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PHY and MAC Proposal for IEEE 802.11n

PHY and MAC Proposal for IEEE 802.11n. Andreas F. Molisch, Daqing Gu, Jinyun Zhang, Neelesh Mehta Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA (molisch, dgu, jzhang ,mehta)@merl.com Yukimasa Nagai, Hiroyoshi Suga, Fumio Ishizu, Keishi Murakami

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PHY and MAC Proposal for IEEE 802.11n

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  1. PHY and MAC Proposal for IEEE 802.11n Andreas F. Molisch, Daqing Gu, Jinyun Zhang, Neelesh Mehta Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA (molisch, dgu, jzhang ,mehta)@merl.com Yukimasa Nagai, Hiroyoshi Suga, Fumio Ishizu, KeishiMurakami Mitsubishi Electric Corporation 5-1-1 Ofuna, Kamakura Kanagawa, Japan, 2478501 (yuki-n, hsuga, ishizu, kmurak)@isl.melco.co.jp Jianxuan Du, Ye (Geoffrey) Li Georgia Institute of Technology (jxdu, liye@ece.gatech.edu) Jeffrey (Zhifeng) Tao Polytechnic University (jefftao@photon.poly.edu) Yuan Yuan University of Maryland, College Park (yuanyuan@cs.umd.edu) Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  2. Outline • Introduction • Proposal for High Rate PHY • Baseline system • Proposed technologies • Statistical rate allocation • RF-baseband processing for antenna selection • QBD-LDPC space time coding for layered structure • Summary • Proposal for High Efficiency MAC • MAC structure • Proposed techniques • ADCA for CP • SCCA for CFP • Frame aggregation • Block ACK enhancement • Summary • Conclusions Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  3. Introduction • Goals • Dramatic increase of data rate in PHY • 100 Mbps required throughput at MAC SAP • High MAC efficiency and QoS • Backward compatibility • Compatible with existing 802.11 standards • Low complexity • Our approach • Maintain backward compatibility • Rely on mature technology & existing standard framework • Be innovative • Develop new technologies which can be easily incorporated to achieve high data rate and high efficiency • Focus on inexpensive solution • Optimize the performance/cost ratio Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  4. PHY Baseline • Basic MIMO-OFDM system with layered structure (VBLAST) • Receiver uses linear processing and successive interference cancellation • 2x2 antenna modes with 20 MHz channelization as mandatory, 3x3 and 4x4 as optional • Convolutional codes, with coding rates of ½, 2/3, ¾, and 7/8, mandatory for backward compatibility. • Low-density parity check (LDPC) codes as options Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  5. System Block Diagram (2x2 Case) Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  6. Proposed Key Technologies • Statistical rate allocation for different layers • RF-baseband processing for antenna selection • QBD-LDPC coding for layered systems • Each above technology, or any form of their combination, can be used for performance enhancement Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  7. Proposed Key Technologies • Statistical rate allocation for different layers • RF-baseband processing for antenna selection • QBD-LDPC coding for layered systems Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  8. Rate Allocation • Problems with existing layered systems (e.g. V-BLAST) • The information rates for all layers are the same • The first layer to be detected has low channel quality due to the loss of signal energy after linear nulling • The errors from previous layers propagate to later layers by successive interference cancellation (SIC) • Sorting layers by SNR does not improve the situation significantly in frequency-selective channels • Layer-dependent rates • It is proved that with instantaneous rate feedback and SIC, the layered structure can achieve the open-loop capacity • Problem: requires instantaneous feedback; that can be sensitive to channel variations Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  9. Statistical Rate Allocation • Our proposed solution • We propose to statistically determine the optimal data rates for different layers to avoid instantaneous rate feedback • Detection order of the layers is fixed; different layers cycle through different transmit antennas • Different layers have different data rates that are statistically determined by the channel quality. Due to V-BLAST principle, different layers have different capacities • Data rates for the layer are chosen so that meeting a certain outage probability is guaranteed! Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  10. Data P Channel QAM IFFT Encoder Modulator Input Demultiplexer P Channel QAM IFFT Encoder Modulator Statistical Layer Rate Allocation Transmitter Structure with Statistical Rate Allocation Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  11. Algorithm and Advantages • Algorithm • Compute the means and variances of different layer capacities based on the past observations • Determine the data rates for each layer for a given outage probability • Choose the closest rate from the supported data rates set as data transmission rate • Advantages • No instantaneous rate feedback is needed. Thus no explicit feedback mechanism is necessary. • Only the first and second moment statistics of each layer capacity are used to determine the modulation and code rate for each layer. • Statistical information can be collected from ACK packets sent from the receiver. Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  12. Simulation Result 1000-byte packets Channel model B Conventional system: 64 QAM, rate ¾ CC; 108 Mbps Statistical rate allocation: layer 1: 16 QAM, rate ¾ CC; 36 Mbps layer 2: 64 QAM, rate 7/8 CC; 63 Mbps rates optimized for outage probability: 1% Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  13. Proposed Key Technologies • Statistical rate allocation for different layers • RF-baseband processing for antenna selection • QBD-LDPC coding for layered systems Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  14. The First Idea: Antenna Selection • Additional costs for MIMO • More antenna elements (cheap) • More signal processing (Moore’s law) • One RF chain for each antenna element • Basic idea of antenna selection: • Have many antenna elements, but select only best for down-conversion and processing • Diversity order is determined by number of antenna elements, not by number of RF chains • Hybrid antenna selection: select best L out of available N antenna elements, use those for processing • Need as many RF chains as data streams Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  15. One Step Better: RF-Preprocessing with Antenna Selection • Problem with antenna selection: significant loss of SNR in correlated channels • Mean SNR gain is determined by number of RF chains • Our solution: • Perform processing in RF domain, i.e., before selection is done • Reduce implementation cost by using only phase-shifter and adder in RF processing • Solution can be based on instantaneous channel state information (CSI), average CSI, or no CSI • Maintains diversity gain AND mean-SNR gain Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  16. 1 2 Selection of the Preprocessing Matrix • No Channel Information • FFT based Pre-Processing • Simple • Beam pattern cannot adapt to the angle of arrival • Instantaneous Channel Information • Orient the beams with the angle of arrival of the incoming rays • Require continuous updating of entries of pre-processor Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  17. Channel Statistics-Based Pre-Processing • Pre-processor depends on channel statistics • Orients the beam with the mean angle of arrival • Optimal solution performs principal component decomposition on columns of H • Advantages • Continuous updating of entries of M not required • Optimum patterns independent of frequency! Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  18. Transmitter Structure Data P Channel QAM IFFT Encoder Modulator Input Joint RF- Demultiplexer baseband Processing P Channel QAM IFFT Encoder Modulator Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  19. Simulation Result 1000-byte packets Channel model B, D 64 QAM, rate ¾ CC; 108 Mbps Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  20. Proposed Key Technologies • Statistical rate allocation for different layers • RF-baseband processing for antenna selection • QBD-LDPC coding for layered systems Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  21. Why LDPC? • Capacity approaching performance • Parallelizability of decoding, suitable for high speed implementation • Flexibility: LDPC is simply a kind of linear block code and its rate can be adjusted by puncturing, shortening, etc. Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  22. Quasi-Block Diagonal LDPC Space-time Coding (QBD-LDPC) for Layered Systems • Feature: The encoding of different layers is correlated as compared with conventional layered systems. • Advantage: The space-time LDPC is designed such that the code can be decoded partially with the help of other layers (undecoded part) by the introduction of correlation between different layers Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  23. System Diagram for QBD-LDPC QAM IFFT Data Modulator Input QBD-LDPC P Space-time Encoder QAM IFFT Modulator Decoder FFT Soft Output Demodulator - P 1 + QBD-LDPC Decoder FFT Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  24. Parity check matrix for conventional LDPC-coded V-BLAST. Parity check matrix for QBD-LDPC. Parity Check Structure of QBD-LDPC Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  25. Encoding of QBD-LDPC • Wn Hn= [PnI] by Gaussian elimination. • The parity check bits for layer n are given by Pnun+ WnCn-1bn-1 , where is un the input information bit vector for layer n, and bn-1 is codeword for layer n-1. • With the given structure, the information about layer n-1 is also contained in layer n. Therefore, information from layer n can help decoding layer n-1. Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  26. Decoder of QBD-LDPC Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  27. Decoding of QBD-LDPC • The decoding is based on linear nulling and interference cancellation, which is made possible by the lower-triagular structure of the parity check matrix. • The LLR’s of bits in successfully decoded subcodes are set to maximum or minimum value, depending on the output, to avoid ambiguity caused by the introduction of connection matrices • The decoding of layer n is stopped as soon as is satisfied, where bn-1 is fixed based on decoded layer n-1. Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  28. Simulation Results 1152 bits/block Channel model F Code rate: ½ Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  29. Summary of PHY Technologies • The proposed solution provides a good tradeoff between performance, complexity and compatibility requirements and cost. • Low complexity: The complexity of linear processing + SIC scales linearly with the number of layers. • Low cost: Joint RF-baseband processing reduces the number of RF chains needed in antenna selection. • Backward compatibility: • Existent convolutional codes can be used. • No explicit feedback mechanism is needed. • Flexibility: Multiple modes for various number of receive antennas. Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  30. Outline • Introduction • Proposal for High Rate PHY • Baseline system • Proposed technologies • Statistical rate allocation • RF-baseband processing for antenna selection • QBD-LDPC space time coding for layered structure • Summary • Proposal for High Efficiency MAC • MAC structure • Proposed main techniques • ADCA for CP • SCCA for CFP • Frame aggregation • Block ACK enhancement • Summary • Conclusions Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  31. MAC Structure • Retain 802.11e super frame structure • Enhance 802.11e for high efficiency • Maintain the same QoS support as 802.11e • Backward compatible with 802.11/802.11e Superframe Superframe CFP CP CFP CP C F E N D B E A C O N B E A C O N B E A C O N SCCA ADCA SCCA C F E N D ADCA Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  32. Proposed Main Techniques • Adaptive Distributed Channel Access (ADCA) • Sequential Coordinated Channel Access (SCCA) • Frame Aggregation • Efficient Block Ack • All above technologies can used together or separately to increase 802.11/802.11e MAC efficiency Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  33. Proposed Main Techniques • Adaptive Distributed Channel Access (ADCA) • Sequential Coordinated Channel Access (SCCA) • Frame Aggregation • Efficient Block Ack Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  34. ADCA Overview • CSMA/CA based • Proved to be a robust, scalable, wide-deployed technology • Adaptive batch transmission • Transmit multiple frames in one channel access • Support BlockAck with adaptive block size • Leverage Multi-Rate Capabilities • Favor high data rate stations • Provide long-term temporal fairness for low rate stations • QoS support • 4 access categories (AC) with different channel contention parameters, same as 802 .11e • Proved to be an efficient way to provide service differentiation Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  35. R > Rf Yes No B = (Bf x Sf / Rf) + credit T = Bf x Sf / Rf; Transmit up to Min{ TxR/S, B } frames credit < Thrsh && B < 1 No Yes Credit++; Resume backoff Transmit up to Max{B, 1} frames; Credit = 0 Batch Size Control int Bf = 0; //reference batch size int Rf = 0; //reference rate int Sf = 0; //reference frame size int B = 0; //local batch size int R = 0; //loca transmission rate int S = 0; //local frame size int credit = 0; //credit counter value int Thrsh; //a constant threshold value Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  36. Frame Frame Frame Frame Frame Frame Frame ACK ACK ACK ACK ACK ACK ACK BlockACK Request BlockACK Request BlockACK Request Frame Frame Frame Frame Frame Frame Frame Frame Frame BlockACK BlockACK BlockACK Frame Frame Frame Frame Frame Frame Frame Frame Frame Frame Frame Frame Frame BlockAck for Batch Transmission Immediate ACK Batch ACK No ACK Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  37. SIFS 16us AIFS[AC0,1] 54us DIFS 34us CWmin[AC0,1] 31 Slot Time 9us CWmax[AC0,1] 1023 ACK Size 14B AIFS[AC2] 43us MAC Header 28B CWmin[AC2] 15 Peak Data-Rate 216Mb/s CWmax[AC2] 500 Base Data-Rate 24Mb/s AIFS[AC3] 34us PLCP Preamble Length 20us CWmin[AC3] 7 PLCP Header Length 4us CWmax[AC3] 100 ADCA Performance Evaluation • Simulation Environments • Simulation platform: Ns-2 (version 2.26) • Physical parameters are based upon the MERL PHY layer proposal. Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  38. ADCA Throughput Gain Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  39. EDCA parameter set element in IEEE 802.11e 1 1 1 4 4 4 4 1 Octet Element ID (12) Length (18) QoS Info Reserved Octet 1 2 1 1 1 1 1 AC_BK Parameter Record AC_VI Parameter Record AC_VO Parameter Record AC_BE Parameter Record ACI/ AIFSN ECWmin/ ECWmax TXOP Limit Reference Packet Size (Sf) Reference Data Rate (Rf) Reference Batch Size (Bf) Reference BlockACK Size (Af) Modified EDCA parameter set element for ADCA Related Message Format • Need to modify the EDCA parameter set element in the beacon Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  40. Proposed Main Techniques • Adaptive Distributed Channel Access (ADCA) • Sequential Coordinated Channel Access (SCCA) • Frame Aggregation • Efficient Block Ack Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  41. SCCA Overview • Scheduled transmission based on request • CSMA/CA with assigned incremental backoff time to each STA • Ensure parameterized QoS • Combine the merits of TMDA and polling mechanisms • Eliminate the polling overhead, and retain its flexibility • Avoid the TDMA’s stringent synchronization, and achieve its efficiency • Consist of five distinct phases • Resource request • Resource allocation • Data transmission • Resource renegotiation • Resource relinquishment Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  42. Resource Request & Allocation STA SCCA Controller Resource Request (RRQ) ADCA Period SIFS ACK Resource Reservation and Allocation . . . Beacon SIFS Resource Allocation (RAL) . . . SCCA Period Data Data Transmission Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  43. TXDT 1 frame 1 frame 2 frame At time t0 SIV 1 2 3 STA S1 S2 AP At time t1 At time t2 STA SIV TXDT STA SIV TXDT S1 NA 0 D A T A S1 NA 0 P I F S S L O T S2 1 1 frame S2 NA 0 AP 2 2 frame AP 1 2 frame C F E N D B E A C O N R A L S L O T D A T A S I F S D A T A S I F S S I F S P I F S P I F S t3 P I F S P I F S A C K A C K t1 At time t3 STA SIV TXDT S1 NA 0 S2 NA 0 D A T A P I F S S L O T S I F S AP NA 0 A C K t2 t0 CP: ADCA CP: ADCA CFP: SCCA Data Transmission S1 AP S2 Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  44. STA SCCA Controller Beacon SIFS Resource Allocation (RAL) . . . Data Data from STA x Resource Request (RRQ) ACK . . . Resource Renegotiation Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  45. SCCA Controller STA Beacon SIFS Resource Allocation (RAL) . . . Resource Relinquishment (RRL) Resource Relinquishment SIFS ACK . . . Resource Relinquishment Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  46. 2 6 6 6 2 4 2 Frame Control Duration DA SA BSSID Sequence Control Frame Body FCS MAC Header Related Message Format • Introduce 3 signaling messages • Resource request (RRQ) • Resource allocation (RAL) • Resource relinquishment (RRL) • Share common frame format • Designed based upon IEEE 802.11e ADDTS request, ADDTS response and DELTS Common frame format Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  47. Order Information 1 Category 2 Action 3 Dialog Token 4 ~n Multi-TSPEC Related Message Format: RRQ RRQ Message Format Octet 1 2 x 1 2 y Frame format of Multi-TSPEC Element ID Length TSPEC Bitmap 1 TSPEC 1 . . . TSPEC Bitmap n TSPEC n 3 2 2 4 4 4 4 4 Maximum Service Interval Inactivity Interval Suspension Interval Service Start Time TS Info Nominal MSDU Size Maximum MSDU Size Minimum Service Interval Frame format of TSPEC Octet 4 4 4 4 2 2 4 4 Minimum Data Rate Medium Time Mean Data Rate Peak Data Rate Maximum Burst Size Delay Bound Minimum PHY Rate Surplus Bandwidth Allowance Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  48. Order Information 1 Category 2 Action 14 1 2 14 3 Dialog Token … Multi-Schedule Element n Element ID Length Multi-Schedule Element 1 4 ~n Multi-Schedule 2 2 2 4 2 2 AID Specification Interval Schedule Info SIV Service Interval TXDT Bit 0 Bit 1 - 4 Bit 5 - 6 Bit 7 - 15 Reserved TSID Direction Reserved Related Message Format: RAL RAL Message Format Multi-Schedule Schedule Information subfield Schedule Information subfield Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  49. Order Information 1 Category 2 Action 3 ~ n RRL element Bit 0 Bit 11 - 13 Bit 1 - 4 Bit 5 - 6 Bit 7 - 8 Bit 9 Bit 10 Bit 14 - 15 Bit 16 Bit 17 - 23 Traffic Type APSD User Priority TSID Direction Access Policy Aggregation TS Info ACK Policy Schedule Reserved 2 3 AID TS Info 5 Bytes Related Message Format: RRL RRL Message Format RRL element TS Info Andreas F. Molisch et al, Mitsubishi (USA, Japan)

  50. Proposed Main Techniques • Adaptive Distributed Channel Access (ADCA) • Sequential Coordinated Channel Access (SCCA) • Frame Aggregation • Efficient Block Ack Andreas F. Molisch et al, Mitsubishi (USA, Japan)

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