Context -aware data caching for mobile computing environments

The deployment of wireless communications coupled with the popularity of portable devices has led to significant research in the area of mobile data caching. Prior research has focused on the development of solutions that allow applications to run in wireless environments using proxy based techniques. Most of these approaches are semantic based and do not provide adequate support for representing the context of a user (i.e., the interpreted human intention.). Although the context may be treated implicitly it is still crucial to data management. In order to address this challenge this dissertation focuses on two characteristics: how to predict (i) the future location of the user and (ii) locations of the fetched data where the queried data item has valid answers. Using this approach, more complete information about the dynamics of an application environment is maintained. ^ The contribution of this dissertation is a novel data caching mechanism for pervasive computing environments that can adapt dynamically to a mobile user's context. In this dissertation, we design and develop a conceptual model and context aware protocols for wireless data caching management. Our replacement policy uses the validity of the data fetched from the server and the neighboring locations to decide which of the cache entries is less likely to be needed in the future, and therefore a good candidate for eviction when cache space is needed. The context aware driven prefetching algorithm exploits the query context to effectively guide the prefetching process. The query context is defined using a mobile user's movement pattern and requested information context. Numerical results and simulations show that the proposed prefetching and replacement policies significantly outperform conventional ones. ^ Anticipated applications of these solutions include biomedical engineering, tele-health, medical information systems and business. ^

Asynchronous distributed parallelization of mobile network optimization algorithms

It has been years since the introduction of the Dynamic Network Optimization (DNO) concept, yet the DNO development is still at its infant stage, largely due to a lack of breakthrough in minimizing the lengthy optimization runtime. Our previous work, a distributed parallel solution, has achieved a significant speed gain. To cater for the increased optimization complexity pressed by the uptake of smartphones and tablets, however, this paper examines the potential areas for further improvement and presents a novel asynchronous distributed parallel design that minimizes the inter-process communications. The new approach is implemented and applied to real-life projects whose results demonstrate an augmented acceleration of 7.5 times on a 16-core distributed system compared to 6.1 of our previous solution. Moreover, there is no degradation in the optimization outcome. This is a solid sprint towards the realization of DNO.

Secure Transmission in Cooperative Relaying Networks with Multiple Antennas

We investigate the secrecy performance of dualhop amplify-and-forward (AF) multi-antenna relaying systems over Rayleigh fading channels, by taking into account the direct link between the source and destination. In order to exploit the available direct link and the multiple antennas for secrecy improvement, different linear processing schemes at the relay and different diversity combining techniques at the destination are proposed, namely, 1) Zero-forcing/Maximal ratio combining (ZF/MRC), 2) ZF/Selection combining (ZF/SC), 3) Maximal ratio transmission/MRC (MRT/MRC) and 4) MRT/Selection combining (MRT/SC). For all these schemes, we present new closed-form approximations for the secrecy outage probability. Moreover, we investigate a benchmark scheme, i.e., cooperative jamming/ZF (CJ/ZF), where the secrecy outage probability is obtained in exact closed-form. In addition, we present asymptotic secrecy outage expressions for all the proposed schemes in the high signal-to-noise ratio (SNR) regime, in order to characterize key design parameters, such as secrecy diversity order and secrecy array gain. The outcomes of this paper can be summarized as follows: a) MRT/MRC and MRT/SC achieve a full diversity order of M + 1, ZF/MRC and ZF/SC achieve a diversity order of M, while CJ/ZF only achieves unit diversity order, where M is the number of antennas at the relay. b) ZF/MRC (ZF/SC) outperforms the corresponding MRT/MRC (MRT/SC) in the low SNR regime, while becomes inferior to the corresponding MRT/MRC (MRT/SC) in the high SNR. c) All of the proposed schemes tend to outperform the CJ/ZF with moderate number of antennas, and linear processing schemes with MRC attain better performance than those with SC.

Energy efficiency optimization in hardware-constrained large-scale MIMO systems

Large-scale multiple-input multiple-output (MIMO) communication systems can bring substantial improvement in spectral efficiency and/or energy efficiency, due to the excessive degrees-of-freedom and huge array gain. However, large-scale MIMO is expected to deploy lower-cost radio frequency (RF) components, which are particularly prone to hardware impairments. Unfortunately, compensation schemes are not able to remove the impact of hardware impairments completely, such that a certain amount of residual impairments always exists. In this paper, we investigate the impact of residual transmit RF impairments (RTRI) on the spectral and energy efficiency of training-based point-to-point large-scale MIMO systems, and seek to determine the optimal training length and number of antennas which maximize the energy efficiency. We derive deterministic equivalents of the signal-to-noise-and-interference ratio (SINR) with zero-forcing (ZF) receivers, as well as the corresponding spectral and energy efficiency, which are shown to be accurate even for small number of antennas. Through an iterative sequential optimization, we find that the optimal training length of systems with RTRI can be smaller compared to ideal hardware systems in the moderate SNR regime, while larger in the high SNR regime. Moreover, it is observed that RTRI can significantly decrease the optimal number of transmit and receive antennas.

Massive MIMO in sparse channels

Massive multi-user multiple-input multiple-output (MU-MIMO) systems are cellular networks where the base stations (BSs) are equipped with hundreds of antennas, N, and communicate with tens of mobile stations (MSs), K, such that, N ≫ K ≫ 1. Contrary to most prior works, in this paper, we consider the uplink of a single-cell massive MIMO system operating in sparse channels with limited scattering. This case is of particular importance in most propagation scenarios, where the prevalent Rayleigh fading assumption becomes idealistic. We derive analytical approximations for the achievable rates of maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Furthermore, we study the asymptotic behavior of the achievable rates for both MRC and ZF receivers, when N and K go to infinity under the condition that N/K → c ≥ 1. Our results indicate that the achievable rate of MRC receivers reaches an asymptotic saturation limit, whereas the achievable rate of ZF receivers grows logarithmically with the number of MSs.

A probabilistic approach to user mobility prediction for wireless services.

Mobile and wireless networks have long exploited mobility predictions, focused on predicting the future location of given users, to perform more efficient network resource management. In this paper, we present a new approach in which we provide predictions as a probability distribution of the likelihood of moving to a set of future locations. This approach provides wireless services a greater amount of knowledge and enables them to perform more effectively. We present a framework for the evaluation of this new type of predictor, and develop 2 new predictors, HEM and G-Stat. We evaluate our predictors accuracy in predicting future cells for mobile users, using two large geolocation data sets, from MDC [11], [12] and Crawdad [13]. We show that our predictors can successfully predict with as low as an average 2.2% inaccuracy in certain scenarios.

Traffic Light Using Multiple Wireless Technologies

This paper presents the study and experimental tests for the viability analysis of using multiple wireless technologies in urban traffic light controllers in a Smart City environment. Communication drivers, different types of antennas, data acquisition methods and data processing for monitoring the network are presented. The sensors and actuators modules are connected in a local area network through two distinct low power wireless networks using both 868 MHz and 2.4 GHz frequency bands. All data communications using 868 MHz go through a Moteino. Various tests are made to assess the most advantageous features of each communication type. The experimental results show better range for 868 MHz solutions, whereas the 2.4 GHz presents the advantage of self-regenerating the network and mesh. The different pros and cons of both communication methods are presented.

Wireless solutions for the industrial internet of things

The fourth industrial revolution is paving the way for Industrial Internet of Things applications where industrial assets (e.g., robotic arms, valves, pistons) are equipped with a large number of wireless devices (i.e., microcontroller boards that embed sensors and actuators) to enable a plethora of new applications, such as analytics, diagnostics, monitoring, as well as supervisory, and safety control use-cases. Nevertheless, current wireless technologies, such as Wi-Fi, Bluetooth, and even private 5G networks, cannot fulfill all the requirements set up by the Industry 4.0 paradigm, thus opening up new 6G-oriented research trends, such as the use of THz frequencies. In light of the above, this thesis provides (i) a broad overview of the main use-cases, requirements, and key enabling wireless technologies foreseen by the fourth industrial revolution, and (ii) proposes innovative contributions, both theoretical and empirical, to enhance the performance of current and future wireless technologies at different levels of the protocol stack. In particular, at the physical layer, signal processing techniques are being exploited to analyze two multiplexing schemes, namely Affine Frequency Division Multiplexing and Orthogonal Chirp Division Multiplexing, which seem promising for high-frequency wireless communications. At the medium access layer, three protocols for intra-machine communications are proposed, where one is based on LoRa at 2.4 GHz and the others work in the THz band. Different scheduling algorithms for private industrial 5G networks are compared, and two main proposals are described, i.e., a decentralized scheme that leverages machine learning techniques to better address aperiodic traffic patterns, and a centralized contention-based design that serves a federated learning industrial application. Results are provided in terms of numerical evaluations, simulation results, and real-world experiments. Several improvements over the state-of-the-art were obtained, and the description of up-and-running testbeds demonstrates the feasibility of some of the theoretical concepts when considering a real industry plant.

Interference mitigation techniques for cloud-RAN deployments in 5G and beyond systems

The deployment of ultra-dense networks is one of the most promising solutions to manage the phenomenon of co-channel interference that affects the latest wireless communication systems, especially in hotspots. To meet the requirements of the use-cases and the immense amount of traffic generated in these scenarios, 5G ultra-dense networks are being deployed using various technologies, such as distributed antenna system (DAS) and cloud-radio access network (C-RAN). Through these centralized densification schemes, virtualized baseband processing units coordinate the distributed access points and manage the available network resources. In particular, link adaptation techniques are shown to be fundamental to overall system operation and performance enhancement. The core of this dissertation is the result of an analysis and a comparison of dynamic and adaptive methods for modulation and coding scheme (MCS) selection applied to the latest mobile telecommunications standards. A novel algorithm based on the proportional-integral-derivative (PID) controller principles and block error rate (BLER) target has been proposed. Tests were conducted in a 4G and 5G system level laboratory and, by means of a channel emulator, the performance was evaluated for different channel models and target BLERs. Furthermore, due to the intrinsic sectorization of the end-users distribution in the investigated scenario, a preliminary analysis on the joint application of users grouping algorithms with multi-antenna and multi-user techniques has been performed. In conclusion, the importance and impact of other fundamental physical layer operations, such as channel estimation and power control, on the overall end-to-end system behavior and performance were highlighted.

NB-IoT via non terrestrial networks

Massive Internet of Things is expected to play a crucial role in Beyond 5G (B5G) wireless communication systems, offering seamless connectivity among heterogeneous devices without human intervention. However, the exponential proliferation of smart devices and IoT networks, relying solely on terrestrial networks, may not fully meet the demanding IoT requirements in terms of bandwidth and connectivity, especially in areas where terrestrial infrastructures are not economically viable. To unleash the full potential of 5G and B5G networks and enable seamless connectivity everywhere, the 3GPP envisions the integration of Non-Terrestrial Networks (NTNs) into the terrestrial ones starting from Release 17. However, this integration process requires modifications to the 5G standard to ensure reliable communications despite typical satellite channel impairments. In this framework, this thesis aims at proposing techniques at the Physical and Medium Access Control layers that require minimal adaptations in the current NB-IoT standard via NTN. Thus, firstly the satellite impairments are evaluated and, then, a detailed link budget analysis is provided. Following, analyses at the link and the system levels are conducted. In the former case, a novel algorithm leveraging time-frequency analysis is proposed to detect orthogonal preambles and estimate the signals’ arrival time. Besides, the effects of collisions on the detection probability and Bit Error Rate are investigated and Non-Orthogonal Multiple Access approaches are proposed in the random access and data phases. The system analysis evaluates the performance of random access in case of congestion. Various access parameters are tested in different satellite scenarios, and the performance is measured in terms of access probability and time required to complete the procedure. Finally, a heuristic algorithm is proposed to jointly design the access and data phases, determining the number of satellite passages, the Random Access Periodicity, and the number of uplink repetitions that maximize the system's spectral efficiency.

Multi-mode communications in near field exploiting OAM properties

This dissertation analyzes the exploitation of the orbital angular momentum (OAM) of the electromagnetic waves with large intelligent surfaces in the near-field region and line-of-sight conditions, in light of the holographic MIMO communication concept. Firstly, a characterization of the OAM-based communication problem is presented, and the relationship between OAM-carrying waves and communication modes is discussed. Then, practicable strategies for OAM detection using large intelligent surfaces and optimization methods based on beam focusing are proposed. Numerical results characterize the effectiveness of OAM with respect to other strategies, also including the proposed detection and optimization methods. It is shown that OAM waves constitute a particular choice of communication modes, i.e., an alternative basis set, which is sub-optimum with respect to optimal basis functions that can be derived by solving eigenfunction problems. Moreover, even the joint utilization of OAM waves with focusing strategies led to the conclusion that no channel capacity achievements can be obtained with these transmission techniques.

Enhanced aeroelastic energy harvesting by exploiting combined nonlinearities: theory and experiment

Converting aeroelastic vibrations into electricity for low power generation has received growing attention over the past few years. In addition to potential applications for aerospace structures, the goal is to develop alternative and scalable configurations for wind energy harvesting to use in wireless electronic systems. This paper presents modeling and experiments of aeroelastic energy harvesting using piezoelectric transduction with a focus on exploiting combined nonlinearities. An airfoil with plunge and pitch degrees of freedom (DOF) is investigated. Piezoelectric coupling is introduced to the plunge DOF while nonlinearities are introduced through the pitch DOF. A state-space model is presented and employed for the simulations of the piezoaeroelastic generator. A two-state approximation to Theodorsen aerodynamics is used in order to determine the unsteady aerodynamic loads. Three case studies are presented. First the interaction between piezoelectric power generation and linear aeroelastic behavior of a typical section is investigated for a set of resistive loads. Model predictions are compared to experimental data obtained from the wind tunnel tests at the flutter boundary. In the second case study, free play nonlinearity is added to the pitch DOF and it is shown that nonlinear limit-cycle oscillations can be obtained not only above but also below the linear flutter speed. The experimental results are successfully predicted by the model simulations. Finally, the combination of cubic hardening stiffness and free play nonlinearities is considered in the pitch DOF. The nonlinear piezoaeroelastic response is investigated for different values of the nonlinear-to-linear stiffness ratio. The free play nonlinearity reduces the cut-in speed while the hardening stiffness helps in obtaining persistent oscillations of acceptable amplitude over a wider range of airflow speeds. Such nonlinearities can be introduced to aeroelastic energy harvesters (exploiting piezoelectric or other transduction mechanisms) for performance enhancement.

A new joint coding and modulation scheme for channels with clipping

A major limitation in any high-performance digital communication system is the linearity region of the transmitting amplifier. Nonlinearities typically lead to signal clipping. Efficient communication in such conditions requires maintaining a low peak-to-average power ratio (PAR) in the transmitted signal while achieving a high throughput of data. Excessive PAR leads either to frequent clipping or to inadequate resolution in the analog-to-digital or digital-to-analog converters. Currently proposed signaling schemes for future generation wireless communications suffer from a high PAR. This paper presents a new signaling scheme for channels with clipping which achieves a PAR as low as 3. For a given linear range in the transmitter's digital-to-analog converter, this scheme achieves a lower bit-error rate than existing multicarrier schemes, owing to increased separation between constellation points. We present the theoretical basis for this new scheme, approximations for the expected bit-error rate, and simulation results. (C) 2002 Elsevier Science (USA).

SAR calculation using FDTD simulations

The main intend of this work, is to determinate the Specific Absorption Rate (SAR) on human head tissues exposed to radiation caused by sources of 900 and 1800MHz, since those are the typical frequencies for mobile communications systems nowadays. In order to determinate the SAR, has been used the FDTD (Finite Difference Time Domain), which is a numeric method in time domain, obtained from the Maxwell equations in differential mode. In order to do this, a computational model from the human head in two dimensions made with cells of the smallest possible size was implemented, respecting the limits from computational processing. It was possible to verify the very good efficiency of the FDTD method in the resolution of those types of problems.

Improving a Cluster Based Directional Channel Model in Realistic Macro-Cell Environment

In this paper a realistic directional channel model that is an extension of the COST 273 channel model is presented. The model uses a cluster of scatterers and visibility region generation based strategy with increased realism, due to the introduction of terrain and clutter information. New approaches for path-loss prediction and line of sight modeling are considered, affecting the cluster path gain model implementation. The new model was implemented using terrain, clutter, street and user mobility information for the city of Lisbon, Portugal. Some of the model's outputs are presented, mainly path loss and small/large-scale fading statistics.