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Tuning the Receiver Structure and the Pilot-to-Data Power Ratio in Multiple Input Multiple Output Systems Gabor Fodor Ericsson Research Royal Institute of Technology 5G: Scenarios & Requirements Traffic capacity multi-Gbps data rates

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Tuning the Receiver Structure and the Pilot-to-Data Power Ratio in Multiple Input Multiple Output Systems

Gabor Fodor Ericsson Research Royal Institute of Technology

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Tuning the MU-MIMO Receiver and the PDPR | UMD Seminar | 2018-05-23 | Page 2

5G: Scenarios & Requirements

Enhanced mobile broadband

multi-Gbps data rates ms latency

Smart buildings Critical infrastructure Industrial processes Traffic safety/control

…

5G network

Traffic capacity Achievable user data-rates [Mbit/s] Spectrum and bandwidth flexibility Latency [ms] Reliability Massive number

  • f devices

Network and device energy performance Mobility and coverage

10000 1000 100 10 high high high 500 50 5 0.5 high 1000 100 10 1 high

IMT-Advanced Future IMT IMT-2000

IMT-Advanced Future IMT IMT-2000

  • R. Baldemair, E. Dahlman, G. Fodor, G. Mildh, S. Parkvall, Y. Selén, "Evolving Wireless Communications:

Addressing the Challenges and Expectations of the Future", IEEE Vehicular Technology Magazine, Vol. 8, No. 1, pp. 24-30, Mar. 2013 MBB: Mobile Broadband MTC: Machine Type Communications IMT: International Mobile Telecommunications

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5G Technology Components

Extension to Higher Frequencies

Complementing lower frequencies for extreme capacity and data rates in dense areas

Ultra-lean Design

Minimize transmissions not related to user data Separate delivery of user data and system information

Higher data rates and enhanced energy efficiency

Access/backhaul Integration

Same technology for access and backhaul Same spectrum for access and backhaul

Device-to-Device Communication

Direct communication Device-based relaying Cooperative devices ⁞

Spectrum Flexibility

  • Unlicensed
  • Shared licensed

Complementing dedicated licensed spectrum

(Full) Duplex Flexibility Spectrum sharing

  • D. Astely, E. Dahlman, G. Fodor, S. Parkvall and J. Sachs, "LTE Release 12 and Beyond", IEEE Comm. Mag., Vol. 51, No. 7, pp. 154-160, July 2013.

Multi-antenna Technologies

For higher as well as lower frequencies

Beam-forming for coverage Multi-user MIMO for capacity

Multi-site Coordination

Multi-site transmission/reception Multi-layer connectivity

  • H. Shokri-Ghadikolaei, F. Boccardi, C. Fischione, G. Fodor and M. Zorzi, "Spectrum Sharing in mmWave Cellular Networks via Cell

Association, Coordination, and Beamforming", IEEE J. on Selected Areas in Communications, Vol. 34, Issue 11, pp. 2902-2917, 2016

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Mimo evolution

MU-MIMO SU-MIMO Massive MU-MIMO Massive SU-MIMO Massive multi-layer MU-MIMO Multi-layer MU-MIMO

More antennas

LTE: Long Term Evolution SU MIMO: Single User Multiple Input Multiple Output MU MIMO: Multiuser Multiple Input Multiple Output

  • G. Fodor, N. Rajatheva, W. Zirwas, L. Thiele, M. Kurras, K. Guo, A. Tolli, J. H. Sorensen, E. de Carvalho,

"An Overview of Massive MIMO Technology Components in METIS", IEEE Communications Magazine, Vol. 55, Issue 6, pp. 155-161, June 2017.

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Why Full Dimension MIMO ?

› The vector channel to a desired user becomes orthogonal to the vector channel

  • f a random interfering user;

› Rejecting interference becomes possible simply by aligning the BF vector with the desired channel; CSI is important ! › Ultimate limitation is CSI error

› Uniform Linear Array › 10 users › Perfect CSI

› The capacity performance of conjugate BF and ZF become asymptotically identical. [Yang, Marzetta, IEEE JSAC 2013] BS: Base Station CSI: Channel State Information ZF: Zero Forcing BF: Beam Forming

  • V. Saxena, G. Fodor, E. Karipidis, "Mitigating Pilot Contamination by Pilot Reuse and Power

Control Schemes for Massive MIMO Systems", IEEE VTC Spring, Glasgow, Scotland, May 2015.

N r 1

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› How can we improve the performance of the MMSE receiver in the presence of CSI errors in terms of: – Mean squared error of the received data symbols; – Spectral efficiency › What are the gains of CSI error aware receivers over naïve receivers ? › Do such gains increase/decrease as the number of antennas grows large ? › What is the impact of correlated antennas ?

UL MU MIMO Receiver Design Questions

UL: Uplink MU MIMO: Multiuser Multiple Input Multiple Output MMSE: Minimum Mean Squared Error CSI: Channel State Information

  • N. Rajatheva, S. Suyama, W. Zirwas, L. Thiele, G. Fodor, A.Tölli, E. Carvalho, J. H. Sorensen, "Massive Multiple Input Multiple Output (MIMO) Systems",

Chapter 8 in: A. Osseiran, J. F. Monserrat, P. Marsch, "5G Mobile and Wireless Communications Technology", Cambridge University Press, 2016.

  • L. S. Muppirisetty, T. Charalambous, J. Karout, G. Fodor, H. Wymeersch, "Location-Aided Pilot Contamination Avoidance for Massive MIMO Systems",

IEEE Trans. Wireless Comm, April 2018.

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Pilot-Based Channel Estimation

Trade-offs:

Higher pilot power Better channel estimate SNR degradation for data + increased pilot contamination More pilot symbols Better channel estimate Less aggressive pilot reuse More users for MU multiplexing Less data symbols

MU: Multi user SNR: Signal-to-Noise-Ratio

  • G. Fodor, P. D. Marco, M. Telek, “Performance Analysis of Block and Comb Type Channel Estimation for Massive MIMO Systems”,

1st International Conference on 5G for Ubiquitous Connectivity, Levi, Finland, Nov. 2014.

  • K. Guo, Y. Guo, G. Fodor and G. Ascheid, "Uplink Power Control with MMSE Receiver in Multicell Multi-User

Massive MIMO Systems", IEEE International Conference on Communications (ICC), Sydney, Australia, June 2014.

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› 3GPP Technical Report: Study on Elevation Beamforming and FD-MIMO for LTE

› See also:

  • 36.873 Study on 3D Channel Model for LTE
  • 37.105 Active Antenna System BS Radio Transmission and Reception

› Full Dimension MIMO (FD-MIMO)

› Greater number of antenna ports › Efficient MU MIMO Spatial Multiplexing › Robustness against CSI Impairments (e.g. intercell interference)

Full Dimension in 3GPP

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MU MIMO Uplink Signal Model

The naïve G minimizes the MSE of the received data symbols when perfect channel estimation is available at the receiver.

Tagged User

Data signal model:

  • G. Fodor, P. Di Marco and M.Telek, "On the Impact of Antenna Correlation on the Pilot-Data Balance in

Multiple Antenna Systems" IEEE International Conference on Communications (ICC), London, UK, June 2015.

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Related Works on MMSE Receivers/Estimators

CSI Errors are not considered Focuses on channel estimation only Uses the naïve receiver

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Preliminaries I

Pilot signal model: Estimated channel: Conditional channel distribution:

  • G. Fodor, M. Telek, “On the Pilot-Data Trade Off in Single Input Multiple Output Systems”,

European Wireless ’14, pp. 485-492, Barcelona, May 2014.

“channel estimation noise” Covariance of the estimated channel:

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Preliminaries II

Data signal model: MU MIMO Receiver at the BS:

  • N. Rajatheva, S. Suyama, W. Zirwas, L. Thiele, G. Fodor, A.Tölli, E. Carvalho, J. H. Sorensen, "Massive Multiple Input Multiple Output (MIMO) Systems", Chapter 8 in: A.

Osseiran, J. F. Monserrat, P. Marsch, "5G Mobile and Wireless Communications Technology", Cambridge University Press, June 2016. ISBN: 9781107130098

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How to find the (true) MMSE Receiver ?

  • - Approach 1

› Determine the MSE of a tagged User as a function of G and the estimated channel › Determine the MSE of a tagged User as a function of G and the actual channel

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How to find the (true) MMSE Receiver ?

  • - Approach 2

› Determine the MSE of a tagged User as a function of G and the estimated channel of all users › Determine the MSE of a tagged User as a function of G and the actual channel of all users

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Results

› Closed form expression for the MMSE receiver in the presence of CSI errors › Closed form expression for the MSE when using the naïve and the MMSE receiver › Closed form expressions for the optimum pilot-to-data power ratio when using the MMSE receiver

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  • G. Fodor, P. Di Marco, M. Telek “On Minimizing the MSE in Multiple Antenna Systems in the Presence of Channel State Information Errors”,

IEEE Communications Letters, Vol. 19, No. 9, pp. 1604-1607, September 2015.

How to find the (true) MMSE Receiver ?

Elements of proof: Quadratic Form:

MU-MIMO Interference CSI error compensation by 2nd order statistics (D and Q)

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How to find the (true) MMSE Receiver ?

MU-MIMO Interference CSI error compensation by 2nd order statistics (D and Q)

Approach 2:

“Perfect” “Estimate”

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Comparing Analytical and Simulation Results

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Comparing Simulation and Analytical Results

Naïve Naïve MMSE MMSE

Gap with 500 antennas Gap with 20 antennas

The gain of the (true) MMSE receiver over the naïve receiver increases when the number of antennas increases.

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Simulation Setup

Tagged User

  • Single user system, that is no MU MIMO interference
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PL=50 dB PL=45 dB PL=40 dB PL=40 dB PL=45 dB PL=50 dB MMSE Naïve Gain of using the (true) MMSE Detector

  • ver Naïve ~8 dB

The optimal receiver yields significant gains over the whole CDF, including the 10 and 90 percentiles and for various levels of the path loss.

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Optimum Pilot Power Setting

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Naïve MMSE Naïve MMSE Minimum value Gain The gain of the optimal receiver increases with increasing number of antennas. With the true MMSE, the transmit power that minimizes the MSE, does not depend

  • n the number of receive antennas.
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Naïve MMSE Optimal MMSE

How does the Gain depend on the Number of Antennas ?

Gain increases

This plot shows the minimum MSE, that is the MSE that is achieved when using the optimal pilot power.

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Comparison With Perfect CSI

Nr=4 Nr=20 Nr=100

naive

  • pt

perfect

Data transmit power decreases

With large number of antennas, the MSE performance of the optimal receiver remains close to the perfect CSI performance, whereas the performance of the naïve receiver is far from the perfect CSI case. Therefore, with larger number of antennas, the importance of applying the optimal receiver increases.

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› The gain of the optimal receiver increases with increasing number of antennas. In the massive MIMO domain, this gain can be up to 8-10 dB in terms of MSE; › The true MMSE receiver well approximates the perfect channel estimation case, independently of the number of antennas (as opposed to the naïve receiver); › With the true MMSE, the transmit power that minimizes the MSE, does not depend

  • n the number of receive antennas (as opposed to the naïve receiver);

Take Away

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Tuning the Pilot-to-Data Power Ratio

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Key Take-Away

Multiuser MIMO Pilot Setting Fixed pilot resources Adaptive pilot resources Centralized Algorithms Decentralized/Hybrid Algorithms

e.g. LTE Demodulation Reference Signals

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data pilot ( ) Regularized MMSE Receiver

Single Cell MU MIMO Model

  • P. Zhao, G. Fodor, G. Dan, and M. Telek, "A Game Theoretic Approach to Setting the Pilot Power Ratio in Multi-User MIMO Systems",

IEEE Transactions on Communications, Vol. 66, Issue 3, March 2018.

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Best Response Power Allocation:

Each user tunes his PPR to minimize the own MSE.

transmit power of all other players pilot data

MU MIMO Game

  • P. Zhao, G. Fodor, G. Dan, and M. Telek, "A Game Theoretic Approach to Setting the Pilot Power

Ratio in Multi-User MIMO Systems", IEEE Transactions on Communications, December 2017.

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› BS can help User- by signaling to

pilot

› Each user minimizes the own MSE by setting the PPR › BPA converges to a pure strategy Nash equilibrium Best Pilot-Data Power Ratio Algorithm

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pilot

Best Pilot-Data Power Ratio Algorithm (BPA)

Non-cooperative Game: Mapping from to : : Best response power allocation of the tagged MS, as a function of the currently used transmit power of all other MSs.

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pilot

Best Pilot-Data Power Ratio Algorithm (BPA)

Best response power allocation of the tagged MS, as a function of the currently used transmit power of all other MSs.

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Outline

› What is the Pilot-to-Data Power Ratio ? › MU MIMO Game › Numerical Results › Conclusions

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Single Cell Parameter Setting

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2-Player Game

› 2-3 iterations are needed to converge to the Nash equilibrium › MSE of MS 1 is hit by the data power of MS 2 ( ) › Large gain of increasing the number

  • f antennas
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2 and 6-Player Game

› Adaptive PPR is superior to fixed PPR › BPA is close to the optimal PPR

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Conclusions

› Adaptive rather than fixed PPR is beneficial for reducing the MSE › A game theoretic, decentralized PPR setting algorithm quickly converges to a near optimal setting

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Key Take-Away

Multiuser MIMO Pilot Setting Fixed pilot resources Adaptive pilot resources Centralized Algorithms Decentralized/Hybrid Algorithms

e.g. LTE Demodulation Reference Signals