MIMO Wireless Communication Per Hjalmar Lehne, Telenor Guest lecture at UniK 1 March 2012

Agenda What is MIMO? Different gains of multiple antenna systems Fundamental Limits of Wireless Transmission Shannon capacity of Wireless Channels Multiple antennas at one end Capacity of MIMO Links Data transmission over MIMO Systems General principles Diversity using Space Time Block Codes Spatial Multiplexing Wireless channel modelling Theoretical Models Heurestic Models Broadband Channels Measured Channels System Level Issues Optimum use of multiple antennas MIMO in Mobile Broadband MIMO Transmission Scheme for HSPA and LTE

Agenda What is MIMO? Different gains of multiple antenna systems Fundamental Limits of Wireless Transmission Shannon capacity of Wireless Channels Multiple antennas at one end Capacity of MIMO Links Data transmission over MIMO Systems General principles Diversity using Space Time Block Codes Spatial Multiplexing Wireless channel modelling Theoretical Models Heurestic Models Broadband Channels Measured Channels System Level Issues Optimum use of multiple antennas MIMO in Mobile Broadband MIMO Transmission Scheme for HSPA and LTE

What is MIMO? MIMO: Multiple input – multiple output Given an arbitrary wireless communication system: ”A link for which the transmitting end as well as the receiving end is equipped with multiple antenna elements” The signals on the transmit antennas and receive antennas are ”combined” to improve the quality of the communication (ber and/or bps) MIMO systems use space-time processing techniques Time dimension is completed with the spatial dimension

Agenda What is MIMO? Different gains of multiple antenna systems Fundamental Limits of Wireless Transmission Shannon capacity of Wireless Channels Multiple antennas at one end Capacity of MIMO Links Data transmission over MIMO Systems General principles Diversity using Space Time Block Codes Spatial Multiplexing Wireless channel modelling Theoretical Models Heurestic Models Broadband Channels Measured Channels System Level Issues Optimum use of multiple antennas MIMO in Mobile Broadband MIMO Transmission Scheme for HSPA and LTE

Different gains of multiple antenna systems ”Smart antenna” gain Beamforming to increase the average signal-to-noise (SNR) ratio through focussing energy into desired directions Spatial diversity gain Receiving on multiple antenna elements reduces fading problems. The diversity order is defined by the number of decorrelated spatial branches Spatial multiplexing gain A matrix channel is created, opening up the possibility of transmitting over several spatial modes of the matrix channel increasing the link throughput at no additional frequency, timer or power expenditure

Multiple antenna fundamentals 7 Channel Coding, modulation, weigthing/mapping Weighting, /demapping, demodulation, decoding Data Data stream Tx antenna ports Rx antenna ports Data Recovered data stream

Multiple antenna fundamentals 8 Coding, modulation, weigthing/mapping Weighting, /demapping, demodulation, decoding Data Data stream Tx antenna ports Rx antenna ports Data Recovered data stream N transmit antennas M receive antennas Channel matrix

Multiple antenna fundamentals A1 A2 A3 A4 Coding, modulation, weigthing/mapping Weighting, /demapping, demodulation, decoding Data Data stream Tx antenna ports Rx antenna ports Data Recovered data stream

Multiple antenna fundamentals Spatial multiplexing The different data streams are divided in space Coding, modulation, weigthing/mapping Weighting, /demapping, demodulation, decoding Data Data stream Tx antenna ports Rx antenna ports Data Recovered data stream rank(H) determines how many streams are possible to transmit

Multiple antenna fundamentals Transmit diversity A1 A2 A3 A4 Redundancy: The data streams contain the same data Redundancy: The data streams contain the same data Coding, modulation, weigthing/mapping Weighting, /demapping, demodulation, decoding Data Data stream Tx antenna ports Rx antenna ports Data Recovered data stream

Multiple antenna fundamentals Beamforming A1 A2 A3 A4 Only the best spatial channel is used to maximize C/N Coding, modulation, weigthing/mapping Weighting, /demapping, demodulation, decoding Data Data stream Tx antenna ports Rx antenna ports Data Recovered data stream

Agenda What is MIMO? Different gains of multiple antenna systems Fundamental Limits of Wireless Transmission Shannon capacity of Wireless Channels Multiple antennas at one end Capacity of MIMO Links Data transmission over MIMO Systems General principles Diversity using Space Time Block Codes Spatial Multiplexing Wireless channel modelling Theoretical Models Heurestic Models Broadband Channels Measured Channels System Level Issues Optimum use of multiple antennas MIMO in Mobile Broadband MIMO Transmission Scheme for HSPA and LTE

Fundamental limits of wireless transmission Shannon capacity of Wireless Channels: h is the unit power complex Gaussian amplitude of the channel –h is a random variable Multiple antennas at one end: Capacity of MIMO Links: Average capacity C a Outage capacity C o

Shannon capacity of Wireless Channels Ideal Rayleigh Channel

Agenda What is MIMO? Different gains of multiple antenna systems Fundamental Limits of Wireless Transmission Shannon capacity of Wireless Channels Multiple antennas at one end Capacity of MIMO Links Data transmission over MIMO Systems General principles Diversity using Space Time Block Codes Spatial Multiplexing Wireless channel modelling Theoretical Models Heurestic Models Broadband Channels Measured Channels System Level Issues Optimum use of multiple antennas MIMO in Mobile Broadband MIMO Transmission Scheme for HSPA and LTE

Data transmission over MIMO systems Two main categories: Data rate maximization –Sending as many independent signals as antennas –Spatial multiplexing Diversity maximization –The individual streams can be encoded jointly –Protect against transmission errors caused by channel fading –Minimize the outage probability

Maximizing diversity with space-time block codes Alamouti’s scheme: The block of symbols s 0 and s 1 is coded across time and space Normalization factor ensures total energy to be the same the case of one transmitter Reception: The receiver collects the observation, y, over two symbol periods Tx0 Tx1 Rx

Spatial multiplexing Extending the Space- Time Block Coding Transmitting independent data over different antennas The receiver must un- mix the channel Limited diversity benefit CHY

Spatial multiplexing - decoding Zero-forcing (ZF) Inverting matrix H Simple approach Dependent on low-correlation in H Maximum likelihood (ML) Optimum Comparing all possible combination with the observation High complexity Nulling and cancelling Matrix inversion in layers Estimates one symbol, subtracts and continues decoding successively

Transmission scheme performance Same transmission rate Alamouti Spatial multiplexing – zero forcing Spatial multiplexing – maximum likelihood Combined STBC spatial multiplexing

Agenda What is MIMO? Different gains of multiple antenna systems Fundamental Limits of Wireless Transmission Shannon capacity of Wireless Channels Multiple antennas at one end Capacity of MIMO Links Data transmission over MIMO Systems General principles Diversity using Space Time Block Codes Spatial Multiplexing Wireless channel modelling Theoretical Models Heuristic Models Broadband Channels Measured Channels System Level Issues Optimum use of multiple antennas MIMO in Mobile Broadband MIMO Transmission Scheme for HSPA and LTE

Wireless channel modelling The promise of high MIMO capacities largely relies on the decorrelation properties: Between antennas Full-rankness of the MIMO channel matrix H –E.g. spatial multiplexing becomes completely inefficient if the channel has rank 1 Aim of channel modelling: Get an understanding of what performance can be reasonably expected form MIMO systems To provide the necessary tools to analyze the impact of selected antenna or propagation parameters –Spacing, frequency, antenna height.. To influence the system design in the best way

Wireless channel modelling Four approaches Theoretical Models –E.g. the ”idealistic” channel matrix of perfectly uncorrelated (i.i.d.) random Gaussian elements Heurestic Models –In practice, MIMO channels will not fall completely into any of the theoretical cases Broadband Channels –Frequency selective fading is experienced a new MIMO matrix is obtained at each frequency/sub-band Measured Channels –Validate the models, provide acceptance of MIMO systems into wireless standards

Theoretical channel models Ideal channel (i.i.d.): Corresponds to a rich multipath environment Emphasizing the separate roles Antenna correlation (transmit or receive) Rank of the channel –Uncorrelated High Rank (UHR aka i.i.d.) –Correlated Low Rank (CLR) –Antennas are placed too close to each other, or –Too little angular spread at both transmitter and receiver –Uncorrelated Low Rank (ULR) –”pin-hole” model

Heuristic channel models Display a wide range of MIMO channel behaviours through the use of as few relevant channel parameters as possible, with as much realism as possible What is the typical capacity of a MIMO channel? What are the key parameters governing capacity? Under what simple conditions do we get full rank channel? The model parameters should be controllable or measurable

Antenna correlation at transmitter or receiver A MIMO channel with correlated receive antennas: For ”large” values of the angle spread and/or antenna spacing, R will converge to the identity matrix For ”small” values of θ r, d r, R becomes rank deficient (eventually rank one) causing fully correlated fading Generalized model includes correlation on both sides:

The double scattering model – ”pinhole” channels Uncorrelated low rank: Significant local scattering around both the BTS and the subscriber’s antennas Local scatterer’s are considered as virtual receive antennas –When the virtual aperture is small, either on transmit or receive, the rank of the overall MIMO channel will fall

Broadband channels Frequency selective channels are experienced MIMO capacity benefits OFDM systems with MIMO Additional paths contribute to the selectivity as well as a greater overall angular spread Improving the average rank of the MIMO channel across frequencies H(f)H(f)

Measured channels Channel matrix is measured using multiple antennas at transmitter and receiver Results confirm the high level of MIMO capacity potential, at least in urban and suburban areas Eigenvalue analysis –A large number of the modes of MIMO channels can be exploited to transmit data 30 SISO 2x2 MIMO 4x4 MIMO LOS NLOS

Agenda What is MIMO? Different gains of multiple antenna systems Fundamental Limits of Wireless Transmission Shannon capacity of Wireless Channels Multiple antennas at one end Capacity of MIMO Links Data transmission over MIMO Systems General principles Diversity using Space Time Block Codes Spatial Multiplexing Wireless channel modelling Theoretical Models Heurestic Models Broadband Channels Measured Channels System Level Issues Optimum use of multiple antennas MIMO in Mobile Broadband MIMO Transmission Scheme for HSPA and LTE

System level issues – optimum use of multiple antennas Multiple antenna usage is not new in mobile systems: Spatial diversity systems Different goals: Beamforming is optimum using a large number of closely spaced antennas: –Directional beamforming imposes stringent limits on spacing, typically a half wavelength –Best performance in line-of-sight (LOS) MIMO algorithms focusses on diversity or data rate maximization: –Antennas will use as much space as possible to realize decorrelation between antennas –Turning rich multipath into an advantage and lose the gain in LOS cases

MIMO in mobile broadband A unfavourable aspect: Increased cost and size of the subscriber’s equipment Limits applicability on simple mobile devices A better opportunity: Wireless LAN modems – tablets - laptops

Agenda What is MIMO? Different gains of multiple antenna systems Fundamental Limits of Wireless Transmission Shannon capacity of Wireless Channels Multiple antennas at one end Capacity of MIMO Links Data transmission over MIMO Systems General principles Diversity using Space Time Block Codes Spatial Multiplexing Wireless channel modelling Theoretical Models Heurestic Models Broadband Channels Measured Channels System Level Issues Optimum use of multiple antennas MIMO in Mobile Broadband MIMO Transmission Scheme for HSPA and LTE

MIMO transmission schemes for LTE LTE supports downlink transmissions on one, two or four cell-specific antenna ports Up to two transport blocks can be transmitted simultaneously on up to four layers The use of multiple antennas in the DL of LTE comprises several modes The system adaptively switches between each mode to obtain the best possible performance as the propagation conditions vary LTE Transmission modes 1Single eNB antenna 2Tx diversity (SFBC) 3Open-loop SM 4Closed-loop SM 5Multi-user MIMO 6Beamforming 7UE specific RS

Downlink multi-antenna support in LTE Up to 4x4 antennas on downlink 8x8 on LTE-advanced Single-user schemes Transmit diversity (2) Spatial multiplexing (3, 4) Beamforming (6) Multi-user MIMO (5) A common physical layer architecture: September Single eNB antenna 2Tx diversity (SFBC) 3Open-loop SM 4Closed-loop SM 5Multi-user MIMO 6Beamforming 7UE specific RS

Downlink multi-antenna support in LTE Up to 4x4 antennas on downlink 8x8 on LTE-advanced Single-user schemes Transmit diversity (2) Spatial multiplexing (3, 4) Beamforming (6) Multi-user MIMO (5) A common physical layer architecture: September Single eNB antenna 2Tx diversity (SFBC) 3Open-loop SM 4Closed-loop SM 5Multi-user MIMO 6Beamforming 7UE specific RS

Transmit Diversity with 2 Tx antennas Alamouti scheme Transmitted diversity streams are orthogonal: Subcarrier (frequency) Port (antenna) Antenna port 0 x 1 x 2 Antenna port 1 -x 2 * x 1 * OFDM subcarriers 07 September

Downlink multi-antenna support in LTE Up to 4x4 antennas on downlink 8x8 on LTE-advanced Single-user schemes Transmit diversity (2) Spatial multiplexing (3, 4) Beamforming (6) Multi-user MIMO (5) A common physical layer architecture: September Single eNB antenna 2Tx diversity (SFBC) 3Open-loop SM 4Closed-loop SM 5Multi-user MIMO 6Beamforming 7UE specific RS

Downlink spatial multiplexing for 2x2 antennas The number of codewords equals the transmission rank and codeword n is mapped to layer n Rank one precoders are column subsets of the rank two precoders Recommendations on transmission rank and which precoder matrix to use is obtained via feedback from the subscriber equipment (UE) The base station (eNB) can override the rank recommended by the UE Codeword to layer mapping: Codeword 1Codeword 2 Rank 1Layer 1 Rank 2Layer 1Layer 2 Rank 3Layer 1Layer 2 and 3 Rank 4Layer 1 and 2Layer 3 and 4 07 September

Downlink multi-antenna support in LTE Up to 4x4 antennas on downlink 8x8 on LTE-advanced Single-user schemes Transmit diversity (2) Spatial multiplexing (3, 4) Beamforming (6) Multi-user MIMO (5) A common physical layer architecture: September Single eNB antenna 2Tx diversity (SFBC) 3Open-loop SM 4Closed-loop SM 5Multi-user MIMO 6Beamforming 7UE specific RS

DL peak throughputs in LTE September QAM Modulation MIMO config

Downlink MIMO for HSPA (3G) HSPA supports downlink closed-loop MIMO rank September 2011

Other multiple antenna schemes Multi-user (MU-) MIMO Spatial multiplexing to different UEs in the same cell Also called Spatial Division Multiple Access (SDMA) September 2011

Summary MIMO is using multiple antennas at both transmitter and receiver ends to set up a wireless link MIMO gains can be beamforming, diversity or spatial multiplexing Wireless link capacity can be multiplied by min(M,N) Data transmission exploits the spatial dimension by maximizing either data rate or diversity Wireless channel modelling is a tool to get the necessary understanding of perfoemence and be atool to analyze the impact of the design Optimum use of multiple antennas contain conflicting goals in the system design, especially when it comes to antenna sizes and design Both HSPA and LTE enables practical use of MIMO

Literature David Gesbert and Jabran Akhtar: ”Breaking the Barriers of Shannon’s Capacity: An Overview of MIMO Wireless Systems”. Telektronikk, 98(1), p53-54, G Americas White paper: "MIMO Transmission Schemes for LTE and HSPA Networks”, chapter 4, p Extra reading for those interested: David Gesbert etal.:” From Theory to Practice: An Overview of MIMO Space-Time Coded Wireless Systems”. IEEE Journal on Selected Areas in Comunications, 21(3), p , April A. Sibille, C. Oestges, A Zanella. ”MIMO: From Theory to implementation”. Academic Press, ISBN-10: , ISBN- 13: