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MULTIUSER DETECTION
AND INTERFACEDETECTION
PRESENTED BY:DISHANT KHOSLAM.TECH(1`st Year)ECE
ROLL NO.11092025
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INTRODUCTION
The idea of MUD was proposed by Sergio Verdu in theearly 1980s.
Modern wireless communication systems are required toaccommodate many users simultaneously, while providinghigh data rates and on-demand data transfers. The multiusercommunication system consists of many users attempting tocommunicate with a single receiver over a common set ofchannel resources.
The primary idea of Multi User Detection (MUD)
techniques is to cancel the interference caused by otherusers. This is done by exploiting the available sideinformation of the interfering users, rather than ignoring the
presence of other users like in Single User Detection (SUD)techniques.
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Example of a Multiuser Wireless MultipleAccess Communication System
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Multiuser Detection
Multiuser detection considers all users as signals for each other ->joint detection
Reduced interference leads to capacity increase
Alleviates the near/far problem
MUD can be implemented in the BS or mobile, or both In a cellular system, base station (BS) has knowledge of all the chip
sequences
Size and weight requirement for BS is not stringent
Therefore MUD is currently being envisioned for the uplink (mobileto BS)
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MUD Features
It has the capability to reject the interference created by thenarrow band
Capable to achieve diversity in frequency
It tremendously reduces the complexity and it increases the
spectral efficiency
Robustness to multipath fading
The use of modern DSP makes MC-CDMA implementationfeasible and attractive
MC-CDMA translates the time operations to the frequencydomain
Effect of ISI and delay spread is mitigated
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The Evolution Path to 3G
Systems
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CDMA System Model
Data of User 1Data of User 1
Spreading SequenceSpreading Sequence
of user 1of user 1
Chip shapingChip shaping
filterfilter 1X
Data of User 1Data of User 1
Spreading SequenceSpreading Sequence
of user 2of user 2
Chip shapingChip shaping
filterfilter 2X
Data of User 1Data of User 1
Spreading SequenceSpreading Sequence
of user Kof user K
Chip shapingChip shaping
filterfilter KX
AWGNAWGN
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Types of SS communication
system
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Anti-jamming (AJ) propertyof DS-SS system
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Near-Far Effect
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Near-Far Effect
Factors causing near-far effect (unequal RxSignal powers from different users) incellular CDMA
Distance loss Shadow loss
Multipath fading (Most detrimental. Dynamicrange of fade power variations: about 60 dB)
Two common approaches to combat near-far effect Transmit Power Control
Near-far Resistant Multiuser Detectors
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Types of multiple access
communications
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Synchronous CDMA System
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MUD Algorithms
Optimal
MLSE
Decorrelator MMSE
Linear
Multistage Decision-feedback Successiveinterference
cancellation
Non-linear
Suboptimal
Multiuser
Receivers
Linear detectors apply
linear transformations tomatched filter outputs to
minimize MAI. Simple to
implement but can get
complex.
Non-Linear detectors are
more complex calculation
wise than linear detectors
due to nonlinearity, however
they perform better under
severe conditions
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Linear Algorithms Practical Linear Algorithms:
- Decorrelating Detector
- Minimum-mean squared error (MMSE)
- Blind (adaptive non-adaptive) techniques
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Adaptive Minimum Mean Squared
Error (MMSE) Detector
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Adaptive MMSE Detector
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Blind Adaptive MMSE Detector Blind adaptive detector characteristics:
The detector doesnt require the training sequence inorder to calculate the channel impulse response
Requires the knowledge of the signature waveforms andtiming information of the desired user
The limitation is that it works only for short codes The major disadvantage of the adaptive MMSE detector
over the blind adaptive MMSE is that it requires thetraining sequences this results on a waste of the bandwidthwhich is populated with signals that do not carry anycommunication data. Therefore for the Blind adaptive we
have a clear benefit when it is compared to other detectorssince it does not require any training sequence thats why iscalled blind.
Adaptive MMSE detectors also are advantageous over othernon-adaptive detectors because they can adapt to unknownand time-varying channel conditions
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Non-Linear Algorithms
Non-Linear Algorithms:Estimate the interference caused by each user on theothers, re-spread and cancel from the received signal. This
is done through multitude of stages.
Practical Non-Linear Detectors: Multistage Detector Decision Feedback Detector
Subtractive Interference cancellation Successive Interference Cancellation (SIC) Parallel Interference Cancellation (PIC) Selective Parallel Interference Cancellation
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Optimal MLSE Detector
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Decision-Feedback Detectors
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Decision-Feedback Detectors
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Successive Interference Cancellers The SIC detectors start to subtract off the
strongest remaining signals in a
successive fashion from the rest of thesignals
By canceling the strongest signalfrom the rest we gain most of thebenefit and it is the most reliablecancellation
The other similar alternative is the PICmethod. This starts to simultaneouslysubtract off all of the users signals fromall of the others unlike the serialcancellation that starts with the strongestsignal user .
It works better than SIC when all ofthe users are received with equalstrength since it is much easier todetect them and hence decreases theprobability of error
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The main advantages are:
1) The weakest user will see atremendous signal gain from theMAI reduction since all of theinterfering channel will add up assignals to the weakest user. Hence
every user is on a win-win situation.2) For severe conditions if we remove the
strongest user the rest of weakerusers will benefit hence the signalcan be recovered
3) Can recover from near-far effects
The main disadvantages are:
1) If the strongest estimate is not highlyreliable it results on performance
degradation
2) As the power profile changes the
signals must be reordered
3) Every stage introduces a delay
1) More vulnerable to
near-far issues2) Complicated circuitry
1) Because of the
parallel nature no
delays/stage required!
2) Simpler than otherlinear detectors
SuccessiveInterference Cancellers
VS. Parallel InterferenceCancellers
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BENEFITS OF MULTIUSER
DETECTION
Significant capacity improvement
Reduced MAI and near-far effect
More efficient power utilization
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LIMITATION OF MULTIUSER
DETECTION
Potential capacity improvements in cellular systems are not
enormous but certainly nontrivial (2.8x upper bound)
Capacity improvements only on the uplink would only bepartly used anyway in determining overall system capacity
Cost of doing MUD must be as low as possible so that there
is a performance/cost tradeoff advantage
If the neighboring cells are not included interference
cancellation efficiency is greatly reduced.
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CONCLUSION AND FUTURE
WORK
It has been shown that CDMA systems suffer fromsevere multi- user interference. Although strong errorcontrol coding is able to ensure reliable transmissions formedium system loads, it is beneficial to apply multi- user
detection especially for high system loads. . MUD research is still in a phase that would not justify
making it a mandatory feature for 3G WCDMA standards.
Currently other techniques such as smart antenna seemto be more promising.
Though MUD has not been a mandatory feature of the
wireless standards so far, the rapid advances in DSParchitectures promise the evolution of MUD asintegrated feature of future wireless standards toprovide better capacity and data rates.
Feasible VLSI implementations for Mobiles
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REFERENCES
Multi user detection using CDMA by Sergio
Verdu.
www. Wikipedia.com
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THANKS