This research contains three essays that explore the speed of adjustment, volatility and noise in the Indonesia Stock Exchange. The first essay explores the speed of adjustment in the Indonesia Stock Exchange at daily interval from 2000 to 2004. The model employed is the speed of adjustment with noise. Firstly, I work on the estimation of the speed of adjustment. The estimated speed of adjustment coefficient concludes that the large size leads the smaller size group to adjust to new information. Secondly, I analyse the component in the noise that contributes significantly to the speed of adjustment level. It is confirmed that the factor determining the noise is bid-ask fluctuations. Therefore, it is reasonable to infer the component in the noise from bid-ask component. The decomposition of bid-ask spread into transaction cost and asymmetric information reveals that the latter is found to be a significant component determining the speed of adjustment level. The second essay analyses the fine grain dynamics of the speed of price adjustment to new information from 2000 to 2007. The exact time of adjustment is estimated at intraday frequency instead of at daily frequency. In this work, as an alternative of first moment estimation, second moment model-free estimation using volatility signature plot to estimate of the speed of adjustment is proposed. Both first and second moment estimation of the speed of adjustment provide consistent result of 30 minute adjustment period. Negative relation after 5-minute return interval between speed of adjustment estimate and realized variance is found implying lower noise leads to smaller deviation between observed and equilibrium price. In the third essay, I concentrate the work on the second moment of continuously compounded returns from 2000 to 2007 in the Indonesia Stock Exchange. The main purpose of the last essay is to estimate the noise and efficient variance in the Indonesia Stock Exchange. The realized variance based estimator is employed in the third essay. During the period of the study, noise variance decreases indicating smaller deviation between the observed and equilibrium price, hence improving market quality in the Indonesia Stock Exchange. The optimal frequency to estimate the efficient variance, on average, is nine minutes. The variance ratio of daily efficient variance to daily open-to-close reveals significant private information underlying price process in the Indonesia Stock Exchange.

Future fifth generation (5G) cellular networks have to cope with the expected ten-fold increase in mobile data traffic between 2015 and 2021. To achieve this goal, new technologies are being considered, including massive multiple-input multiple-output (MIMO) systems and millimeter-wave (mmWave) communications. Massive MIMO involves the use of large antenna array sizes at the base station, while mmWave communications employ frequencies between 30 and 300 GHz. In this thesis we study the impact of these technologies on the performance of channel estimators. Our results show that the characteristics of the propagation channel at mmWave frequencies improve the channel estimation performance in comparison with current, low frequency-based, cellular networks. Furthermore, we demonstrate the existence of an optimal angular spread of the multipath clusters, which can be used to maximize the capacity of mmWave networks. We also propose efficient noise variance estimators, which can be employed as an input to existing channel estimators.