Multi carrier communication signals

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General principle of MC based TDOA estimation

The general communication block diagram dealing with TDOA estimation process is illustrated in the Figure 2-1.
Multiple OFDM signals travel through the channel toward the receiver. Once the main communication parameters (SNR, Roll of factor etc.) properly defined and evaluated, one can perform, at the receiving part, energy detection or channel estimation to extract the useful TDOA, i.e., time difference of arrival, between the receiver and the OFDM sources. As it will be demonstrated in chapter III, received power and the channel behavior are directly impacted by TDOA, and of course by the other communication environmental factors. So some practical solutions will be suggested to reinforce the effect of useful TDOA Channel parameters LOS, NLOS, ISI OFDM sources Communication parameters SNR,β…etc. Receiver and TDOA and to reduce the negative effects of the communication environmental factors. So each part and process of the multicarrier communication system has to be studied clearly to find out whether its influence on the precision of the proposed method is good or not.

Multi carrier based positioning systems and OFDM solutions

For communications issues, and due to evident reasons linked to redundancy principle, the structure of the multicarrier systems is more powerful, against the multipath channel, than any single carrier schemes. It also seems, due to the relative bandwidth, well suited for the time delay based location device. The following sections present a short overview of multicarrier based positioning solutions.

Blind solution

Many references cited in [1] underline the fact that, in a given network, each sensor independently can identify boundaries in a received MC signal, by looking for the repetition of a given sequence in this signal. Then it can calculate some statistical features (e.g., the sample mean or variance) of each sequence, and transmit, for positioning purpose, the sequence repetition times and the associated feature values to another sensor. This approach, which does not require any knowledge of the transmitted signal, is defined as blind, and is well suited for OFDM based communication. Indeed, in OFDM format, the insertion of a cyclic prefix (CP) before each sequence of data makes the beginning and the end of each sequence identical, leading to the notion of repeated sequence and hence can allow, as mentioned former, the blind identification. Let’s note that for our purpose, CP leads also to the concept of circular convolution, which is of great interest to handle, in an easy way, OFDM signal both in the time domain and in the frequency domain.

Training solution

At the opposite side, there are several other papers in the literature that perform positioning by using a specially designed training MC signal. For example, the work reported in [2], based on the Schmidl–Cox [3] and Minn [4] synchronization algorithms, uses such a cooperative scheme, and with the same concept, the authors in [5] uses, for accurate positioning, cooperative transmitters and receivers in an indoor positioning system. With using of a known transmitted signal, the authors in [6] look for time delay induced by phase rotations across subcarriers at the receiver. With the same way in IEEE 802.11a wireless LANs, the authors in [7] and [8] correlate the received signals with a training sequence.

Alternative solution

Two other positioning methods are tangentially relevant since they involve multi carrier signals. First in [9], the authors combine the standard TDOA positioning with Cell ID positioning. Recall that the Cell ID method works only if the received power levels indicate that the mobile is within the base station scope of coverage. Second in [10], the extraction of both time of arrival and direction of arrival information is done using several receivers; each of them has an antenna array. All the measurements from all antennas of all receivers are available, at all times, in a central station. Moreover, training data is assumed to be available and will help to solve location problem.

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OFDM based positioning

Generally, the positioning methods using OFDM signals in the existing literature are divided into two categories. The first category locates the boundaries of OFDM signals by using the traditional or improved timing synchronization algorithms [11], [12] [13]. The idea behind those methods is to deal with timing synchronization as with the TDOA estimation.
Essentially, timing synchronization and TDOA estimation are the same tasks for the receiver. However, the sampling rate limits the accuracy of such methods, and generally, an error of several meters is expected. The second category uses modern spectral estimation techniques [14], [15], [16] and deals with the high-resolution algorithms such as multiple signal classification MUSIC, ESPRIT. These algorithms are applied to the frequency-domain channel estimation to extract a more accurate estimate of the first path in the time domain. Again, the sampling rate limits these mathematical methods that are, by the way, too sensitive to the model. However their accuracy is much better than that of the first category.

OFDM communication system

OFDM is a well popular multicarrier technique that offers many advantages to perform high quality and high data rate communication in complex channels. This section presents the different steps required for implementing OFDM modulation technique needed for channel estimation and leading further to TDOA extraction.

Table of contents :

Acknowledgments
Abstract
Contents
General Introduction
Chapter 1: Context and State of the art of Indoor Positioning
1.1 Introduction
1.2 Indoor positioning: a critical need
1.2.1 Context
1.2.2 General definitions
1.3 Indoor Positioning Systems
1.3.1 Infrared (IR) Positioning Systems
1.3.2 Ultra-sound Positioning Systems
1.3.3 Radio Frequency (RF) Positioning Systems
1.3.4 Alternative systems
1.4 Measuring Principles
1.4.1 RF Metrics for Wireless Localization
1.4.2 Scene Analysis
1.4.3 Proximity
1.4.4 Conclusion
1.5 Objectives
1.6 Conclusion
Bibliography
Chapter 2: Multi carrier communication signals
2.1 Introduction
2.2 General principle of MC based TDOA estimation
2.3 Multi carrier based positioning systems and OFDM solutions
2.3.1 Blind solution
2.3.2 Training solution
2.3.3 Alternative solution
2.3.4 OFDM based positioning
2.3.5 Conclusion
2.4 Data modulation
2.4.1 QPSK Based Communication System
2.4.2 SNR calculation
2.4.3 Simulation results
2.5 OFDM communication system
2.5.1 Transmitter/Receiver module
2.5.2 Guard Interval
2.5.3 Guard Band and roll of factor
2.6 MATLAB implementation
2.6.1 Transmission part
2.6.2 Reception part
2.7 Channel Estimation
2.7.1 Pilot block
2.7.2 Mathematical derivation
2.7.3 Channel estimation testing
2.8 Conclusion
Bibliography
Chapter 3: OFDM based TDOA estimation
3.1 Introduction
3.2 Algorithms for TDOA-based positioning
3.3 Definition of the direct model
3.3.1 Frequency limitation
3.3.2 Signal model
3.3.3 Energy based approach
3.3.4 Channel based approach
3.4 Inverse problem: TDOA extraction
3.4.1 Large TDOA
3.4.2 Small TDOA
3.4.3 Very small TDOA
3.4.4 Cramer Rao Bound Limit
3.5 Communication parameters effect
3.5.1 Estimation of the coefficients 􀢻􀫚, 􀢻􀫛
3.5.2 Number of pilots
3.6 Communication environment effect
3.6.1 Multipath modeling
3.6.2 Emulating Multipath
3.6.3 Multipath effect reduction
3.7 Conclusion
Bibliography
Chapter 4: Experimental setup and results
4.1 Presentation of the environment
4.1.1 The controlled electromagnetic room
4.1.2 Radiating devices
4.1.1 Amplifier
4.1.2 Arbitrary waveform generator
4.1.3 Digital storage oscilloscope
4.1.4 Conclusion
4.2 SISO communication system setup
4.2.1 The transmitter
4.2.2 The receiver
4.2.3 Signal acquisition and I-Q constellation
4.2.4 OFDM communication performances
4.2.5 Channel estimation
4.3 Direct and Inverse models validation
4.3.1 MISO configuration
4.3.2 Baseline calculation
4.3.1 Calibration MISO system
4.3.2 MISO configuration for TDOA estimation
4.3.3 Direct model validation
4.3.4 Inverse model validation
4.3.5 Multipath effects
4.4 Conclusion
Conclusions and perspectives
Abbreviations
List of Figures
List of Tables

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