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Wireless Networking

Fundamentals and Applications

Kate C.-J. Lin (林靖茹)

Network & Mobile System Lab Research Center for IT Innovation

Academia Sinica

(2)

Agenda

•  Auto Rate Adaptation

•  Orthogonal Frequency Division Modulation (OFDM)

•  Multi-Input Multi-Output Systems (MIMO)

2

(3)

Auto Rate Adaptation

•  Modulations and bit-rates

•  SNR and bit-error rate

•  Bit-rate selection algorithms

(4)

Modulations

I   Q  

BPSK  

1  à  1+0i   0  à  -­‐1+0i  

Constella)on  Points  

Modulate digital bits

to a complex number (sample)

(5)

Modulations

64QAM  

I   Q  

I   Q  

BPSK  

1   0  

QPSK  

I   Q  

01   11  

10   00  

16QAM  

I   Q  

1000   1100  

1001   1101  

1011   1111  

1010   1110  

0100   0000  

0101   0001  

0111   0011  

0110   0010  

(6)

Demodulation

Map the received complex number back to digital bits

I   Q  

BPSK   Received  sample  

Closet  constella?on  point  

If Tx is actually sending ‘0’, bit error occurs

(7)

Bit-Rates in 802.11

Bit- 802.11 DSSS Modulation Bits Coding Mega-

rate Stan- or per Rate Symbols

dards OFDM Symbol per

second

1 b DSSS BPSK 1 1/11 11

2 b DSSS QPSK 2 1/11 11

5.5 b DSSS CCK 1 4/8 11

11 b DSSS CCK 2 4/8 11

6 a/g OFDM BPSK 1 1/2 12

9 a/g OFDM BPSK 1 3/4 12

12 a/g OFDM QPSK 2 1/2 12

18 a/g OFDM QPSK 2 3/4 12

24 a/g OFDM QAM-16 4 1/2 12

36 a/g OFDM QAM-16 4 3/4 12

48 a/g OFDM QAM-64 6 2/3 12

54 a/g OFDM QAM-64 6 3/4 12

Figure 2-1: A summary of the 802.11 bit-rates. Each bit-rate uses a specific combination of modulation and channel coding. OFDM bit-rates send 48 symbols in parallel.

a channel. In the presence of fading, multi-path interference, or other interference that is not additive white Gaussian noise, predicting the combinations of modulation and channel coding that will be most effective at masking bit errors is difficult.

All 802.11 packets contain a small preamble before the data payload which is sent at a low bit-rate. The preamble contains the length of the packet, the bit-rate for the data payload, and some parity information calculated over the contents of the preamble. The preamble is sent at 1 megabit in 802.11b and 6 megabits in 802.11g and 802.11a. This results in the unicast packet overhead being different for 802.11b and 802.11g bit-rates; a perfect link can send approximately 710 1500-byte unicast packets per second at 12 megabits (an 802.11g bit-rate) and 535 packets per second at 1 megabit (an 802.11b bit-rate). This means that 12 megabits can sustain nearly 20% loss before a lossless 11 megabits provides better throughput, even though the bit-rate is less than 10% different.

2.2 Medium-Access Control (MAC) Layer

For the purposes of this thesis, the most important properties of the 802.11 MAC layer are the medium access mechanisms and the unicast retry policy.

To prevent nodes from sending at the same time, 802.11 uses carrier sense multiple access

14

(8)

Coding Rate

•  Avoid random errors

„ 

1/2: Add 1x redundant bits

„ 

3/4: Add 1/3x redundant bits

•  Haven’t solved the problem yet

„ 

Data input: 1, 1, 0, 1, 0, 1, 1, 0, …

„ 

After encoding:

1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, ….

„ 

Still one bit error à Suffer from bursty errors

(9)

Interleave and De-interleave

So ur ce c od in g Inter leav e Mo dul atio n D/A

channel

noise

+  

1,  1,  0,  1,  0   1,  1,  1,  1,  0,    

0,  1,  1,  0,  0   1,  0,  0,  1,  0,    

1,  1,  0,  1,  1   1,  -­‐1,  -­‐1,  1,  -­‐1,     1,  1,  -­‐1,  1,  1  

D ec od in g De-inter leav e De-mo dulatio n A/ D

1,  0,  1,  1,  0,    

0,  1,  1,  1,  0   1,  0,  1,  0,  0,    

1,  1,  0,  1,  1   1,  -­‐1,  1,  -­‐1,  -­‐1,     1,  1,  -­‐1,  1,  1   1,  1,  0,  1,  0  

Transmitter

Receiver Create  a  more  uniform  

distribu?on  of  errors  

(10)

Channel Quality vs. Bit-Rate

•  When channels are very good

„ 

Encode more bits as a sample

•  When channels are noisy

„ 

Encode fewer bits as a sample

Why is it affected by the channel quality?

(11)

Error Probability vs. Modulations

I   Q  

BPSK  

‖ noise‖

SNR = 10log10 (‖signal‖/‖noise‖)

‖ signal‖

Decode correctly

QPSK  

I   Q  

01   11  

10   00  

‖ noise‖

Decode incorrectly

Given the same SNR

Given the same SNR, decodable for BPSK,

but un-decodable for QPSK

(12)

SNR vs. BER (Bit Error Rate)

1e-05 0.0001 0.001 0.01 0.1 1

0 5 10 15 20 25 30 35

Bit Error Rate

S/N (dB)

BPSK (1 megabit/s) QPSK (2 megabits/s) QAM-16 (4 megabits/s) QAM-64 (6 megabits/s)

Figure 1-2: Theoretical bit error rate (BER) versus signal-to-noise ratio for several modu- lation schemes assuming AGWN. The y axis is a log scale. Higher bit-rates require larger S/N to achieve the same bit-rate as lower bit-rates.

9

802.11 operating region 5dB

(13)

SNR vs. PER (Packet Error Rate)

•  In 802.11, a packet is received correctly if it passes the CRC check ( all bits are correct )

„ 

Receive all or none

0 1 2 3 4 5 6

5 10 15 20 25 30

Throughput (Megabits per Second)

S/N (dB) BPSK (1 megabit/s) QPSK (2 megabit/s) QAM-16 (4 megabits/s) QAM-64 (6 megabits/s)

Figure 1-4: Theoretical throughput in megabits per second using packets versus signal-to- noise ratio for several modulations, assuming AGWN and a symbol rate of 1 mega-symbol per second.

packets can be estimated using the following equation:

throughput = (1− BER)n∗ bitrate

This equation assumes the transmitter sends packets back-to-back, the receiver knows the location of each packet boundary, the receiver can determine the integrity of the data with no overhead, there is no error correction, and the symbol rate is 1 mega-symbol per second.

Packets change the throughput versus S/N graph dramatically; Figure 1-4 shows through- put in megabits per second versus S/N for 1500-byte packets after accounting for packet losses caused by bit-errors. The range where each modulation delivers non-zero throughput but suffers from loss is much smaller in Figure 1-4 than in Figure 1-3. For most S/N values in the range from 5 to 30 dB, the best bit-rate delivers packets without loss.

Bit-rate selection is easier for links that behave as in Figure 1-4 than as in Figure 1-3;

the sender can start on the highest bit-rate and switch to another bit-rate whenever the

11

PER = 1-(1-BER)

n

Throughput

= (1-BER)

n

* bit-rate

Throughput degrades

quickly even with a

little BER

(14)

Bit-Rate Selection

•  Given the SNR, select the bit-rate that can achieve the highest throughput

0 1 2 3 4 5 6

5 10 15 20 25 30

Throughput (Megabits per Second)

S/N (dB) BPSK (1 megabit/s) QPSK (2 megabit/s) QAM-16 (4 megabits/s) QAM-64 (6 megabits/s)

Figure 1-4: Theoretical throughput in megabits per second using packets versus signal-to- noise ratio for several modulations, assuming AGWN and a symbol rate of 1 mega-symbol per second.

packets can be estimated using the following equation:

throughput = (1− BER)n∗ bitrate

This equation assumes the transmitter sends packets back-to-back, the receiver knows the location of each packet boundary, the receiver can determine the integrity of the data with no overhead, there is no error correction, and the symbol rate is 1 mega-symbol per second.

Packets change the throughput versus S/N graph dramatically; Figure 1-4 shows through- put in megabits per second versus S/N for 1500-byte packets after accounting for packet losses caused by bit-errors. The range where each modulation delivers non-zero throughput but suffers from loss is much smaller in Figure 1-4 than in Figure 1-3. For most S/N values in the range from 5 to 30 dB, the best bit-rate delivers packets without loss.

Bit-rate selection is easier for links that behave as in Figure 1-4 than as in Figure 1-3;

the sender can start on the highest bit-rate and switch to another bit-rate whenever the

11

QPSK  

64QAM  

(15)

Difficulties with Rate Adaptation

•  Channel quality changes very quickly

„ 

Especially when the device is moving

•  Can’t tell the difference between

„ 

poor channel quality due to noise/interference/

collision (high ‖ noise‖)

„ 

poor channel quality due to distance (low

‖ signal‖)

Ideally, we want to decrease the rate due to low

signal strength, but not interference/collision

(16)

Types of Auto-Rate Adaptation

Transmitter-based Receiver-Based

SNR-based RBAR, OAR

ACK-based ARF, AARF Throughput-based SampleRate

(default in Linux)

RRAA Selected by Tx

(Less accurate)

Selected by Rx

(Higher overhead)

(17)

Sync. ACK vs. Async ACK

•  Sync. ACK

„ 

Cost the minimum overhead

„ 

Only know whether the packet is transmitted correctly

„ 

Don’t know whether the packet error is due to incorrect rate selection or collision

•  Async. ACK

„ 

Cost extra overhead

„ 

Can include more detailed information Tx  

Rx  

backoff   Data  

ACK   SIFS  

backoff   A-­‐ACK   DIFS  

(18)

Robust Rate Adaption Algorithm (RRAA)

•  Dynamically enable RTS/CTS before data transmission

•  Detect that the low throughput is due to the incorrect bit-rate selection or

collision (hidden terminals)

•  Estimate the correct number of transmissions to keep RTS/CTS

•  Disable RTS/CTS if it does not help

S.  Wong,  H.  Yang,  S.  Lu,  V.  Bharghavan,  “Robust  Rate  Adapta?on  for  

802.11  Wireless  Networks,”  ACM  MOBICOM,  2006    

(19)

SampleRate

•  Periodically send packets at bit-rates other than the current bit-rate

•  Calculate the transmission time of each packet

„ 

packet length, bit-rate, number of retries, backoff time

pkt1   pkt1’   pkt1’’   pkt2   …   pkt10   r*  

retry  1  

pkt   r’  

pkt  

retry  2   retry  1  

• Look up the destination and add the transmission time to the total transmission times for the bit-rate.

• If the packet succeeded, increment the number of successful packets sent at that bit- rate.

• If the packet failed, increment the number of successive failures for the bit-rate. Oth- erwise reset it.

• Re-calculate the average transmission time for the bit-rate based on the sum of trans- mission times and the number of successful packets sent at that bit-rate.

• Set the current-bit rate for the destination to the one with the minimum average transmission time.

• Append the current time, packet status, transmission time, and bit-rate to the list of transmission results.

SampleRate’s remove stale results() function removes results from the transmission results queue that were obtained longer than ten seconds ago. For each stale transmission result, it does the following:

• Remove the transmission time from the total transmission times at that bit-rate to that destination.

• If the packet succeeded, decrement the number of successful packets at that bit-rate to that destination.

After remove stale results() performs these operations for each stale sample, it re-

calculates the minimum average transmission times for each bit-rate and destination. remove stale results() then sets the current bit-rate for each destination to the one with the smallest average trans-

mission time.

To calculate the transmission time of a n-byte unicast packet given the bit-rate b and number of retries r, SampleRate uses the following equation based on the 802.11 unicast retransmission mechanism detailed in Section 2.2:

tx time(b, r, n) = dif s + backof f (r) + (r + 1) ∗ (sifs + ack + header + (n ∗ 8/b) (5.1)

37

(20)

Sample Rates

•  Select the rate that has the smallest predicted average packet transmission time

•  Do not sample the rates that

„ 

have failed four successive times

„ 

are unlikely to be better than the current one

•  Is thought of the most efficient scheme for static environments

J.  Bicket,  “Bit-­‐rate  Selec?on  in  Wireless  Networks,”  Ph.D  Thesis,  MIT,  2005  

(21)

Rate Adaptation for Multicast?

•  Can only assign a single rate to each packet

•  Possible Solutions

„ 

For reliable transmission: select the rate based on the worst node

„ 

For non-reliable transmission: provide clients

heterogeneous throughput

(22)

Recent Proposals

•  ZipTx

K. Lin, N. Kushman and D. Katabi, “Harnessing Partial Packets in 802.11 Networks,” ACM MOBICOM, 2008

Exploit partial packets with consideration of bit-rate adaptation

•  SoftRate

M. Vutukuru, H. Balakrishnan and K. Jamieson, “Cross-Layer Wireless Bit Rate Adaptation,” ACM SIGCOMM, 2009

Exploit soft information to improve selection accuracy

•  FARA

H. Rahul, F. Edalat, D. Katabi and C. Sodini, “Frequency-Aware Rate Adaptation and MAC Protocols,” ACM MOBICOM, 2009

Adapt the bit-rate for every OFDM subcarrier

•  ESNR

D. Halperin, W. Hu, A. Sheth and D. Wetherall, “Predictable 802.11 Packet Delivery from Wireless Channel Measurements”, ACM SIGCOMM, 2010

Consider frequency selective fading

(23)

Frequency-Aware Rate Adaptation (FARA)

H. Rahul, F. Edalat, D. Katabi, C. Sodini

MOBICOM 2009

(24)

Frequency Diversity

•  Frequency diverse across 100MHz of 802.11a spectrum

•  The SNRs of different frequencies can be as much as 20dB on a single link

•  Different receivers could prefer different frequencies

Frequency-Aware Rate Adaptation and MAC Protocols

Hariharan Rahul

, Farinaz Edalat

, Dina Katabi

, and Charles Sodini

Massachusetts Institute of Technology

RKF Engineering Solutions, LLC

ABSTRACT

There has been burgeoning interest in wireless technologies that can use wider frequency spectrum. Technology advances, such as 802.11n and ultra-wideband (UWB), are pushing toward wider fre- quency bands. The analog-to-digital TV transition has made 100- 250 MHz of digital whitespace bandwidth available for unlicensed access. Also, recent work on WiFi networks has advocated discard- ing the notion of channelization and allowing all nodes to access the wide 802.11 spectrum in order to improve load balancing. This shift towards wider bands presents an opportunity to exploit frequency diversity. Specifically, frequencies that are far from each other in the spectrum have significantly different SNRs, and good frequencies differ across sender-receiver pairs.

This paper presents FARA, a combined frequency-aware rate adaptation and MAC protocol. FARA makes three departures from conventional wireless network design: First, it presents a scheme to robustly compute per-frequency SNRs using normal data trans- missions. Second, instead of using one bit rate per link, it en- ables a sender to adapt the bitrate independently across frequencies based on these per-frequency SNRs. Third, in contrast to traditional frequency-oblivious MAC protocols, it introduces a MAC protocol that allocates to a sender-receiver pair the frequencies that work best for that pair. We have implemented FARA in FPGA on a wide- band 802.11-compatible radio platform. Our experiments reveal that FARA provides a 3.1× throughput improvement in comparison to frequency-oblivious systems that occupy the same spectrum.

Categories and Subject Descriptors

C.2.2 [Computer Sys- tems Organization]: Computer-Communications Networks

General Terms

Algorithms, Design, Performance

Keywords

Wireless, Cognitive Radios, Wideband, Rate Adapta- tion, Cross-layer

1 I

NTRODUCTION

Wireless technologies are pushing toward wider frequency bands than the 20 MHz channels employed by existing 802.11 networks.

802.11n already includes a 40 MHz mode that bonds together two 20 MHz bands [23]. Emerging ultra-wideband (UWB) technolo- gies employ hundreds of MHz to support multimedia homes and offices [24, 50, 9, 40]. The FCC has recently permitted unlicensed

Permission to make digital or hard copies of all or part of this work for per- sonal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to re- publish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

MobiCom’09, September 20–25, 2009, Beijing, China.

Copyright 2009 ACM 978-1-60558-702-8/09/09 . . . $10.00.

0 5 10 15 20 25 30

-40 -20 0 20 40

SNR (dB)

Freq (Mhz)

Figure 1: Frequency diversity across 100 MHz of 802.11a spec- trum as observed by two receivers for transmissions from the same sender. The figure shows that the SNRs of different frequen- cies can differ by as much as 20 dB on a single link. Further, different receivers prefer different frequencies.

use of digital TV whitespaces that occupy 100-250 MHz of spectrum vacated by television bands in the analog-to-digital transition [12].

Furthermore, recent empirical studies show that the 802.11 chan- nelization model which limits each node to a single 20 MHz chan- nel can lead to severe load imbalance [19, 28, 37]. They advocate discarding channelization and allowing all nodes to access the en- tire 802.11 spectrum based on demand [19, 37]. This push towards wider bands is further enabled by the constantly lowering prices of high-speed ADC and DAC hardware [38, 31].1 In particular, today, wireless cards that span over 100 MHz of spectrum can be built us- ing off-the-shelf hardware components [35].

As wireless networks push towards wider bands, we can no longer afford to ignore frequency diversity. Specifically, multipath effects cause frequencies that are far away from each other in the spectrum to experience independent fading. Thus, different frequencies can exhibit very different SNRs for a single sender-receiver pair. Further, the frequencies that show good performance for one sender-receiver pair may be very different than the frequencies that show good per- formance for another pair. Fig. 1 shows empirical measurements of the SNRs across 100 MHz of the 802.11a spectrum, as observed by 2 clients for transmissions from the same AP (see §9 for exper- imental setup). The figure reveals that different frequencies show a difference in SNR of over 20 dB both for a single link and across links. Existing bitrate adaptation and MAC protocols however are frequency-oblivious. They assign the same bitrate to all frequencies and allocate the medium in a time-based manner, ignoring the fact that different frequencies work better for different sender-receiver pairs. Thus, current rate adaptation and MAC protocols can neither deal with the challenge nor exploit the opportunities introduced by the frequency diversity of wide bands or unchannelized 802.11.

1The wider the band, the faster the ADC and DAC have to sample the signal.

(25)

FARA

•  Instead of assigning the same rate to the entire frequency band, it allows each

OFDM sub-carrier to pick a modulation and a code rate that match its SNR

Frequency-Aware Rate Adaptation and MAC Protocols

Hariharan Rahul

, Farinaz Edalat

, Dina Katabi

, and Charles Sodini

Massachusetts Institute of Technology

RKF Engineering Solutions, LLC

ABSTRACT

There has been burgeoning interest in wireless technologies that can use wider frequency spectrum. Technology advances, such as 802.11n and ultra-wideband (UWB), are pushing toward wider fre- quency bands. The analog-to-digital TV transition has made 100- 250 MHz of digital whitespace bandwidth available for unlicensed access. Also, recent work on WiFi networks has advocated discard- ing the notion of channelization and allowing all nodes to access the wide 802.11 spectrum in order to improve load balancing. This shift towards wider bands presents an opportunity to exploit frequency diversity. Specifically, frequencies that are far from each other in the spectrum have significantly different SNRs, and good frequencies differ across sender-receiver pairs.

This paper presents FARA, a combined frequency-aware rate adaptation and MAC protocol. FARA makes three departures from conventional wireless network design: First, it presents a scheme to robustly compute per-frequency SNRs using normal data trans- missions. Second, instead of using one bit rate per link, it en- ables a sender to adapt the bitrate independently across frequencies based on these per-frequency SNRs. Third, in contrast to traditional frequency-oblivious MAC protocols, it introduces a MAC protocol that allocates to a sender-receiver pair the frequencies that work best for that pair. We have implemented FARA in FPGA on a wide- band 802.11-compatible radio platform. Our experiments reveal that FARA provides a 3.1× throughput improvement in comparison to frequency-oblivious systems that occupy the same spectrum.

Categories and Subject Descriptors

C.2.2 [Computer Sys- tems Organization]: Computer-Communications Networks

General Terms

Algorithms, Design, Performance

Keywords

Wireless, Cognitive Radios, Wideband, Rate Adapta- tion, Cross-layer

1 I

NTRODUCTION

Wireless technologies are pushing toward wider frequency bands than the 20 MHz channels employed by existing 802.11 networks.

802.11n already includes a 40 MHz mode that bonds together two 20 MHz bands [23]. Emerging ultra-wideband (UWB) technolo- gies employ hundreds of MHz to support multimedia homes and offices [24, 50, 9, 40]. The FCC has recently permitted unlicensed

Permission to make digital or hard copies of all or part of this work for per- sonal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to re- publish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

MobiCom’09, September 20–25, 2009, Beijing, China.

Copyright 2009 ACM 978-1-60558-702-8/09/09 . . . $10.00.

0 5 10 15 20 25 30

-40 -20 0 20 40

SNR (dB)

Freq (Mhz)

Figure 1: Frequency diversity across 100 MHz of 802.11a spec- trum as observed by two receivers for transmissions from the same sender. The figure shows that the SNRs of different frequen- cies can differ by as much as 20 dB on a single link. Further, different receivers prefer different frequencies.

use of digital TV whitespaces that occupy 100-250 MHz of spectrum vacated by television bands in the analog-to-digital transition [12].

Furthermore, recent empirical studies show that the 802.11 chan- nelization model which limits each node to a single 20 MHz chan- nel can lead to severe load imbalance [19, 28, 37]. They advocate discarding channelization and allowing all nodes to access the en- tire 802.11 spectrum based on demand [19, 37]. This push towards wider bands is further enabled by the constantly lowering prices of high-speed ADC and DAC hardware [38, 31].1 In particular, today, wireless cards that span over 100 MHz of spectrum can be built us- ing off-the-shelf hardware components [35].

As wireless networks push towards wider bands, we can no longer afford to ignore frequency diversity. Specifically, multipath effects cause frequencies that are far away from each other in the spectrum to experience independent fading. Thus, different frequencies can exhibit very different SNRs for a single sender-receiver pair. Further, the frequencies that show good performance for one sender-receiver pair may be very different than the frequencies that show good per- formance for another pair. Fig. 1 shows empirical measurements of the SNRs across 100 MHz of the 802.11a spectrum, as observed by 2 clients for transmissions from the same AP (see §9 for exper- imental setup). The figure reveals that different frequencies show a difference in SNR of over 20 dB both for a single link and across links. Existing bitrate adaptation and MAC protocols however are frequency-oblivious. They assign the same bitrate to all frequencies and allocate the medium in a time-based manner, ignoring the fact that different frequencies work better for different sender-receiver pairs. Thus, current rate adaptation and MAC protocols can neither deal with the challenge nor exploit the opportunities introduced by the frequency diversity of wide bands or unchannelized 802.11.

1The wider the band, the faster the ADC and DAC have to sample the signal.

54Mb/s  

6Mb/s  

(26)

FARA

•  Receiver driver protocol

„ 

Initially, the sender transmit few symbols using the lowest bit-rate for all sub-carriers

„ 

The receiver selects the bit-rate based on an SNR-Rate mapping table

where H

i

is the channel, x

i

[k ] is the k

th

transmitted signal sam- ple in subband i , and n

i

[k ] is the corresponding noise sample. The receiver knows H

i

for all subbands because it is estimated using known OFDM symbols in the preamble [20]. In the case of a pi- lot subband, x

i

[k ] is also known at the receiver since pilot subbands contain a known data sequence. As a result, the receiver can estimate the noise samples, n

i

[k ], and the noise power, N

0

, as:

n

i

[k ] = y

i

[k ] − H

i

x

i

[k ] (4) N

0

= E

i ,k

(n

i

[k ]

2

) (5) where the function E (.) is the mean computed using all pilot bits across all symbols in the data packet.

Thus, every received packet allows the receiver to obtain a new SNR measurement for each OFDM subband. The receiver maintains a time weighted moving average of the SNR in each subband, which it updates on the reception of a data packet.

A few points are worth noting:

(a) What happens when the data packet is corrupted (i.e. does not pass the checksum test)? Even when the packet is corrupted, the receiver can still compute an accurate estimate of the per-subband SNRs. This is because the receiver can compute the average received power, regardless of whether the packet is corrupted or not. Further- more, the receiver can still obtain an accurate estimate of the noise power since this only requires the pilots which are known, and sent at BPSK, which is the most robust modulation rate and hence al- low synchronization and packet recovery even at low SNRs. Thus, FARA can get accurate estimates of the per-subband SNRs from ev- ery captured packet, including corrupted packets.

(b) How accurate are FARA’s SNR estimates? We note that since FARA has access to the PHY layer, it can collect accurate SNR estimates. In particular, traditional estimates of the SNR use RSSI readings, which measure the received power of a few samples at the beginning of the packet (i.e., the AGC gain) [6], or infer the SNR using just the correlation of header symbols in the preamble of the packet [49]. In contrast, FARA exploits the known pilot bits to ac- curately estimate the noise power and utilize it in its SNR compu- tation. Furthermore, FARA computes its signal and noise estimates over the whole packet and not just a few samples at the beginning of the packet, which allows it to obtain more stable estimates.

(c) Do different choices of bitrate affect the accuracy of FARA’s SNR estimation? OFDM data subbands use a different modulation scheme depending on the choice of bitrate. The modulation scheme in a subband, however, does not affect our per-subband SNR esti- mate. The estimation of SNR involves only the measured power in each subband and hence can be performed on any packet indepen- dent of the modulation and coding schemes used by the transmitter.

6 F REQUENCY -A WARE R ATE A DAPTATION

The goal of rate adaptation is to determine the highest bitrate that a channel can sustain at any point in time. Traditional 802.11 rate adaptation schemes are frequency-oblivious, and use the same modulation scheme and coding rate across all frequencies. Thus, they cannot exploit the frequency diversity present across the 802.11 spectrum. In contrast, FARA exploits this frequency diversity via a frequency-aware rate adaptation scheme that picks different bitrates for different frequencies depending on their SNRs.

6.1 PHY Architecture

In 802.11, a particular bit rate implies a single modulation scheme and code rate over all OFDM subbands in the entire packet. For

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(a) Schematic of 802.11 PHY

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(b) Schematic of FARA-enabled 802.11 PHY

Figure 3: OFDM PHY semantics with and without FARA. In FARA-enabled devices, the choice of modulation and FEC code rate is done independently for each OFDM subband.

Minimum Required SNR Modulation Coding

<3.5 dB Suppress subband

3.5 dB BPSK 1/2

5.0 dB BPSK 3/4

5.5 dB 4-QAM 1/2

8.5 dB 4-QAM 3/4

12.0 dB 16-QAM 1/2

15.5 dB 16-QAM 3/4

20.0 dB 64-QAM 2/3

21.0 dB 64-QAM 3/4

Table 1: Minimum required SNR for a particular modulation and code rate (i.e., bitrate). Table is generated offline using the WiGLAN radio platform by running all possible bit rates for the whole operational SNR range. The SNR field refers to the minimum SNR required to maintain the packet loss rate below 1% (see §9 for experimental setup).

example, a bitrate of 24 Mbps corresponds to 16-QAM modula- tion scheme and a half-rate code. 802.11 has 4 possible modulation schemes (BPSK, 4-QAM, 16-QAM, and 64-QAM), and 3 possible code rates (1/2, 2/3, and 3/4). In current 802.11, a transmitter imple- ments a particular bitrate by first taking the input bit stream, passing it to the convolutional coder, and puncturing to achieve the desired coding rate. The bits are then interleaved, modulated and striped over the OFDM subbands, as shown in Fig. 3(a). The process is reversed on the receiver as shown in the figure.

FARA makes a few modifications to the existing 802.11 PHY layer, as shown in Fig. 3(b). Specifically, FARA employs the same set of modulation schemes and code rates supported by the existing 802.11. However, it allows each OFDM subband to pick a modu- lation scheme and a code rate that match its SNR, independently from the other subbands. Note that this design does not require addi- tional modulation/demodulation or coding/decoding modules in the PHY layer. In particular, since we use standard 802.11 modulation and coding options, we only need to buffer the samples and process them through the same pipeline.

6.2 Mapping Subband SNRs to Optimal Bitrates

The receiver needs to map the average SNR in each subband to

the optimal bitrate for that band. To do so, the receiver uses an SNR

characterization table like the one in Table 1 that lists the minimum

SNR required for a particular combination of modulation and cod-

(27)

Predictable 802.11 Packet Delivery from Wireless Channel Measurements

D. Halperin, W. Hu, A. Sheth and D. Wetherall

ACM SIGCOMM, 2010

(28)

SNR-based Rate Adaptation

•  SNR-based rate adaptation is usually inaccurate because we

„ 

Assume frequency-flat fading

„ 

Select the bit-rate based on “average SNR”

across bins

•  However, this will over-estimate the channel quality because

„ 

A packet will fail to pass the CRC check even if

only few bits are erroneous due to frequency-

selective fading

(29)

Effective SNR

•  Bias toward the weaker sub-carrier SNRs

BER

eff ,k

= 1

52 BER

k

(SNR

s

) SNR

eff ,k

= BER

k!1

(BER

eff ,k

)

OFDM

Demodulator Deinterleaver Convolutional

Decoder Descrambler

(0) Received

signal

MIMO Stream Separation

Separated signals for each spatial stream

(1)

Scrambled, coded bits

(3)

(2)

Scrambled, interleaved, coded bits

(4)

Scrambled bits

(5) Received bitstream

Packet processing

Figure 3: The 802.11n MIMO-OFDM decoding process. MIMO receiver separates the RF signal (0) for each spatial stream (1).

Demodulation converts the separated signals into bits (2). Bits from the multiple streams are deinterleaved and combined (3) followed by convolutional decoding (4) to correct errors. Finally, scrambling that randomizes bit patterns is removed and the packet is processed (5).

Modulation Bits/Symbol (k) BER

k

(ρ)

BPSK 1 Q �√

2ρ �

QPSK 2 Q �√ ρ �

QAM-16 4

34

Q ��

ρ/5 �

QAM-64 6

127

Q ��

ρ/21 �

Table 2: Bit error rate as a function of the symbol SNR ρ for narrowband signals and OFDM modulations. Q is the standard normal CDF.

likely to be variable, and simply knowing when the link starts to work is useful information in practice.

802.11 Packet Reception. The model must account for the action of the 802.11 receiver on the received signal. This is a complex pro- cess described in many pages of the 802.11n specification [1]. Our challenge is to capture it well enough with a fairly simple model.

We begin by describing the main steps involved (Figure 3).

First, MIMO processing separates the signals of multiple spatial streams that have been mixed by the channel. As wireless chan- nels are frequency-selective, this operation happens separately for each subcarrier. The demodulator converts each subcarrier’s sym- bols into the bits of each stream from constellations of several dif- ferent modulations (BPSK, QPSK, QAM-16, QAM-64). This hap- pens in much the same way as demodulating a narrowband channel.

The bits are then deinterleaved to undo an encoding that spreads errors that are bursty in frequency across the data stream. A paral- lel to serial converter combines the bits into a single stream. For- ward error correction at any of several rates (1/2, 2/3, 3/4, and 5/6) is then decoded. Finally, the descrambler exclusive-ORs the bit- stream with a pseudorandom bitmask added at the transmitter to avoid data-dependent deterministic errors.

Modeling Delivery. We build our model up from narrowband de- modulation. Standard formulas summarized in Table 2 relate SNR (denoted ρ) to bit-error rate (BER) for the modulations used in 802.11 [8]. CSI gives us the SNR values (ρ

s

) to use for each sub- carrier. For a SISO system, ρ

s

is given by the single entry in H

s

.

In OFDM, decoding is applied across the demodulated bits of subcarriers. If we assume frequency-flat fading for the moment, then all the subcarriers have the same SNR. The link will behave the same as in our wired experiments in which RSSI reflect real performance and it will be easy to make predictions for a given SNR and modulation combination. We can use Figure 1(a) to measure the fixed transition points between rates and thus make our choice.

Frequency-selective fading complicates this picture as some weak subcarriers will be much more likely to have errors than others that are stronger. To model a link in this case, we turn to the notion of an effective SNR. This is defined as the SNR that would give the same

error performance on a narrowband channel [18]. For example, the links in Figure 2 will have effective SNR values that are nearly equal because they perform similarly, even though their RSSIs are spread over 15 dB.

The effective SNR is not simply the average subcarrier SNR; in- deed, assuming a uniform noise floor, that average is indeed equiv- alent to the packet SNR derived from the RSSI. Instead, the effec- tive SNR is biased towards the weaker subcarrier SNRs because it is these subcarriers that produce most of the errors. If we ignore coding for the moment, then we can compute the effective SNR by averaging the subcarrier BERs and then finding the corresponding SNR. That is:

BER

eff,k

= 1 52

� BER

k

s

) (1)

ρ

eff,k

= BER

−1k

(BER

eff,k

) (2)

We use BER

−1k

to denote the inverse mapping, from BER to SNR.

We have also called the average BER across subcarriers the effec- tive BER, BER

eff

. SoftRate estimates BER using internal receiver state [28]. We compute it from channel measurements instead.

Note that the BER mapping and hence effective SNR are func- tions of the modulation (k). That is, unlike the RSSI, a particular wireless channel will have four different effective SNR values, one describing performance for each of the modulations. In practice, the interesting regions for the four effective SNRs do not overlap be- cause at a particular effective SNR value only one modulation will be near the transition from useless (BER ≈0.5) to lossless (BER

≈0). When graphs in this paper are presented with an effective SNR axis, we use all four values, each in the appropriate SNR range.

For 802.11n, we also model MIMO processing at the receiver.

To do this we need to estimate the subcarrier SNRs for each spa- tial stream from the channel state matrix H

s

. Although the stan- dard does not specify receiver processing, we assume that a Min- imum Mean Square Error (MMSE) receiver is used. It is compu- tationally simple, optimal and equivalent to Maximal-Ratio Com- bining (MRC) for a single stream, and near optimal for multiple streams. All of these make it a likely choice in practice. The SNR of the i

th

stream after MMSE processing for subcarrier s is given by ρ

s,i

= 1/Y

ii

− 1, where Y = �

H

sH

H

s

+ I �

−1

for i ∈ [1, N] and NxN identity matrix I [27]. For MIMO, the model computes the effective BER averaged across both subcarriers and streams.

Coding interacts with the notion of effective SNR in a way that is difficult to analyze. One challenge is that the ability to correct bit errors depends on the position of the errors in the data stream.

To sidestep this problem, we rely on the interleaving that random- izes the coded bits across subcarriers and spatial streams. Assum- ing perfect interleaving and robust coding, bit errors in the stream should look no different from bit errors for flat channels (but at a

162

(30)

Effective SNR

•  Look up the SNR-MAP table using ESNR

Figure 4: Our indoor 802.11n testbeds, T1 and T2. T1 consists of 10 nodes spread over 8 100 square feet, and T2 consists of 11 nodes spread over 20 000 square feet. The nodes are placed to ensure a large number of links between them, a variety of distance between nodes, and diverse scattering characteristics.

8 12 16 20 24

-28 -14 0 14 28

SNR (dB)

Subcarrier index

BPSK QPSK QAM-16 QAM-64

Packet SNR Subcarrier SNRs

Figure 5: Sample faded link showing the packet SNR and ef- fective SNRs for different modulations. BPSK has the lowest effective SNR, but it needs less energy to decode.

lower SNR). Thus our estimate of the effective BER in Eq. (1) will accurately reflect the uncoded error performance of the link. Our algorithm now proceeds as in the case of a flat-fading channel de- scribed above: we take the computed effective SNR value and use the measurements from a flat-fading link (Figure 1(a)) to determine transmission success or failure. As in CHARM [10], we support different packet lengths with different SNR thresholds.

Note that this procedure differs from the typical approach of simulation-based analyses [11, 15, 19], that instead map the un- coded BER estimate such as we compute to a coded BER esti- mate by means of a simple log-linear approximation. They then use the coded BER estimate, and the length of the target transmis- sion, to directly compute the packet delivery rate of the link. We believe our method of thresholding the effective SNR is better be- cause it directly accommodates variation in the receiver implemen- tation. Different devices may have different noise figures, a measure of how much signal strength is lost in the internal RF circuitry of the NIC. They may implement soft Viterbi decoders with more or fewer soft bits for their internal state, or indeed might do hard de- coding instead. A receiver could use the optimal Maximum Like- lihood MIMO decoder that has exponential complexity for small constellations like BPSK, but revert to the imperfect but more ef- ficient MMSE at higher modulations. All of these can be easily expressed, albeit maybe approximately, as (perhaps modulation- dependent) shifts in the effective SNR thresholds. In contrast, chang- ing these parameters in the simulation approach involves changing the internals of the calculation.

Protocol Details. Effective SNR calculations can be performed by either receiver or transmitter, and each has advantages. For it to make decisions, the transmitter must know the receiver’s thresholds

for the different rates; these are fixed for a particular model of NIC and can be shared once, e.g., during association. The transmitter also needs up-to-date CSI: either from feedback or estimated from the reverse path. Alternately, the receiver can request rates and se- lect antennas directly using the new Link Adaptation Control field of any 802.11n QoS packet [1, §7.1.3.5a]. This obviates sending CSI, but the calculation instead requires that the transmitter share its spatial mappings, i.e. how it maps spatial streams to transmit an- tennas. These are likely to change less frequently than the channel, if at all. Finally, when operating in either mode with fewer trans- mit streams than antennas, the transmitter must occasionally send a short probe packet with all antennas to measure the full CSI.

Summary and Example. Combining the above steps, our model consists of the following: 1) CSI is obtained and a test config- uration is chosen; 2) the MMSE expression is used to compute per-stream, subcarrier SNRs from the CSI for the test number of streams; 3) the effective SNR is computed from the per-stream, subcarrier SNRs for the test modulation; and 4) the effective SNR is compared against the pre-determined threshold for the test mod- ulation and coding to predict whether the link will deliver packets.

As an example, Figure 5 shows the CSI for a SISO link (steps 1–

2) as a fading profile across subcarriers, with the computed effective SNRs for all modulations (step 3). These effective SNRs are com- pared with pre-determined thresholds (step 4, see §5) to correctly predict that the best working rate will be 39 Mbps. Note that these effective SNRs are well below the RSSI-based packet SNR that is biased towards the stronger subcarriers (note the logarithmic y-axis scale). This link does a poor job of harnessing the received power because it is badly faded, so its RSSI is a poor predictor of rate.

Applications can use this model to find useful configurations without sending packets to test them. For example, the highest rate can be predicted by running the model for all candidate rates and selecting the best working rate. Alternatively, we could predict the minimum transmit power to support a rate.

4. TESTBEDS

We conduct experiments using two stationary wireless testbeds deployed in indoor office environments, T1 and T2 (Figure 4). T1 consists of 10 nodes spread over 8 100 square feet. T2 is less dense by comparison with 11 nodes over 20 000 square feet. Each testbed covers a single floor of a multi-story building and has a variety of links in terms of maximum supported rate and line-of-sight versus multi-path fading. We conduct mobile experiments using laptops that interact with testbed nodes and are configured in the same way.

163

參考文獻

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