MIMO in 3G STATUS. MIMO for high speed data in 3G systems. Outline. Information theory for wireless channels
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1 MIMO in G STATUS MIMO for high speed data in G systems Reinaldo Valenzuela Wireless Communications Research Department Bell Laboratories MIMO (multiple antenna technologies) provides higher peak data rates and better coverage than conventional systems. Up to Mbps in.mhz bandwidth MIMO Status Developed fundamental MIMO theories (BLAST) Applied these theories to CDMA data systems Active in GPP and GPP MIMO standards Standards progress slow MIMO in EV-DO/DV in or years Terminal ASICs Developed key MIMO ASICs Demonstrated MIMO G high speed data system using these ASICs. of of Outline What are the ultimate limits of wireless communication? What are the characteristics of the wireless channel? What algorithms would we use to achieve high peak data rates? How do we design a practical implementation? How do we put it all together? of Information theory of multiple antenna links Channel Measurements System design for BLAST in G downlink data systems BLAST ASIC design BLAST demonstrations and prototypes Information theory for wireless channels of Conventional wireless communication systems use a single antenna at the transmitter and receiver. Transmitter CC Receiver Capacity C bps for a given SNR and bandwidth How do we achieve higher data rates without increasing transmit power or bandwidth?
2 Information theory for wireless channels It was recently shown that the maximum capacity of wireless systems grows linearly with the number of antennas [Jerry Foschini, Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas, Bell Labs Technical Journal, Vol., No., 99.]... N NC NC NW log ( + SNR) bps = + = Mbps /.8MHz log ( )bps SNR = db Information theory for wireless channels High capacities are achieved by sending N independent data streams. Required that the antennas are spatially uncorrelated. Channel has rich scattering: not line of sight Large angle spread Antennas are spaced sufficiently far apart. What are the characteristics of wireless channels with multiple antennas? of of Overview of multiple antenna channel measurements Several million channel realizations were measured in midtown Manhattan (dense urban environment). transmitters receivers Transmitter x8 transmitter array of patch antennas with polarizations. Carrier frequency. GHz, tones each separated by KHz. Transmitter placed on a 8th floor balcony of New Yorker Hotel in midtown Manhattan. Transmit power per antenna is dbm. Shannon capacities computed based on measurements. 8 9 lambda lambda = m = ft. lambda of 8of
3 Transmitter s view from balcony Receiver x receiver array of patch antennas with polarizations. / wavelength element spacing. Placed on side of van at height of m. bit A/D converters for each channel coefficient. One channel realization is a = x complex matrix of coefficients. One channel realization measured each. ms. Received SNR is at least db to ensure sufficient accuracy. 9of of Receiver on measurement van Drive route and capacity HOTEL Tx Rx db System SNR > db Measured SNR Mile Range RED Very High to 88 bps/hz YELLOW High to bps/hz GREEN Med. to bps/hz BLUE Low. to bps/hz of of
4 Cumulative distribution of capacity System design Probability Capacity > Abscissa CCDF of Measured Capacity & Theoretical Capacities (,) conventional (,) measured channels (,) ideal closed loop transmit diversity (,) uncorrelated channels CDF is of entire dataset Measured capacity is 9% of ideal capacity. Conclusion: dense urban channels have sufficient richness to support MIMO. What algorithms would we use to realize the high capacities predicted by information theory and channel measurements? Link level: transmit multiple data streams simultaneously System level: incorporate into a G downlink data system. 8 Capacity in bps/hz at db SNR of of of System design: Link level Bell Labs Space-Time (BLAST) architecture is a technique for achieving Shannon capacity. Multiple simultaneous transmissions with independent data rates. Signals overlap in time and frequency. Spreading codes are reused for each antenna BLAST for CDMA is called code reuse. Example: N = antennas High speed data stream demultiplexer Coding, rate / Coding, rate / QAM mapping QPSK mapping demultiplexer demultiplexer Spreading code Spreading code Scrambling code of System design: G downlink packet data systems Conventional transmission Data rate depends on channel quality (SINR) Channel quality is fed back to base on uplink Transmit to a single user at a time. BLAST transmission Data rate for each transmit antenna depends on its SINR. Channel quality for each antenna is fed back to base Transmit to a single user at a time using from to N antennas.
5 System design: Conventional transmission System design: BLAST transmission 8 possible data rates N = Kbps code rate constellation. / QAM high 8. / 8PSK 8.8 / PSK. / PSK. / PSK. / PSK.8 / PSK 8. / PSK low SINR 9 N - possible data rates Use only a subset to reduce feedback bits. ( ) Selection transmit diversity is a special case where transmission is from one antenna. ( ) Simultaneous low data rate transmissions are not spectrally efficient. For low geometry, transmit with single antenna. Ant N = Ant of 8 of Example of a G downlink packet data system Simulation methodology Achievable data rate Mbps 8Kbps Mbps Kbps time user served A B C D actual data rate for d = to D drops sample geometry (average SINR) of K users. for f = to F frames. Compute channel matrix H for each user (evolved according to a generalized Jakes model).. Compute SINR for each user. Determine requested data rates for each user. Base station selects user to transmit to based on proportional fair scheduling algorithm. end end A, C A A, B, C D transmit antennas used 9 of of
6 Simulation assumptions EV-DO system (. MHz bandwidth) Ideal channel estimation Ideal uplink feedback (no errors or delays) Proportional fair scheduler Mobile speed km/hr Flat fading Spatially uncorrelated channel Perfect steering (ideal db gain) for steered PARC Architectures considered (x,y): x transmit antennas, y receive antennas per mobile (,) : baseline (,),(,): or antenna rx diversity (,),(,) STD: no code reuse, selection diversity only (,),(,) CR: code reuse with MMSE-IC receiver. of of Sector throughput (kbps) of Average sector throughput (,) (,) (,) (,) (,), STD (,), CR 8 Number of users.... Sector throughput (kbps) Throughput gain versus (,) (,) (,) (,), STD (,), CR 8 Number of users per sector.... of Probability of achieving high data rates Probability of achieving rate R (,) (,) CR (,) CR R < Kbps Kbps R <.Mbps R.Mbps Achieve higher data rates much more often with MIMO.
7 Implementation: Mobile System Architecture Implementation: BLASTCHIP algorithm MLAPP algorithm: External Interface Delay Locking Input MEMORY Freq Offset Comp INTERFACE Control Interface (CQI) RF FRONTEND CE Interface INTERFACE Optimal solution for detector is to use Maximum Likelihood (ML). Soft information derived by approximating the a posteriori probability (APP) on each bit Maximum likelihood is computationally feasible for the antenna counts and constellation sizes proposed for GPP and GPP. DESPREADER MIMO DETECTOR x QPSK x QAM x 8PSK CHANNEL ESTIMATION LLR MEM MIMO (BLASTCHIP) ASIC H-ARQ, interleaving Control H-ARQ, interleaving, rate matching (off-chip SRAM) Input MEMORY ACK/ NACK OUTPUT MEM TURBO DECODER TURBO ASIC Almost a db gain over V-BLAST * Maximum likelihood search xˆ J = arg min J x ML = r Hˆ x ML Computation of soft information (approximate log likelihood ratio) LLR ( i) = min J min b ( x ML J ) = ( x ) = i b ML i Looks at next closest cost on per bit basis of * V-BLAST is still attractive for larger constellations & more antennas. of of Implementation: BLASTCHIP Architecture Main units: HX unit : generate and cache post-channel candidates. LLR units : Compute soft information 8 of Implementation: Turbo decoder Raw channel rates up to Mbps must be processed. Achieved by compressing radix- trellis into radix- trellis Two trellis time steps compressed into one Simplified add-compare-select unit to process combined transitions Radix- Radix- t- t- t t- t
8 9 of ASICs:.8µm CMOS,.88MHz,.8V core. HX cache OUTPUT BUFFER INPUT SYMBOL HISTORY MEMORY BLASTCHIP receiver TR Turbo Decoder Incorporates despreading, channel estimation, and Mbps throughput using MLAPP detection top frequency = MHz Supports up to x QPSK, x QAM, x 8PSK Full LogMAP soft-in soft-out (SISO) Supports up to, length spreading codes Fully GPP compliant.mm core area.mm core area. Proves feasibility of MIMO for G data of HSDPA demonstration at CTIA At CTIA- (March th, in New Orleans, USA ).Mbps raw data rate, ~.Mbps protected bit rate Running on Commercial Flexent OneBTS Base Station with prototype channel card Two laptops running kbps video and browser applications Full (but lean) protocol stack including MAC-hs and MAC-d with Ethernet connection to laptops Mobile receiver prototyped with BLASTCHIP and turbo decoder ASICs. Powerful and reconfigurable channel card for evaluating variations and proposals (MIMO, HSUPA, etc.) First (probably) G data demonstration with full protocol stack HSDPA demo: Flexent OneBTS Base Station HSDPA demo: User equipment Ethernet RF Receivers to laptop 8 Power PC Uplink cable (ACK/NAKs on cable due to Spectrum constraints at CTIA) INTER- LEAVER ADDR TABLES INTER- LEAVER / DEINTER- LEAVER UCR- Radio cards (only needed) CTU Timing board TMSC DSP XCV FPGA TR Turbo MIMO ASICs on reverse side of board of Ethernet to Server XCU (in test card) of (Pictured with emulators and test connections attached.)
9 Conclusions G data systems enhanced with MIMO technologies provide higher peak data rates and better coverage than conventional systems. Bell Laboratories has made substantial MIMO technologies Developed fundamental theories Comprehensive MIMO channel measurements Leading MIMO standards in GPP and GPP Developed key MIMO ASICs Demonstrated MIMO G data prototype of
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