The document discusses the concept of adaptive transmission and its applications in wireless communications, emphasizing the importance of creating systems that can adapt to varying conditions to optimize performance. It outlines various strategies for improving channel capacity, including using multiple antennas and rate allocation techniques, while also addressing the trade-offs between reliability, integrity, and complexity. Additionally, it highlights the versatility of adaptive transmission across different OSI layers and its potential benefits for emerging ad-hoc networks.

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FINNISH WIRELESS COMMUNICATIONS WORKSHOP ’01
The Concept of Adaptive Transmission
Pavel Loskot, Matti Latva-aho
Centre for Wireless Communications
University 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 control
it; 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 − )C
• decoder probability p
• complexity χ( , p) in operations per information bit
• lim
→0
χ( , p), lim
p→0
χ( , 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 inadequate
attention 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 = max
xt
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]
1
From 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
C(1) = 1
2 log2(1 + SNR)
C(n) = n
2 log2(1 + SNR
n )
C(1) = B log2(1 + P
N0B)
C(n) = nB log2(1 + P
N0nB)
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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
[Verd´u,Goldsmith]
• joint estimation–detection (adaptive receiver)
Adaptive transmitter1
:
• joint channel state estimation–control
hypothesis: “holds for ergodic source, and stationary ergodic channel”
• distributed control (what information and when is available)
• feedback control best viewed from System Theory perspective
1
From 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 ∈ C(K,1)
yt = diag(ht)xt + nt
• For stationary and ergodic (delay-unlimited) channels:
power rate BER
instant. average instant. average instant. average
tr(xtxH
t ) E[tr(xtxH
t )] 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 without
spreading reaches the same spectral efficiency over stationary ergodic
flat-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 a
simple 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 broadband
and cellular systems
• Adaptive transmission especially appealing for emergning ad-hoc networks
– FWCW’01 – c Pavel Loskot 2001/10/23 16(16)