Massive MIMO in TDD wireless networks depends crucially on channel reciprocity, which can be established by calibration. Existing calibration approaches, however, have been proven to be impractical for deployment in 5G NR and 802.11. This presentation introduces terminal-assisted calibration, which is shown to overcome the drawbacks of existing approaches and to enable various massive MIMO modes in 5G NR and 802.11.

1. Blue Mountain Wireless, Inc.
Blue Mountain Wireless
June 2019

2. 2Blue Mountain Wireless, Inc.
Relevant Key Features in 5G NR
 TDD (time-division duplex) takes a much more active role
 Provides flexibility in downlink/uplink allocation
 Various MIMO modes benefit from channel reciprocity in TDD
 Beamforming
 Spatial multiplexing (multi-user MIMO)
 Distributed MIMO (CoMP)
 TDD is to dominate in higher frequency band (> 3 GHz)
 Massive MIMO
 Up to 256 antennas below 28 GHz, many more in mmWave band
 2D antenna array ⇒ 3D beamforming
 Self-contained TDD subframes – providing a platform for
massive MIMO
data (DL)
guard
period
ACK (UL)
adaptive DL/UL allocation within 1-ms subframe
training signal for DL MIMO by channel reciprocity

3. 3Blue Mountain Wireless, Inc.
TDD/MIMO Gains and Assumptions
 4× cell throughput gain
 The gain is all by TDD and massive MIMO
 Source: Qualcomm
 TDD/MIMO gain assumes channel reciprocity
 Channel reciprocity – downlink channel and uplink channel are
reciprocal
 In reality, downlink and uplink channels are not reciprocal
 Reciprocity holds only between broadcaster antennas and terminal
antennas
 Taken into account transceiver chains of broadcasters and
terminals, downlink and uplink channels are no longer reciprocal
 In practice, calibration is needed to restore channel
reciprocity and to realize the multi-fold gain

4. 4Blue Mountain Wireless, Inc.
Channel Reciprocity
 With transceiver chains taken into account:
 and : diagonal matrices capturing transceiver properties of
broadcasters and terminals
 Reciprocity does not hold unless and are (proportional to)
identity matrices
T,0( )b f
T, 1( )mb f
R,0( )b f
R, 1( )mb f
T,0( )t f
R,0( )t f
R, 1( )nt f
T, 1( )nt f
( )fH

5. 5Blue Mountain Wireless, Inc.
Calibration
 Calibration – obtaining and so downlink channel can be
derived from uplink channel
 Situations where alone is sufficient
 Multi-user MIMO (spatial multiplexing)
 Distributed MIMO (CoMP)
 Beamforming with single-antenna terminals
 In beamforming with multiple-antenna terminals, both and are
needed

6. 6Blue Mountain Wireless, Inc.
Existing Art – Self Calibration
 Self-calibration is the prevalent calibration method
 Self-calibration means calibration among broadcasters only
 Broadcaster examples
 eNBs in 4G/LTE
 gNBs in 5G
 APs in 802.11
 In self-calibration:
 No terminals are involved
 Can only obtain knowledge on ⇒ only suitable in MIMO
scenarios where is not needed
 Types of self-calibration
 Single broadcaster with multiple antennas [1-3]
 Multiple broadcasters, each with one or more antennas [4-6]

7. 7Blue Mountain Wireless, Inc.
Drawbacks of Self-Calibration
 Long calibration time
 Calibration has to be in a serial fashion – antennas send
calibration signal one at a time to avoid interference
 Calibration has to be performed for each transceiver gain setting
 Each gain setting introduces different gain and phase offsets that
have to be calibrated
 Each transceiver chain can have hundreds to thousands of gain
setting combinations (from various RF/analog stages)
 Calibration also depends on carrier frequency
 Serial processing for antenna, gain setting, and carrier results in
thousands to millions of combinations for calibration
 Calibration process can take minutes or hours
 Infeasible for massive MIMO

8. 8Blue Mountain Wireless, Inc.
Drawbacks of Self-Calibration
 Calibration process, while long, also needs to be repeated
 Gain/frequency-dependency changes over time
 Drift and aging in analog components
 Change in operational environment – temperature, antenna load, etc.
 Calibration must repeat periodically
 Repeated long calibrations reduce network capacity
 More difficult for multiple broadcasters (antenna arrays in
different locations)
 Multiple broadcasters run on independent oscillators – calibration
must include oscillator synchronization
 GPS-based oscillator synchronization is expensive and requires
line-of-sight satellite paths
 Over-the-air (non GPS-based) oscillator synchronization
 Additional signaling protocol – further lengthening calibration process
 More Tx power than single broadcaster calibration – creating
interferences in the network

9. 9Blue Mountain Wireless, Inc.
Drawbacks of Self-Calibration
 Service disruption
 Network service has to stop during calibration processes
 Not acceptable in cellular networks where uninterrupted services
are expected
 Self-calibration is not applicable in certain beamforming
scenarios
 Recall that self-calibration only calibrates
 Both and are needed in beamforming to multi-antenna
terminals
 Difficult to standardize
 Numerous self-calibration schemes exist, many of which are highly
device-dependent and include ad-hoc solutions
 Ad-hoc nature hinders standardization thus industry-wide
acceptance and large-scale deployment

10. 10Blue Mountain Wireless, Inc.
Ramifications
 There have been no feasible calibration methods for upcoming
5G and evolving 802.11 networks
 Without efficient and accurate channel calibration, promise of
multi-folds capacity gain cannot be realized
 Recall the unfulfilled promises by LTE CoMP
 A feasible calibration method should ideally be
 Fast – much shorter calibration time
 Device-independent – no gain-dependent calibration
 Accurate
 Undisruptive to network services
 Capable of calibrating , as well as if needed
 Easy to standardize – accelerating acceptance and deployment
 Generic – applicable to any wireless network, including 5G and
802.11

11. Blue Mountain Wireless, Inc.
Terminal-Assisted Calibration

12. 12Blue Mountain Wireless, Inc.
Overview
 Terminal-assisted calibration is an alternative to self-calibration
 Broadcasters send downlink (DL) pilot signal to terminals
 Terminals estimate DL channel and feed back to broadcasters
 Terminals also send uplink (UL) pilot signal to broadcasters
 Broadcasters estimate UL channel and derive and/or from UL
channel and feedback
 Compared to self-calibration, terminal-assisted calibration has
been considered to be disadvantageous
 Need to involve terminals
 Large feedback overhead
 We will describe a terminal-assisted calibration approach that
 Overcomes drawbacks of self-calibration
 Has extremely low feedback overhead
 Fulfils desired properties of ideal calibrations

13. 13Blue Mountain Wireless, Inc.
Efficient Calibration Signal
 Calibration signal is very efficient
 Tones from all antennas can be mapped into one OFDM symbol
 Antenna calibration function over the signal bandwidth can be
interpolated from calibration tones
 Smoothness of calibration function ensures interpolation quality
 Calibration signal can also be multi-symbol
 Increases tone density thereby improving calibration accuracy

14. 14Blue Mountain Wireless, Inc.
Low Signaling and Feedback Overhead
 Calibration overhead includes signaling and feedback1
 Calibration signal can be as short as one OFDM symbol
 An example for 20-MHz LTE with 256 antennas
 There are 1200 subcarriers in one OFDM symbol
 Each antenna can use four subcarriers for calibration
 One Tx OFDM symbol suffices if long-term component2 is known
 Types of feedback
 Digital – about 5 symbols per Tx symbol per terminal antenna3
 Analog – 2 ~ 4 symbols per Tx symbol per terminal antenna4
 Thus calibration overhead can be well below 1 ms5
 For multi-symbol calibration signal and multiple terminal antennas,
the overhead can still be on the order of milliseconds
 For wider bandwidth, the overhead decreases accordingly
1 Low-overhead calibration tone design is based on principles described in US 8478203 and US 8792372
2 See later slides
3 Assumptions: 16 bits per (I,Q) symbol (8 bits for each of I and Q), and uplink spectral efficiency of 3.2 bits per subcarrier
4 Let  = number of feedback symbols per Tx symbol. Assuming same SNR in uplink and downlink,  = 1, 2, 4 correspond to
SNR degradation of 3.01, 1.76, 0.97 dB, respectively, due to analog feedback
5 For 20-MHz LTE, 1 ms = 14 OFDM symbols

15. 15Blue Mountain Wireless, Inc.
Simple Protocols
 Terminal-assisted calibration follows very simple protocols
1. DL calibration signal is sent to participating terminals
 Calibration signal consists of tones from all broadcaster antennas
2. Terminals send UL reference signals (RS)
 For UL channel estimation
 Existing UL RS in LTE/5G-NR can be used
3. Terminals feeds back received DL calibration tones
 With UL channel estimation and DL calibration tone feedback,
broadcaster is able to derive and

16. 16Blue Mountain Wireless, Inc.
Gain Agnostic
 Self-calibration process has to exhaust all gain settings
 Calibration results are stored with respect to gain settings
 The broadcaster chooses calibration result corresponding to
current gain setting
 In terminal-assisted calibration, MIMO session immediately
follows calibration
 Calibration always corresponds to most recent gain setting
 Gain control is generally a low-rate process – one calibration can
apply to many subsequent MIMO sessions
 A new calibration is only needed when gain setting is changed

17. 17Blue Mountain Wireless, Inc.
Improving Calibration Accuracy
 In general, calibration quantities (dependency on frequency
added) have two components
 Long-term component – fixed/slow-varying
 Relative amplitude profiles
 Relative nonlinear phases (there may also be none)
 Relative antenna delays within same broadcaster
 Short-term component – may change from time to time
 Relative amplitude gains
 Relative antenna phases
 Relative antenna delays among broadcasters (e.g., in distributed
MIMO)
 Short-term component consists of only scalar parameters,
instead of functions of frequencies as in long-term component
 Assuming known long-term component, calibration turns into
parameter estimation
 In general, estimating a few parameters has much higher accuracy
than reconstructing functions over the signal bandwidth

18. 18Blue Mountain Wireless, Inc.
Long-Term Component
 Acquisition options for long-term component in
 By initial calibration – longer than “normal” calibrations
 Initial calibration can be designed to extract long-term component
with desired accuracy
 By accumulating over normal calibrations
 Avoid long initial calibrations
 Calibration quality improves over time
 Tracking and updating long-term component in
 Updating information can be derived from normal calibrations to
track the slow variations in long-term component
 What about ?
 “Long-term” concept does not apply to terminals1 – each
calibration session may involve different terminals
 can still be obtained accurately without long-term component
 More broadcaster antennas than terminal antennas – e.g., 256 vs. 2
 This translates to “processing gain” in estimating
1 “Long-term” concept is still relevant in terminal-centric calibrations – for example, in a “broadcaster-assisted calibration”
for a multi-antenna terminal

19. 19Blue Mountain Wireless, Inc.
More MIMO Modes
 Terminal-assisted calibration is applicable to more MIMO modes
 Modes where alone suffices
 DL beamforming – one broadcaster, one single-antenna terminal
 MU-MIMO – one broadcaster, multiple terminals
 Distributed MIMO – multiple broadcasters, multiple terminals
 Modes where both and are needed1
 DL beamforming – one broadcaster, one multi-antenna terminal
 MU-beamforming – one broadcaster, multiple multi-antenna terminals
 Distributed MIMO/beamforming – multiple broadcasters, multiple multi-
antenna terminals
 UL beamforming1: from a multi-antenna terminal to a broadcaster
 Same calibration principle but with “role reversal” between the
broadcaster and the terminal
1 Self-calibration is not able to support these MIMO modes

20. 20Blue Mountain Wireless, Inc.
Flexible Terminal Selection
 Terminal-assisted calibration has full flexibility in selecting
terminals
 Calibration requires only one terminal antenna (for calibrating )
but can include more terminals or terminal antennas
 Terminals in calibration are not necessarily the same as ones in
MIMO sessions
 Benefits from terminal-selection flexibility
 Choose terminals with best channel quality to maximize calibration
accuracy
 Choose multiple terminals (or multiple terminal antennas),
including terminals not participating subsequent MIMO sessions
 Multiple terminal antennas reduces impact of channel nulls, offering
effect of antenna diversity
 Calibration accuracy is proportional to number of terminal antennas

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Ease of Standardization
 Recall that it is difficult to standardize self-calibration
 Self-calibration schemes depend highly on antenna-array properties and
many solutions are ad-hoc
 Service disruption which is incompatible with cellular networks
 In contrast, terminal-assisted calibration relies on generic principles
and operates on standard protocols
 Calibration tones fit OFDM waveform in 4G, 5G-NR, and 802.11
 DL/UL interaction fits in L1/L2 signaling of 3GPP
 Well-known and standard signal processing algorithms
 Calibration can be made a natural and integral part of MIMO operations
 Wide adoption and deployment is possible only if calibration is
standardized
 LTE-A CoMP fails to deliver multi-fold gain because of incomplete
standardization
 Full CoMP is not feasible due to network constraints
 Standardization barriers such as huge feedback overhead
 Terminal-assisted calibration has none of the above issues

22. 22Blue Mountain Wireless, Inc.
 WiFi is considered to play an indispensable role in 5G
 WiFi networks are in many ways complementary to cellular
 Operates in different bands from cellular
 Small, self-organizing, and asynchronous
 Wider bandwidth in sub-6 GHz: 160 MHz vs. 20 MHz in LTE
 Free
 WiFi is more prone to interferences and congestion
 A single AP (access point) is ill-equipped serving large number of
terminals
 Multiple APs interfere each other due to self-organizing and
asynchronous nature
 WiFi throughput often comes to a standstill when networks are
dense and terminals are many
 Massive MIMO can solve above WiFi issues and terminal-
assisted calibration enables it
802.11 Applications

23. 23Blue Mountain Wireless, Inc.
 Coordinated AP transmission has been lacking in WiFi networks
 Primary tool in reducing interference is interference avoidance
 Interference avoidance in 802.11 has been primitive
 Use difference channels in frequency domain
 “Back-off” in time domain
 Huge spectral inefficiency as a result
 Distributed MIMO is more attractive than interference avoidance
 802.11-based distributed MIMO was demonstrated in [3][4]
 Self-calibration was used to restore channel reciprocity
 Exhaustive calibration in gain space
prevents industry-wide adoption
 Terminal-assisted calibration makes
distributed MIMO feasible
 Phase-synchronizes APs
 Eliminates gain-dependent calibration
802.11 – Distributed MIMO

24. 24Blue Mountain Wireless, Inc.
802.11 – Interference Avoidance
 For APs with large antenna size, simultaneous MU-MIMO and
interference avoidance is possible
 Each AP serves terminals in its own BSS and align its emission
nulls to neighboring APs and to terminals in other BSSs
 Again, terminal-assisted calibration restores the needed channel
reciprocity
 Simultaneous MU-MIMO and interference avoidance offer an
equally attractive alternative to distributed MIMO
 No inter-AP backhaul is needed
 No need for synchronizing APs – APs can operate in native
asynchronous WiFi mode

25. 25Blue Mountain Wireless, Inc.
Conclusion
 The multi-fold gain of massive MIMO in TDD network can only be
realized by efficient channel calibrations
 Terminal-assisted calibration possesses all desired properties
 Fast – calibration time is on the order of milliseconds or less
 Device-independent – no need to calibrate over the gain space
 Accurate – separation of long-term and short-term components
improves calibration quality
 Low signaling and feedback overhead
 Capable of calibrating both broadcaster and terminal antennas –
supports more MIMO modes
 Easy to standardize and undisruptive to network services –
calibration can be implemented with existing signaling protocols in
cellular networks
 Applicable to both 5G and 802.11, and removing interference
bottleneck in 802.11
 Terminal-assisted calibration is an enabling technology to TDD
massive MIMO in 5G

26. 26Blue Mountain Wireless, Inc.
References
[1] J. Vieira et al., “Reciprocity for massive MIMO: proposal, modeling, and validation”, IEEE
Trans. Wireless Comm., vol. 16, no. 5, pp. 3042–3056, May 2017.
[2] K. Gopala and D. Slock, “Optimal algorithms and CRB for reciprocity calibration in
massive MIMO”, IEEE International Conference on Acoustics, Speech and Signal
Processing, Calgary, Alberta, Canada, April 15-20, 2018.
[3] O. Raeesi et al., “Performance analysis of multi-user massive MIMO downlink under
channel non-reciprocity and imperfect CSI”, IEEE Trans. Comm., vol. 66, no. 6, pp. 2456–
2471, June 2018.
[4] US 9236998, “Transmitter and receiver calibration for obtaining the channel reciprocity for
time division duplex MIMO systems”, January 12, 2016.
[5] R. Rogalin et al., “Scalable synchronization and reciprocity calibration for distributed
multiuse MIMO”, IEEE Trans. Wireless Comm., vol. 13, no. 4, pp. 1815–1831, April 2014.
[6] H. Rahul et al., “MegaMIMO: scaling wireless capacity with user demands”, ACM
SIGCOMM 2012, Helsinki, Finland, August 2012.
[7] E. Hamed et al., “Real-time distributed MIMO systems”, Proceedings of the 2016 ACM
SIGCOMM conference, pp. 412-425, Florianopolis, Brazil, August 22–26, 2016.
[8] US 8792372, “Carrier-phase difference detection with mismatched transmitter and receiver
delays”, July 29, 2014.
[9] US 8478203, “Phase synchronization of base stations via mobile feedback in multipoint
broadcasting”, July 2, 2013.
[10] US 15/869042, “Phase synchronization and channel reciprocity calibration of antennas via
terminal feedback”, January 12, 2018.

27. 27Blue Mountain Wireless, Inc.