PHY and MAC Proposal for IEEE n

PHY and MAC Proposal for IEEE 802.11n
August 2004 PHY and MAC Proposal for IEEE n Andreas F. Molisch, Daqing Gu, Jinyun Zhang, Neelesh Mehta Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA (molisch, dgu, jzhang Jianxuan Du Georgia Institute of Technology Jeffrey (Zhifeng) Tao Polytechnic University Yuan Yuan University of Maryland, College Park Ye (Geoffrey) Li Georgia Institute of Technology Andreas F. Molisch et al, MERL

Outline Introduction Proposal for High Rate PHY
August 2004 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 Conclusions Andreas F. Molisch et al, MERL

Introduction Challenges Our approach
August 2004 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 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

PHY Baseline Basic MIMO-OFDM system with layered structure (VBLAST)
August 2004 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

System Block Diagram (2x2 Case)
August 2004 System Block Diagram (2x2 Case) Andreas F. Molisch et al, MERL

Proposed Key Technologies
August 2004 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

Statistical Rate Allocation
August 2004 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

Transmitter Structure with Statistical Rate Allocation
August 2004 Transmitter Structure with Statistical Rate Allocation Data Input Demultiplexer Channel Encoder QAM Modulator P IFFT Statistical Layer Rate Allocation Andreas F. Molisch et al, MERL

Algorithm and Advantages
August 2004 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

August 2004 Simulation Result Andreas F. Molisch et al, MERL

The First Idea: Antenna Selection
August 2004 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

One Step Better: RF-Preprocessing with Antenna Selection
August 2004 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

RF Pre-Processing: Block Diagram
August 2004 RF Pre-Processing: Block Diagram . Down Conv LNA A/D Sig Proc 1 L 2 Nr S W I T C H Baseband Demodulator Pre- (M) RF Andreas F. Molisch et al, MERL

Selection of the Preprocessing Matrix
August 2004 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 1 2 Andreas F. Molisch et al, MERL

Channel Statistics-Based Pre-Processing
August 2004 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

Transmitter Structure
August 2004 Transmitter Structure Data Channel P QAM IFFT Input Encoder Modulator Joint RF- Demultiplexer baseband Processing Channel P QAM IFFT Encoder Modulator Andreas F. Molisch et al, MERL

August 2004 Simulation Result Andreas F. Molisch et al, MERL

Why LDPC? Capacity approaching performance
August 2004 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

August 2004 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

System Diagram for QBD-LDPC
August 2004 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

Parity Check Structure of QBD-LDPC
August 2004 Parity Check Structure of QBD-LDPC Parity check matrix for conventional LDPC-coded V-BLAST. Parity check matrix for QBD-LDPC. Andreas F. Molisch et al, MERL

Encoding of QBD-LDPC Wn Hn= [Pn I] by Gaussian elimination.
August 2004 Encoding of QBD-LDPC Wn Hn= [Pn I] by Gaussian elimination. The parity check bits for layer n are given by Pnun+ Wn Cn-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

August 2004 Decoder of QBD-LDPC Andreas F. Molisch et al, MERL

August 2004 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

August 2004 Simulation Results Andreas F. Molisch et al, MERL

Summary of PHY Technologies
August 2004 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

Outline Introduction Proposal for High Rate PHY
August 2004 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 Conclusions Andreas F. Molisch et al, MERL

MAC Structure Enhance 802.11e for high efficiency
August 2004 MAC Structure Enhance e for high efficiency Retain e super frame structure Maintain the same QoS support as e Backward compatible with IEEE /802.11e B E A C O N SCCA CAP F D ADCA CFP CP Superframe Andreas F. Molisch et al, MERL

MAC Protocol ADCA (Adaptive Distributed Channel Access)
August 2004 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

ADCA: Overview CSMA/CA Based Channel Access Mechanism
August 2004 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 e Proved to be an efficient way to provide service differentiation Andreas F. Molisch et al, MERL

ADCA: Algorithm Details
August 2004 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

Adaptive Batch Transmission: Illustration
August 2004 Adaptive Batch Transmission: Illustration IEEE MAC DIFS Frame ACK SIFS DIFS Frame ACK Backoff SIFS Backoff Adaptive Packet Batch Transmission SIFS Frame ACK IEEE n MAC Andreas F. Molisch et al, MERL

ADCA Performance Evaluation
August 2004 ADCA Performance Evaluation Simulation Environments Simulation platform: Ns-2 (version 2.26) Physical parameters are based upon the MERL PHY layer proposal. 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] PLCP Preamble Length 20us CWmin[AC3] 7 PLCP Header Length 4us CWmax[AC3] 100 Andreas F. Molisch et al, MERL

August 2004 ADCA Throughput Gain Andreas F. Molisch et al, MERL

Effect of Reference Batch Size(Bf)
August 2004 Effect of Reference Batch Size(Bf) Andreas F. Molisch et al, MERL

ADCA Comprehensive Simulation
August 2004 ADCA Comprehensive Simulation We have conducted extensive simulations according to the usage model (UM) released by IEEE n 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

Sample Simulation Results
August 2004 Sample Simulation Results Home Scenario Andreas F. Molisch et al, MERL

Sample Simulation Results
August 2004 Sample Simulation Results Home Scenario Cont’ Andreas F. Molisch et al, MERL

Effect of Increasing Frame Size
August 2004 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

Related Message Format
August 2004 Related Message Format Need to modify the EDCA parameter set element in the beacon 1 Element ID (12) Length (18) Reserved Octet QoS Info AC_BE Parameter Record AC_BK AC_VI AC_VO 4 ACI/ AIFSN ECWmin/ ECWmax TXOP Limit 2 Reference Packet Size (Sf) Data Rate (Rf) Batch Size (Bf) BlockACK Size (Af) EDCA parameter set element in IEEE e Modified EDCA parameter set element for ADCA Andreas F. Molisch et al, MERL

SCCA: Overview Highly efficient and flexible channel utilization
August 2004 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

SCCA: Algorithm Details
August 2004 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

SCCA: Resource Request & Allocation
August 2004 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

SCCA: Data Transmission
August 2004 SCCA: Data Transmission 1 frame 2 S2 TXDT SIV STA 3 1 2 frame AP S1 At time t0 NA At time t2 At time t1 CFP: SCCA CP: ADCA t0 t1 t2 t3 R A L P I F S C K O T D At time t3 B E N Andreas F. Molisch et al, MERL

SCCA: Resource Renegotiation
August 2004 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

SCCA: Resource Relinquishment
August 2004 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

SCCA Throughput Identical simulation settings
August 2004 SCCA Throughput Identical simulation settings Simplified scenario and focus solely the core SCCA mechanism Andreas F. Molisch et al, MERL

Effect of Increasing Frame Size
August 2004 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

Related Message Format
August 2004 Related Message Format Introduce 3 signaling messages Resource request (RRQ) Resource allocation (RAL) Resource relinquishment (RRL) Share common frame format Designed based upon IEEE e ADDTS request, ADDTS response and DELTS Common frame format Frame Control Duration DA SA BSSID Sequence Body FCS MAC Header 2 6 4 Andreas F. Molisch et al, MERL

Frame format of Multi-TSPEC
August 2004 RRQ Order Information 1 Category 2 Action 3 Dialog Token 4 ~n Multi-TSPEC RRQ Message Format Octet Frame format of Multi-TSPEC 1 1 2 x 2 y Element ID Length TSPEC Bitmap 1 TSPEC 1 . . . TSPEC Bitmap n TSPEC n 3 2 2 4 4 4 4 4 TS Info Nominal MSDU Size Maximum MSDU Size Minimum Service Interval Maximum Service Interval Inactivity Interval Suspension Interval Service Start Time Frame format of TSPEC Octet 4 4 4 4 4 4 2 2 Minimum Data Rate Mean Data Rate Peak Data Rate Maximum Burst Size Delay Bound Minimum PHY Rate Surplus Bandwidth Allowance Medium Time Andreas F. Molisch et al, MERL

RAL August 2004 Message Format RAL Multi-Schedule …
Order Information 1 Category 2 Action 3 Dialog Token 4 ~n Multi-Schedule Message Format RAL 1 2 Element ID Length Multi-Schedule Element 1 Element n … 14 Multi-Schedule 2 4 Schedule Info SIV Service Interval TXDT Specification AID Schedule Information subfield Bit 0 Bit 1 - 4 Bit 5 - 6 Bit Reserved TSID Direction Schedule Information subfield Andreas F. Molisch et al, MERL

RRL August 2004 Message Format RRL element RRL TS Info Order
Information 1 Category 2 Action 3 ~ n RRL element Message Format RRL TS Info 5 Bytes AID 2 3 element RRL Bit 1 - 4 Bit 5 - 6 Bit 7 - 8 Bit 9 Bit 10 TSID Direction Access Policy Aggregation APSD Traffic Type Bit 0 Bit Bit 16 Bit TS Info ACK Policy Schedule Reserved User Priority Bit TS Info Andreas F. Molisch et al, MERL

BlockACK Enhancement …….
August 2004 BlockACK Enhancement The blockACK bitmap field in the legacy BlockACK frame contains more than it needs A more streamlined BlockACK frame design can save 80% of bandwidth TID B0 B12 Mode Selection Block Size B8 Reserved B11 B15 B2 B3 B9 BAR/BA field 16 Bytes 8 Bytes Fragment Number Concatenation Sequence Number Bitmap BlockACK Bitmap 64 4-bit-long Fragment Number Concatenation ……. Fragment Number (4bits) Fragment Number (4bits) Fragment Number (4bits) Fragment Number (4bits) Fragment Number (4bits) Fragment Number (4bits) Andreas F. Molisch et al, MERL

Summary of Proposed MAC Technologies
August 2004 Summary of Proposed MAC Technologies We proposed three major enhancements for e MAC Adaptive Batch transmission Sequentially coordinated channel access & frame format BlockACK enhancement All these enhancements improve e MAC efficiency while retaining the reliability, simplicity, interoperability and QoS support of /802.11e MAC Andreas F. Molisch et al, MERL

August 2004 Conclusions We have proposed a variety of important techniques for performance enhancements to both PHY and MAC These techniques can be individually or jointly included in the upcoming n standard We will continue to develop further enhancements for possible adoption We are open for any collaboration to establish a baseline proposal for n standard Andreas F. Molisch et al, MERL

PHY and MAC Proposal for IEEE n

Similar presentations

Presentation on theme: "PHY and MAC Proposal for IEEE n"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

PHY and MAC Proposal for IEEE n

Similar presentations

Presentation on theme: "PHY and MAC Proposal for IEEE n"— Presentation transcript:

Similar presentations

About project

Feedback