Advanced reception techniques for 3GPP LTE wireless cellular telecommunications systems

In the first introductive part of the thesis, we discuss the classical Orthogonal Frequency Division Multiplexing (OFDM) principle highlighting its advantages such as low-required decoding complexity in case of multipath propagation channel together with its well-known limitations induced by impaired reception. Furthermore, we carefully examine its application to next generation 3GPP Long Term Evolution (LTE) wireless telecommunication system. In this sense, LTE OFDMA physical-layer system parameters are detailed and their dimensioning explained from the 3GPP standard perspective.

In the second part of the thesis, we first consider the design of Reference Signals in LTE and the wireless propagation channel model. We then approach the Channel Estimation problem. In particular, we study the impact of LTE system parameters on common linear channel estimation techniques and introduce several new methods applicable in this specific context. Furthermore, we propose a general framework for the performance analysis of classical and proposed methods.

In the last part of the thesis, we consider impaired OFDM reception in the case of selective channels. As a first step, we deal with linear OFDM equalization in highly doubly selective channels. In order to avoid complex matrix inversion entailed by straightforward application of linear equalization, we develop iterative equalization methods which show to be very attractive from an implementation point of view. Exploiting Basis Expansion Model of the frequency-selective time-varying channel and preconditioning, we show that the complexity of such methods are roughly linearly proportional to the OFDM FFT order but yet attaining MMSE equalizer performance within an acceptable performance loss. Finally, we discuss Alamouti block-code reception for OFDM in case highly selective channel. We determine useful Maximum Likelihood (ML) detection bounds and then revise linear and non-linear detection approaches. To overcome known sub-optimality of such methods, we present a Lattice Reduction aided near-ML technique which reveals to offer optimal diversity-order detection performance with negligible coding gain loss.