PHY and MAC Proposal for IEEE 802.11n

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 • Jianxuan Du • Georgia Institute of Technology (jxdu@ece.gatech.edu) • Jeffrey (Zhifeng) Tao • Polytechnic University (jefftao@photon.poly.edu) • Yuan Yuan • University of Maryland, College Park (yuanyuan@cs.umd.edu) • Ye (Geoffrey) Li • Georgia Institute of Technology (liye@ece.gatech.edu) Andreas F. Molisch et al, MERL

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 • ADCA mechanism for CP • SCCA for CFP • Block ACK enhancement • Summary • Conclusions Andreas F. Molisch et al, MERL

3. Introduction • Challenges • 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, MERL

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, MERL

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

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, MERL

7. Statistical 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) • Proposed solution • It is proved that with instantaneous rate feedback and SIC, the layered structure can achieve the open-loop capacity • We propose to statistically determine the optimal data rates fro different layers to avoid instantaneous rate feedback • Detection order is fixed • Different layers cycle through different transmit antennas • Different layers have different data rates that are statistically determined by the channel quality Andreas F. Molisch et al, MERL

8. 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, MERL

9. 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 a nominal channel data rate • 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, MERL

10. Simulation Result Andreas F. Molisch et al, MERL

11. 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 Andreas F. Molisch et al, MERL

12. 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, MERL

13. RF Demodulator Baseband S W I T C H 1 1 Sig Proc A/D LNA 2 Pre- Proc (M) Down Conv Down Conv . . . . L A/D LNA Nr RF Pre-Processing: Block Diagram Andreas F. Molisch et al, MERL

14. 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, MERL

15. 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, MERL

16. 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, MERL

17. Simulation Result Andreas F. Molisch et al, MERL

18. 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, MERL

19. 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, MERL

20. 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, MERL

21. 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, MERL

22. 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, MERL

23. Decoder of QBD-LDPC Andreas F. Molisch et al, MERL

24. 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, MERL

25. Simulation Results Andreas F. Molisch et al, MERL

26. 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, MERL

27. 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 • ADCA mechanism for CP • SCCA for CFP • Block ACK enhancement • Summary • Conclusions Andreas F. Molisch et al, MERL

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

29. MAC Protocol • ADCA (Adaptive Distributed Channel Access) • CSMA/CA based random access • Significantly boost the channel efficiency by reducing overhead • Ensure long-term fairness, which legacy MAC cannot accomplish • SCCA (Sequential Coordinated Channel Access) • Coordinate contention-free medium access • Remove polling overhead, and retain the flexibility and simplicity • Provide reservation-based per-flow QoS • Achieve high efficiency without having to maintain the stringent synchronization and timing Andreas F. Molisch et al, MERL

30. ADCA: Overview • CSMA/CA Based Channel Access Mechanism • Defer, backoff, collision resolution • Proved to be a robust, scalable, wide-deployed technology • Adaptive Batch Transmission • Reference Parameter Set (RPS) • Supporting BlockACK • Leveraging Multi-Rate Capabilities • Select stations in good channel condition • Provide long-term temporal fairness among stations • QoS Support • Four access categories (AC) with different channel contention parameters, similar to IEEE 802 .11e • Proved to be an efficient way to provide service differentiation Andreas F. Molisch et al, MERL

31. ADCA: Algorithm Details • AP broadcasts the Reference Parameters Set (RPS) (includes reference rate, packet size, batch size) of the BSS in a control packet (e.g., beacon) periodically. • Each STA computes the number of packets that can be fit into the batch (a.k.a. actual batch size) based on its current transmission rate and packet size. • If the actual batch size is less than a packet, the STA skips the current transmission opportunity, and increments its batch size credit accordingly. • If the actual batch size is equal or larger than one packet, the STA can transmit a batch of packets up to the actual packet size, if packets available. • During the batch transmission, the NAV in each data packet is set so that the channel time for the next packet in the same batch is reserved. • Once the batch transmission is completed, the transmitting STA releases the channel. Andreas F. Molisch et al, MERL

32. DIFS DIFS SIFS SIFS . . . . . Backoff Frame ACK Backoff Frame ACK Adaptive Packet Batch Transmission DIFS SIFS SIFS SIFS SIFS SIFS . . . . . Backoff Frame ACK Frame ACK Frame ACK Adaptive Batch Transmission: Illustration IEEE 802.11 MAC IEEE 802.11n MAC Andreas F. Molisch et al, MERL

33. 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, MERL

34. ADCA Throughput Gain Andreas F. Molisch et al, MERL

35. Effect of Reference Batch Size(Bf) Andreas F. Molisch et al, MERL

36. ADCA Comprehensive Simulation • We have conducted extensive simulations according to the usage model (UM) released by IEEE 802.11n TG. The results prove that ADCA satisfies the most stringent requirements set aside in UM. • The throughput observed at MAC SAP on a point to point link is 106Mbps, which exceeds the 100Mbps requirement. Andreas F. Molisch et al, MERL

37. Sample Simulation Results • Home Scenario Andreas F. Molisch et al, MERL

38. Sample Simulation Results • Home Scenario Cont’ Andreas F. Molisch et al, MERL

39. Effect of Increasing Frame Size • Large frame size and frame aggregation, among other technologies can be integrated with ADCA to achieve even higher throughput Andreas F. Molisch et al, MERL

40. 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, MERL

41. SCCA: Overview • Highly efficient and flexible channel utilization • Ensure parameterized QoS • Combine the merits of TMDA and polling mechanisms • Eliminate the overhead of polling, and retain its flexibility • Avoid the rigidity of TDMA, and achieve its efficiency • Consist of five distinct phases • Resource request • Resource allocation • Data transmission • Resource renegotiation • Resource relinquishment Andreas F. Molisch et al, MERL

42. SCCA: Algorithm Details • To join SCCA, STAs need to send a resource reservation packet to AP . • Based on reservation request received from the STAs, AP assigns a TXDT, and an integer sequence index value (SIV) to each STA sequentially. SIV starts from 1 to N (Max. number of admitted). • AP distributes SIVs and TXDTs in a control packet at the beginning of each CFP period. • STAs listen to the channel, retrieves its own SIV and TXDT from the control frame and then accesses the channel in CFP period as follows • Start to backoff SIV time after the channel is idle for PIFS time • Stop the backoff once channel becomes busy. Restart it again when the channel is cleared • Transmit for a duration of TXDT if SIV is decremented to zero • The controller inside AP schedules the downlink traffic (from AP to STA) in the same way as uplink traffic. Andreas F. Molisch et al, MERL

43. SCCA: 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, MERL

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

45. SCCA: Resource Renegotiation STA SCCA Controller Beacon SIFS Resource Allocation (RAL) . . . Data Data from STA x SCCA Period Resource Request (RRQ) ACK . . . Andreas F. Molisch et al, MERL

46. SCCA: Resource Relinquishment SCCA Controller STA Beacon SIFS Resource Allocation (RAL) . . . Resource Relinquishment (RRL) SCCA Period Resource Relinquishment SIFS ACK . . . Andreas F. Molisch et al, MERL

47. SCCA Throughput • Identical simulation settings • Simplified scenario and focus solely the core SCCA mechanism Andreas F. Molisch et al, MERL

48. Effect of Increasing Frame Size • SCCA can achieve even higher throughout, with other augmentations such as large frame size, frame aggregation, etc. Andreas F. Molisch et al, MERL

49. 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, MERL

50. Order Information 1 Category 2 Action 3 Dialog Token 4 ~n Multi-TSPEC 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, MERL