Adaptive Radio Links

1(15)© Pavel Loskot: Presentation in AWICS-seminar 18.12.2000

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The Concept of Adaptive Radio Links

AWICS-seminar, 18.12.2000

Pavel Loskot

[email protected]

Centre for Wireless CommunicationsUniversity of Oulu, Finland


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Introduction and Motivation

ARL - Modern Concept of Telecommunications

Optimization Problems (PHY)Nothing Is Ideal (Trade-offs)

Adaptive Modulation Scheme (AMS)

Coding and AMSAdaptive Multicarrier Modulation and Adaptive CDMA

Other Techniques at PHY

ARL in Today’s Cellular SystemsConclusions

Outline

N.B.: Adaptive Radio Links ≅ ARL


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Introduction and Motivation

�

The transmission problem– power and bandwidth constraint

→→→→ channel capacity– delay and complexity constraint

�

The noise source– distortion in time and frequency (noise, fading, multipath … etc.)

→→→→ channel state information (CSI)– multiuser interference

→→→→ traffic– time-varying

� Solution– adaptive receiver– adaptive transmitter– combination of both

� Adaptive transmitter only– ideally fading channel → Gaussian channel

aposterioryapriory


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�

Conventional solution– adaptive receiver only, so that the system design for the worth case or average channel– wasting the channel capacity

� New solution– exploit all the available (time-varying) channel capacity– adaptive transmitter with partial/perfect channel state information and/or traffic situation

to compensate it apriory– transmitter = function( channel( time ) , traffic( time ))

� CSI is obtained through– channel reciprocity (TDD) - open loop adaptation

• relatively faster fading channels but interference limited– channel feedback (FDD) - closed loop adaptation

• feedback is usually limited (latency, overhead)– cf. power control

� Restrictions– point-to-point duplex connection

Introduction and Motivation - Cont.

Tx RxCh

Tx RxCh

Tx Rx

Rx Tx

BS MS


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�

What is adaptive– physical layer

• adaptive modulation scheme (AMS) - power and data rate• coding - both source and channel• antennas (e.g. adaptive beamforming, or antenna switching)

– link layer• radio resource management - avoid or minimize collisions, retransmissions,

interference (e.g. Dynamic Frequency Allocation, adaptive MAC, ARQ)– higher layers

• routing (e.g. Ad-Hoc networks, ODMA)– adaptive users

� Areas– information theory– detection theory– estimation theory– signal processing– etc.

ARL - Modern Concept of Telecommunications

Communications ≈30.000/65.000

ARL ≈10.000

MCM ≈2.000AMS ≈200

bit loading ≈50

IEL Online


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�

Goal– maximize link throughput - spectral efficiency (bits/s/Hz)

(in fact #of users/link)– maximize network throughput - area spectral efficiency (bits/s/Hz/m2)– minimize power (less stringent SNR requirements, or less complex computation)

�

Problem– distribution of information in Time-Frequency-Space ?

� Data sources (interface to PHY)– variable bit rate source (VRS), e.g. data transfer– constant rate source (CRS), e.g. speech– available rate source (ARS), e.g. video

� QoS– at PHY - delay (maximum, jitter) and BER (average) - e.g. voice ver. data– higher layers - goodput– multimedia services - distortion rather than BER

ARL - Modern Concept of Telecommunications - Cont.

timefrequency

space

time

frequency


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�

Assumptions– fading channel with being the instantaneous SNR with distribution – variable power with average power constraint – variable rate [bits/symbol] as to vary symbol period is impractical

� Problem definition#1 (VBR)– maximize the average throughput– subject to average power constraint– subject to instantaneous BER constraint

(more restrictive than average BER)

� Problem definition#2 (CBR)– minimize the average power– subject to average throughput– subject to average BER constraint

� Solution– method of LaGrange multipliers to obtain optimum rate/power adaptation policy– leads to a water-filling (in different dimensions)

�

Practical restriction– define fading regions - cutoff rate– average fading rate duration (AFRD) → channel model as a Marcov process

Optimization Problems (PHY)

γ )(γp

)(γS S

)(γk

�∞0 )()( γγγ dpk

�

=∞0 )()( SdpS γγγ

BERBER =)(γ

�∞0 )()( γγγ dpS

Kdpk =�∞

0 )()( γγγBERdpBER =

�∞0 )()( γγγ

}0{},;{)( /∪<+Ζ∈∈ Niiik γ),[)1,0[)0,0[ ∞∪∪∪=+ℜ∈ Nγγγγγ 0γ


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Nothing Is Ideal (Trade-offs)

�

Principle of uncertainty (physics) - arise with a practical implementation

� General trade-offs (for Communications Engineering)– BER ver. throughput (implied by channel coding theorem)– performance ver. delay and complexity (not implied by Shannon theory)– power efficiency ver. spectral efficiency (e.g. CDMA ver. OFDM)– spectral efficiency ver. area spectral efficiency

� Antennas– physical size ver. performance

� Coding and modulation– channel coding ver. source coding (not implied by Shannon theory)– modulation level ver. coding– coding gain ver. complexity– amount of feedback ver. complexity– single link ver. cellular network

�

System related– CDMA - spreading ver. coding (CDMA, MC-CDMA)– OFDM - crest factor ver. out of band radiation

spreading

coding loading?

hardware

?software

baseband

passband


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Adaptive Modulation Scheme (AMS)

�

AMS is– technique to approach the channel capacity by varying (cf. solution to optimization problem)

• power only (cf. power control)• rate only• both, power and rate.

� Special attention to– AMS with coding (discussed later)– AMS in network

• optimization of one link ↑ spectral efficiency of that user, however, create MUI and ↓ area spectral efficiency (tradeoff)

– AMS with multiple antennas– AMS as a multicarrier modulation and/or with spreading (discussed later)

� Limitations– Doppler spread

• fast fading - the channel cannot be tracked and the performance is poor• slow fading - long outage periods infers large data buffers and significant link latency

– delay spread• complexity ver. performance tradeoff in adaptive multicarrier modulation• single carrier modulation - problem to explicitly evaluate BER as a function of SNR


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Adaptive Modulation Scheme (AMS) - Cont.

�

System model - both TDD and FDD

� Steps to be taken– estimate the channel

• outdated or erroneous estimates significantly impair the performance• hence, prediction rather than estimation

– select new transmission format• instantaneous/average SNR based• instantaneous/average BER based (decoder)

– signaling of the new format to the receiver (overhead) or blind detection

� Effects of AMS– bursts of errors removed, and constant BER supports well-established codes to be used– if the delay is not a problem, the gain of AMS can be enormous– in practice, usually adaptive MQAM (e.g. no Tx, BPSK, 4QAM, 16QAM)

AMS and coding Tx power Channel

channel estimate

Demodulationand decoding

data data

delay τ

error ε


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Coding and AMS

�

Source coding– source and channel code trade-off– e.g. unequal error protection, layered coding for multimedia

� Block codes (variable block length)

�

Convolutional codes (variable interleaving and puncturing)

� Coset codes (separation of coding and modulation) (6dB from capacity)– trellis and lattice codes

(variable coset size)

� Turbo codes (3dB from capacity)– cannot separate coding and modulation– BER curves necessary - upper bound– additional constraint of block length– adapt the channel encoder itself


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Adaptive Multicarrier Modulation and Adaptive CDMA

�

Adaptive MCM– in fact, MCM means that frequency dimension is available– by SVD a set of independent parallel flat fading channels– joint adaptation of subcarriers - bit and power loading– mitigate the latency problem over slow fading channels– decomposition also via DFT - requires finite block length

otherwise backward/forward adaptation to attain the channel capacity– other degrees of freedom

• number of subcarriers (related to coherence bandwidth)• cyclic prefix

� Adaptive CDMA– degrees of freedom

• multicode• variable processing gain• multilevel modulation

� Adaptive MCM-CDMA– great flexibility (the degrees of freedom)– ranges from OFDM to FH/DS-CDMA


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Other Techniques at PHY

�

Precoding– pre-Rake, or design of spreading codes to force zero MUI in GMC-CDMA

�

Predistortion and preequalization– prevent noise enhancement at the receiver– power limited channel inversion at the transmitter (keep constant SNR)– receiver can be simpler

� Beamforming and smart antennas– MIMO → SISO structure– 1D coding followed by precoding (antenna weights) can achieve the capacity

� Uncorrelated antennas– antenna selection/switching - good performance and complexity tradeoff– MCM preceding each antenna create the set of flat fading channels

� Variable packet length– related to MAC and also to coding


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�

Today’s cellular systems

– assure 90-95% coverage for certain QoS– hence, most area excessive SNR - support of higher data rates

� Constraints– adaptation on frame by frame basis

– slow feedback (≈ 10-100 ms)

� Historical note– the first idea late 60’s, however, short lived due to hardware limitations and lack of good

channel estimation techniques– renewed interested late 80’s for meteor-burst communications– early 90’s, variable rate MQAM by Steel and Webb

ARL in Today’s Cellular Systems

WCDMA CDMA2000 IS-95B

variable spreadingand coding

variable spreadingand coding

code aggregation

GPRS GPRS-136 EGPRSadaptive coding andslots aggregation

adaptive modulation andslots aggregation

adaptive modulation andslots aggregation


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Conclusions

� ARL is a modern concept of communications– the transmission format is change according to predicted channel conditions– the traffic is scheduled in order to avoid interference

� The main motivation is the lack of spectrum (can be only worse in future)– the spectrum shall be used as efficiently as possible

� Adaptive transmitter to approach time-varying channel capacity– the more degrees of freedom the better the performance– at PHY layer

• water-filing in time (adaptive modulation)• water-filing in frequency (bit and power loading)• water-filing in time and frequency (not studied, yet)

– upper layers• avoid interference and optimize routing

� ARL principles in all current standards– cellular

• WCDMA, CDMA2000, IS-95, GPRS, GPRS-136, EGPRS– broadband

• HIPERLAN/2, IEEE 802.11

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