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…………..