Upload
sakanat5152
View
179
Download
0
Embed Size (px)
DESCRIPTION
this ppt will help to understand the mimo channel modelling in brief.
Citation preview
MIMO Channel Modelling For
Indoor Environment
Presented by:
Hussain Bohra
Outline
Introduction MIMO System Overview MIMO Technology: Benefits MIMO System: Transmitter & Receiver MIMO Channel Models MIMO Channel Matrix Formulation Measurement-Based Channel Modeling Multi-User MIMO
Introduction Multiple Input Multiple Output(MIMO) systems are basically designed to achieve high data rate transmissions using spatial diversity technique
with no increase in bandwidth.
MIMO can be implemented in GSM, CDMA, Wimax, UMTS, etc wireless technology.
MIMO Channel Modeling creates the perfect and realistic environment to test the Tx-Rx equipment.
MIMO Channel models are also helpful to recreate the conditions of signal diversity.
MIMO Channel Modeling helps in analyzing the capacity of the channels, fading effects , correlation among links, scattering environment etc.
MIMO System : An Overview
2x2 MIMO System
MIMO : Channel Matrix
The 2x2 MIMO system channel matrix is given as
where the coefficients hmR mT represents the spatial channel between each Tx-Rx antenna pair.
The input-output relationship of a MIMO system is given by linear model as
y = Hs + n
where H is the narrowband MIMO channel matrix.
MIMO Technology : Benefits
Array gain
Spatial diversity gain
Interference reduction and avoidance
Spatial multiplexing gain
MIMO System: Transmitter
ENCODER AND
PUNCTRING
SPACE FREQUENCY INTERLEAVE
R
MODULATION (BPSK,QPSK,16 & 64 QAM)
SPATIAL MAPPING
IFFT and
Add CP
IFFT and
Add CP
OFDM Modulator
MIMO System: Receiver
V- BLASTSOFT
DECISION
SPACE FREQUENCY
INTERLEAVER
DEPUNCTURE & DECODER
FFT and Remove
CP
FFT and Remove
CP
OFDM Demodulator
ANTENNA SELECTION
MIMO: Spatial Multiplexing Scheme
SPATIAL MULTIPLEXING
SCHEME
SPATIAL MULTIPLEXING
SCHEME
TX
TX
RX
RX
MIMO : Spatial Multiplexing Receivers
1. Maximum Likelihood (ML) receiver
2. Zero-forcing receiver
3. Minimum mean square error receiver (MMSE)
4. Successive cancelation receiver
5. V-BLAST receiver
6. D-BLAST receiver
MIMO : Antenna selection
Spatial diversity involves placing two receive antennas at a specific distance from each other.
The objective is that when one antenna is in a deep fade, the other antenna still has a strong signal.
Antennas should be spaced by more than one coherence distance apart.
Antenna spacing on the order of 0.4λ – 0.6λ is adequate for independent fading.
MIMO: Space–time coding
It improves the downlink performance.
It imparts coding gain in addition to the spatial diversity gain.
It does not require channel state information (CSI) at the transmitter.
Space–time codes: trellis codes, block codes and turbo codes are widely used.
MIMO : Channel ModelsPHYSICS – BASED
DETERMINISTIC :1.Ray Tracing Model2.Finite difference time domain(FDTD)3.Methods of Moments(MoM)
Stochastic :1.Geometric Based Stochastic Model.2.Non Geometric Based Stochastic Model.
Measurement Based :1.Measurement system independent2.Application Specific
ANALYTICAL- BASED
1.Full Correlation.2.Spatially White(i.i.d)3.Kronecker Model.4.Weischelberger (WB) Model.
MIXED BASED
1.Finite Scatterer Model (FSM).2.Virtual Channel Representation (VCR).3.Maximum Entropy Model.
PHYSICS: Deterministic Propagation Models
Ray tracing : 1. It is typically based on the uniform geometrical theory of
diffraction.2. The idea is to find all possible paths that the signal can travel
between the Tx and the Rx.3. It is best applicable in man made environments.
MoM and FTTD : These are also very accurate field prediction models, but due to
computational complexity, their applicability is constrained to structures with limited dimensions.
GCSM
Stochastic: Geometric Based Model
Examples GSCM type model1. COST273 channel model.2. IST-WINNER model.3. 3GPP model.
Examples Non GSCM type model1. Saleh-Valenzuela Angular (SVA) model.2. Zwick model.
PHYSICS: Stochastic Channel Model
PHYSICS: Measurement-Based Channel Modeling
1. It refers to the method where a measurement system along with parameter estimation techniques are employed.
2. The results are specific to the measured environment and no environment database is explicitly required.
3. It is often needed for determining the cluster/multipath parameter statistics for the stochastic models.
Analytical Models
Correlation-Based Analytical Models
Spatial Whiteness Hi.i.d = σ Hw
Kronecker Model
Weichselberger Model
Mixed models
Finite Scatterer Model (FSM)
Virtual Channel Representation (VCR)
Maximum Entropy Model
MIMO : Channel Matrix Formulation
where H is Channel Matrix, HF is the fixed matrix and Hv is Rayleigh matrix.
MIMO: Measurement-Based Channel Modeling
The MBCM framework includes the following procedures:
1. Conducting MIMO channel sounding measurements .2. Estimating the channel model parameters from measurement
data.3. Deriving model statistics for parameterizing and improving
current Channel models.4. Reconstructing channel realizations for simulation purposes
using measurement-based parameters or, alternatively.5. Applying the measured channels directly in simulations .
Multi-User MIMOMULTI USER COMMUNICATIONS
Decentralized (Ad-hoc) caseCentralized case
Multi-User Communications vs. Classical MIMO
Added complexity: (1)Concurrent transmission creates interference. (2)Multiple power constraints. (3)Need for advanced scheduling (centralized) or self-organization (ad-hoc
case).
Benefits: (1)More degrees of freedom available for resource allocation. (2)Multiuser diversity.
Thank You…………..