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Multiple-Input-Multiple- Output (MIMO) Systems

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Presentation on theme: "Multiple-Input-Multiple- Output (MIMO) Systems"— Presentation transcript:

1 Multiple-Input-Multiple- Output (MIMO) Systems
Basic principles, Algorithms and Networking Applications HARISH GANAPATHY

2 Topics Motivations for the development of MIMO systems
MIMO System Model and Capacity Studies Design Criterion for MIMO Systems (Diversity Vs Spatial Multiplexing) Some actual architectures based on these criterion MIMO-OFDM Networking Applications: MAC protocol for MIMO PHY layer Conclusions


4 Aspirations (Mathematical) of a System Designer
Achieve “Channel Capacity (C)” High data rate Quality Minimize Probability of Error (Pe) Minimize complexity/cost of implementation of proposed System Minimize transmission power required (translates into SNR) Minimize Bandwidth (frequency spectrum) Used Real-life Issues

5 Antenna Configurations
Single-Input-Single-Output (SISO) antenna system channel User data stream Theoretically, the 1Gbps barrier can be achieved using this configuration if you are allowed to use much power and as much BW as you so please! Extensive research has been done on SISO under power and BW constraints. A combination a smart modulation, coding and multiplexing techniques have yielded good results but far from the 1Gbps barrier

6 MIMO Antenna Configuration
Use multiple transmit and multiple receive antennas for a single user User data stream . 1 2 MT MR channel Now this system promises enormous data rates!

7 Data Units Will use the following terms loosely and interchangeably,
Bits (lowest level): +1 and -1 Symbols (intermediate): A group of bits Packets (highest level): Lots and lots of symbols

8 Shannon’s Capacity (C)
Given a unit of BW (Hz), the max error-free transmission rate is C = log2(1+SNR) bits/s/Hz Define R: data rate (bits/symbol) RS: symbol rate (symbols/second) w: allotted BW (Hz) Spectral Efficiency is defined as the number of bits transmitted per second per Hz R x RS bits/s/Hz W As a result of filtering/signal reconstruction requirements, RS ≤ W. Hence Spectral Efficiency = R if RS = W If I transmit data at a rate of R ≤ C, I can achieve an arbitrarily low Pe

9 Spectral Efficiency Spectral efficiencies of some widely used modulation schemes Scheme b/s/Hz BPSK 1 QPSK 2 16-QAM 4 64-QAM 6 The Whole point: Given an acceptable Pe , realistic power and BW limits, MIMO Systems using smart modulation schemes provide much higher spectral efficiencies than traditional SISO

10 MIMO System Model y = Hs + n . Channel Matrix H s1 s2 sM s y1 y2 yM y
User data stream . Channel Matrix H s1 s2 sM s y1 y2 yM y Transmitted vector Received vector h11 h12 y = Hs + n h11 h …….. hM1 h12 h …….. hM2 h1M h2M …….. hMM …….. . MT MR hij is a Complex Gaussian random variable that models fading gain between the ith transmit and jth receive antenna Where H =

11 Types of Channels

12 Fading Channels Fading refers to changes in signal amplitude and phase caused by the channel as it makes its way to the receiver Define Tspread to be the time at which the last reflection arrives and Tsym to be the symbol time period Time-spread of signal Frequency-selective Frequency-flat Tsym Tspread time Tsym Tspread time freq 1/Tsym freq 1/Tsym Occurs for wideband signals (small Tsym) TOUGH TO DEAL IT! Occurs for narrowband signals (large Tsym) EASIER! Fading gain is complex Gaussian Multipaths NOT resolvable

13 Channel Matrix H Channel Time-variance Time-spread
In addition, assume slow fading MIMO Channel Response Time-spread Channel Time-variance Taking into account slow fading, the MIMO channel impulse response is constructed as, a and b are transmit and receive array factor vectors respectively. S is the complex gain that is dependant on direction and delay. g(t) is the transmit and receive pulse shaping impulse response Because of flat fading, it becomes, With suitable choices of array geometry and antenna element patterns, H( ) = H which is an MR x MT matrix with complex Gaussian i. i. d random variables Accurate for NLOS rich-scattering environments, with sufficient antenna spacing at transmitter and receiver with all elements identically polarized

14 Capacity of MIMO Channels
y = Hs + n Let the transmitted vector s be a random vector to be very general and n is normalized noise. Let the total transmitted power available per symbol period be P. Then, C = log 2 (IM + HQHH) b/s/Hz where Q = E{ssH} and trace(Q) < P according to our power constraint Consider specific case when we have users transmitting at equal power over the channel and the users are uncorrelated (no feedback available). Then, CEP = log 2 [IM + (P/MT)HHH] b/s/Hz Telatar showed that this is the optimal choice for blind transmission Foschini and Telatar both demonstrated that as MT and MR grow, CEP = min (MT,MR) log 2 (P/MT) + constant b/s/Hz Note: When feedback is available, the Waterfilling solution is yields maximum capacity but converges to equal power capacity at high SNRs

15 Capacity (contd) The capacity expression presented was over one realization of the channel. Capacity is a random variable and has to be averaged over infinite realizations to obtain the true ergodic capacity. Outage capacity is another metric that is used to capture this So MIMO promises enormous rates theoretically! Can we exploit this practically?

16 MIMO Design Criterion MIMO Systems can provide two types of gain
Spatial Multiplexing Gain Diversity Gain Maximize transmission rate (optimistic approach) Use rich scattering/fading to your advantage Minimize Pe (conservative approach) Go for Reliability / QoS etc Combat fading If only I could have both! As expected, there is a tradeoff System designs are based on trying to achieve either goal or a little of both

17 Diversity Each pair of transmit-receive antennas provides a signal path from transmitter to receiver. By sending the SAME information through different paths, multiple independently-faded replicas of the data symbol can be obtained at the receiver end. Hence, more reliable reception is achieved A diversity gain d implies that in the high SNR region, my Pe decays at a rate of 1/SNRd as opposed to 1/SNR for a SISO system The maximal diversity gain dmax is the total number of independent signal paths that exist between the transmitter and receiver For an (MR,MT) system, the total number of signal paths is MRMT 1 ≤ d ≤ dmax= MRMT The higher my diversity gain, the lower my Pe

18 Spatial Multiplexing y = Hs + n  y’ = Ds’ + n’ (through SVD on H)
where D is a diagonal matrix that contains the eigenvalues of HHH Viewing the MIMO received vector in a different but equivalent way, CEP = log 2 [IM + (P/MT)DDH] = log 2 [1 + (P/MT)גi] b/s/Hz Equivalent form tells us that an (MT,MR) MIMO channel opens up m = min (MT,MR) independent SISO channels between the transmitter and the receiver So, intuitively, I can send a maximum of m different information symbols over the channel at any given time

19 Practical System 1 2 MT Channel coding Symbol mapping Space-Time Coding . R bits/symbol rs : number of different symbols N transmitted in T symbol periods rs = N/T Redundancy in time Coding rate = rc Spectral efficiency = (R*rc info bits/symbol)(rs)(Rs symbols/sec) w = Rrcrs bits/s/Hz assuming Rs = w rs is the parameter that we are concerned about: 0 ≤ rs ≤ MT ** If rs = MT, we are in spatial multiplexing mode (max transmission rate) **If rs ≤ 1, we are in diversity mode Non-redundant portion of symbols Space- time redundancy over T symbol periods Spatial multiplexing gain = rs

20 V-BLAST – Spatial Multiplexing (Vertical Bell Labs Layered Space-Time Architecture)
This is the only architecture that goes all out for maximum rate. Hope the channel helps me out by ‘splitting’ my info streams! . s1 s2 sM s User data stream y1 y2 yM y H V-BLAST Processing MT ≤ MR Split data into MT streams  maps to symbols  send Assume receiver knows H Uses old technique of ordered successive cancellation to recover signals Sensitive to estimation errors in H rs = MT because in one symbol period, you are sending MT different symbols

21 V-BLAST (Experimental Results)
The prototype in an indoor environment was operated at a carrier frequency of 1.9 GHz, and a symbol rate of 24.3 ksymbols/sec, in a bandwidth of 30 kHz with MT = 8 and MR = 12 Results shown on Block-Error-Rate Vs average SNR (at one received antenna element); Block = 100 symbols ; 20 symbols for training Each of the eight substreams utilized uncoded 16-QAM, i.e. 4 b/symb/trans Spec eff = (8 xmtr) ( 4 b/sym/xmtr )(24.3 ksym/s) 30 kHz = bps/Hz In 30 kHz of bandwidth, I can push across 621Kbps of data!! Wireless spectral efficiencies of this magnitude are unprecedented, and are furthermore unattainable using traditional techniques

22 Alternate Receivers Can replace OSUC by other front-ends; MMSE, SUC, ML for instance OSUC ML

23 D-BLAST – a little of both (Diagonal Bell Labs Layered Space-Time Architecture)
In D-BLAST, the input data stream is divided into sub streams which are coded, each of which is transmitted on different antennas time slots in a diagonal fashion For example, in a (2,2) system receiver first estimates x2(1) and then estimates x1(1) by treating x2(1) as interference and nulling it out The estimates of x2(1) and x1(1) are fed to a joint decoder to decode the first substream MT ≤ MR After decoding the first substream, the receiver cancels the contribution of this substream from the received signals and starts to decode the next substream, etc. Here, an overhead is required to start the detection process; corresponding to the 0 symbol in the above example Receiver complexity high

24 Alamouti’s Scheme - Diversity
Transmission/reception scheme easy to implement Space diversity because of antenna transmission. Time diversity because of transmission over 2 symbol periods Consider (2, MR) system Receiver uses combining and ML detection rs = 1 V-BLAST SUC If you are working with a (2,2) system, stick with Alamouti! Widely used scheme: CDMA 2000, WCDMA and IEEE OFDM-256 Alamouti

25 Comparisons Scheme Spectral Efficiency Pe Implementation Complexity

26 Orthogonal Frequency Division Multiplexing (OFDM)
As the data rate increases in a multipath environment, the interference goes from flat fading to frequency selective (last reflected component arrives after symbol period). This results in heavy degradation Most popular solution to compensate for ISI: equalizers As we move to higher data rates (i.e.> 1 Mbps), equalizer complexity grows to level of complexity where the channel changes before you can compensate for it! Alternate solution: Multi-carrier Modulation (MCM) where channel is broken up into subbands such that the fading over each subchannel becomes flat thus eliminating the problem of ISI Multi-carrier Modulation FDMA OFDM

27 OFDM Spectral Efficiency
Rs symbols/s Rs/ 3 symbols/s The spectral efficiency of an OFDM-(PSK/ASK) system is same as compared to using the (PSK/ASK) system alone Spec eff = log 2 M bits/s/Hz However, you have successfully converted an ugly channel into a channel that you can use easy to implement Used in IEEE A, .11G, HiperLAN, IEEE

28 MIMO-OFDM OFDM extends directly to MIMO channels with the IFFT/FFT and CP operations being performed at each of the transmit and receive antennas. MIMO-OFDM decouples the frequency-selective MIMO channel into a set of parallel MIMO channels with the input–output relation for the ith (i = 0, 2,…,L-1) tone, yi = Hisi + ni i = 0, 2,…, L-1

29 IEEE MAC (DCF Mode) As a result of the CSMA/CA with RTS/CTS MAC protocol, two issues arise -the unfairness problem -extreme throughput degradation (ETD) Throughtput T23 > Throughtput T01 Unfairness Both throughtput T23 and throughtput T01 are equally affected ETD

30 MIMO-Based Solutions Use multiple transmit and receive antennas
Again, MIMO provides -increase in rate -decrease in Pe (as a result of diversity, interference cancellation abilities etc) Simply use with MT= MR=2. VBLAST-ZF is an option Two data streams transmitted from node 0 to 1 instead of 1. Increases transmission rate. Increases overall capacity of network Does not address unfairness and ETD

31 MIMO-Based Solutions MIMA-MAC Protocol
Mitigating Interference using Multiple Antennas (MIMA) MAC protocol [Tang, Park, Nettles, Texas at Austin, submitted to Proc. ACM Mobicom, Philadelphia, PA, USA on Sep. 26 – Oct. 1, 2004] Each transmitter views it as a (1,2) MIMO system. Unfairness (SDT): node 1 concentrates on node 0 and supresses node 2. Increase in throughput Tro1 ETD (ODT): node 1 concentrates on node 0 and supresses node 3 stream. node 2 concentrates on node 3 and supresses node 0 stream. Increase in throughput Tro1 and Tr32

32 Throughput degradation
Simulation Results SDT Unfairness ODT Throughput degradation

33 Summary/Conclusions MIMO Systems are getting us closer to the 1Gbps landmark (aspiration 1) At the same time, they provide reliable communications (aspiration 2) Different architectures available for use Developing efficient network protocols for a MIMO PHY layer is an area of open research

34 References “Layered Space-Time Architecture for Wireless Communication in a Fading Environment When using Multi-Element Antennas”, G.J.Foschini, Bell Labs Tech Journal, 1996 “An Overview of MIMO Communications – A Key to Gigabit Wireless”, A.J Paulraj, Gore, Nabar and Bolcskei, IEEE Trans Comm, 2003 “Improving Fairness and Throughput of Ad Hoc Networks Using Multiple Antennas”, Park, Choi and Nettles, submitted Mobicom 2004 “From Theory to Practice: An Overview of MIMO Space-Time Coded Wireless Systems”, Gesbert et al.,IEEE Sel Comm, 2003 “On Limits of Wireless Communications in a Fading Environment”, Foschini and Gans, Wireless Personal Comm, 1998 “A Simple Transmit Diversity Technique for Wireless Communications”, Alamouti, IEEE Sel Comm, 1998 “Diversity and Multiplexing: A Fundamental Tradeoff in Multiple-Antenna Channels”, Zheng and Tse, IEEE Trans Info Theory, 2003 “V-BLAST: An Architecture for Realizing Very High Data Rates Over the Rich-Scattering Wireless Channel”, Wolniansky, Foschini, Golden and Valenzuela, Electronic Letters, 1999 “MIMO-OFDM Systems for High Data Rate Wireless Networks”, Whu

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