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FINNISH WIRELESS COMMUNICATIONS WORKSHOP ’01

The Concept of Adaptive Transmission

Pavel Loskot, Matti Latva-aho

Centre for Wireless CommunicationsUniversity of Oulu, Finland

{loskot,matla}@ee.oulu.fi

23rd October 2001

– FWCW’01 –

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Goal

• to draw the Concept of Adaptive Transmission (CAT)

• to explain issues not emphasized in literature

• to keep discussion on a general level

• each slide carries an independent topic

– FWCW’01 – c©Pavel Loskot 2001/10/23 2(16)

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“We may have knowledge of the past but cannot controlit; we may control the future but have no knowledge of it.”

-- Claude E. Shannon --

– FWCW’01 – c©Pavel Loskot 2001/10/23 3(16)

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System Model View

source destination

source destinationtransmitter receiver

source destination

noisy message

noise

noise

message

add. noisemult. noise

mult. noise add. noise

dem det

CC(2,1,5) GMSK FD VE DECRPE-LTP

COST#207

encod mod

interference

• general −→ particular, theory −→ practice, academy −→ industry

• “everything is coding” (and information theory)

• upper bound research → more accurate, lower bound → optimized

– FWCW’01 – c©Pavel Loskot 2001/10/23 4(16)

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Dimensions, Degrees of Freedom

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Dimensions• time, frequency, space × users

Degrees of freedom• resources allocated within dimensions (time-variant)• typically constrained, e.g. CPU and memory are complexity constaints

Capacity• time-invariant quantity above degrees of freedom

More degrees of freedom• orthogonalization within degrees of freedom, e.g. spreading code

– FWCW’01 – c©Pavel Loskot 2001/10/23 5(16)

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Reliability–Integrity–Complexity Trade-off

How difficult is to approach channel capacity ?

• transmission rate R = (1− ε)C• decoder probability p

• 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

• reliability–integrity–complexity trade-off is unavoidable

• Design with prescribed delay, memory and comput. complexity is unknown

– FWCW’01 – c©Pavel Loskot 2001/10/23 6(16)

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Channel Capacity

“Real time issues and feedback in communication problems received inadequateattention in Information Theory.” -- The first Shannon lecture (1973)1 --

Ergodic or memoryless channels:

signal x(t), xt wave− field x(t, s)channel h(t, τ) spatial channel h(t, τ ; s, σ)

Ct = maxxt

I(xt, yt) area spectral efficiency [bits/s/Hz/m2]

C = E[Ct] C → C(sTx, sRx)

[Wolfowitz, 1978] [Alouini, Goldsmith, 1999][Goldsmith, V arayia, 1997]

1From S. K. Mitter, “Control with Limited Information: the Role of Systems Theory and Information Theory”,ISIT 2000, Sorrento, Italy, plenary talk.

– FWCW’01 – c©Pavel Loskot 2001/10/23 7(16)

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How to Increase Channel Capacity ?

Answer

• create parallel channels

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C(n) = nB log2(1 + PN0nB

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• E.g.: multiple antennas, BPSK ver. QPSK, horiz. and vert. polarizations

• N.B.: general −→ particular (opposite in literature)

– FWCW’01 – c©Pavel Loskot 2001/10/23 8(16)

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Adaptive Transmission

a priori a posteriori

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• transmitting conditions: noise and traffic

• conventional design for the worst case or average conditions

– sacrify BER or waste power

• new design for all conditions

– avoid bad transmit/receive conditions

– FWCW’01 – c©Pavel Loskot 2001/10/23 9(16)

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Feedback

transmitter receiverforward channel

data in

feedback channel

data out

Feedback

• may both simplify and complicate the system design

• implicit (reciprocity, TDD), explicit (FDD)

• allow coordination of users

Fading

• stationary and ergodic (equivalent to memorylessness)

– FWCW’01 – c©Pavel Loskot 2001/10/23 10(16)

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Channel Knowledge in Tx/Rx

1. nothing is known

2. fading statistics known

• generally difficult to solve• optimum power/rate allocation can be fading-distribution independent• mean and covariance feedback (Gaussian fading)

3. fade value known to Rx

• coherent detection

4. fade value known to Tx

• causual or noncausual

– FWCW’01 – c©Pavel Loskot 2001/10/23 11(16)

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Separation Principle

• very general, “independet problems might not have independet solution”

Example

• joint source–channel coding, separability holds for stationary channels[Verdu,Goldsmith]

• joint estimation–detection (adaptive receiver)

Adaptive transmitter1:

• joint channel state estimation–controlhypothesis: “holds for ergodic source, and stationary ergodic channel”

• distributed control (what information and when is available)

• feedback control best viewed from System Theory perspective

1From S. K. Mitter, “Control with Limited Information: the Role of Systems Theory and Information Theory”,ISIT 2000, Sorrento, Italy, plenary talk.

– FWCW’01 – c©Pavel Loskot 2001/10/23 12(16)

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Optimization Problems

• yt,ht,xt and nt are vectors ∈6 C(K,1)

yt = diag(ht)xt + nt

• For stationary and ergodic (delay-unlimited) channels:

power rate BERinstant. average instant. average instant. average

tr(xtxHt ) E[tr(xtx

Ht )] b(xt) E[b(xt)]

e(xt)b(xt)

E[

e(xt)b(xt)

]

b(.)= bits, e(.)= bits in errors

• so there are total 24 basic optimization problems

• plus more for distortion constraints (multimedia)

– FWCW’01 – c©Pavel Loskot 2001/10/23 13(16)

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Theorem of Delay-Unlimited Transmission

Theorem Any uncoded single- or multicarrier- modulation with or withoutspreading reaches the same spectral efficiency over stationary ergodicflat-fading channel with an arbitrary fading statistics.

Proof To be submitted.

– FWCW’01 – c©Pavel Loskot 2001/10/23 14(16)

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Summary of Adaptive Techniques

Physical Layer (fading)• Adaptive modulation

– optimum power and rate allocation is scenario-dependent, e.g.,delay-unlimited → water-filling, delay-limited → channel inversion

– usage bounds from below and above due to Doppler and delay spread– multicarrier and space-time modulation are special cases

• (Joint) source and channel coding

• Multiple antennas– beamforming (accurate channel knowledge)– switched diversity (moderate channel knowledge)– space-time coding (no channel knowledge)

Higher Layers (traffic)• Radio resource management (DCA, scheduling)

– avoid retransmission, collisions

• Routing, active networks

Adaptive Users (Internet)

– FWCW’01 – c©Pavel Loskot 2001/10/23 15(16)

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Conclusions

• Adaptive modulation borrows ideas from channel capacity

• Principle of adaptive modulation can be extended to all OSI layers

• Hence, the Concept of Adaptive Transmission is very versatile, but with asimple idea (at least for delay-unlimited systems)

“avoid transmission in bad conditions”

• We can go even further

“adapt to time-varying source (and channel)”

• Optimization at the transmitter side is already embedded in all broadbandand cellular systems

• Adaptive transmission especially appealing for emergning ad-hoc networks

– FWCW’01 – c©Pavel Loskot 2001/10/23 16(16)