Abstract The present dissertation deals with initial synchronization in Code Division Multiple Access (CDMA) networks. In the first part of this thesis an extensive and up-to-date review of the literature is presented. The basic theory of code acquisition as well as different techniques and structures used to achieve the initial synchronization are discussed. A survey of the most common theoretical approaches allowing performance characterization of the acquisition process is included. The effect of noise, interference, carrier Doppler, multipath propagation, fading and data modulation on system performance are reviewed. Advanced code acquisition approaches exploiting interference suppression techniques and multiple antennas are also described. A summary of the results obtained within the area of code acquisition in CDMA networks is also embraced by this thesis. The distinctive assumption is to consider the actual variable effect of multiple access interference on the delay-domain search process, instead of the usual constant approximation. Three directions of research are followed. Models for code acquisition in quasi-synchronous and asynchronous CDMA networks are first developed and analysed. Closed-form expressions for the main performance figures of the acquisition process are derived and analysed. Results show a strong dependence of the mean acquisition time with the nature of the multiple access interference. In the second area of research the previous results are extended to consider code acquisition with a multi-branch (Rake) receiver in a multipath channel. A generic model for Rake receiver code acquisition is considered and developed, in which the synchronization takes place in two phases. The first detected path is allocated to the first finger during the initial synchronization phase, whereas the remaining fingers are successively allocated to other available paths in the postinitial synchronization phase. Performance measures for this acquisition process are also derived and analysed. Finally, based on the use of an antenna array and beamforming techniques, conventional delay-domain code acquisition is extended to the angular domain, resulting in a two-dimensional (delay-angle) search. This technique is found to be feasible, outperforming the synchronization approach exploiting a single-antenna. It is found that there exists an optimal number of antennas that minimises the mean acquisition time. Two-dimensional code acquisition is studied in a variety of scenarios, including single and multipath channels, fixed and fading channels, and with uniform and nonuniform spatial distributions of interference. Different two-dimensional search strategies are studied. A clear dependence of acquisition performance with the search strategy and the particular distribution of interference is pointed out. The performance of two-dimensional code acquisition is found to be seriously deteriorated by the presence of spatially nonuniform interference. Schemes based on search strategy and adaptive detector structures are considered and analysed to combat the performance degradation in the mentioned case. A comparative study of code acquisition exploiting multiple antennas is also presented.

This dissertation focuses on real-time decision-making for large-scale domain search and object classification using Multiple Autonomous Vehicles (MAV). In recent years, MAV systems have attracted considerable attention and have been widely utilized. Of particular interest is their application to search and classification under limited sensory capabilities. Since search requires sensor mobility and classification requires a sensor to stay within the vicinity of an object, search and classification are two competing tasks. Therefore, there is a need to develop real-time sensor allocation decision-making strategies to guarantee task accomplishment. These decisions are especially crucial when the domain is much larger than the field-of-view of a sensor, or when the number of objects to be found and classified is much larger than that of available sensors. In this work, the search problem is formulated as a coverage control problem, which aims at collecting enough data at every point within the domain to construct an awareness map. The object classification problem seeks to satisfactorily categorize the property of each found object of interest. The decision-making strategies include both sensor allocation decisions and vehicle motion control. The awareness-, Bayesian-, and risk-based decision-making strategies are developed in sequence. The awareness-based approach is developed under a deterministic framework, while the latter two are developed under a probabilistic framework where uncertainty in sensor measurement is taken into account. The risk-based decision-making strategy also analyzes the effect of measurement cost. It is further extended to an integrated detection and estimation problem with applications in optimal sensor management. Simulation-based studies are performed to confirm the effectiveness of the proposed algorithms.