Chapter 4 System Level Aspects for Single Cell Scenarios

Chapter 4 System Level Aspects for SingleCell Scenarios

School of Info. Sci. & Eng.Shandong Univ.

CONTENT

4.1 Efficient Analysis of OFDM Channels 4.2 Generic Description of a MIMO-

OFDM-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 Multi-

User 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 of translated and modulated functions：

, ,( )q r q r Zg

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: a Dirac distribution at (v0 ， t0) representing a time- and Doppler-shift with attenuation a.

Time-invariant systems:

0 0,H v tS a

0( , ) ( ) ( )HS v t h t 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 aMIMO-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 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

on subcarrier k with a variance of

kH

k k kH H H

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.


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.


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.


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.


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.


Sum-Rate Maximization in MIMO-OFDM


Minimum rate requirements in SISO-OFDM


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.


Minimum rate requirements in MIMO-OFDM


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


Idle resources represented by spectrum holes


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 non-orthogonal multicarrier system

Multi-antenna concepts to reduce the Doppler spread.


BER-performance for SIMO-OFDM with a single transmit antenna and

different receiver configurations


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.


BER-performance for (2 × 2)-OFDM and different receiver configurations

Combination of Adaptive and Non-AdaptiveMulti-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-AdaptiveMulti-User OFDMA Schemes

(a) System data rate and (b) Number UA of adaptive users vs V

MAC Frame for SDMA Operation and SpatialGrouping

A MAC frame supporting SDMA operation

MAC Frame for SDMA Operation and SpatialGrouping

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 run-time 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 compute according to:,i c

SESAM with MMSE Filters

denotes the transmit filter in the dual uplink for the j-th data stream on carrier c and is equal to the unit-norm eigenvector belonging to the principal eigenvalue of the matrix:

,j ct

,j ct

SESAM with Zero-Forcing Filters

With zero-forcing filters the subchannel gains

are given by :

,i c

Numerical Results

Combined Radar and CommunicationSystems Using OFDM

Channel limitations for the OFDM parameters


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:


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.

Chapter 4 System Level Aspects for Single Cell Scenarios

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