Scalable and accurate channel models for analysis …[3GPP] 3GPP, “Study on channel model for frequency spectrum above 6 GHz (Release 14) “, TR 38.900 V14.2.0, Dec. 2016. [NYU]

• Quasi-Deterministic Channel Models

• Spatial Channel Models

• Analytical Channel Models

Scalable and accurate channel models for analysis and large-scale simulations at mmWaves

Mattia Lecci, Paolo Testolina, Michele Polese, Marco Giordani, Michele ZorziUniversity of Padova – Padova, Italy – [email protected]

mmWave Networks Simulations

mmWave Channel Models

Channel Models Comparison

Visit mmWave.dei.unipd.it to know more on our research on mmWave networks

Channel model

Application and network stack

3GPP cellular stack 3GPP cellular stack

Application and network stack

Packet

Propagation

Fading Beamforming

InterferenceSINR

Error model

Channel model is key for wireless system level simulationsAccuracy - Complexity

mmWave frequencies introduce new challenges for channel modeling:• Beamforming and MIMO with many antenna elements• Rapid channel variations due to LOS/NLOS transitions• Sparsity in the angular domain

Pros:• Simple and widely-used for analytical papers on mmWaves• Rayleigh, Nakagami-m, etc

Cons:• Non-geometric model• Usually coupled with simple sectorized beamforming model

Main lobeBacklobe

SCM Text Description SCM-132

15

AoAmn ,,D Offset for the mth (m = 1 … M) subpath of the nth path with respect to AoAn,d . 1

AoAmn ,,q Absolute AoA for the mth (m = 1 … M) subpath of the nth path at the MS with respect 2

to the BS broadside. 3 4

v MS velocity vector. 5

vq Angle of the velocity vector with respect to the MS broadside: vq =arg(v). 6

7

The angles shown in Figure 1 that are measured in a clockwise direction are assumed to be 8

negative in value. 9

BSq

AoDn,d

, ,n m AoDD

AoDmn ,,q

BSW

N

NCluster n

AoAmn ,,q

, ,n m AoAD

,n AoAd

MSW

MSq

qv

BS array broadside

MS array broadside

BS array

MS directionof travel

MS array

Subpath m

v

10

Figure 3-2. BS and MS angle parameters 11

12

For system level simulation purposes, the fast fading per-path will be evolved in time, although 13

bulk parameters including angle spread, delay spread, log normal shadowing, and MS location 14

will remain fixed during the its evaluation during a drop. 15

The following are general assumptions made for all simulations, independent of environment: 16

1. Uplink-Downlink Reciprocity: The AoD/AoA values are identical between the uplink and 17

downlink. 18

2. For FDD systems, random subpath phases between UL, DL are uncorrelated. (For TDD 19

systems, the phases will be fully correlated.) 20

3. Shadowing among different mobiles is uncorrelated. In practice, this assumption would 21

not hold if mobiles are very close to each other, but we make this assumption just to 22

simplify the model. 23

4. The spatial channel model should allow any type of antenna configuration (e.g. whose size 24

is smaller than the shadowing coherence distance) to be selected, although details of a 25

given configuration must be shared to allow others to reproduce the model and verify the 26

results. It is intended that the spatial channel model be capable of operating on any given 27

antenna array configuration. In order to compare algorithms, reference antenna 28

configurations based on uniform linear array configurations with 0.5, 4, and 10 wavelength 29

inter-element spacing will be used. 30

3GPP TR 25.996 - V14.0.0, Spatial channel model for Multiple Input Multiple

Output (MIMO) simulations

Cons:• Compute a channel matrix with ! × # × $ elements• Fading is computationally intensive• Cannot be used for analysis

Number of TX antennasNumber of RX antennas

Number of clusters

Pros:• Model complex interactions – interaction with beamforming vectors• Chosen by 3GPP for system level evaluation of 5G networks

Open issues and limitations

TCP experiment: Nakagami-m vs. 3GPP Cellular Model (from [1])

• 3 mmWave gNBs• 1 sub-6 GHz LTE eNB• 1 user moving across

the scenario with handovers

• Similar trend for throughput

• Latency diverges asRLC buffer size increases

[1] M. Polese and M. Zorzi, "Impact of Channel Models on the End-to-End Performance of Mmwave Cellular Networks," IEEE SPAWC 2018.[2] A. K. Gupta et al, “On the feasibility of sharing spectrum licenses in mmwave cellular systems,” IEEE Trans. Commun., vol. 64, no. 9, Sep 2016.

[2]

Vehicular experiment: 3GPP Vehicular Model vs. others

• Urban sidelink pathloss• Comparison of different

combinations ofo LOS pathlosso NLOSv pathloss (from vehicles)o NLOSb pathloss (other blockages)

• Clearly distinguish twopathloss groups

• Two classes of models identified:o Pessimistico Optimistic

0 100 200 300 400 500

80

100

120

140

160

V2V distance [m]

Path

loss

[dB

]

3GPP_V/HUA_V/HUA_V 3GPP_C/HUA_V/HUA_Bck3GPP_V/HUA_Bck/3GPP_C 3GPP_V/3GPP_V/3GPP_V

Figure 10: NLOSb path loss vs. inter-vehicle distance d for urban and highway scenarios in a medium-traffic density environment. Option A dropping option isapplied.

• It is interesting to notice that, although referred to different scenarios and channel characterizations, all theconsidered combinations of models show similar trends.

• In case of urban propagation, the path loss characterization obtained when the 3GPP_V model is applied liesin the middle of the curves referred to all the other combinations of models, thereby validating the accuracyand the validity of the 3GPP_V measurements.

• In case of highway propagation, we clearly distinguish two path loss groups, identifying a lower and a upperbound for the sidelink path loss, respectively. The 3GPP_V model is one of the conservative models.

Finally, in Fig. 11, we investigate the impact of different traffic density values when applying the path losscharacterization defined in [1].

• As observed in Fig. 3, the higher the vehicle density, the more probable the NLOS state and, therefore, thelarger the overall sidelink path loss. Nevertheless, such difference is minimal (i.e., in the order of 2-3 dB) and,in case of urban propagation, only affects short-range communications.

• As expected, the urban path loss is significantly larger than its highway counterpart (although the waveguideeffect provoked by the more likely signal reflections and scattering from walls of buildings in street canyonsgenerally results in reduced attenuation) due to the much larger probability of blockage intersection.

REFERENCES

[1] 3GPP, “Study on evaluation methodology of new Vehicle-to-Everything V2X use cases for LTE and NR (Release 15),” TS 37.885, 2018.[2] 3GPP, “V2X sidelink channel model,” Huawei, HiSilicon – Tdoc R1-1803671, 2018.[3] 3GPP, “Study on channel model for frequencies from 0.5 to 100 GHz (Release 14),” TR 38.901, 2018.[4] 3GPP, “V2X sidelink measurement results,” Huawei, HiSilicon – Tdoc R1-1801398, 2018.[5] A. Yamamoto, K. Ogawa, T. Horimatsu, A. Kato, and M. Fujise, “Path-loss prediction models for intervehicle communication at 60 GHz,”

IEEE Transactions on Vehicular Technology, vol. 57, no. 1, pp. 65–78, Jan 2008.[6] M. Boban, X. Gong, and W. Xu, “Modeling the evolution of line-of-sight blockage for V2V channels,” in IEEE 84th Vehicular Technology

Conference (VTC-Fall). IEEE, 2016.[7] V. Va, T. Shimizu, G. Bansal, and R. W. Heath, “Millimeter wave vehicular communications: A survey,” Foundations and Trends R� in

Networking, vol. 10, no. 1, pp. 1–113, 2016. [Online]. Available: http://dx.doi.org/10.1561/1300000054[8] M. Boban, T. T. V. Vinhoza, M. Ferreira, J. Barros, and O. K. Tonguz, “Impact of Vehicles as Obstacles in Vehicular Ad Hoc Networks,”

IEEE Journal on Selected Areas in Communications, vol. 29, no. 1, pp. 15–28, January 2011.[9] D. Krajzewicz, J. Erdmann, M. Behrisch, and L. Bieker, “Recent development and applications of SUMO - Simulation of Urban MObility,”

International Journal On Advances in Systems and Measurements, vol. 5, no. 3&4, pp. 128–138, December 2012.

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[3GPP_V] 3GPP, “Study on evaluation methodology of new Vehicle-to-Everything V2X use cases for LTE and NR (Release 15),” TS 37.885, 2018.[HUA_V] 3GPP, “V2X sidelink channel model,” Huawei, HiSilicon – Tdoc R1-1803671, 2018.[HUA_Bck] 3GPP, “V2X sidelink measurement results,” Huawei, HiSilicon – Tdoc R1-1801398, 2018. [3GPP_C} 3GPP, “Study on channel model for frequencies from 0.5 to 100 GHz (Release 14),” TR 38.901, 2018.

LOS NLOSv NLOSb

Pros:• In the right conditions, proven to be extremely accurate

Cons:• Need a detailed model of the environment• Difficult to code and debug• Computationally extremely demanding

Performance comparison of 3GPP-compliant model (TR 38.900)

between MATLAB custom implementation and ns-3

Profiling highlighting the portion of time taken by pure sums/products to create the H

matrix, sampling of random variables, and other operations plus functions’ overhead

Cellular experiment: 3GPP Cellular Model vs. NYU Channel Model

[3GPP] 3GPP, “Study on channel model for frequency spectrum above 6 GHz (Release 14) “, TR 38.900 V14.2.0, Dec. 2016.[NYU] Akdeniz, Mustafa Riza, et al, "Millimeter wave channel modeling and cellular capacity evaluation”, in IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1164-1179, June 2014.

Comparison of mean SINR [dB] obtained with different deployment parameters

(antenna array size, antenna element spacings, downtilt)

Mean SINR [dB] CDF obtained with fixed deployment parameters and different channel models.

• Second order statistics, such as the temporal and spatial autocorrelation, are not considered in the vast majority of the geometry based channel models (GSCMs).

• This limits their applicability to dynamic scenarios, e.g., for V2V communications, where a complete model for the temporal evolution of the channel is still missing.

• The role played by ground reflection is often underestimated, especially in mobile and vehicular settings.

• Measurements campaigns generally employ horns antennas to simulate large, highly directive beamforming arrays, thus ignoring specific issues concerning large arrays.

• The impact of the aforementioned limitations and approximations is often not clear, and that strongly calls for further investigation and validation.Work partially supported by NIST under Award No. 70NANB18H273 ("Channel abstractions and modeling

approaches with scalable accuracy and complexity for mmWave wireless communication systems")