The cellular network operators have witnessed significant growth in data traffic in the past few decades. This growth occurs due to the increases in the number of connected mobile devices, and further, the emerging mobile applications developed for rendering video-based on-demand services. As the frequency bandwidth for cellular communication is limited, significant effort was dedicated to improve the utilization of the available spectrum and increase the system performance via new technologies. For example, 3G and 4G networks were designed to facilitate high data traffic in cellular networks in past decades. Nevertheless, there is a necessity for new cellular network technologies to accommodate the ever-growing data traffic demand. 5G is behind the corner to deal with the tremendous data traffic requirements that will appear in cellular networks in the next decade. Massive MIMO (multiple-input-multi-output) is one of the backbone technologies in 5G networks. Massive MIMO originated from the concept of multi-user MIMO. It consists of base stations (BSs) implemented with a large number of antennas to increase the signal strengths via adaptive beamforming and concurrently serving many users on the same time-frequency blocks. As an outcome of using Massive MIMO technology, there is a notable enhancement of both sum spectral efficiency (SE) and energy efficiency (EE) in comparison with conventional MIMO based cellular networks. Resource allocation is an imperative factor to exploit the specified gains of Massive MIMO. It corresponds to properly allocating resources in the time, frequency, space, and power domains for cellular communication. Power control is one of the resource allocation methods to deliver high spectral and energy efficiency of Massive MIMO networks. Power control refers to a scheme that allocates transmit powers to the data transmitters such that the system maximizes some desirable performance metric. In the first part of this thesis, we investigate reusing the resources of a Massive MIMO system, for direct communication of some specific user pairs known as device-to-device (D2D) underlay communication. D2D underlay can conceivably increase the SE of traditional Massive MIMO systems by enabling more simultaneous transmissions on the same frequencies. Nevertheless, it adds additional mutual interference to the network. Consequently, power control is even more essential in this scenario in comparison with conventional Massive MIMO systems to limit the interference that is caused between the cellular network and the D2D communication, thereby enabling their coexistence. In this part, we propose a novel pilot transmission scheme for D2D users to limit the interference to the channel estimation phase of cellular users in comparison with the case of sharing pilot sequences for cellular and D2D users. We also introduce a novel pilot and data power control scheme for D2D underlaid Massive MIMO systems. This method aims at assuring that D2D communication enhances the SE of the network in comparison with conventional Massive MIMO systems. In the second part of this thesis, we propose a novel power control approach for multi-cell Massive MIMO systems. The new power control approach solves the scalability issue of two well-known power control schemes frequently used in the Massive MIMO literature, which are based on the network-wide max-min and proportional fairness performance metrics. We first explain the scalability issue of these existing approaches. Additionally, we provide mathematical proof for the scalability of our proposed method. Our scheme aims at maximizing the geometric mean of the per-cell max-min SE. To solve this optimization problem, we prove that it can be rewritten in a convex form and then be solved using standard optimization solvers.

To this day, most of the communication networks are characterized by a "monolithic" operating approach. Network elements are configured and operate without any reconfiguration for long time periods. Softwarization, whereby dedicated elements are being replaced by more general-purpose devices, has been lately challenging this existing approach. Virtualizing the infrastructure through the softwarization can provide significant benefits to end users and operators, supporting more flexible service deployment, providing real time monitoring and operational changes.  In Paper I we study a novel allocation technique and traffic optimization process for the access network. Cellular network technologies (i.e. UMTS, LTE, LTE-A) will coexist with non-cellular small cells and offload traffic from cellular to non-cellular networks mainly operating in 3GPP Wi-Fi (IEEE 802.11 standards). This is a scenario for indoor wireless access implementations where offloading mechanisms can improve the QoS offered by the operators, and reduce the traffic handled by the access fronthaul. The analysis of a novel optimization algorithm exhibited a holistic solution for access-core interworking where LWA (LTE-WiFi Aggregation) offers improved performance for the end users.  In order to optimize core network operations factors such as the operational costs should be addressed. Following this approach in Paper II we analysed how environmental factors (e.g. temperature, humidity) can affect the power consumption of core network data centers (cooling systems). By applying machine learning techniques using data from a data center, we were able to forecast the power consumption based on to atmospheric weather conditions and analyse its accuracy.  Optimizing the access network operations and the interworking (resource allocation, scheduling, offloading) can lead to highly configurable and secure operations. These have been factors of great concern as wireless connectivity increases in denser populated areas. In Paper III we examine the physical layer secrecy aspects of a collaborative small cell network in the presence of parallel connections and caching capabilities at the edge nodes. Using tools from the probability theory, we examined how the power allocation for the transmissions can ensure secrecy in the presence of an eavesdropper. / <p>Funding agencies: Europen Union's Horizon 2020 Marie Sk lodowska-Curie Actionsproject WiVi-2020 (H2020-MSCA-ITN-2014-EID 642743-WiVi-2020)</p>

This thesis report investigates how good position estimations that can be done outdoors with received signal strength indication (RSSI) trilateration for sub 1-GHz radio nodes. A custom network and an empirical distance model have been created as well as a technique for weighting multiple distance estimations, using wrapped normal probability density functions. The developed localization system was tested on an open area of one square kilometer, to estimate a node’s position on various locations. To evaluate the localization system, mean and median error of the estimated positions were calculated.

The work is devoted in the development and the exploration of the capabilities of the state of art unlicensed 60GHz (V-Band) mm-wave band that has raised so much interest and attention from numerous companies and laboratories for implementing Multi-gigabit communications [17] and especially for the 5th generation of cellular network and wireless systems. Implementing a high wireless data transfer system requires a high bandwidth and the one around the 60GHz frequency turned out to be a very promising candidate [13]. In this thesis, different protocols were investigated and simulated on MATLAB and implemented on low-cost Field-Programmable Gate Arrays (FPGAs) in order to test its performance with different transmission protocols and systems and insure a robust communication system at the frequency band around 60GHz. Furthermore, the system was tested with a series of different binary sequences such as pseudo-random bi-nary sequences (PRBS-7, PRBS-15, PRBS-23 and PRBS-31) and a high data rate communication link also in the design. The link has been tested in the lab environment and two systems have successfully achieved a relatively low bit-error rate.

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In the last decade we have experienced a rapid increase in the demand for high data rates over cellular networks. This increase has been partly satisfied by the introduction of multi-user multiple-input multiple-output (MU-MIMO). In such systems, the base station (BS) is equipped with multiple antennas and the users share the time-frequency resources. However, modern communication systems are highly power inefficient. Further, the increase in demand for higher data rates is expected to accelerate in the years to come due to the popularity of mobile devices like smartphones and tablets. Hence, next generation cellular systems arerequired to exhibit high energy efficiency as well as low power  consumption. Recently, it has been shown that the deployment of a large excess of base station (BS) antennas in comparison to the served users can be a promising candidate to meet these contradictory requirements. These systems are termed as Massive MIMO. When the number of BS antennas grows large, the channels between different users become orthogonal and low complexity transceiver processing exhibits sum-rate performance that is close to optimal. In order to realize the promised gains of Massive MIMO systems, it is required that power efficient and inexpensive components are used. In contemporary cellular systems, multi-carrier transmission is used since it facilitates simple equalization at the receiver side. However, multi-carrier signals exhibit high peak-to-average-power-ratio (PAPR) and require expensive highly linear power amplifiers. Power amplifiers in this regime are also very power inefficient. On the other hand single carrier signals exhibit lower PAPR and are suitable for signal design that is more robust to non-linear power amplifiers. Further, single-carrier signals are less vulnerable to hardware impairments, such as phase noise. In this thesis we study the fundamental limits of Massive MIMO systems in terms of sum-rate performance with single-carrier transmission and phase noise and provide important insight on the design of Massive MIMO under these scenarios.

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Given synchronized video sequences from a number of cameras withoverlapping fields of view, the detection and tracking of a prioriunknownnumber of individuals entering a determined area is considered,showingthat a generative model can accurately follow the individuals and handleeffectively such problems as occlusions in each view independently. Theaim of this thesis is to implement the exchange of information betweenthe cameras where the detection and tracking processes take place. Theinputs are obtained from synchronized videos and the frames are takenindividually to treat them as independent images. The proposed algo-rithm was implemented in MATLAB and results obtained on a personalcomputer are presented. The results show that the algorithm achievesgood tracking accuracy, has relatively low computational complexity, andat the same time it allows to observe the communication requirementsbetween the cameras and the processing node.

QC 20120119