"Communications in wireless MIMO channels: Channel models, baseband algorithms, and system design"
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1 Postgraduate course on "Communications in wireless MIMO channels: Channel models, baseband algorithms, and system design" Lectures given by Prof. Markku Juntti, University of Oulu Prof. Tadashi Matsumoto, University of Oulu/ Elektrobit Docent Ian Oppermann, University of Oulu/ Southern Poro Comm. Docent Juha Ylitalo, Nokia/University of Oulu
2 Course description Dates 1. Introduction (2h) --Juha Capacity limits of MIMO channels (4h) Markku 22.10, MIMO radio channel models (4h) Juha/Ian 29.10, Beamforming and diversity (2h) --Juha Adaptive antenna algorithms (4h) --Juha 7.11, Example: BLAST (PARC) approach for MIMO (2h) --Juha Transmit diversity (4h) Matsumoto , Example: Transmit diversity techniques for WCDMA (2h) --Juha Advanced receivers for MIMO: space-time equalisation (4h) Tad 28.11, Future prospects for MIMO/ Panel discussion (2h) --All at 1pm Lectures on Tuesdays and Thursdays Place: Room TL201 (Tutkijantie 2E) Time: 14:15-16 (except lecture 10)
3 Course description, cont'd Exam : Date to be determined. Please remember to register for the exam. Literature: Mainly journal articles (to appear soon) Prerequisites: Necessary: Signals and systems, Digital Filters, Random Signals, Digital Communications I, Digital Communications II, Coding Methods, Radio Communication Channels. Recommended: Statistical Signal Processing. Useful background: Information Theory. Requirements: Final exam and a few homework problems Credit units: To be determined
4 Course description, cont'd As a part of the course an optional homework project will be arranged. To receive extra credit units a student may design and perform a simple study using Matlab. The study may consist of Monte-Carlo simulations for Shannon capacity or design of a simple CDMA transmitterchannel-receiver chain with multiple antennas and its performance evaluation compared to a single antenna transmitter/receiver. A report of the study shall be written. Report could be in a form of a 5-page conference paper.
5 Introduction to the MIMO course Short historical note Advantages of multi-antenna techniques Single signal through "Smart" antennas (=adaptive antennas) correlated channels - Beamforming: spatial focusing of correlated signals - Rx/Tx diversity: combining of decorrelated signals - MIMO: increasing spectral efficiency/ data rates Single signal through decorrelated channels Simple example: SINR improvement Definition of MIMO Spatial correlation matrix Example: Diversity & MIMO in WCDMA Multiple parallel signals through decorrelated channels
6 Historical Note Multiple antenna transmission used by Marconi in 1901 Four 61m high tower antennas (circular array) Morse signal for "S" from England to Signal Hill, St. John, Newfoundland, distance 3425km Submarine sonar during 1910's Acoustic sensor arrays 1910's RF radars 1940's Ultrasonic scanners from 1960's
7 Advantages of Multiple Antenna Techniques Resistivity to fading (quality) Increased coverage Increased capacity Increased data rate Improved spectral efficiency Reduced power consumption Reduced cost of wireless network Some challenges: - RF: Linear power amplifiers, calibration - Complex algorithms: DSP requirements, cost - Network planning & optimisation Demonstration by Lucent with 8 Tx /12 Rx antennas: 1.2 Mbit/s in 30kHz
8 What are Smart Antennas? A smart antenna system consists of several antenna elements, whose signals are processed adaptively in order to exploit the spatial dimension of the mobile radio channel. Weight Adaptation RF IF RF IF + RF IF Baseband processing It is not the antenna that is smart, but the antenna system!
9 Introduction - Beamforming Conventional BTS: radiation pattern covers the whole cell area Smart Antenna BTS: adaptive radiation pattern, "spatial filter" transmission/reception only to/from the desired user direction minimise antenna gain to direction of other users Conventional BTS radiation pattern Smart Antenna BTS
10 Introduction - Beamforming Beamforming = phasing the antenna array elements 0-5 DOA = 0 deg. 1 DOA = 30 deg. 2 M=8 Array Gain [db] Azimuth [deg]
11 Introduction - Beamforming (cnt.) Individual antenna elemens experience small delay differences coherence between elements assumed element spacing ~λ/2 Basic assumptions: plane waves impinging array geometry known ( "spatial reference" ) transceivers calibrated d narrowband signals ( run time over array << inverse of system bandwidth ) kd sin(θ) θ 1 2 Μ 1 Μ Observed phase difference can be used for direction-of-arrival (DOA) estimation Delay difference => phase difference: θ τ = (d sin θ) /c ϕ = k d sin θ k=2π / λ
12 Introduction - RX Diversity Basic Principles: uncorrelated (statistically independent) signals received spatial combining andof polarisation independently diversity fading arrangements signals: Diversity antenna Maximum Ratio Combining (MRC) λ/2 Interference Rejection Combining (IRC) coverage improvement in WCDMA utilisation of GSM footprint for data services RX Beamforming array RX RX RX db 10 Separation in space-wcdmand/or in polarisation domain Transceiver Received signal power -10 SRC Rx diversity Seconds, 3km/h Combined received signal SRC= Smart Radio Concept (4-branch Rx diversity)
13 Introduction - Transmission Diversity Multiple antennas available at the BTS for RX diversity Conventional terminal: only one antenna downlink suffers from lack of diversity RX diversity in MS is not favored due to complexity reasons (cost, power consumption) Downlink: Use TX instead of RX diversity Uncorrelated fading Signal #1 Signal #2 (1) Gain against fading TX diversity gain: Gain against fading Feedback modes: coherent combining ("beamforming") gain (2) Coherent combining gain (only feedback modes) Downlink capacity improvement
14 MIMO Definition Starting point: SISO, SIMO Single-Input, Single-Output channel suffers from fading Single-Input, Multiple-Output channel: receive diversity Data stream Single-Input Single-Output SISO radio channel Data stream Data stream SIMO radio channel Single-Input Multiple-Output Combiner Data stream
15 Definition of MIMO Multiple-Input, Multiple-Output channel Mapping of a data stream to multiple parallel data streams and de-mapping multiple received data streams into a single data stream Aims at high spectral efficiency / high data rate Data stream Serial/ parallel mapping R x x MIMO radio channel R y y Parallel/ serial mapping Data stream Aims at high spectral efficiency Requires rich scattering environment
16 Spectral efficiency: WCDMA Capacity UL Load Factor (N speech users): η E b UL = + / N 0 N a (1 ) W / R i DL Load Factor (N speech users): η DL N ( Eb / N0) j = a j [(1 bj ) + i W / R j= 1 j j ] E b /N 0 = required SINR at the receiver, W= CDMA chip rate, R= user bit rate, α= activity factor, i= intercell interference, b j = orthogonality factor
17 144kbpsCoverage/CapacityinMacro Cells Max. allowed path loss [db] Downlink load curve Better coverag e Coverage is uplink limited Capacity is downlink limited Uplink load curve with RX diversity for kbps Load per sector [kbps]
18 Nokia Smart Radio Concept Phase 1: Increase Uplink Coverage Max. allowed path loss [db] Uplink load curve with SRC db coverage improveme nt with SRC Uplink load curve without SRC Load per sector [kbps]
19 Nokia Smart Radio Concept Phase2:IncreaseDownlinkCapacity Max. allowed path loss [db] Downlink with TX diversity, 20W per branch 160 Downlink 20W no diversity % increase in capacity Load per sector [kbps]
20 Introduction to MIMO concepts Reference: Foschini and Gans, "On limits of wireless communications in a fading environment when using multiple antennas", Wireless Personal Communications, vol. 6, no.3, 1998
21 Introduction to MIMO Maximum Gain: Transmit Diversity s1, s2, s3, s4 a) Maximum Capacity: Parallel channel transmission V1 V2 V3 V4 Same signal on all antennas, i.e. conventional Tx diversity s1 s2 s3 s4 V1 V2 V3 V4 Different signals on Tx antennas. i.e. true MIMO BLAST (PARC) type b) of tranmission scheme is considered as MIMO, whereas STTD is a hybrid, considered as a Tx diversity scheme
22 Channel capacity (Shannon) Represents the maximum error-free bit rate Capacity depends on the specific channel realization, noise, and transmitted signal power. log 1 P C + α 2 2 Single-input single-output (SISO) channel σ n y ( t) = α x( t) + n( t) = 2 Multi-input multi-output (MIMO) channel y ( t) = Hx( t) + n( t) C 1 det + σ n = H log2 I HQH 2 -Qis the covariance matrix of the transmitted vector
23 Power allocation strategies - Uniform power distribution Transmission power has to be properly distributed over the antennas to maximize the capacity For unknown channel uniform power distribution over the antennas can be applied Q = P n T I which gives C det + P / n σ = T H log2 I HH 2 n For fading channel ergodic capacity can be found by Monte-Carlo simulations
24 Power allocation strategies Water-filling For known channel optimum power distribution using the water-filling technique can be applied The water-filling algorithm can be derived after converting the MIMO channel into a set of L parallel channels using a SVD of the channel matrix H = UDV ~ y ( t) = λ ~ x ( t) + n~ ( t) k k k k H 1 k L p k = K σ 2 n λk yielding the following optimum power allocation
25 Capacity results Uncorrelated Rayleigh MIMO channel (I) Capacity CDFs for uncorrelated flat-freq. Rayleigh channels ( db) Probability(capacity>abcisa) SISO MIMO(1,2) Unknow n MIMO(2,1) Know n MIMO(2,1) MIMO(1,4) Unknow n MIMO(4,1) Know n MIMO(4,1) Capacity in bits per second per Hertz
26 Capacity results Uncorrelated Rayleigh MIMO channel (II) 0.99 Capacity CDFs for uncorrelated flat-freq. Rayleigh channels ( db) 1 Probability(capacity>abcisa) SISO Unknow n MIMO(2,2) Know n MIMO(2,2) Unknow n MIMO(2,4) Know n MIMO(2,4) Unknow n MIMO(4,2) Know n MIMO(4,2) Unknow n MIMO(4,4) Know n MIMO(4,4) Capacity in bits per second per Hertz
27 Capacity results Fully correlated Rayleigh MIMO channel (I) Probability(capacity>abcisa) Capacity CDFs for correlated flat-freq. Rayleigh channels ( db) SISO MIMO(1,2) Unknow n MIMO(2,1) Know n MIMO(2,1) MIMO(1,4) Unknow n MIMO(4,1) Know n MIMO(4,1) Capacity in bits per second per Hertz
28 Capacity results Fully correlated Rayleigh MIMO channel (II) 1 Capacity CDFs for correlated flat-freq. Rayleigh channels ( db) Probability(capacity>abcisa) SISO Unknow n MIMO(2,2) Know n MIMO(2,2) Unknow n MIMO(2,4) Know n MIMO(2,4) Unknow n MIMO(4,2) Know n MIMO(4,2) Unknow n MIMO(4,4) Know n MIMO(4,4) Capacity in bits per second per Hertz
29 MIMO versus Rx/Tx Diversity (theoretical) Spectral efficiency of one channel, no diversity: C=log 2 (1+SNR) [b/s/hz] MIMO with N Tx and M Rx antennas, unknown channel: C=Nlog 2 (1+SNR*M/N) [b/s/hz] M=N=>C=Nlog 2 (1+SNR) [b/s/hz] Rx & Tx diversity: N Tx and M Rx antennas, known channel: C=log 2 (1+SNR*M*N) [b/s/hz]
30 MIMO vs. diversity approaches True MIMO has a theoretical potential at high SNRs, while conventional Rx schemes are more attractive at low SNRs
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