A Gentle Introduction to AdaptiveTransmission

Pavel Loskot

University of Alberta, Edmonton, Alberta, Canada

March 19, 2002

2/34An example

• Fig.: 3-sectored single cell communication system with power control,beamforming and perhaps scalable sectors

3/34An example - cont.

10 20 30 40 50 60 70 80 90 100−20

−10

0

10

20Received power

Pow

er [d

B]

10 20 30 40 50 60 70 80 90 100−20

−10

0

10

20Power control

Pow

er [d

B]

10 20 30 40 50 60 70 80 90 100−20

−10

0

10

20Power control + Beamforming

sample #

Pow

er [d

B]

• Fig.: impact of beamforming and power control to the received SNR

4/34An example - cont.

DEM

CH.EST.

AGC

controlpower

γ̂

F (γ̂)

MODdata

pilots

S̄SNR ≈ γ

data

∆S(γ)

feedback

transmitter receiverchannel

• a single link (point-to-point connection)• output power

St(γ) = St−1(γ) ∆S(γ)

5/34An example - cont.

10 20 30 40 50 60 70 80 90 1000

0.5

1

1.5

2Transmitted Pilot Symbols

10 20 30 40 50 60 70 80 90 1000

0.5

1

1.5

2Channel Realization

10 20 30 40 50 60 70 80 90 1000

0.5

1

1.5

2

sample #

Received Pilot Symbols

• Fig.: channel estim. via Pilot Symbol Assisted Modulation (PSAM)

6/34A number of questions...

• What are the optimization criteria ? Is channel inversion the best ?

• How often and how much to update transmit power ?

St(γ) = St−1(γ) ∆S(γ)

– Impact of channel delay and Doppler spread ?

• How to obtain channel knowledge at the transmitter ?

• How will the realistic feedback affect the performance ?

• How different is the single link and multiuser case ?

• Channel knowledge at the transmitter, what else can we do ?

7/34Conventional solution

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• Fig.: bit-error rate (BER) performance

• system design for the worst case or average channel conditions⇒ we sacrifice BER or waste power

• improvements– adaptive receivers to track the channel– multiuser detection to resolve multiuser interference

8/34New solution

a priori a posteriori�� �� �� � � �� � � � � � � �

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• Fig.: general communication system model

• now the channel knowledge at transmitter is available⇒ a new design for all channel and traffic conditions, i.e.

match the transmission to the channel and traffic conditions

– “match” in a sense of some optimization criterion(ia)– almost always it means to avoid bad transmit/receive conditions

9/34We may go further...

Transmitter Receiver

Noise Source

Traffic Source

a priori a posteriori

Source

• Fig.: even more general communication system model

• “forward-feedback” to inform receiver about current source properties– adapt receiver algorithms– match the source to the channel

• source types– ideal EP-IID data symbol source– variable bit rate, e.g. video– constant bit rate, e.g. speech– available bit rate, e.g. data transfer

10/34Optimization problems

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�% $ # � �� & �# & "' ( �

)�

• Maximize (data) rate

• Minimize– power (energy), bandwidth, complexity, delay and BER– plus distortion for multimedia transmissions

• Always constrained optimization– instantaneous– short-term average of instantaneous– long-term average of instantaneous

11/34Reliability–Integrity–Complexity Trade-off

• How difficult is to approach channel capacity ?– let transmission rate R = (1− ε)C and decoder probability p– let complexity χ(ε, p) in operations per information bit

limε→0

χ(ε, p) , limp→0

χ(ε, p) ?

• Reliability– performance, robustness, BER, power efficiency

• Integrity– throughput, capacity, spectral efficiency

• R-I-C Trade-off cannot be avoided– design with prescribed delay, memory and computational

complexity is unknown

12/34Feedback

transmitter receiverforward channel

data in

feedback channel

data out

• the only way to get channel knowledge, a.k.a channel-stateinformation (CSI), at the transmitter

• whether negative or positive depends on the optimization problem– positive feedback – to maximize efficiency– negative feedback – to stabilize system

• the same principle for TDD and FDD systems– TDD systems - channel reciprocity holds (implicit feedback)– FDD systems - explicit feedback

13/34Feedback - cont.

• it may both simplify and complicate the system design

• it allows– coordination of users– iterative solution to optimization problems– tracking of time varying channel and traffic

• also internal feedbacks inside the transmitter and receiver,(e.g. turbo decoding)

• it is only a “channel” (implicit or explicit), hence realistic feedback is– power and bandwidth limited– noise limited– delay limited

14/34Adaptation at physical layer

• Source coding– unequal error protection, layer and subband coding (multimedia),

• Channel coding– to cover SNR gap and/or increase SNR margin– block and convolutional codes are spectrally inefficient– coset codes (lattice and trellis) better but still “far” from capacity– turbo codes, lots of hope

• Modulation– Adaptive Modulation Scheme (AMS)– multidimensional - multicarrier (MCM) and multiantenna∗ beamforming (accurate channel knowledge)∗ switched-diversity (moderate channel knowledge)∗ space-time coding (no channel knowledge)

– adaptive CDMA

15/34Adaptation at physical layer - cont.

• Precoding– beamforming, switched diversity– predistorition, pre-equalization (prevent noise enhancement and

simplify the receiver)– pre-Rake

• Other– design of spreading codes (to match the channel)– wavelets

16/34Adaptation at higher layers

L7 Applications Layer

L6 Presentation Layer

L5 Session Layer

L4 Transport Layer

L3 Network Layer

L2 Link Layer

L1 Physical Layer

Medium

Application Programs, Users A B

• Fig.: the OSI reference model

• mutual synergy among all layers

• adaptive protocols and intelligence nodes

• “adaptive users” (Internet)

17/34Adaptation at higher layers - cont.

• Medium Access Protocol (MAC) and Radio Resource Management– dynamic channel allocation– variable packet length– avoid or minimize interference, packet collisions, retransmissions

• Packet Reservation Multiple Access (PRMA)

• ARQ– hybrid type II, incremental retransmission

• routing– find the “best” route– combinatorial optimization problem

18/34AMS - the steps of operation

• Transmission

1. Predict the channel quality2. Choose new transmission format/parameters3. Optional signaling of new the format/parameter

• Reception

1. Estimate channel quality2. Decide on transmission format/parameters

⇒ coordination between transmitter and receiver necessary (!)

19/34Step 1: Channel prediction

• channel knowledge at transmitter is necessary (in our case)

Hence• fully noncoherent systems do not apply• it requires bidirectional link (point-to-point connection)• prediction in order to make the system causal• typically use channel quality measures (SNR, BER)

• TDD systems– open-loop adaptation– packetized transmission– faster tracking (however, channel decorrelates over time)

• FDD systems– feedback link necessary– realistic channel-feedback impair the performance– better performance in presence of interference

20/34Step 2: Choice of new parameters

• to solve given optimization problem

• Data rate– constellation size, typically linear modulations (MQAM)– to vary symbol size is impractical (synchronization, bandwidth)

• Power

• In ergodic channels, capacity is achieved via– variable rate and power

A. Goldsmith, “The capacity of downlink fading channels with variable rate andpower”, IEEE Trans. Vehic. Tech., vol. 46, no. 3, pp. 569–580, Aug. 1997

– variable power (fixed rate)G. Caire, G. Tarico, and E. Biglieri, “Optimum power control over fadingchannels”, IEEE Trans. Inform. Th., vol. 45, no. 5, pp. 1468–1489, July 1999

21/34Step 3: Signaling of parameters1

− Evaluate perceived channel quality− Signal the requested trans. mode

− Evaluate perceived channel quality− Signal the requested trans. mode

− Evaluate perceived channel quality− Decide on MS trans. mode

− Evaluate perceived channel quality− Decide on BS trans. mode

− Evaluate perceived channel quality− Infer the BS trans. mode blindly− Decide on MS trans. mode

− Evaluate perceived channel quality− Infer the MS trans. mode blindly− Decide on BS trans. mode

BSMS Uplink

Downlink

(b) Non−reciprocal channel, closed−loop signalling

(a) Reciprocal channel, open−loop control

Signal modem modes to be used by BS

Signal modem modes to be used by MS

BSMS

Downlink

Signal modem modes used by MS

Signal modem modes used by BS

Uplink

BSMS Uplink

Downlink

no signalling

no signalling

(c) Reciprocal channel, blind modem−mode detection

• note: blind decisions on modem mode increases efficiency

1

1From: L. Hanzo, W. Webb, T. Keller, Single- and Multi-carrier QAM, 2nd ed., Wiley, 2000

22/34Practical considerations

• closed-form solutions to optimization problem hardly exist

– iterative solution, greedy algorithms, (non)linear programming etc.

• AMS is affected by

– finite information granularity (bits)– maximum constellation size– finite update rate– latency problem in slowly fading (large buffers)– tracking problem in fast fading

• realistic feedback

• channel estimation errors

23/34Channel knowledge

• a.k.a Channel State Information (CSI)

1. nothing is known

2. fading statistics known

• typically difficult to deal with

3. fade values known at receiver

• coherent detection

4. fade values known at receiver and transmitter

• both transmitter and receiver can be adaptive• channel knowledge at transmitter can be causal or noncausal (!)

24/34How beneficial is adaptive transmitter ?

1. single link - ergodic channel• Shannon capacity = maximum error-free data rate• for most distributions the gain is negligible

M.-S. Alouini and A. Goldsmith, “Comparison of fading channel capacity underdifferent csi assumptions”, in Proc. VTC, 2000, vol. 4, pp. 1844–1849.

2. single link - delay limited• capacity-versus-outage (maximum rate during non-outage) and

delay-limited a.k.a zero-outage capacity (maximum rate in allfading conditions)• the less ergodic, the higher gain

E. Biglieri, G. Caire, and G. Tarico, “Limiting performance of block-fadingchannels with multiple antennas”,IEEE Trans. Inform. Th., vol. 47, no. 4, pp.1273–1289, May 2001

3. multiple users• gain significant for all scenarios

25/34AMS in networks

• users mutually interfere– to optimize one link create more multiuser interference

• optimization problems– orders of magnitude more complex to solve– to find global optimum is difficult– individual or joint constraints

• it is not clear how to distribute control– centralized versus distributed control– note that feedback links allow coordination of users

• AMS especially appealing for emerging ad-hoc networks

26/34Ad-hoc versus cellular networks

BA

A

B

• random network topology– dynamic resource allocation and routing– multiple hops to connect two arbitrary nodes⇒ robust design

• WLAN, battle-field, emergency networks, medical sensors

• evolution of cellular systems

• Which one provides higher (network) capacity2 ?

2

2[Goldsmith, Cover, 1999]

27/34Loading algorithms

Subchannelindex0 1 2 3 54 6 7

Energy

const

transmit power channel gain

• Task– find the distribution of bits and power over subchannels– conditioned on the perfect knowledge of the subchannel gains– subject to power constraints or fixed data rate

• Solution– always water-filling (optimum and near optimum algorithms due to

J. Cioffi3 et al. however suitable only to wireline applications)– combinatorial optimization problem, “traveling salesman”– hence it is NP-complete (=very complex)

3

3http://www.stanford.edu/class/ee379c/

28/34Historical perspective - 60s

1962 R. Price, “Error probabilities for adaptive multichannel reception over of binarysignals”, MIT Tech. Rep.

1963 J. C. Hancock and W. C. Lindsey, “Optimum performance of self-adaptivesystems operating through Rayleigh-fading”, IEEE Trans. Comm. Syst.

1964 J. L. Holsinger, “Digital communications over fixed time-continuouschannels”, MIT Tech. Rep. (water-filling in colored Gaussian channels)

1965 G. L. Turin, “Signal design for sequential detection systems with uncertaintyfeedback”, IEEE Trans. Inform. Th.

1966 J. P. M. Schalkwijk, “A coding scheme for additive noise channels withfeedback”, IEEE Trans. Inform. Th.

1968 J. F. Hayes, “Adaptive feedback communications”, IEEE Trans. Com.

29/34Historical perspective - 70s, 80s

1972 J. K. Caver, “Variable-rate transmission for Rayleigh fading channels”, IEEETrans. Com.

1973 C. E. Shannon, ”Feedback in communication problems received inadequateattention in Information Theory”, the first Shannon lecture

1972 F. E. Glave, “Communication over fading dispersive channels with feedback”,IEEE Trans. Inform. Th.

1974 R. Srinivasan and R. L. Brewster, “Feedback communications in fadingchannels”, IEEE Trans. Com.

1974 V. O. Hentinen, “Error performance for adaptive transmission on fadingchannels”, IEEE Trans. Com.

• lack of good channel estimation techniques• hardware limitations

1988 J. M. Jacobsmeyer, “An adaptive modulation scheme for bandwidth-limitedmeteor-burst channels”, Proc. Milcom

30/34Historical perspective - 90s

1995 W. T. Webb and R. Steele, “Variable rate QAM for mobile radio”, IEEE Trans.Com.

1997 A. J. Goldsmith and S.-G. Chua, “Variable-rate variable-power MQAM forfading channels”, IEEE Trans. Com.

1997 A. J. Goldsmith, P. Varaiya, “Capacity of fading channels with channel sideinformation”, IEEE Trans. Inform. Th.

1998 S. Sampei, N. Morinaga et. al, “Laboratory experimental results of anadaptive modulation for wireless multimedia communication systems”, Proc.PIMRC

1998 D. L. Goeckel, “Strongly robust adaptive signaling for time-varying channels”,Proc. ICC

31/34Historical perspective - 90s (cont.)

2000 G. Caire, G. Tarico, and E. Biglieri, “Optimum power control over fadingchannels”, IEEE Trans. Inform. Th.

2000 M.-S. Alouini and A. J. Goldsmith, “Adaptive modulation over Nakagamifading channels”, Kluwer Journal on Wireless Communications

2000 T. Keller and L. Hanzo, “Adaptive multicarrier modulation: a convenientframework for time-frequency processing in wireless communications”, IEEEProc.

2000 L. Hanzo, W. Webb and T. Keller, “Single- and Multi-carrier QuadratureAmplitude Modulation”, Wiley, 2nd ed.

2001 S. T. Chung and A.J. Goldsmith, “Degrees of Freedom in AdaptiveModulation: A Unified View”, IEEE Trans. Com.

2001 E. Biglieri, G. Caire, and G. Tarico, “Limiting performance of block-fadingchannels with multiple antennas”, IEEE Trans. Inform. Th.

32/34Current cellular systems and standards4

• coverage 90− 95% for certain Quality-of-service (QoS)• hence excessive SNR to support higher data rates

Standard Method Method Feedback Peakof rate of format of channel dataadaptation indication quality rate

IS-95B code aggregation separate messages pilot strength 64 kbits/smeasurements

CDMA2000 variable spreading separate message pilot strength 614 kbits/sand coding blind detection measurement,

power control bitsW-CDMA variable spreading separate fields in Measurements: 2048 kbits/s

and coding each frame •pilot strength•SINR•BER•path loss

GPRS time slot separately ARQ message: 160 kbits/saggregation and coded bits •SINRadaptive coding •av. BER

•BER varianceGPRS-136 time slot aggreg. separately ARQ message in 44 kbits/s

AMS, incremental coded field uplink, downlinkredundancy packet feedback

EGPRS time slot aggreg. separately ARQ message: 474 kbits/sset of mod.+coding coded field •SINRschemes, incr. red., •BERaggresive reuse fac. •fading rate

4

4From: S. Nanda and K. Balachandran and S. Kumar, “Adaptation techniques in wireless packet dataservices”, Comm. Mag., vol. 38, no. 1, Jan. 2000

33/34Current broadband standards

• Hiperlan II– wireless ATM in 5 GHz by ETSI– physical layer

- adaptive OFDM with 52 subcarriers in 20 MHz channel- variable data rate , 9, 12, 18, 27, 36 Mb/s, and optional 54 Mb/s- M-ary QAM with punctured convolutional (turbo) codes

– link layer - adaptive MAC, ad-hoc capabilities

• IEEE 802.11-a– also in 5 GHz band but not H/2 compatible, by IEEE WLAN

standardization body– physical layer

- adaptive OFDM with 52 subcarriers in 20 MHz channel- data rate 6, 12, 24 Mb/s and optional 9, 18, 36, 48 and 54 Mb/s

– link layer common to all 802.11 family standards

34/34Conclusions

Signal−to−noise ratio

Performance

o p e n r e s e a r c h a r e a

Ergodic capacity

Fixed schemes

Delay−limited capacity

Goldsmith et al., Biglieri et al.

Biglieri et al.

Goeckel

Hanzo et al., Morinaga et al.

• Fig.: upper and lower bound approach to adaptive modulation