machine learning for reliable mmwave systems: blockage … · 1 machine learning for reliable...

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Machine Learning for Reliable mmWave Systems:Blockage Prediction and Proactive Handoff

Ahmed Alkhateeb† and Iz Beltagy‡† Arizona State University, Email: [email protected]

‡ Allen Institute for Artificial Intelligence, Email: [email protected]

Abstract—The sensitivity of millimeter wave (mmWave) signalsto blockages is a fundamental challenge for mobile mmWavecommunication systems. The sudden blockage of the line-of-sight(LOS) link between the base station and the mobile user normallyleads to disconnecting the communication session, which highlyimpacts the system reliability. Further, reconnecting the user toanother LOS base station incurs high beam training overheadand critical latency problems. In this paper, we leverage machinelearning tools and propose a novel solution for these reliabilityand latency challenges in mmWave MIMO systems. In thedeveloped solution, the base stations learn how to predict that acertain link will experience blockage in the next few time framesusing their past observations of adopted beamforming vectors.This allows the serving base station to proactively hand-over theuser to another base station with a highly probable LOS link.Simulation results show that the developed deep learning basedstrategy successfully predicts blockage/hand-off in close to 95% ofthe times. This reduces the probability of communication sessiondisconnection, which ensures high reliability and low latency inmobile mmWave systems.

I. INTRODUCTION

Reliability and latency are two main challenges for millime-ter wave (mmWave) wireless systems [1]–[3]: (i) The highsensitivity of mmWave signal propagation to blockages andthe large signal-to-noise ratio gap between LOS and non-LOSlinks greatly affect the link reliability, and (ii) the frequentsearch for new base stations (BSs) after link disconnectionscauses critical latency overhead [4]. This paper leveragesmachine learning tools to efficiently address these challengesin mobile mmWave systems.

The coordination among multiple BSs to serve the mobileuser has been the main approach for enhancing the reliabilityof mmWave communication links [1]–[3]. In [1], extensivemeasurements were done for coordinated multi-point trans-mission at 73 GHz, and showed that simultaneously servingthe user by a number of BSs noticeably improves the networkcoverage. This coverage performance gain was also confirmedby [2] in heterogeneous mmWave cellular networks usingstochastic geometry tools. To overcome the large trainingoverhead and increase the effective achievable rate in coordi-nated transmissions, especially for highly-mobile applications,[3] proposed to use machine learning tools to predict thebeamforming directions at the coordinating BSs from low-overhead features. Despite the interesting coverage gains ofcoordinated transmission shown in [1]–[3], BSs coordinationis associated with high cooperation overhead and difficultsynchronization challenges.

In this paper, we develop a novel solution that enhancesmmWave system reliability in high-mobile applications with-out requiring the high cooperation overhead of coordinatedtransmission. In our strategy, the serving BS uses the sequenceof beams that it used to serve a mobile user over the past periodof time to predict if a hand-off/blockage is going to happen inthe next few moments. This allows that user and its servingBS to pro-actively hand-over the communication session tothe next BS, which prevents sudden link disconnections dueto blockage, improves the system reliability, and reducesthe latency overhead. To do that, we develop a machinelearning model based on gated recurrent neural networks thatare best suited for dealing with variable-length sequences.Simulation results showed that the proposed solution predictsblockages/hand-off with almost 95% success probability andsignificantly improves the reliability of mmWave large antennaarray systems.

Notation: We use the following notation: A is a matrix,a is a vector, a is a scalar, and A is a set. AT , A∗ are thetranspose and Hermitian (conjugate transpose) of A. [a]n isthe nth entry of a. A ◦B is Hadamard product of A and B.N (m,R) is a complex Gaussian random vector with mean mand covariance R.

II. SYSTEM AND CHANNEL MODELS

In this section, we describe the adopted mmWave systemand channel models. Consider the communication setup inFig. 1, where a mobile user is moving in a trajectory. Atevery step in this trajectory, the mobile user gets connectedto one out of N candidate base stations (BSs). For simplicity,we assume that the mobile user has a single antenna whilethe BS is equipped with M antennas. Extending the resultsof this paper to the case of multi-antenna users is straight-forward. Let hn,k denote the M × 1 uplink channel vectorfrom the user to the nth BS at the kth subcarrier. If the useris connected to the nth BS, this BS applies a beamformingvector fn to serve this user. In the downlink transmission, thereceived signal at the mobile user on the kth subcarrier canthen be expressed as

yk = h∗n,kfns+ v, (1)

where data symbol s ∈ C satisfies E[|s|2]

= P , with P thetotal transmit power, and v ∼ NC

(0, σ2

)is the receive noise at

the mobile user. Due to the high cost and power consumptionof mixed-signal components in mmWave large antenna array

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WHAT STARTS HERE CHANGES THE WORLD(c) 2014 Robert W Heath Jr.

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BS 1

BS n

BS NBlockage

Mobile UserUser Trajectory

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The final goal of training the model is to find the parametersembd,Wr,Wz,Wh,Ur,Uz,Uh,Wf , cr, cz, ch, cf thatminimize this loss for all training instances.

V. SIMULATION RESULTS

h1,k

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VI. CONCLUSION

REFERENCES

[1] F. B. Mismar and B. L. Evans, “Partially blind handovers for mmwavenew radio aided by Sub-6 GHz LTE signaling,” in Proc. of 2012 IEEEICC Workshops, Kansas City, MO, USA, May 2018.

[2] A. Alkhateeb, O. El Ayach, G. Leus, and R. Heath, “Channel estimationand hybrid precoding for millimeter wave cellular systems,” IEEE Journalof Selected Topics in Signal Processing, vol. 8, no. 5, pp. 831–846, Oct.2014.

[3] A. Alkhateeb, J. Mo, N. Gonzalez-Prelcic, and R. Heath, “MIMOprecoding and combining solutions for millimeter-wave systems,” IEEECommunications Magazine,, vol. 52, no. 12, pp. 122–131, Dec. 2014.

[4] R. W. Heath, N. Gonzlez-Prelcic, S. Rangan, W. Roh, and A. M. Sayeed,“An overview of signal processing techniques for millimeter wave MIMOsystems,” IEEE Journal of Selected Topics in Signal Processing, vol. 10,no. 3, pp. 436–453, April 2016.

[5] V. Va, J. Choi, T. Shimizu, G. Bansal, and R. W. Heath, “Inversemultipath fingerprinting for millimeter wave v2i beam alignment,” IEEETransactions on Vehicular Technology, vol. PP, no. 99, pp. 1–1, 2017.

[6] A. Alkhateeb, S. Alex, P. Varkey, Y. Li, Q. Qu, and D. Tujkovic, “Deeplearning coordinated beamforming for highly-mobile millimeter wavesystems,” IEEE Access, pp. 1–1, 2018.

[7] K. Cho, B. Van Merrienboer, D. Bahdanau, and Y. Bengio, “On theproperties of neural machine translation: Encoder-decoder approaches,”in Proc. of SSST, 2014.

[8] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”arXiv preprint arXiv:1412.6980, 2014.

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h1,k

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VI. CONCLUSION

REFERENCES









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h1,k

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Fig. 4.

VI. CONCLUSION

REFERENCES









Fig. 1. The system model considers one user moving in a trajectory, and isserved by one out of N candidate BSs at every step in the trajectory.

systems, beamforming processing is normally done in theanalog domain using networks of phase shifters [5]. Theconstraints on these phase shifters limit the beamformingvectors to be selected from quantized codebooks. Therefore,we assume that the BS beamforming vector fn is selected froma quantized codebook F with size/cardinality |F| = MCB.The codewords of this codebook are denoted as gm,m =1, 2, ...,MCB. Further, we assume that the beamforming vectorfn is selected from the codebook F to maximize the receivedsignal power, i.e., according to the criterion

fn = arg maxgm∈F

∑

k

|h∗n,kgm|2. (2)

We adopt a wideband geometric mmWave channel model[4] with L clusters. Each cluster `, ` = 1, ..., L is assumedto contribute with one ray that has a time delay τ` ∈ R, andazimuth/elevation angles of arrival (AoA) θ`, φ`. Further, letρn denote the path-loss between the user and the n-th BS,and prc(τ) represents a pulse shaping function for TS-spacedsignaling evaluated at τ seconds. With this model, the delay-dchannel between the user and the nth BS follows

hn,d =

√M

ρn

L∑

`=1

α`p(dTS − τ`)an (θ`, φ`) , (3)

where an (θ`, φ`) is the array response vector of the nth BSat the AoAs θ`, φ`. Given the delay-d channel in (3), thefrequency domain channel vector at subcarrier k, hk,n, canbe written as

hn,k =

D−1∑

d=0

hd,ne−j 2πk

K d. (4)

Considering a block-fading channel model, {hk,n}Kk=1 areassumed to stay constant over the channel coherence time,denoted TC [6] .

III. PROBLEM DEFINITION AND FORMULATION

Maintaining good link reliability is a key challenge formmWave communication systems, especially with mobility.This is mainly due to the high sensitivity of mmWave signals

to blockages, which can frequency cause link disconnections.Further, when the link to the current BS is blocked, themobile user incurs critical latency overhead to get connectedto another BS. To overcome these challenges, can we predictthat a link blockage is going to happen in the next fewmoments? Successful blockage prediction can be very helpfulfor mmWave system operation as it allows for proactive hand-off to the next BS. This proactive hand-off enhances thesystem reliability by ensuring session continuity and avoids thelatency overhead that results from link disconnection. In thissection, we formulate the mmWave blockage prediction andproactive hand-off problem that we tackle in the next section.

Beam sequence and hand-off status: To formulate theproblem, we first define two important quantities, namelythe beam sequence and the hand-off status. Due to the usermobility, the current/serving BS needs to frequently updateits beamforming vector fn ∈ F . The frequency of updatingthe beams depends on a number of parameters including theuser speed and the beam width. A good approximation for theperiod every which the BS needs to update its beam is thebeam coherence time, TB, which is defined as [6]

TB =D

vs sin(α)

Θn

2, (5)

where vs denotes the user speed, D is the distance betweenthe user and the scatterer/reflector (or the BS in the case ofLOS), α is the angle between the direction of travel and thedirection of the main scatterer/reflector (or the BS in the caseof LOS), and Θn defines the beam-width of the beams used byBS n. Now, assuming that the current/serving BS n updates itsbeamforming vector every beam-coherence time and calling ita time step, we define f

(t)n as the beamforming vector selected

by the nth BS to serve the mobile user in the tth time step,with t = 1 representing the first time step after the handing-over to the current BS. With this, we define the beam sequenceof BS n until time step t, denoted Bt as

Bt ={f (1)n , f (2)n , ..., f (t)n

}. (6)

Further, we define st ∈ {1, 2, ..., N}, as the hand-off statusat the tth time step, with st = n indicating the user willwill stay connected to the current BS n in the next time step,and st 6= n indicating that the mobile user will hand-off toanother BS in the t + 1 time step. It is important to notehere that predicting a hand-off in the next time step is moregeneral that predicting a blockage, as the hand-off can happendue to a sudden blockage or a better SNR. Therefore, we willgenerally adopt the hand-off prediction that implicitly includelink blockage prediction.

In this paper, we will focus on stationary blockages, whichleaves the user mobility as the main factor of the time-varyingbehavior in the channel. To complete the formulation, weleverage the note that the beamforming vector that maximizesthe received signal power, as defined in (2), heavily relies onthe user location and the surrounding environment geometry(including blockages) [3]. Therefore, we formulate the prob-lem as a prediction of the hand-off status at time t + 1,i.e., st, given the beam-sequence at time t, Bt. Formally,

3

the objective of this paper is to maximize the probability ofsuccessful blockage/hand-off prediction defined as

P [st = st |Bt ] . (7)

In this next section, we leverage machine learning tools toaddress this problem.

IV. DEEP LEARNING BASED PROACTIVE HAND-OFF

In this section, we explain our proposed solution that usesdeep learning tools, and more specifically recurrent neuralnetworks, to efficiently predict hand-off/blockages. First, wehighlight the key idea and system operation before delvinginto the exact machine learning modeling in Section IV-B.

A. Main Idea and System Operation

In this subsection, we briefly describe the key idea ofthe proposed solution as well as the learning/communicationssystem operation. We model the problem of predicting hand-off/blockage as a sequence labeling problem. In summary,given the sequence of previous beams Bt, the serving BSpredict the most likely base station that the user will connectwith in the next time step. If the predicted base station isdifferent from the current one, that indicates that a proactivehand-off needs to happen. As we mentioned in Section III, byadopting the problem of predicting the hand-off, our systemwill also predict link blockages if they are going to happen inthe next time step as they will require a hand-off.

System operation: The proposed learning/communicationsystem operates in two phases. In the first phase (learning),the mmWave communication system operates as normal: Atevery beam coherence time, the current BS will update itsbeamforming vector that serves the user. If the link is blocked,the user will follow the initial access process to hand-off toa new BS. During this process, the serving BS will feed thebeam sequence Bt and the hand-off status st at every time step(beam coherence time) to its machine learning model that willuse it for training. It is important to note here that these beamsequences have different lengths depending on the speed ofthe user, its trajectory, the time period it is spent connectedto this BS, etc. As will be explained shortly, We designed ourdeep learning model to carefully handle this variable sequencelength challenge.

After the machine learning model is well-trained, the serv-ing BS will leverage it to predict if the link to the user willface a blockage/hand-off in the next time-step. If a hand-off is predicted, the user and its serving BS will pro-activelyinitiate the hand-off process to the next BS to ensure sessioncontinuity and avoid latency problems.

B. Machine Learning Model

Next, we describe the key elements of the proposed machinelearning model which is based on recurrent neural networks.

Input representation: At every time step t, the input toour deep learning model is the tth beam index in the beamsequence Bt. Let bt ∈ {1, 2, ...,MCB} denote the index of the


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Fig. 2. The proposed deep learning model that leverages recurrent neuralnetworks to predict the hand-off BS in the next time step, st, given the pastbeam sequence Bt

Further, we define st 2 {0, 1}, as the hand-off status at thetth time step, with st = 1 indicating the user will hand-off toanother BS in the t + 1 time step, and st = 0 indicating thatthe mobile user will stay connected to the current BS in thenext time step.

In this paper, we will focus on stationary blockages, whichleaves the user mobility as the main factor of the time-varyingbehavior in the channel. To complete the formulation, weleverage the note that the beamforming vector that maximizesthe received signal power, as defined in (2), heavily relies onthe user location and the surrounding environment geometry(including blockages) [6]. Therefore, we formulate the prob-lem as a prediction of the hand-off state at time t+1, i.e., st,given the beam-sequence at time t, Bt. Formally, the objectiveof this paper is to maximize the probability of successfulblockage/hand-off prediction defined as

P [st = st |Bt ] . (7)




We model the problem of predicting blockage and reconnectto base stations as a sequence labeling problem. Given asequence of beams, predict the most likely base station toconnect with in the next time step. At every time step, ourmodel has access to the history of beams, and it uses that topredict the base station. If the predicted base station is differentfrom the current one, that indicates a blockage.

XXX note on variable length sequence



Input representation: At every time step t, the input toour deep learning model is the tth beam index in the beamsequence Bt. Let bt 2 {1, 2, ..., MCB} denote the index of the

tth beam index, then our model starts with an embedding layerthat maps every beam index bt to a vector xt.

xt = embd (bt) (8)

where embd is a lookup table we learn during the training.The main value of this layer is to ... XXX.

Sequence processing: The central component of our modelis a Gated Recurrent Unit (GRU) [7] — a form of neuralnetworks that is best suited for processing variable lengthsequnces. GRU is a recursive network that runs at every timestep of the input, and it maintains a hidden state qt whichis a function of the previous state and the current input. Inaddition, it has a special gating mechanism that helps with longsequences. More formally, GRU is implemented as depictedin Fig. 2, and is described by the following model equations

rt = �g (Wrxt + Urqt�1 + cr) (9)zt = �g (Wzxt + Uzqt�1 + cz) (10)qt = (1 � zt) � qt�1

+ zt � �h (Wqxt + Uq (rt � qt�1) + cq) , (11)

where xt is the input vector, qt is the hidden state, rt and zt

are ”gates” that help the model learn from long distance de-pendencies if needed. The weight matrices Wr, Wz,Ur,Uz

and the bias vectors cr, cz, cq are model parameters that arelearned during the machine learning training phase. Finally, �g

and �h are nonlinear activation functions, the first is sigmoidand the second is tanh. Using non linear activation functionsallows the model to represent complex non-linear functions.

Output: At every time step t, the model has a hiddenstate qt which encompasses what the model learned aboutthe sequence of beams until time step t, and the next stepis to use qt to predict the most likely base station st forthe next time step. For this, we use a fully connected layerwith output size N equals number of possible base stations,followed by a softmax activation that normalizes the outputsinto probabilities (they sum up to 1). The predicted hand-offBS (the final output of the model) can then be expressed as

st = arg maxn2{1,2,...,N}

softmax (Wfqt + cf)n (12)

where Wf and cf are the weights and biases of the fully-connected layer. The softmax(.)n function takes a vector oflength N and outputs the probability that the nth BS is goingto be the BS of the next time step t + 1. The softmax(.)n

function is defined as follows:

softmax(a)n =ean

PNd=1 ead

. (13)

Training: The training objective in our supervised learningmodel is minimizing the cross entropy loss between our modelpredictions st and the actual base station of the following timestep st. We compute this loss at every time step t then sumover all time steps. The cross entropy loss at every time stept is computed as follows:

loss(st, st) = �X

i

sit log si

t. (14)

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h1,k

hN,k

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0 2 4 6 8 10 12 14 16 18


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Fig. 3.

Fig. 4.

VI. CONCLUSION

REFERENCES











xt = embd (bt) (8)

where embd is a lookup table we learn during the training.Sequence processing: The central component of our model

is a Gated Recurrent Unit (GRU) [7] — a form of neuralnetworks that is best suited for processing variable lengthsequnces. GRU is a recursive network that runs at every timestep of the input, and it maintains a hidden state qt whichis a function of the previous state and the current input. Inaddition, it has a special gating mechanism that helps with longsequences. More formally, GRU is implemented as depictedin Fig. 2, and is described by the following model equations

rt = σg (Wrxt + Urqt−1 + cr) (9)zt = σg (Wzxt + Uzqt−1 + cz) (10)qt = (1− zt) ◦ qt−1

+ zt ◦ σq (Wqxt + Uq (rt ◦ qt−1) + cq) , (11)

where xt is the input vector, qt is the hidden state, rt and ztare ”gates” that help the model learn from long distance de-pendencies if needed. The weight matrices Wr, Wz,Ur,Uz

and the bias vectors cr, cz, cq are model parameters that arelearned during the machine learning training phase. Finally, σgand σq are nonlinear activation functions, the first is sigmoidand the second is tanh. Using non linear activation functionsallows the model to represent complex non-linear functions.


st = arg maxn∈{1,2,...,N}


where Wf and cf are the weights and biases of the fully-connected layer. The softmax(.)n function takes a vector of

4

BS 1

BS 2

Blockage

Candidate User

Trajectories

Fig. 3. This figure illustrates the considered simulation setup where twocandidate BSs, each has ULA, serve one vehicle moving in a street.

length N and outputs the probability that the nth BS is goingto be the BS of the next time step t + 1. The softmax(.)nfunction is defined as follows:

softmax(a)n =e[a]n

∑Nd=1 e

[a]d. (13)


loss(st, st) = −∑

i

[p]i,t log [p]i,t. (14)

where the reference prediction vector p has 1 in the entrycorresponding to the index of the correct BS in st, and zerootherwise. Further, the model prediction vector p has the dthentry equals to softmax(a)d,∀d.The final goal of training the model is to find the parametersembd,Wr,Wz,Wh,Ur,Uz,Uh,Wf , cr, cz, ch, cf thatminimize this loss for all training instances.


In this section, we evaluate the performance of the proposeddeep-learning based proactive hand-off solution.

Simulation setup: We adopt the mmWave system andchannel models in Section II, with two candidate BSs toserve one vehicle/mobile user moving in a street, as de-picted in Fig. 3. To generate realistic data for the channelparameters (AoAs/delay/etc.), we use the commercial ray-tracing simulator, Wireless InSite [8], which is widely usedin mmWave research [6], [9], and is verified with channelmeasurements [9]. Each BS is installed on one lamp post atheight 4 m, and employs a 32-element uniform linear array(ULA) facing the street. The mobile user is moving in straight-line trajectories in the street, that can be any where of thestreet width, and with maximum trajectory length of 160m. The trajectory starting point in randomly selected fromthe first 40m of the street, and the user speed is randomlyselected from {8, 16, 24, 32, 40} km/hr. The BS selects itsbeamforming vector from a uniformly quantized beamsteeringcodebook with an oversampling factor of 4, i.e. the codebooksize is MCB = 128. At every beam coherence time, the BS

0 2 4 6 8 10 12 14 16 18


0.55

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Blo

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Fig. 4. This figure shows that the proposed deep-learning proactive hand-offsolution successfully predicts blockages/hand-off with high probabilities whenthe machine learning model is well-trained.

beamforming vector is updated to maximize the receive SNRat the user. During the uplink training, the MS is assumed touse 30dBm transmit power, and the noise variance correspondsto 1GHz system bandwidth. The system is assumed to operateat 60GHz carrier frequency.

We consider the deep learning model described in Sec-tion IV-B. The neural network model has an embedding thatoutputs vectors of length 20 to the GRU unit, with maximumsequence length of 454. Since we have only 2 BSs in outexperiment, the fully-connected layer has only two outputsthat go to the softmax function. We use the Adam optimizer[10]. In the deep learning experimental work, we used theKeras libraries [11] with a TensorFlow backend.

Hand-off/Blockage prediction: To evaluate the perfor-mance of the proposed deep-learning based proactive hand-off solution, Fig. 4 plots the blockage/hand-off successfulprediction probability, defined in (7), versus the training size.Fig. 4 shows that with sufficient dataset size (larger than 12thousand samples), the machine learning model successfullypredicts the hand-off with more than 90% probability, givenonly the sequence of past beams Bt. This illustrates thepotential of the proposed solution in enhancing the reliabilityof next-generation mmWave systems.

VI. CONCLUSION

In this paper, we proposed a novel solution for the reliabilityand latency problem in mobile mmWave systems. Our solutionleveraged deep learning tools to efficiently predict blockageand the need for hand-off. This allows the mobile user toproactively hand-off to the next BS without disconnecting thesession or suffering from high latency overhead due to suddenlink disconnections. The simulation results showed that thedeveloped proactive hand-off strategy can successfully predictsblockage/hand-off with high probability when the machinelearning model is trained with reasonable dataset sizes. In thefuture, it is interesting to extend the developed solution tomulti-user systems and account for mobile blockages.

5

REFERENCES

[1] G. R. MacCartney, T. S. Rappaport, and A. Ghosh, “Base stationdiversity propagation measurements at 73 GHz millimeter-wave for5G coordinated multipoint (CoMP) analysis,” in 2017 IEEE GlobecomWorkshops (GC Wkshps), Dec 2017, pp. 1–7.

[2] D. Maamari, N. Devroye, and D. Tuninetti, “Coverage in mmwavecellular networks with base station co-operation,” IEEE Transactions onWireless Communications, vol. 15, no. 4, pp. 2981–2994, April 2016.


[4] R. W. Heath, N. Gonzlez-Prelcic, S. Rangan, W. Roh, and A. M. Sayeed,“An overview of signal processing techniques for millimeter waveMIMO systems,” IEEE Journal of Selected Topics in Signal Processing,vol. 10, no. 3, pp. 436–453, April 2016.




[8] Remcom, “Wireless insite,” http://www.remcom.com/wireless-insite.[9] Q. Li, H. Shirani-Mehr, T. Balercia, A. Papathanassiou, G. Wu, S. Sun,

M. K. Samimi, and T. S. Rappaport, “Validation of a geometry-basedstatistical mmwave channel model using ray-tracing simulation,” in 2015IEEE 81st Vehicular Technology Conference (VTC Spring), May 2015,pp. 1–5.


[11] F. Branchaud-Charron, F. Rahman, and T. Lee, “Keras.” [Online].Available: https://github.com/keras-team

http://www.remcom.com/wireless-insite

http://arxiv.org/abs/1412.6980

https://github.com/keras-team

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