Transcript Document

Chapter 4 System Level Aspects for Single
Cell Scenarios
School of Info. Sci. & Eng.
Shandong Univ.
CONTENT
 4.1 Efficient Analysis of OFDM Channels
 4.2 Generic Description of a MIMOOFDM-Radio-Transmission-Link
 4.3 Resource Allocation Using Broadcast
Techniques
 4.4 Rate Allocation for the 2-user Multiple
Access， Channel with MMSE Turbo
Equalization
 4.5 Coexistence of Systems
CONTENT
 4.6 System Design for Time-Variant Channels
 4.7 Combination of Adaptive and Non-Adaptive MultiUser OFDMA Schemes in the Presence of User-Dependent
Imperfect CSI
 4.8 Integration of COFDM Systems with Multiple Antennas
and Design of Adaptive Medium Access Protocols
 4.9 Large System Analysis of Nearly Optimum Low
Complex Beam forming in Multicarrier Multiuser Multi
antenna Systems
 4.10 Combined Radar and Communication Systems Using
OFDM
Efficient Analysis of OFDM Channels
 The Channel Matrix G：
A Gabor (or Weyl-Heisenberg )system with
window g and lattice constants a and b is the
sequence ( gq,r )q,rZ of translated and modulated
functions：
Efficient Analysis of OFDM Channels
 A standard Riesz basis series expansion with
this basis gives as follows:
where G is the coefficient mapping
Common Channel Operator Models
 The channel operator H maps an input signal s
to a weighted superposition of time and
frequency shifts of s :
 Line-of-sight path transmission: SH  av0 ,t0 a Dirac
distribution at (v0，t0) representing a time- and Dopplershift with attenuation a.
 Time-invariant systems: SH (v, t )  h(t ) 0 (v)
Computing the Channel Matrix G
 With notation IC,B =[C-B/2, C+B/2] the resulting model is
based on the following assumptions about compact supports
and index sets for the involved functions:
Generic Description of a
MIMO-OFDM-Radio-Transmission-Link
 System Model:
System model of MIMO-OFDM link
Three forward error correction schemes
 Code 1: convolutional code, constraint length
LC=3, code rate RC =1/2, generators G = [7; 5]8
 Code 2: convolutional code, constraint length
LC=9, code rate RC =1/2, generators G = [561;
715]8
 Code 3: parallel turbo code, constraint length
LC=4, code rate RC =1/3, generators G = [13;
15]8
System Model
H
 For channel estimation,
a typical OFDM pilot symbol based
approach is applied.
 At the receiver , the estimation of the channel transfer
function is improved by a noise reduction approach
exploiting the fact that the channel impulse response does
not exceed the guard interval

 This leads to the estimated channel coefficient H k  H k  H k
on subcarrier k with a variance of
k
Performance Analysis
 For subcarrier k and a perfectly known channel coefficient
Hk at the receiver, the link capacity for a discrete input
alphabet X and a continuous output set Y is:
 The average parallel-decoding capacity for an OFDM
symbol with NC subcarriers and m bit-levels becomes:
Performance Analysis
Simulation results (gray), generated models (black) and for different FEC
codes and 16-QAM
Performance Analysis
 Simulation results comparison for 16-QAM, Code 2 and
different channel impulse response lengths NH
Generic Model
 For the code 1 with memory 2, an exponential
function of the form：
 The logarithm of the BER can be approximated by
a straight line：
Generic model parameters for 16-QAM
Generic Model
Resource Allocation Algorithms
 These strategies are based on one or more of
the following techniques:
 Restriction to at most two users per carrier in
order to keep the overhead low.
 Introduction of a new metric to predict
interference caused by broadcast techniques.
 Usage of hybrid allocation strategies combining
orthogonal access with broadcast techniques.
Resource Allocation Algorithms
 Sum-Rate Maximization:
 First, we consider a scenario where the over all
system throughput, defined by the sum over all
achievable user rates, is maximized under a
transmit power constraint.
 Then the optimal power allocation is retrieved
by perform water-filling over the adapted eigen
values.
Resource Allocation Algorithms
 Sum-Rate Maximization with
Minimum Rate Requirements:
 We maximize the sum rate of the system. However
an individual minimum rate requirement for each
user has to be fulfilled. Such a scheme may be
needed in systems where delay critical as well as
non-delay critical data should be sent to each user.
Resource Allocation Algorithms
 Minimum Rate Requirements in
SISO-OFDM systems:
 First, a simple scheduler allocates one user to each
carrier aiming in assigning the minimum rates.
This scheduler performs the “worst selects”
algorithm.
 In the second step, an additional user is added to
each suitable carrier by means of broadcast
techniques.
Resource Allocation Algorithms
 Minimum Rate Requirements in
MIMO-OFDM systems:
 The first strategy, extended eigenvalue update (EEU)
algorithm, is based on the previously discussed
heuristic sum rate maximization algorithm using
eigenvalue updates.
 The second algorithm, the rate based coding (RBC)
algorithm, makes use of the duality of uplink and
downlink, which allows us to determine the allocation
in the dual uplink.
Resource Allocation Algorithms
Sum-Rate Maximization in MIMO-OFDM
Resource Allocation Algorithms
Minimum rate requirements in SISO-OFDM
Resource Allocation Algorithms
 Maximization of the Number of Users:
 We propose an hybrid algorithm, aiming in maximization of
the number of served users.
 In each iteration step it increases the number of users
successively until the rate requirements can not be fulfilled
anymore.
 Then, always the instantaneous worst user is added as
second user to its best suitable carrier as long as the rate
requirements are not fulfilled.
 Finally, the fraction for the power distribution is determined
individually for each carrier.
Resource Allocation Algorithms
Minimum rate requirements in MIMO-OFDM
Resource Allocation Algorithms
User maximization in SISO-OFDM
Turbo Equalization
Structure for a coded multiuser MIMO system with turbo equalization
Rate Allocation using EXIT Charts
 In the 2 -user case the convergence characteristic of the
equalizer is defined by two EXIT functions：
 Let P be the set of admissible convergence curves in the
plane region U ,With the area property of EXIT functions，
we derive in a straight forward manner an upper bound for
the rate region of both users, as：
Rate Allocation using EXIT Charts
 Average total throughput of both users versus ES/N0 for the
proposed rate allocation scheme
Coexistence of Systems
Overviews over scenarios adopted
Coexistence of Systems
Idle resources represented by spectrum holes
Coexistence of Systems
 Promising methods were developed in TAKOKO
which show that in principle OFDM based overlay
systems may be implemented.
 The real implementation of OFDM based overlay
systems requires fundamental decisions in the
political as well as in the regulatory area in order to
define the principle framework.
 The scientific results acquired in the TAKOKO
project are essentially summarized in the doctoral
dissertations.
System Design for Time-Variant Channels
 Three main strategies will be discussed with the
aim to solve the conflict between the transmission
channel’s high time and frequency selectivity:
 The application of a pre-equalizer to shorten the
channel impulse response
 The use of soft impulse shaping for a nonorthogonal multicarrier system
 Multi-antenna concepts to reduce the Doppler
spread.
System Design for Time-Variant Channels
BER-performance for SIMO-OFDM with a single transmit antenna and
different receiver configurations
System Design for Time-Variant Channels
 The MIMO philosophy is based on two pillars: Spatial
transmit diversity is provided by Space-Time codes,
and spatial multiplexing allows to increase the data rate
by transmitting independent data streams.
 The algorithms used for data processing in the
baseband yield a better performance if the individual
antennas for sectorization or spatial interpolation are
decoupled.
 Two scenarios are considered, a high-speed train and
vehicle-to-vehicle environments.
System Design for Time-Variant Channels
BER-performance for (2 × 2)-OFDM and different receiver
configurations
Combination of Adaptive and Non-Adaptive
Multi-User OFDMA Schemes
 For the order of allocating the available subcarriers
to the adaptive and non-adaptive users, two
possibilities are considered:
 Firstly, the subcarriers of non-adaptive users are allocated
in a first step and the remaining subcarriers are then
allocated to the adaptive users in a second step referred to
as Non-Adaptive First(NAF)
 Secondly, first the subcarriers of the adaptive users are
allocated followed by the allocation of the subcarriers of the
non-adaptive users referred to as Adaptive First (AF).
Combination of Adaptive and Non-Adaptive
Multi-User OFDMA Schemes
 (a) System data rate and (b) Number UA of adaptive users
vs V
MAC Frame for SDMA Operation and Spatial
Grouping
A MAC frame supporting SDMA operation
MAC Frame for SDMA Operation and Spatial
Grouping
 The first part of the frame is transmitted omni-directional to
implement broadcast mode. DL-MAP (blue) and UL-MAP
(green) and arrows are shown pointing to the time instants
contained in the MAPs, where up to four radio bursts can be
transmitted, spatially separated.
 A tree-based heuristic algorithm only estimating the most
promising spatial groups appear well suited to reduce runtime complexity to be applicable in real-time condition,
with a grouping gain comparable to that of the greedy
algorithm.
Hardware Implementation of COFDM Systems
with Multiple Antennas
Diversity gain under SIMO with different number of antennas
Hardware Implementation of COFDM
Systems with Multiple Antennas
SNR per user with 4x2 and 4x4 MIMO
Description of Algorithms
 Denoting the number of totally allocated
subchannels on carrier c with Mc, the achievable
sum rate computes according to:
 When the algorithm is run, potential subchannel
gains have to be computed for every user and
carrier to select the most suitable user and carrier
for each data stream.
SESAM with MMSE Filters
 With MMSE filters, the problem of maximizing
sum rate under a total power constraint can be
solved almost optimally at drastically reduced
computational complexity.
 The subchannel gains i ,c compute according to:
SESAM with MMSE Filters
t
 t j ,c denotes the transmit
filter in the dual uplink for the jj ,c
th data stream on carrier c and is equal to the unit-norm
eigenvector belonging to the principal eigenvalue of the
matrix:
SESAM with Zero-Forcing Filters
 With zero-forcing filters the subchannel gains
i ,c are given by :
Numerical Results
Combined Radar and Communication
Systems Using OFDM
 Channel limitations for the OFDM parameters
Combined Radar and Communication
Systems Using OFDM
 Physical parameters of an OFDM frame are sub-carrier
distance, guard interval duration, total bandwidth and the
frame duration. Their choice depends on the required radar
accuracy and the quality of the mobile propagation channels
 The resolution in range and Doppler domain set minimum
limits on bandwidth B and frame duration TF, which can be
estimated by the following equations:
Combined Radar and Communication
Systems Using OFDM
 Application scenario for a combined radar and communication system
The Radar Subsystem
 In the case of H reflecting targets, the relation
between transmitted and received signals is:
 Representing the received signal in the same
matrix notation as FTx can thus be done as
The Radar Subsystem
 The resulting matrix is:
 The MLE for Doppler shift and propagation
time is :
Measurements
 OFDM system parameters
Measurements
 OFDM system setup for a maximum carrier frequency of 6 GHz
Measurements
 The expected radar image SNR amounts to:
Radar image SNR for PTx =-12dBm
Summary
 In this project a detailed concept for a combined
radar and communication system based on OFDM
signals has been elaborate d and evaluated. A
suitable estimator has been developed that allows
for performing range and Doppler measurements
with OFDM signals without any negative impact of
the simultaneously transmitted user information.
Both measurements an d simulations show that
OFDM radar is an interesting and feasible new
technology with some interesting qualities.