Presented by: Phil Ziegler Principal Consultant MIMO: An Introduction Presented by: Phil Ziegler Principal Consultant 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved References E. Teletar, “Capacity of multiantenna Gaussian Channels,” ATT Bell Laboratories, Tech. Memo, June 1995. Notes MIMO Channel Modeling and Emulation Test Challenges, Agilent Technical Memo 5989-8973EN, Agilent Technologies Inc. Jan 22, 2010 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved MIMO for WAN Overview Basic Principles Space Time Coding Spatial Multiplexing Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved MIMO Vocabulary SISO SIMO MISO MIMO CSI CSIT CSIR MRC MMSE Layers Single Input Single Output Single Input Multiple Output Multiple Input Single Output Multiple Input Multiple Output Channel State Information Transmit Channel State Information Receive Channel State Information Maximal Ratio Combining receiver Minimum Mean Square Error receiver or RANK, the number of data streams Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Multiple Input / Multiple Output Antenna Technologies Use multiple antennas at the Base Station and the mobile terminal Increase sector throughput Improve data integrity Using the same spectrum. Critical part of LTE, HSPA+, WiMax, 802.20, 802.11n, 802.11ac How many antennas is enough? Where do I put them? In what environments does this work? What is the expected performance? What parameters should I measure? Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved The Wireless Channel Notes Multipath: Each path has distinct amplitude, phase and delay Fading: Channel fades across time, frequency, space 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Multiple Antennas At least FIVE multi-antenna techniques have been defined to improve the link performance: Receive diversity at the mobile Transmit diversity using Space-Frequency Block Coding (SFBC) at the AP Beam Steering (Beam Forming) MIMO spatial multiplexing at the AP, for one or more users Cyclic Delay Diversity (CDD) at the AP, used in conjunction with spatial multiplexing 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Beamforming Beamforming increases user data rates by focusing the transmit power to the direction of the user, enabling higher receive SINR at the terminal Beamforming Multiple Antenna System Combinations in UMB Both MIMO and SDMA add spatial dimensions, thus increasing sector capacity. SDMA for suburban (low-angular spread channels) environment Location-based user multiplexing Adaptive sectorization gains Users requesting beams from distinct clusters can be served simultaneously using the same time and frequency resources. 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Multipath More multipath is better … Line of Sight (LOS) links do not enable MIMO. MIMO works best in multipath rich environments Indoors is ideal Dense urban is good Suburban and rural have limited multipath Mobility causes the multipath to shift, which is makes it more difficult to take advantage of MIMO techniques. Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved How does MIMO work? Imagine an “ideal” “scattering” object: e.g. a radio telescope A i.e. The NRAO Green Bank Telescope Signals A and B are received separately on two antennas on the same frequency 7 mile distance B B A 1 foot apart two 116 GHz transmitters on the same frequency at the same time 100 x 110 meters ~+-0.003 inch perfect surface Antenna Gain ~ 100 dB@116 GHz 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved How does MIMO work? Imagine an “uneven” scattering object: e.g. a large clump of metal A Signals A and B are combined on two antennas on the same frequency 7 mile distance B otherA +otherB someB +someA 1 foot apart two 116 GHz transmitters on the same frequency an “uneven” scattering surface antenna gain ~ 0 dB 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved How does MIMO work? How to get A and B ? Use a computer. Signals A and B are combined on two antennas on the same frequency A A B 7 mile distance computer “learns” the scattering shape B computer 1 foot apart someA + someB otherB + otherA two 116 GHz transmitters on the same frequency “uneven” scattering surface antenna gain ~ 0 dB 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved How does MIMO work? To illustrate how the MIMO channel uses multi-path to be able to discriminate among differing data streams and improve signal throughput, consider a 2 x 2 MIMO channel. Let’s use the analogy of a piano player striking (and sustaining) playing a chord consisting of 5 notes with the left hand and another chord consisting of another 5 different notes with the right hand at the same time. Think of the individual notes as fundemental elements and each chord as a symbol. The chord played by the left hand is transmitted from transmit antenna A. The chord played by the left hand is transmitted from transmit antenna B. Assume that the propagation path from transmit antenna A results in 3 multi-paths Assume that the propagation path from transmit antenna B results in 4 multi-paths The following animation illustrates how the arrival of each multi-path results in a detectable power change for either symbol through constructive or destructive interference 4/4/16 3/24/14 Copyright © 2016 | CIBET | All rights reserved 13 13

Copyright © 2016 | CIBET | All rights reserved Multi-Path Animation Initially, each receiver hears all the notes and is unable to determine which chord (symbol) each belongs to Based on the changes to the signal strength of the notes, the receiver is able to separate out the notes into individual chords (symbols) Relative signal strength T1 direct path T2 Multi-path A1 arrives T3 Multi-path B1 arrives T4 Multi-path A2 arrives T5 Multi-path B2 arrives T6 Multi-path B3 arrives (deconstr.) It is the detection of the changes in amplitudes which enable the separation of the two resource blocks at each of the downlink Receivers in the UE. How effectively the receivers are able to perform this separation, is what the H matrix coefficients determine. The degree to which each transmission path may be considered a separate channel depends on the independence of the paths. Spatial multiplexing is optimized for high SNR environments, Noisy outdoor industrial environments will produce inconsistent MIMO capacity improvements. T7 Multi-path A3 arrives T8 Multi-path B4 arrives Frequency F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F1, F3, F6, F8 and F10 are parts of symbol 1 F1 F3 F6 F8 F10 F2 F4 F5 F7 F9 F2, F4, F5, F7 and F9 are parts of symbol 2 3/24/14 4/4/16 Copyright © 2016 | CIBET | All rights reserved 14 14

Copyright © 2016 | CIBET | All rights reserved Antennas 2x2, 2x3, 4x4, 8x8 arrays are popular. For best results, there should be at least as many receive antennas as there are transmit antennas. Multipath is no longer a bad thing; it is required for MIMO to work. Not all multipath equally helps. Two medium SNR multipaths are better than one strong SNR and one weak SNR multipath. Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved N x M Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Capacity of Wireless Systems Shannon’s capacity theorem SNR C/B Where: C = Capacity [bits/sec] B = Bandwidth [Hz] S/N = Signal to Noise Ratio forbidden region n-QAM Notes operating region SNR SNR = Signal Power / Noise Power Eb/No = Energy per bit / Noise Power per Hz Signal Power = Energy per bit x R bits/sec Noise Power = No Power per Hz x Bandwidth Hz SNR = ( Eb / No ) x ( R bits/sec ) / Bandwidth Hz n-FSK Eb/No limit = ln(2) = 0.693.. >> minimum energy per bit /No needed to communicate the bit 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Leave this slide alone! Perfect as is. 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved MIMO Channel Model Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved The H Matrix is Born Notes The MIMO receiver has to recover s0 and s1 from r1 and r2 The receiver has to solve (or assume) H This works if H is “nicely behaved” 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved A Capacity Comparison Which is better, a 3dB increase in SIMO power or a MIMO environment? Capacity increases logarithmically with NR for SIMO systems MIMO systems achieve Spatial Multiplexing Gains which offer a linear increase in the transmission rate (or capacity) for the same bandwidth and with no additional power (see notes for an alternative way to look a the capacity of a MIMO system). A 2 x 2 MIMO system produces two spatial streams to effectively double the maximum data rate of what might be achieved in a traditional SISO communications channel. Note the bounds on the values of the H channel information. SIMO Capacity: The SNR of a SIMO channel is modified by the gain factor obtained from having multiple receivers. The channel consists of only NR paths, increasing the SNR by a factor of the number of receivers, ||h||2 = NR . Capacity of a SIMO communications channel represents a logarithmic increase over the capacity of a SISO channel: CSIMO = log2( 1 + NRSNR) bits/s/Hz 4/4/16 3/24/14 Copyright © 2016 | CIBET | All rights reserved 21 21

Capacity of Wireless Systems Shannon’s capacity theorem For MIMO systems, the capacity increases linearly with the min(M,N) where M is the number of transmit antennas and N is the number of receive antennas Where: C = Capacity [bits/sec] B = Bandwidth [Hz] S/N = Signal to Noise Ratio Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Theoretical Shannon Capacity Formula Chapter 3 Topic 4: MIMO Concept Overview Theoretical Shannon Capacity Formula 4/4/16 Copyright © 2016 | CIBET | All rights reserved 23 23

SISO or MIMO Channel Capacity For MIMO systems, the capacity increases linearly with the min(M,N) = n The ratio of singular values is related to the Condition Number, ideally = 0 dB; i.e. 2 paths have equal SNR. n = number of independent transmit/receive pairs singular values of the H matrix Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Single User MIMO Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Single-user MIMO Multi-user MIMO 4/4/16 Copyright © 2016 | CIBET | All rights reserved from 802.11ac: A Survival Guide, O’Reilly.com

Channel State Information Much to know about the channel For each path Attenuation or path loss as a function of frequency Phase as a function of frequency Time delay Changes with location Mobility makes the problem harder The best performing systems require that channel state information is measured at the receiver and sent back to the transmitter. This generates data to send Desirable to minimize this data stream Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Layers Layers are equivalent to the Rank of the MIMO system The number of layers is the number of data streams that the Base Station (Access Point, eNodeB) is sending to the phone: SISO is Rank = 1 MIMO 2x2 is Rank =>1 or 2 The Rank can change quickly Rank values change as the environment changes The Rank varies Different phones may have different Ranks Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Open Loop MIMO No channel knowledge at the transmitter. Multiple transmit antennas typically use orthogonal coding. An example of the orthogonal coding technique is space-time coding. Exploits the independent fading of the paths. Without channel state information, there is no beamforming or array gains. Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Closed Loop MIMO Knows channel state Enables: Optimized performance Different data rates in each layer Types of Closed Loop MIMO: Beamforming Precoding Spatial Multiplexing Note: High SNR is generally required to have accurate channel state information. Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Reference Symbols* Notes *aka: Reference Elements Pilot Tones Pilot channel 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Topics discussed Channel Rank >> Layers >> Capacity CSI Open Loop vs. Closed Loop Beamforming Layers 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Spatial Multiplexing How many antennas is enough? Where do I put them? Depends on the channel, 2 antennas is the current popular number 1 or 2 wavelengths apart In what environments does this work? In multipath rich environments Lots of multipath indoors What is the expected performance increment? Expect performance that is close to 90% the minimum number N or M What parameters should I measure Walk test: Channel Rank, RSRP, Through-put (uplink and downlink) Sending multiple streams to one user is known as Spatial Multiplexing (SM) Can increase the data rate to the user Works best at high SNRs Using the same RF spectrum Notes 4/4/16 Copyright © 2016 | CIBET | All rights reserved

Copyright © 2016 | CIBET | All rights reserved Conclusions Shannon’s law still applies, even for MIMO; MIMO extends Shannon due to channel gain characteristics which support multiple uncorrelated spatial transmission modes by exploiting transmission environments rich in fading, multipath and scattering. Transmission environments rich in fading, multipath and scattering offer the most channel gain whereas environments with a strong line-of-sight (direct) path will exhibit limited MIMO channel gains. MIMO systems offer a combination of both diversity and spatial multiplexing gains to increase system reliability and data throughput. MIMO is unique in that it can support multiple uncorrelated data streams. Spatial Multiplexing Gains offer a linear increase (ignoring channel overhead) in the transmission rate based on the min(NT, NR) for the same bandwidth and with no additional power. In particular, a 2 x 2 MIMO system produces two spatial streams to effectively double the maximum data rate of what might be achieved in a traditional SISO communications channel 3/24/14 4/4/16 Copyright © 2016 | CIBET | All rights reserved 34 34

Copyright © 2016 | CIBET | All rights reserved Thank you! Questions? 3/24/14 4/4/16 Copyright © 2016 | CIBET | All rights reserved 35 35