Skip to main content Accessibility help
Hostname: page-component-5d59c44645-mrcq8 Total loading time: 0 Render date: 2024-02-24T22:19:23.242Z Has data issue: false hasContentIssue false

13 - The 5G wireless propagation channel models

Published online by Cambridge University Press:  05 June 2016

Tommi Jämsä
Jonas Medbo
Pekka Kyösti
Anite Telecoms
Katsuyuki Haneda
Aalto University
Leszek Raschkowski
Fraunhofer Heinrich Hertz Institute
Afif Osseiran
Jose F. Monserrat
Universitat Politècnica de València
Patrick Marsch
Mischa Dohler
King's College London
Takehiro Nakamura
NTT DoCoMo Inc.
Get access


5G wireless propagation channel models are crucial for evaluation and comparison of the performance of different technology proposals, and for assessment of the overall performance of the foreseen 5G wireless system. This chapter elaborates on the main challenges of 5G channel modeling and describes the new proposed channel models.

Two different channel-modeling approaches, stochastic and map-based, are detailed. The purpose of the stochastic approach is to extend the traditional well-established WINNER [1] type of modeling for 5G. Some of the 5G requirements may however be hard to meet with stochastic modeling. For that reason, the map-based model, which is based on ray tracing, was also developed [2]. In order to parameterize and evaluate the models, extensive measurement campaigns have been conducted. The detailed description of the METIS channel models can be found in [2].


The envisioned scenarios, use cases and concepts of 5G wireless communications, as described in Chapter 2, set new critical requirements for radio channel and propagation modeling. Some of the more important and fundamental requirements are the support of

  1. • extremely wide frequency ranges from below 1 GHz up to 100 GHz,

  2. • very wide bandwidths (> 500 MHz),

  3. • full 3-dimensional and accurate polarization modeling,

  4. • spatial consistency, i.e. the channel evolves smoothly without discontinuities when the transmitter and/or receiver moves or turns, for supporting highly dense scenarios,

  5. • coexistence of different types of links in the same area such as cellular links with different cell sizes and Device-to-Device (D2D) connections,

  6. • dual-end mobility, i.e. both link-ends move simultaneously and independently, for supporting D2D and Vehicle-to-Vehicle (V2V) connections as well as moving base stations,

  7. • high spatial resolution and spherical waves for supporting very large antenna arrays, massive MIMO and beamforming,

  8. • elevation extension for supporting 3D models and

  9. • specular scattering characteristics especially for high frequencies.

Moreover, the model should provide spatially consistent characteristics for different topologies and between different users. Realistic small-scale fading, for example, would require multiple users to share a common set of scattering clusters.

Currently recognized and widely used channel models, like the 3GPP/3GPP2 Spatial Channel Model (SCM) [3], WINNER [1][4], ITU-R IMT-Advanced [5], 3GPP 3D-UMi and 3D-UMa [6], and IEEE 802.11ad [7], are found to be inadequate for 5G in that they do not meet these requirements [8].

Publisher: Cambridge University Press
Print publication year: 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)


[1] IST-4-027756 WINNER II project, “Channel models,” Deliverable D1.1.2, version V1.2, February 2008.
[2] ICT-317669 METIS project, “METIS Channel Models,” Deliverable D1.4, version v3, July 2015,
[3] 3GPP TR 25.996, “Spatial channel model for multiple input multiple output (MIMO) simulations,” Technical Report TR 25.996 V6.1.0, Technical Specification Group Radio Access Network, September 2003.
[4] CELTIC CP5-026 WINNER+ project, “Final channel models,” Deliverable D5.3, V1.0, June 2010.
[5] International Telecommunications Union Radio (ITU-R), “Guidelines for evaluation of radio interface technologies for IMT-Advanced,” Report ITU-R M.2135, December 2009,
[6] 3GPP TR 36.873, “Study on 3D channel model for LTE,” Technical Report TR 36.873 V12.2.0, Technical Specification Group Radio Access Network, June 2015.
[7] Maltsev, A., Ergec, V. and Perahia, E., “Channel models for 60 GHz WLAN systems,” Document IEEE 802.11-09/0334r8, 2010.
[8] Medbo, J. et al., “Channel modelling for the fifth generation mobile communications,” in European Conference on Antennas and Propagation, The Hague, April 2014.
[9] ICT-317669 METIS project, “Description of the spectrum needs and usage principles,”Deliverable D5.3, September 2014.
[10] Medbo, J. et al., “Directional channel characteristics in elevation and azimuth at an urban macrocell base station,” in European Conference on Antennas and Propagation, Prague, March 2012.
[11] ICT-317669 METIS project, “Initial channel models based on measurements,” Deliverable D1.2, April 2014,
[12] Jämsä, T. and Kyösti, P., “Device-to-device extension to geometry-based stochastic channel models,” in European Conference on Antennas and Propagation, Lisbon, April 2015.
[13] Wang, Z., Tameh, E. K., and Nix, A. R., “A sum-of-sinusoids based simulation model for the joint shadowing process in urban peer-to-peer radio channels,” in IEEE Vehicular Technology Conference, Dallas, September 2005.
[14] Järveläinen, J. and Haneda, K., “Sixty gigahertz indoor radio wave propagation prediction method based on full scattering model,” Radio Science, vol. 49, no. 4, pp. 293–305, April 2014.Google Scholar
[15] Karttunen, A., Jarvelainen, J., Khatun, A., and Haneda, K., “Radio propagation measurements and WINNER II parametrization for a shopping mall at 61–65 GHz,” in IEEE Vehicular Technology Conference, Glasgow, May 2015.
[16] Fan, W., Jämsä, T., Nielsen, J. Ø., and Pedersen, G. F., “On angular sampling methods for 3-D spatial channel models,” IEEE Antennas and Wireless Propagation Letters, vol. 14, pp. 531–534, February 2015.Google Scholar
[17] Jaeckel, S., Raschkowski, L., Börner, K., Thiele, L., Burkhardt, F., and Eberlein, E., “QuaDRiGa: Quasi deterministic radio channel generator, user manual and documentation,” Fraunhofer Heinrich Hertz Institute, Tech. Rep. v1.2.32-458, 2015.

Save book to Kindle

To save this book to your Kindle, first ensure is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the or variations. ‘’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats