Abstract This thesis concentrates on evaluating and improving the throughput performances of mobile users in high speed vehicles. In particular, high speed train (HST) scenarios are considered. Emphasis is placed on practical designs and methods that take into account distinctive HST characteristics. A two-hop communication link, i.e., base station (BS)-to-HST and HST-to-onboard users (OBUs) is adopted. The main target is to improve the throughput performance on the BS-to-HST communication link, which is assumed to be the main bottleneck in the whole communication link, since the HST-to-OBU communication link is assumed to have good channel quality due to the short link distance with relatively stationary OBUs. The algorithms developed are assessed through link and system level simulations. A theoretical and practical study of the throughput maximization problem in a single and multi-cell multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) train scenario are considered with and without cooperation between train carriages. Two low-complexity transmission schemes based on simple antenna selection (AS) methods with spatial multiplexing (SM) are proposed. The simulation results demonstrate that large antenna arrays with AS and SM transmission strategies have the potential to significantly improve the throughput of the BS-to-train link in HST scenarios. Resource sharing methodologies between the moving relay nodes (MRNs) on the HST and ground macro users (GMUs) were also studied in a multi-cell MIMO-OFDM train scenario. Direct application of existing resource scheduling methods will not be appropriate to efficiently and fairly share resources, since the MRNs and the GMUs have different processing capabilities. Hence, two hybrid resource scheduling methods are analyzed in conjunction with joint and disjoint resource management. The tradeoff between the number of MRNs and receive antennas that should be installed on an HST was also examined in the context of throughput performance and capital expenditure. Results show that joint scheduling does not provide the best overall performance and there is a need to schedule each group of mobile terminals (MTs) separately. Finally, the feasibility of the use of higher frequency bands (HFBs) was examined in HST scenarios. A timer-based beam selection scheme for HST, which does not require any training time to select the appropriate beam is also proposed. The proposed beam selection scheme (PBSS) displays a close performance to the ideal singular value decomposition (SVD) scheme. / Tiivistelmä Tämä väitöskirja keskittyy mobiilikäyttäjien tiedonsiirtonopeuksien arviointiin ja parantamiseen nopeasti liikkuvissa kulkuneuvoissa. Työ käsittelee erityisesti tiedonsiirtoa suurnopeusjunissa. Työssä korostetaan käytännön menetelmiä, jotka ottavat huomioon nopeasti liikkuvien junien tiedonsiirron erityispiirteet. Työssä käytetään kahden hypyn linkkimallia, jossa tiedonsiirto kulkee tukiasemalta junaan ja junasta käyttäjälle, joka on junassa. Päätavoite on parantaa datanopeuksia tukiaseman ja junan välisessä tiedonsiirtolinkissä, jonka uskotaan olevan suurin pullonkaula koko tiedonsiirtolinkissä, koska junan ja lähes paikallaan olevan käyttäjän välinen kanava voidaan olettaa hyvälaatuiseksi linkin lyhyyden vuoksi. Kehitettyjen algoritmien suorituskykyä arvioidaan linkki- ja järjestelmätason simulaatioilla. Työssä tutkitaan tiedonsiirtonopeuden maksimointiongelmaa teoreettisella ja käytännön tasolla yhden ja usean solun MIMO OFDM junaskenaarioissa, joissa junan vaunut tekevät tai eivät tee yhteistyötä. Työssä esitetään kaksi alhaisen kompleksisuuden lähetysmenetelmää, jotka hyödyntävät yksinkertaista antennin valintamenetelmää ja tilatason multipleksointia. Simulointitulokset osoittavat, että suuret antenniryhmät, jotka hyödyntävät näitä lähetysmenetelmiä, voivat parantaa merkittävästi tiedonsiirtonopeutta tukiasemalta junaan päin. Työssä tutkitaan myös resurssien jakomenetelmiä liikkuvien junassa olevien releiden ja maatason makrokäyttäjien välillä monen solun MIMO-OFDM junaskenaariossa. Nykyisten resurssinhallintamenetelmien käyttö ei ole suoraan mahdollista tehokasta ja oikeudenmukaista resurssien jakoa, koska releillä ja makrokäyttäjillä on erilaiset prosessointikyvyt. Tämän vuoksi työssä analysoidaan kahta hybridimenetelmään resurssien skeduloinnille. Tutkimukset selventävät tasapainoa releiden lukumäärän ja junaan asennettavien vastaanotinantennien välillä tiedonsiirtonopeuden ja kustannusten osalta. Tulokset osoittavat, että yhteinen resurssien jako ei saavuta parasta suorituskykyä, eikä ole tarvetta ajoittaa jokaista matkaviestinterminaaliryhmää erikseen. Lopuksi työssä tutkitaan korkeampien taajuusalueiden soveltuvuutta tiedonsiirtoon suurnopeusjunissa. Työssä ehdotetaan ajastinpohjaista keilanvalintamenetelmää, joka ei vaadi opetusjaksoa sopivan keilan valintaan. Ehdotetun menetelmän saavuttama suorituskyky on lähellä ideaalisen singulaariarvohajotelmaa hyödyntävän menetelmän suorituskykyä.

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Parameter estimation and signal detection are the two key components of a wireless communication system. They directly impact the bit-error-ratio (BER) performance of the system. Several criteria have been successfully applied for parameter estimation and signal detection. They include maximum likelihood (ML), maximum a-posteriori probability (MAP), least square (LS) and minimum mean square error (MMSE) etc. In the linear detection framework, linear MMSE (LMMSE) and LS are the most popular ones. Nevertheless, these criteria do not necessarily minimize the BER, which is one of the key aspect of any communication receiver design. Thus, minimization of BER is tantamount to an important design criterion for a wireless receiver, the minimum bit/symbol error ratio (MBER/MSER). We term this design criterion as the minimum-error-probability (MEP). In this thesis, parameter estimation and signal detection have been extensively studied based on the MEP framework for various unexplored scenar-ios of a wireless communication system. Thus, this thesis has two broad categories of explorations, first parameter estimation and then signal detection. Traditionally, the MEP criterion has been well studied in the context of the discrete signal detection in the last one decade, albeit we explore this framework for the continuous parameter es-timation. We first use this framework for channel estimation in a frequency flat fading single-input single-output (SISO) system and then extend this framework to the carrier frequency offset (CFO) estimation of multi-user MIMO OFDM system. We observe a reasonably good SNR improvement to the tune of 1 to 2.5 dB at a fixed BER (tentatively at 10−3). In this context, it is extended to the scenario of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM with pa-rameter estimation error statistics obtained from LMMSE only and checked its effect at the equalizer design using MEP and LMMSE criteria. In the second exploration of the MEP criterion, it is explored for signal detection in the context of MIMO-relay and MIMO systems. Various low complexity solutions are proposed to alleviate the effect of high computational complexity for the MIMO-relay. We also consider various configurations of relay like cognitive, parallel and multi-hop relaying. We also propose a data trans-mission scheme with a rate of 1/Ns (Ns is the number of antennas at the transmitter) with the help of the MEP criterion to design various components. In all these cases, we obtain considerable BER improvement compared to the existing solutions.

Bayesian approaches for sparse signal recovery have enjoyed a long-standing history in signal processing and machine learning literature. Among the Bayesian techniques, the expectation maximization based Sparse Bayesian Learning(SBL) approach is an iterative procedure with global convergence guarantee to a local optimum, which uses a parameterized prior that encourages sparsity under an evidence maximization frame¬work. SBL has been successfully employed in a wide range of applications ranging from image processing to communications. In this thesis, we propose novel, efficient and low-complexity SBL-based algorithms that exploit structured sparsity in the presence of fully/partially known measurement matrices. We apply the proposed algorithms to the problem of channel estimation and data detection in Orthogonal Frequency Division Multiplexing(OFDM) systems. Further, we derive Cram´er Rao type lower Bounds(CRB) for the single and multiple measurement vector SBL problem of estimating compressible vectors and their prior distribution parameters. The main contributions of the thesis are as follows: We derive Hybrid, Bayesian and Marginalized Cram´er Rao lower bounds for the problem of estimating compressible vectors drawn from a Student-t prior distribution. We derive CRBs that encompass the deterministic or random nature of the unknown parameters of the prior distribution and the regression noise variance. We use the derived bounds to uncover the relationship between the compressibility and Mean Square Error(MSE) in the estimates. Through simulations, we demonstrate the dependence of the MSE performance of SBL based estimators on the compressibility of the vector. OFDM is a well-known multi-carrier modulation technique that provides high spectral efficiency and resilience to multi-path distortion of the wireless channel It is well-known that the impulse response of a wideband wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this thesis, we consider the estimation of the unknown channel coefficients and its support in SISO-OFDM systems using a SBL framework. We propose novel pilot-only and joint channel estimation and data detection algorithms in block-fading and time-varying scenarios. In the latter case, we use a first order auto-regressive model for the time-variations, and propose recursive, low-complexity Kalman filtering based algorithms for channel estimation. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the MSE and coded bit error rate performance. • Multiple Input Multiple Output(MIMO) combined with OFDM harnesses the inherent advantages of OFDM along with the diversity and multiplexing advantages of a MIMO system. The impulse response of wireless channels between the Nt transmit and Nr receive antennas of a MIMO-OFDM system are group approximately sparse(ga-sparse),i.e. ,the Nt Nr channels have a small number of significant paths relative to the channel delay spread, and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wire¬less channels are also group approximately-cluster sparse(ga-csparse),i.e.,every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this thesis, we cast the problem of estimating the ga-sparse and ga-csparse block-fading and time-varying channels using a multiple measurement SBL framework. We propose a bouquet of novel algorithms for MIMO-OFDM systems that generalize the algorithms proposed in the context of SISO-OFDM systems. The efficacy of the proposed techniques are demonstrated in terms of MSE and coded bit error rate performance.