Signal Processing Techniques in Mobile Communication Systems - Signal Separation, Channel Estimation and Equalization
|Title:||Signal Processing Techniques in Mobile Communication Systems - Signal Separation, Channel Estimation and Equalization|
|Series:||Blekinge Institute of Technology Doctoral Dissertation Series|
|ISSN:||1653-2090 (felaktigt 1650-2159 i publikationen)|
|Publisher:||Blekinge Institute of Technology|
|Organization:||Blekinge Institute of Technology|
|Department:||School of Engineering - Dept. of Signal Processing (Sektionen för teknik – avd. för signalbehandling)
School of Engineering S- 372 25 Ronneby
+46 455 38 50 00
|Abstract:||Over the last decade there has been an explosive growth in the use of wireless mobile communications. Second generations systems are mature technologies now and third generation systems and beyond are being implemented and researched. Future systems should support a substantially wider and enhanced range of services and thus would require even higher data rates compared to current system in order to deliver these services. A fundamental necessity for being able to provide high data rates is that the physical channel between transmitter and receiver is efficiently utilized. Signal processing algorithms are integral part of any wireless mobile communication systems that makes this possible.
In this thesis, several signal processing techniques for improving the performance and capacity of wireless mobile communications systems are discussed. In Part I a simulation package for the simulation of communication systems is implemented and verified. In Part II, the linear and non-linear Projection Approximation Subspace Tracking with Deflation (PASTD) algorithms are proposed for Blind Source Separation (BSS) and Independent Component Analysis (ICA) of linearly mixed signals, respectively. Here, the signals are transmitted simultaneously from multiple antennas. In Part III, the PASTD algorithm is compared to the Rao-Principe (RP) and the Exact Eigendecomposition (EE) algorithm for the purpose of assessing their performance for different configurations. Beamforming is an important function of receivers, in particular to base stations, and in Part IV an efficient and effective space-time adaptive algorithm is proposed. In Part V, an adaptive blind equalization technique for time varying multipath fading channels is suggested and analyzed, and in Part VI a combined channel estimation algorithm for coherent detection in mobile communication systems is proposed. Finally, Part VII investigates several algorithms for power allocation in Multiple-Input Multiple-Output (MIMO) systems and the impact of channel estimation error on the performance of the system is evaluated. In this thesis, the above mentioned algorithms are implemented in Matlab and their applicability and effectiveness were investigated by using several performance measures via Monte Carlo simulation approach. The simulation results clearly demonstrate the promise of using these different signal processing algorithms for improving the performance of wireless mobile communication systems.
|Keywords:||wireless mobile communications, signal processing, blind source separation, independent component analysis, beamforming, water filling, greedy algorithm, exact eigendecomposition, eigenvector algorithm, multiple-input multiple-output systems, the projection approximation subspace tracking algorithm with and without deflation, linear and non-linear principal component analysis, whitening, decorrelation, equalization, antenna arrays|