Simple Antenna Diversity techniques with inherit directional information for SDMA operation
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Transcript of Simple Antenna Diversity techniques with inherit directional information for SDMA operation
Simple Antenna Diversity techniques with inherit directional information for SDMA operation
Project group 997:Julien GonidecThibaut IngrainFrançois NetMauro PelosiAurélie Villemont
Supervisors: Patrick Eggers
Chenguang Lu
Censor: Jesper Ø. Nielsen
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Why diversity techniques ?
Introduction
Wireless technologies comparison depending on data rate and mobility
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Why WLAN ?
A widespread technology Problems of security New localisation services Convergence of technologies
Introduction
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Choices made
802.11G standard Open office environments Jitter diversity Implementation of diversity techniques only
at the base station Algorithm will provide directional information
Introduction
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Experiment process
Study the recquired theory Apply the Jitter Diversity algorithm to deduce
tendencies Model a more realistic channel model and
apply the jitter diversity on it Study the gain provided by the diversity See how the algorithm can provide
directional information
Introduction
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Antenna Array
Ordered repetition in space of identical radiating elements
5 degrees of freedom:– Geometry– Element spacing– Excitation signal amplitude– Excitation signal phase– Element pattern
Theory
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Antenna Array: features
Portability– Compact configuration
Adaptability– Feeding network– Dynamic beamforming
Theory
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SDMA
Multiplexing technique used together with another multiple access process
Spatial separation of the users at the BS
Advantages: - improves antenna gain
- reduces interferences
- improves capacity
Theory
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SDMA
Principle: concentrate on the desired user and nullify the others
Directional information
Theory
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Diversity techniques
Temporal based techniques– Time diversity– Frequency diversity
Antenna pattern techniques– Space diversity– Polarisation diversity– Angle diversity
Direction information bearing techniques– Phase diversity– Beam diversity– Jitter diversity
Theory
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Jitter diversity
Aim: reduce deep fading effects
Principle: slightly move the antenna beam
Advantages: simple and easy to implement
Theory
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Jitter diversity algorithm
INITIALISATION
Full environment scanto find the global maximum
JITTER PROCESS
Find nearest local maximum
CONDITION
end of operation ?
END
CONDITION
- power level under threshold ?- time deadline reached ?
UPDATE
- full scan again- local scan using previous steps
data
YES
NO
NO
YES
Theory
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Fading types
Large- and medium-scale fading– Pathloss– Shadowing
Small-scale or multipath fading– Frequency selective fading– Flat fading– Fast fading– Slow fading
Theory
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Large- and medium-scale fading
Pathloss: average power decay caused by distance d between Tx and Rx
Where γ is the pathloss exponent
Shadowing: absorption by the local surrounding media
PL d d
Pathloss Shadowing Multipath fadingRapidity of fluctuations of signal’s strength
Theory
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Small-scale fading
Frequency
selective fading
Flat fading
Relationship between signal’s bandwidth and coherence bandwidth
Time-dispersive nature of the channel
Fast fading
Slow fading
Relationship between signal’s and channel’s time-rate of change
Time-varying nature of the channel
Theory
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Small-scale fading
Multipath time
delay
Flat fading
Frequency selective fading
Bc
Bs
Bc
Bs
Doppler spread
Fast fading
Slow Fading
Tc
Ts
Tc
Ts
Theory
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Models of small-scale fading
Rayleigh fading: NLOS between Tx and Rx
Rice fading: LOS between Tx and Rx
Nakagami-m fading:
2
_ 2 2.exp
2Rayleigh z
x xp x
2 2
_ 02 2 2
( ).exp .
2Rice z
x x v x vp x I
2 1 2
_
2 1 p .exp
(m) 2
m m
Nm z mp p
m x mxx m
Theory
Rician Factor K
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Models of small-scale fading
Rician PDF
Theory
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Models of small-scale fading
Nakagami-m PDF
Theory
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Scatterers repartition: Lee’s model
Scatterers uniformly spaced on a circle centred on the MS
Useful for correlation calculation Not the best model for indoor description
Theory
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Scatterers repartition: GBSB model
Spatial scatterer density function
GBSB Elliptical Model
Theory
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Scatterers repartition: Saleh-Valenzuela model
Accurate indoor channel representation Clustered scatterers
Extended model including AOA
Theory
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Choices for our model
Rayleigh fading
Clustered scatterers
Elliptical repartition
Theory
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Jitter diversity simulation in a simplified environment
Steps of the simulation
Modelling a simplified indoor channel Generation of an ideal antenna pattern Jitter process description Results and tendencies
Monte-Carlo simulations
the user’s location is randomly defined at each step
Simulation 1
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Environment implementation (1)
Clustered scattering Investigations concentrated on rays from an unique cluster AOA power distribution approximated by a Laplacian
distribution PowerLaplace_a(θAOA)
Environment response
Where The amplitude is defined by
The phase is defined by
,, , . er AOAj xAOA er AOAer x x e
_
1
., . 1 ,
1 ,R
R Laplace a AOAk
er i AOAk i AOAkN
n AOAkn
N Px temp x
temp x
, 2 . 2 ,er AOA AOAx temp x
Simulation 1
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Environment implementation (2)
“a“ parameter controls the shape of the environment
10-6 < a < 10-1
BWenv: half-power width of the mean environment response
Simulation of various type of environment by varying the a parameter
Simulation 1
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Antenna pattern
Choice of an ideal beam pattern (no side and back lobes)
Amplitude of the pattern
“α“ parameter controls the antenna beamwidth
sin,
0AOA BO AOA BO
AOA BO
ifa
otherwise
Simulation 1
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Transfer function
At each realisation all beam’s orientation are performed
Discrete transfer function
Influence of the environment width on the fades
1
, , . ,AOAN
BO AOAn AOAn BOn
h x er x a
Simulation 1
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Jitter process
We want to compare 3 different algorithms: JRDA (Jitter with respect to the Reference Direction Algorithm) BPP (best possible process algorithm) FB (fixed beam algorithm) as a reference
Explanation of the JRDA process
1. Reference direction θrefk is found at the kth step
2. is compared to and
3. The orientation of the maximum value is chosen θpathk
4. is the whole of the collected h module
,k refkh x ,k refk jitth x
,k refk jitth x
, pathh x
Simulation 1
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JRDA results
Simulation 1
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Standard deviation of the JRDA
Simulation 1
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Total power gain of the JRDA
Total power gain at the 1% level of probability:
We define the total power gain at the 1% level of probability as the difference between the cumulative density values of
and at the 1% level of probability
( , )path dBh x ( , )FB dB
h x
_1%JRDATPG
Simulation 1
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Diversity gain at the 1% level of probability : Definition of the normalised power :
_( , ) ( , ) ( , )path path pathdB norm dB dBh x h x h x We define the diversity gain at the 1% level
of probability as the difference between the cumulative density values of and at the 1% level of probability
_( , )path dB normh x
_( , )FB dB normh x
Diversity gain of the JRDA (1)
Simulation 1
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Diversity gain of the JRDA (2)
Simulation 1
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Simulations with a more realistic channel model
Simulation aim:
• Derive a more realistic channel model
• Compare the efficiency of diversity techniques in the aforementioned channel model
• Provide simple Directional Information
Simulation 2
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Environment description:modified elliptical model
Simulation 2
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Beam scanning power at the base station over spatial iterations
Simulation 2
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Location of the maxima of the beam scanning power over spatial iterations
Simulation 2
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Jittering algorithms
Optimum jittering We do periodical updating of the global maxima on
the beam scanning power
Dominant jittering We do not have updating; we only initialise the
algorithm with an angular value corresponding to the angular center of gravity of the dominant cluster
Simulation 2
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Reference algorithm
Dominant fixed beam algorithm (no diversity)
• In this case we choose a fixed beam orientation for
our antenna, which will remain the same for all the
spatial iterations of the mobile station; the algorithm
is first initialised with the angular position of the
center of gravity of the dominant cluster
Simulation 2
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Diversity gain calculation
Simulation 2
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Simulation sets
Dominant cluster’s angular width variation Scatterer’s complex gain variation Scatterer’s distribution variation Modified elliptical model with 4 clusters
Simulation 2
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Modified elliptical model with 4 clusters
Simulation 2
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Simulation set 1
•Scatterer’s complex gain variation
•50°< Dominant cluster < 90°
Simulation 2
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Simulation set 2
•Scatterer’s distribution variation
•50°< Dominant cluster < 90°
Simulation 2
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Simulation set 3
•Scatterer’s complex gain variation
•60°< Dominant cluster < 90°
Simulation 2
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Simulation set 4
•Scatterer’s distribution variation
•60°< Dominant cluster < 90°
Simulation 2
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Simulation set 5
•Scatterer’s complex gain variation
•70°< Dominant cluster < 90°
Simulation 2
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Simulation set 6
•Scatterer’s distribution variation
•70°< Dominant cluster < 90°
Simulation 2
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Simulation results
All the previous results show that we get a fair diversity gain even when we change the width of the dominant cluster.
Though the optimum jittering algorithm shows the best performances, the dominant jittering, that has a lower complexity, behaves also good. This would suggest the sub-optimality of the dominant jittering, that though has no updating part in the jittering process, leads to good results.
Simulation 2
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Simple directional information
This information will be provided by the dominant jittering algorithm
The steering beam path on the beam scanning power over spatial iterations is then investigated
Simulation 2
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Steering beam over spatial iterations
Simulation 2
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Directional information results
As we can see from the previous figure, the dominant jittering is nearly confined in the dominant cluster neighbourhood
If sometimes the jittering path escapes from the dominant cluster bounds, it means that the algorithm simply finds less fading in that direction
A suggestion for future investigations would be a sort of inter-cluster hand-over, because during the moving of the mobile station the dominance of a new cluster may replace the previous one
Simulation 2
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Conclusions
A simplified channel model has been investigated, with the main conclusion that the jitter diversity takes advantage of a spread angular repartition of the rays together with the use of a narrow antenna beamwidth
A more realistic channel model has been studied, with the definition of a modified elliptical model including the effect of scatterers clustering
A fair diversity gain has been found nearly in all the simulations, suggesting the sub-optimality of the dominant jittering algorithm
Simple directional information has been provided
Conclusion
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Future work
Introduction of a realistic antenna pattern Variation of the input parameters of the
simulations in order to have more average information
Extension of directional information and study of a possible inter-cluster hand over
Conclusion