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Large Intelligent Surfaces - Massive MIMO Evolution or Revolution? - - PowerPoint PPT Presentation

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Large Intelligent Surfaces - Massive MIMO Evolution or Revolution? Rui Dinis 12 1 Instituto de Telecomunicac oes 2 Nova University of Lisbon Outline 1 Motivation 2 MIMO 3 Massive MIMO 4 LIS Concept Challenges 5 Conclusions Lisbon, Nov.

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SLIDE 1

Large Intelligent Surfaces - Massive MIMO Evolution

  • r Revolution?

Rui Dinis12

1Instituto de Telecomunicac

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  • es

2Nova University of Lisbon

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SLIDE 2

Outline

1 Motivation 2 MIMO 3 Massive MIMO 4 LIS

Concept Challenges

5 Conclusions

Lisbon, Nov. 25, 2019 2/15

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SLIDE 3

Digital Communications

  • Low error rates
  • Higher and higher bit rates
  • Spectral efficiency [bps/Hz]
  • Power savings

Lisbon, Nov. 25, 2019 3/15

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SLIDE 4

Channel Capacity

C = B log2(1 + SNR)

  • Channel coding
  • Turbo codes
  • LDPC codes
  • Polar codes
  • Equalization
  • MLSE
  • OFDM
  • SC-FDE
  • Synchronization and channel estimation

Lisbon, Nov. 25, 2019 4/15

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SLIDE 5

MIMO Channel

C = log2

  • det
  • I + SNR

NTx HHH

  • Channel capacity grows with the number of antennas
  • Gain relatively the SISO case upperbounded by min(NTx, NRx)
  • Suitable for OFDM and SC-FDE schemes
  • Optimum receiver too complex
  • Practical receivers based on MMSE with excellent

performance/complexity trade-offs

Lisbon, Nov. 25, 2019 5/15

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SLIDE 6

Massive MIMO

  • Conventional MIMO schemes suitable for systems up to about 8 × 8
  • Massive MIMO not a scaled version of MIMO!
  • Low complexity implementations (low resolution DACs and ADCs,

strongly nonlinear amplifiers, simplified equalization/pre-coding, etc.)

  • Common elements (RF chains, mixers, DAC/ADC, etc.)
  • Channel estimation challenges (e.g., pilot contamination)

Lisbon, Nov. 25, 2019 6/15

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SLIDE 7

LIS - Large Intelligent Surfaces

  • Evolution of massive MIMO
  • Much more antenna elements
  • Short range
  • Near field communication
  • LoS communication

Lisbon, Nov. 25, 2019 7/15

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SLIDE 8

LIS Access

  • Antennas switched on and off according to user position and/or user

requirements

  • Resource allocation at the space domain

Lisbon, Nov. 25, 2019 8/15

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SLIDE 9

LIS for Positioning

  • Antennas with different RSS and/or AoA/AoD
  • Accurate positioning

Lisbon, Nov. 25, 2019 9/15

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SLIDE 10

LIS for Communication

  • Communication aided by positioning information
  • Low complexity transmission and detection schemes
  • Huge capacity and coverage gains
  • Robustness to interference and imperfections

Lisbon, Nov. 25, 2019 10/15

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SLIDE 11

LIS for Energy Harvesting

  • Beamforming to compensate losses in energy harvesting
  • Better range and/or energy harvesting efficiency than traditional

techniques

  • Ranges of 1m or more

Lisbon, Nov. 25, 2019 11/15

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SLIDE 12

Transceiver Design

  • Need for very low complexity transceivers
  • On/off approaches
  • Beamforming
  • Skip equalizers?
  • Interference cancellation
  • Low resolution DAC/ADC (1 bit quantizers?)
  • Low complexity amplifiers (saturated or even switched amplifiers)

Lisbon, Nov. 25, 2019 12/15

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SLIDE 13

Channel Estimation

  • Too many channel to estimate
  • Parameterized channel models
  • Position-aided channel estimation
  • Channel tracking

Lisbon, Nov. 25, 2019 13/15

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SLIDE 14

Resource Allocation

  • Space-domain resource allocation
  • Aided by position information
  • LIS split in panels
  • Many antennas per panel
  • Small number of outputs per panel
  • A user can be associated to several panels

Lisbon, Nov. 25, 2019 14/15

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SLIDE 15

Conclusions

  • Path from SISO to LIS
  • LIS with very high potential
  • Challenges
  • It is possible!

Lisbon, Nov. 25, 2019 15/15