are User Equipments (UEs) andbase stations (eNodeB/eNBs and gNBs according to4G and 5G terminology respectively) with diverseradio access technology (RAT). Our framework canalso interact with other sources, such as the Net-Work Data Analytics Functions (NWDAF), which3GPP has introduced in Rel. 15, and RAN DataAnalytics Function (RAN-DAF - not within 3GPP’sumbrella) to extract user location, cell/slice ID,cell/slice load, channel quality, amount of trans-mitted/received data, etc. This allows to collectdata (collector c) close to where it can undergopre-processing, so as to expose properly formattedinputs to deep learning algorithms by applying a setof transformations to map the measurements into atensor format that learning algorithms can process.

According to the specific optimization objective,the training and inference phases take place eitherin edge clouds or at the level of individual base sta-tions/routers/UEs, depending on the required com-puting capabilities. Traditionally, training an MLmodel requires to move all the data into a centrallocation, which is the result of trading privacy andbandwidth (e.g., backhaul links) to obtain accuratemodels. With federated learning, an ML model is

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trained in a distributed manner, which suits betterscenarios with the UE in the loop and entailsminimal communication overhead, e.g., with datacompression or reduction on the number of updatesper node. This allows to harness the computing re-sources of the federated nodes, while partial modelparameter updates affect the model’s convergencespeed only marginally. Once the output parametersare determined, e.g., an update of the scheduling orrouting policy, they are distributed to the sink nodes(e.g., the RATs in Fig. 3).

III. USE CASES FOR NETWORK OPTIMIZATION

We now cover a diverse set of use cases that ourframework can serve.

Semi-persistent, elasticity- and latency-awarescheduling: Different types of traffic have differentlevels of latency requirements, ranging from ex-tremely small (e.g., ultra-reliable low-latency com-munications – URLLC), to medium (interactivevoice or video), all the way to slack latency require-ments (media streaming). ML can extract specificflow characteristics and the associated latency re-quirements, and feed this information to schedulers.

With semi-persistent scheduling, periodic uplinkflows can be assigned transmission slots withoutnotifying the scheduler of the user equipment’squeue occupancy. Such allocations will change dy-namically over time, to adapt to changes in themodulation and coding scheme (MCS) that reflectthe perceived channel quality. Reserving resourcesin advance brings significant advantages, i.e., re-duced control overhead and signaling load (whichis critical in dense networks) and notifications toneighboring cells of these reservations, to bettercoordinate transmissions and limit inter-cell inter-ference. While this technique has many advantages,it requires flow classification, i.e., inferring whethera given flow is amenable to this type of scheduling,its periodicity, and the resources required for eachperiod.

Elastic flows can (within limits) adapt to theavailable capacity; e.g., HTTP video streaming ap-plications can switch between codecs of differentrates. Dropping packets of a flow directly reduces itsrate by the corresponding fraction of packets in caseof UDP. When TCP is employed, packet droppingindirectly reduces the rate of that flow by triggeringcongestion control. Further, dropping packets ofelastic flows reduces the Quality of Experience

(QoE) of the corresponding users significantly lessthan reducing the rate of inelastic traffic. In contrast,URLLC traffic of industrial automation and tactileapplications poses extreme requirements in terms oflatency and reliability (1 ms target latency and 10−5

reliability as per 3GPP Rel. 15 specifications).A prompt and correct classification of latency

requirements enables to prioritize and schedule dif-ferent flows to meet their deadlines, if necessaryat the expense of serving less important trafficwith higher delay. The classification of the level ofelasticity allows (i) to intelligently drop packets ofspecific flows, so as to minimize QoE degradationand (ii) to shape the traffic of known applicationsearly on, thereby preventing congestion.

Load balancing: Advance knowledge of be-havior and characteristics of different flows, such asthe average/peak rate, and level of burstiness, allowsto intelligently assign different flows to base stationsand scale in and out VNF resources.

Load balancing may be based on average loador the statistical behavior of the different flowsand the probability that they congest the corre-sponding base station. More importantly, prediction-based load balancing can occur proactively ratherthan reactively, i.e., when some base stations havealready become congested. In 5G scenarios, usersmay benefit from being simultaneously attachedto conventional LTE and New Radio (NR) basestations. NR will typically operate in mm-wavebands, which offer much higher data rates, but aremore susceptible to blockage. Early prediction ofblockage on NR links allows to steer traffic overproper fallback LTE links.

Load balancing is also essential for VNF orches-tration. The placement of the different VNFs servinga given flow depends on (i) the traffic generated bythat flow and (ii) the latency requirements of theapplication. Since moving or start-up new VNFsincur high overheads, it is important to predictthese features as far in advance as possible thusminimizing the disruption of their operation. Forexample, co-location in the same rack of VNFsexecuting RAN and core services that exchange asignificant amount of signaling traffic (e.g., Accessand Mobility Function (AMF) and Session Manage-ment Functions (SMF)) is beneficial to reduce loadpeaks, queuing delays, and energy consumption.Additionally, traffic prediction is an enabler for jointoptimization of radio configuration (MCS) and CPU

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allocation of VNFs executing RAN services [11].Unlike core services for which statistics about con-trol traffic are required to optimize resource alloca-tion and orchestration of VNFs, RAN services andflow assignment require data traffic statistics.

Mobile traffic routing: Efficient route manage-ment is essential for mobile networks, especiallyfor high rate and low latency traffic flows, whichmay originate in edge clouds, are routed to thecore, and then continue on to servers responsible forC-RAN processing. Poorly chosen paths lead to net-work congestion and degrade the quality of service.While typically routing protocols assign (static ordynamic) weights to links, the increasing popularityof SDN offer new degrees of freedom for routing(e.g., flow-based routing). With an ML algorithmthat can learn flow statistics with high accuracyand mine traffic patterns, more sophisticated andaccurate routing policies can be devised. In the nextsection, we present a realization of our frameworkthat addresses this particular routing use case.

IV. ORCHESTRATION AND USE CASEIMPLEMENTATION

We demonstrate how the proposed ML-basedoptimization framework can be instantiated to or-chestrate simultaneous ML pipelines for learning-driven routing and VNF scaling. Further, we assessthe benefits of ML in the former use case.

Let us consider part of a mobile core network,a cluster of eNBs deployed in the city of Milan(see Fig. 4). We envision a set of routing nodesthat connect the eNBs to the core network viadiverse backhaul links. The traffic measurementswere collected by Telecom Italia between Nov–Dec2013 [14]. The experiments preserve the trends inthe original dataset, but we scale up the demands bya factor of 10, according to current market studieson traffic consumption growth. Fig. 5 illustratesdifferent 30-min snapshots of the ground truth ob-servations and the demand forecast with our D-STNat 4 selected eNBs (as numbered in Fig. 4).

Orchestration: In a 5G core network withcontrol-user plane separation, the User Plane Func-tion (UPF) takes care of the data path while AMFand SMF cover the control path. AMF performsfunctions related to user authentication, authoriza-tion and handles mobility management. SMF is incharge of all session management functions, allo-cates IP addresses and selects the UPF (see Fig. 4).

Our ML framework (see Fig. 1) will orchestratesimultaneously the scaling of AMF and routing.The orchestrator instructs one ML-pipeline to gathercontrol traffic to determine the number of acceptedUEs and the time they need to attach to the net-work [7]. A second ML-pipeline is instructed toaggregate data traffic into minute-granularity sum-maries along with timing information and the co-ordinates of the corresponding source nodes (“Ex-traction of Input Parameters”). Both ML pipelinesextract these parameters from mobile traffic ob-served at different eNodeBs (“Measurement Points”and “Data” in Fig. 1). Input parameters are fed tospecific ML-algorithms, e.g., LSTM, and ConvL-STM and 3D-CNN structures for VNF scaling androuting respectively (“Traffic Forecasting” module).Finally, the outputs, i.e., the optimal number ofVNFs and paths are fed to the (“Decision Module”)that instruct the VNF orchestrator and routers.

Learning Driven Proactive Routing: We nowquantify the benefits of a specific ML-pipeline. Ourapproach stems from the observation that traditionalshortest-path routing is increasingly phased out,because of the highly dynamic nature of traffic de-mands within mobile networks and the progressivelydenser network deployments. More often, alterna-tives like backpressure routing [15] that forwardpackets based on information about queue sizesalong possible paths are preferred. DL was provento outperform conventional OSPF by taking routingdecisions based on observed traffic patterns [6].However, both load-based and early DL-poweredsolutions select routing paths a posteriori, i.e., onlyafter network conditions changed. This increasesdelays, as traffic demand information may be staleby the time forwarding decisions are enforced.

By forecasting future traffic demands and makerouting decisions proactively can circumvent theselimitations. Unfortunately, widely used forecastingtechniques (e.g. ARIMA) require to be fed con-tinuously with measurement time series, which isexpensive. Furthermore, such tools must be re-configured each time they are deployed in a newnetwork topology. In contrast, our framework adoptsDL techniques that are trained offline once, andafterwards can provide city-scale traffic predictionsto routing logic without retraining.

Therefore, we incorporate our recently developeddeep spatio-temporal neural network (D-STN) [13],which through ConvLSTM and 3D-CNN structures

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UPF

AMF SMF

Core

Fig. 4. Example of eNBs cluster in the city of Milan, with our ML framework orchestrating in the core network VNF scaling and proactiverouting both based on traffic forecast measured per eNB.

eNB#1 eNB#2 eNB#3 eNB#4

0 5 10 15 20 25 30

400

600

800

Time (min)

Traf

fic(M

B)

Ground Truth D-STN

Fig. 5. Traffic forecast with a deep spatio-temporal neural network(D-STN) at the 4 eNBs indicated in Fig. 4. Note the predictionsfollow closely ground truth measurements.

0 8 16 24 32 400.0

0.2

0.4

0.6

0.8

1.0

Delay (ms)

Prob

abili

ty

BackpressureForecast drivenPerfect knowledge based

Fig. 6. Delay CDF for the topology shown in Fig. 4. The pathselection is made using a vanilla backpressure algorithm, an enhancedversion that uses mobile traffic forecasts, and the ideal case whereperfect knowledge of future traffic is available.

extracts abstract spatial and temporal features ofmobile traffic, achieving high forecasting accuracywith only limited measurements. Our approach doesnot require fine-grained information about individ-ual flows because it makes predictions based onaggregate traffic volumes. Further, by mixing pre-dictions with historical information, this solutionestimates the future traffic demand with small errorsfor long periods of time.

We assume the backhaul routers are intercon-nected with wireless backhaul links that employ 2×2MIMO transceivers (up to 300 Mb/s nominal datarates). We emulate the traffic flowing from each ofthe eNBs (see Fig. 4), assuming UDP packets with1000 Byte payload. UDP is commonly employed

to tunnel user traffic traveling from eNBs to thecore network. To assess the gains attainable withtraffic forecasting driven routing, we measure thedelay that packets experience from the moment theyare injected into the backhaul at eNB level, untilthey reach the core network. We examine this delaywhen routes are established with a vanilla back-pressure algorithm that makes a posteriori decisions(without any ML logic) to balance queue sizes astraffic arrives. We compare the performance of thisapproach with an enhanced version that makes pathcomputation decisions based on traffic forecastingobtained with D-STN. Decisions are made with thegranularity of one traffic prediction step (1 second),whereas the computation time is dominated by theinference time of the neural network (in the order ofmilliseconds). Fig. 6 shows the cumulative distribu-tion function (CDF) of the delays. Observe that evenin the small topology considered, the median of thepacket delay is 3× smaller with traffic forecastingdriven routing. To appreciate the potential negativeeffects of incorrect traffic forecasts, we also showthe latency performance in the ideal case whereperfect knowledge of future traffic is available (i.e.,no prediction errors). Our approach follows closelythe ideal scenario, as the median of the delay is onlymarginally higher which confirms that the proposedML-based framework can bring substantial perfor-mance benefits in (beyond) 5G mobile networks.

V. CONCLUSIONS

In this paper, we presented a ML-based frame-work to optimize the operation of (beyond) 5Gnetworks. Unlike current approaches that embed MLdirectly within network control systems, our frame-work does not require to design use-case specificML algorithm and change existing network algo-rithms. Our framework instantiates ML pipelines

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to characterize traffic features and predict futuretraffic demands. The predictions are subsequentlyfed into existing network control mechanisms. Ourapproach brings together and harmonizes conceptsfrom ITU-T, 3GPP and other specifications, in a sin-gle comprehensive framework. We showed how ourframework can instantiate multiple ML pipelines fordifferent objectives. We implemented and tested ourframework for one, i.e., proactive routing. Resultsindicate that even in small topologies, our solutionreduces packet delay significantly.

ACKNOWLEDGMENT

This work is partially supported by the MadridRegional Government through the TAPIR-CM pro-gram (S2018/TCS-4496) and the Juan de la Ciervagrant (FJCI-2017-32309). Paul Patras acknowledgesthe support received from the Cisco UniversityResearch Program Fund (2019-197006).

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Claudio Fiandrino (S’14–M’17) is a postdoctoral researcher theWireless Networking Group (WNG) at IMDEA Networks Institute.His primary research interests include ML-driven network optimiza-tion, multi-access edge computing and crowdsensing.

Chaoyun Zhang is a final year Ph.D.student in the School ofInformatics at the University of Edinburgh. His is interested inapplications of deep learning to problems in the computer networkingdomain, including traffic analysis and network control.

Paul Patras (M’11-SM’18) is Reader (Associate Professor) andChancellor’s Fellow in the School of Informatics at the Universityof Edinburgh. His research interests include, mobile intelligence,performance optimization, IoT security and privacy.

Albert Banchs (M’04-SM’12) is currently a Full Professor with theUniversity Carlos III of Madrid (UC3M), and Deputy Director ofthe IMDEA Networks institute. His research interests include theperformance evaluation and algorithm design in wireless and wirednetwork.

Joerg Widmer (M’06–SM’10–F’20) is Research Professor as wellas Research Director of IMDEA Networks in Madrid, Spain. Hisresearch focuses on wireless networks, ranging from extremely highfrequency millimeter-wave communication and MAC layer design tomobile network architectures.