Mikko Valkama et al.Tampere University (TAU), Finland

mikko.valkama@tuni.fi

Joint Radio-based Sensing and Communications in 5G and Beyond: Prospects, Algorithms and Waveform

Optimization

.. shortly about myselfEducation• M.Sc. (Tech.) and D.Sc. (Tech.) degrees in Electrical Engineering at former

Tampere University of Technology, Finland, in 2000 and 2001, respectively

Current posts• Professor of Communications Engineering and Radio Systems at Tampere University (TAU), Finland, since 2008• Founder and Chairman of Wireless System Engineering Finland Ltd, established 2012

Research areas, competence and some numbers• Development of new methods, concepts and algorithms for radio communications,

radio positioning and radio-based sensing/radars – all with signal processing flavor• 5G and beyond mobile radio networks is one central umbrella• Supervised so far 22 doctoral students and 130+ masters students, all in

communications engineering field• Published 500+ scientific referee articles, raised 15+ MEUR external research funding

Personal• Happily married, proud father of 4 sons (Tuukka, Veikka, Joona, and Onni)

Outline• Some general trends in wireless

• OFDM radar and 5G/6G

• TX-RX isolation and self-interference in monostatic OFDM radars

• JCAS – beamforming optimization

• JCAS – waveform optimization

Big thanks to following colleagues: Carlos Baquero Barneto, Sahan D. Liyanaarachchi, Matias Turunen, Dr. Jukka Talvitie, Dr. Lauri Anttila, Dr. Mikko Heino and Assist. Prof. Taneli Riihonen – Thanks Folks !

Some General Trends in Wireless Communications

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5G NR and future 6G networks:• Peak data rates• Network capacity• Number of connected devices• Radio access latency & reliability• …• Positioning and sensing

RF convergence• Joint communications and

sensing (JCAS) systems• Sharing same transmit waveforms

and even hardware platforms• Large interest also in mobile cellular

networks

In this presentation, I’m providing snap-shot examples of our recent research in Tampere, towards facilitating high-efficiency JCAS/sensing capabilities in 5G/6G networks. Also trying to provide example recent articles for further reading.

Example: TDD 5G NR base-station as monostatic OFDM radar

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Downlink Signal

Reflections

• Using NR Base Station (gNB) also as monostatic radar, while transmitting the downlink signal

• Merging Communication and Radar functionalities to same frequency bands and potentially to same hardware platforms

• Appealing from the sensing performance point of view since the complete IQ transmit waveform is known

• Further opportunities at UE side and/or through distributed processing

gNBgNB

Communication Sensing

Autonomous vehicles networks Unmanned aerial vehicle (UAV)

Building analytics Digital health monitoring

Search and tracking of potential usersSearch potential reflectors in case of NLOS

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OFDM Radar Resolution and 5G NR / 6G Waveform

C. Baquero Barneto, M. Turunen, S. D. Liyanaarachchi, L. Anttila, A. Brihuega, T. Riihonen, and M. Valkama, “High-accuracy radio sensing in 5G New Radio networks: Prospects and self-interference challenge,” in Proc. Asilomar Conference on Signals, Systems, and Computers, Nov. 2019

Excellent prospects, especially in NR long term evolution and 6G/sub-THz systems with 1-10 GHz scalechannel bandwidths: even cm level resolution !

∆𝑑 = 𝑐2𝐵∆𝑣 = 𝑐2𝑇𝑓

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Mono-static OFDM radar and self-interferenceRADAR PROCESSING RADAR IMAGE WITH LOW SI

RADAR IMAGE WITH HIGH SI• In OFDM radar, the receiver must be operating simultaneously while transmitting

• otherwise no targets within tens of kilometers could be detected• implementation challenge: sufficient transmitter–receiver isolation

• Essentially Inband Full-Duplex Radio• direct TX-RX leakage can be interpreted as a strong static target at a very very

short distance• powerful SI component can largely mask the true echoes and targets• particularly those that are static, but also other slowly moving targets• efficient isolation mechanisms needed, though not as high as in actual two-way comms

C. Baquero Barneto, T. Riihonen, M. Turunen, L. Anttila, M. Fleischer, K. Stadius, J. Ryynänen, and M. Valkama, “Full-duplex OFDM radar with LTE and 5G NR waveforms: Challenges, solutions, and measurements,” IEEE Trans. Microw. Theory Techn., vol. 67, no. 10, pp. 4042–4054, Oct. 2019

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C. Baquero Barneto, T. Riihonen, M. Turunen, L. Anttila, M. Fleischer, K. Stadius, J. Ryynänen, and M. Valkama, “Full-duplex OFDM radar with LTE and 5G NR waveforms: Challenges, solutions, and measurements,” IEEE Trans. Microw. Theory Techn., vol. 67, no. 10, pp. 4042–4054, Oct. 2019

Mono-static OFDM radar, example measurements

• 40 MHz NR waveform @ 2.4 GHz ISM band• 1200 sub-carriers, SCS 30 kHz• 10 ms radio frame

• Transmission power +20 dBm• Dual antenna setup

• Tx: Horn antenna (10 dBi)• Rx: Horn antenna (10 dBi)

• Antenna isolation is ~40 dB• Inhouse active analog/RF cancellation ~40 dB• Inhouse digital cancellation ~35 dB

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JCAS:some selected challenges and opportunities

Simultaneous transmission and reception• From the sensing perspective, TX and RX need to

operate simultaneously (OFDM radar concept)• Self-interference (SI) challenge, noted already earlier

Beamforming design and optimization• Separate simultaneous beams for communication and

sensing• TX and RX beamforming optimization taking both comms

and sensing capabilities, as well as the TX-RX self-interference into account

Waveform optimization• OFDM/multicarrier waveforms with empty subcarriers

=> optimize waveform by considering both communication and sensing performance

C. Baquero Barneto, S. D. Liyanaarachchi, M. Heino, T. Riihonen, and M. Valkama, “Full-duplex radio/radar technology: The enabler for advanced joint communication and sensing,” IEEE Wireless Communications, 2021, in press.

C. Baquero Barneto, S. D. Liyanaarachchi, M. Heino, T. Riihonen, and M. Valkama, “Joint Multi-User Communication and MIMO Radar Through Full-Duplex Hybrid Beamforming,” IEEE Symposium on Joint Communications and Sensing, 2021, submitted.

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Multi-beam optimization - exampleEffective TX pattern (baseband + RF)

Subarrays’ RX patterns (RF)

TX-RX self-interference suppression

TX and RX beamforming weights are optimized to provide multiple beams for communications and simultaneously a dedicated beam for sensing, while also supressing the SI leakage

C. Baquero Barneto, S. D. Liyanaarachchi, T. Riihonen, L. Anttila, and M. Valkama, “Multibeam design for joint communication and sensing in 5G New Radio networks,” in Proc. International Conference on Communications, June 2020.

C. Baquero Barneto, S. D. Liyanaarachchi, M. Heino, T. Riihonen, L. Anttila and M. Valkama, “Beamforming and waveform optimization for OFDM-based joint communications and sensing at mm-waves,” in Proc. Asilomar Conference on Signals, Systems, and Computers, Nov. 2020

Design of Phased Array Architectures forFull-Duplex Joint Communications and Sensing

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• The self-interference is suppressed by optimizing the RX and TX weights, while providing communication and radar beams to desired directions

• The self-interference coupled to the RX array is correlated with the TX weights

• With certain TX communication and radar directions and array geometries, it turns out that it is not possible to cancel the SI and maintain beam integrity Happens when sensing direction is zero or when

sensing and comms directions are mirrors of each other Degrades the sensing performance

• Antenna array design can be used to avoid this problem

TX array

RX array

Solution to the mirror beam problem

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M. Heino, C. Baquero Barneto, T. Riihonen, M. Valkama, “Design of Phased Array Architectures for Full-Duplex Joint Communications and Sensing”, European Conference on Antennas and Propagation (EuCAP), 2021, accepted

• An example illustration on right (top), in case when the sensing and comms beam directionsare mirrors of each other

• The problem of mirror beams can be solved byrandomizing the coupling delay betweenindividual RX/TX antenna pairs in the arraydesign Increases the phase variance of the coupled SI

and enables the RX beamformer optimizationto suppress the SI without degrading the RX beam pattern

Illustrated on the right

TXRX

TXRX

Undesired mirror beam in RX pattern

Sensing and Indoor Mapping with mmWave 5G NR Signals

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User-centric 5G mm-wave indoor mapping system

• A moving UE senses the surrounding environment by steering its TX and RX beam patterns towards different directions synchronously

• 5G NR uplink waveform• Simultaneous localization and mapping (SLAM) type

post-processing

C. Baquero Barneto, T. Riihonen, M. Turunen, M. Koivisto, J. Talvitie, and M. Valkama, “Radio-based sensing and indoor mapping with millimeter-wave 5G NR signals,” in Proc. International Conference on Localization and GNSS, June 2020

(a) mm-wave measurement setup at 28 GHz (b) Indoor measurement scenario

Corridor walls

Sensing and Indoor Mapping with mmWave 5G NR Signals

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Raw radar/sensing processing Final indoor map

C. Baquero Barneto, T. Riihonen, M. Turunen, L. Anttila, M. Fleischer, K. Stadius, J. Ryynänen, and M. Valkama, “Full-duplex OFDM radar with LTE and 5G NR waveforms: Challenges, solutions, and measurements,” IEEE Trans. Microw. Theory Techn., vol. 67, no. 10, pp. 4042–4054, Oct. 2019

C. Baquero Barneto, M. Turunen, S. D. Liyanaarachchi, L. Anttila, A. Brihuega, T. Riihonen, and M. Valkama, “High-accuracy radio sensing in 5G new radio networks: Prospects and self-interference challenge,” in Proc. Asilomar Conference on Signals, Systems, and Computers, Nov. 2019

Measurement data available athttps://doi.org/10.5281/zenodo.3754175

• Example results at 28 GHz, 400 MHz CBW, 120 kHz SCS• Additional filtering and thresholding to reach the final

mapping results (on the right)• Currently developing more advanced filtering and tracking

algorithms

Metallic lockers

Metallic lockers

Waveform optimization

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• In multicarrier systems like 5G NR, there exist commonly empty/unused subcarriers • They can be filled with optimized complex samples to improve the radar/sensing performance in JCAS context• Different optimization problems can be formulated, taking into account both the comms perf and radar/sensing

perf

S. D. Liyanaarachchi, C. B. Barneto, T. Riihonen and M. Valkama, "Joint OFDM Waveform Design for Communications and Sensing Convergence," IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020

Metallic lockers

Waveform optimization - example

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S.D. Liyanaarachchi, T. Riihonen, C. Baquero Barneto, and M. Valkama, “Optimized Waveforms for 5G-6G Communication with Sensing: Theory, Simulations and Experiments,” IEEE Transactions on Wireless Communications, submitted

• Jointly minimizing lower bounds for the error variances of range and velocity estimates such that Radar subcarriers achieve a given proportion of the total power Power density of an individual radar subcarrier in frequency domain is upper-bounded PAPR of the waveform is properly limited

• As illustrated, the optimized waveform has lower error variances than the unoptimized waveform (left figure)

• Additionally, the so-called peak side-lobe (PSL) of range and velocity profiles can also be improved, through proper optimization (example on the right)

All nasty details and large collection of results available in the below paper:

Metallic lockers

Waveform optimization –measurement example

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Mapping with unoptimized waveform

• Unoptimized and optimized waveforms are used in mapping an outdoor environment at 28 GHz

• A standard-compliant 5G NR baseline waveform is used with 20 OFDM symbols, 264 PRBs, 120kHz carrier spacing, constituting a 400 MHz channel bandwidth

• In this case, the optimization focuses on minimizing the ambiguity (maximizes PSL) of the radar map’s range profile

S.D. Liyanaarachchi, C. Baquero Barneto, T. Riihonen, and M. Valkama, “Experimenting Joint Vehicular Communications and Sensing with Optimized 5G NR Waveform,” IEEE Vehicular Technology Conference, Apr. 2021, accepted

Mapping with optimized waveform

Some recent works by TAU Team

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• C. Baquero Barneto, S. D. Liyanaarachchi, M. Heino, T. Riihonen, and M. Valkama, “Full-duplex radio/radar technology: The enabler for advanced joint communication and sensing,” IEEE Wireless Communications, 2021, in press.

• C. Baquero Barneto, S. D. Liyanaarachchi, T. Riihonen, L. Anttila, and M. Valkama, “Multibeam design for joint communication and sensing in 5G New Radio networks,” IEEE International Conference on Communications, June 2020.

• C. Baquero Barneto, S. D. Liyanaarachchi, M. Heino, T. Riihonen, L. Anttila and M. Valkama, “Beamforming and waveform optimization for OFDM-based joint communications and sensing at mm-waves,” Asilomar Conference on Signals, Systems, and Computers, Nov. 2020.

• C. Baquero Barneto, T. Riihonen, M. Turunen, M. Koivisto, J. Talvitie, and M. Valkama, “Radio-based sensing and indoor mapping with millimeter-wave 5G NR signals,” Proc. International Conference on Localization and GNSS, June 2020.

• C. Baquero Barneto, T. Riihonen, M. Turunen, L. Anttila, M. Fleischer, K. Stadius, J. Ryynänen, and M. Valkama, “Full-duplex OFDM radar with LTE and 5G NR waveforms: Challenges, solutions, and measurements,” IEEE Trans. Microw. Theory Techn., vol. 67, no. 10, pp. 4042–4054, Oct. 2019.

• C. Baquero Barneto, M. Turunen, S. D. Liyanaarachchi, L. Anttila, A. Brihuega, T. Riihonen, and M. Valkama, “High-accuracy radio sensing in 5G new radio networks: Prospects and self-interference challenge,” Asilomar Conference on Signals, Systems, and Computers, Nov. 2019.

• M. Heino, C. Baquero Barneto, T. Riihonen, M. Valkama, “Design of Phased Array Architectures for Full-Duplex Joint Communications and Sensing”, European Conference on Antennas and Propagation (EuCAP), 2021, accepted.

• S. D. Liyanaarachchi, C. B. Barneto, T. Riihonen and M. Valkama, "Joint OFDM Waveform Design for Communications and Sensing Convergence," IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020.

• D. Solomitckii, C. Baquero Barneto, M. Turunen, M. Allen, Y. Kucheryavy, and M. Valkama, “Millimeter-Wave Automotive Radar Scheme with Passive Reflector for Blind Corner Conditions,” European Conference on Antennas and Propagation (EuCAP), 2020.

• S.D. Liyanaarachchi, T. Riihonen, C. Baquero Barneto, and M. Valkama, “Optimized Waveforms for 5G-6G Communication with Sensing: Theory, Simulations and Experiments,” IEEE Transactions on Wireless Communications, submitted.

• C. Baquero Barneto, S. D. Liyanaarachchi, M. Heino, T. Riihonen, and M. Valkama, “Joint Multi-User Communication and MIMO Radar Through Full-Duplex Hybrid Beamforming,” IEEE Symposium on Joint Communications and Sensing, 2021, submitted.

• S.D. Liyanaarachchi, C. Baquero Barneto, T. Riihonen, and M. Valkama, “Experimenting Joint Vehicular Communications and Sensing with Optimized 5G NR Waveform,” IEEE Vehicular Technology Conference, Apr. 2021, accepted.

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TAU hosts state-of-the-art measurement instruments for waveform generation and analysis up to 110 GHz, such as• Keysight Infiniium UXR-Series Real-

Time Oscilloscopes• Keysight M8195A 65 GSa/s Arbitrary

Waveform Generator• Rohde & Schwarz FSW85 high-

performance signal and spectrum analyser

• NI Vector Signal Transceiver 2 • NI mmWave Transceiver System• The environment supports also true

over-the-air (OTA) testing through various beam-steering and directive antenna capabilities

Small add

Mikko ValkamaTampere University (TAU), Finlandmikko.valkama@tuni.fi+358408490756

Wireless Research Group

Further information on the research areas and the infrastructure available athttps://research.tuni.fi/wireless/

Kiitos, thanks !