Relay assisted device-to-device communication with channel uncertainty

The gains of direct communication between user equipment in a network may not be fully realised due to the separation between the user equipment and due to the fading that the channel between these user equipment experiences. In order to fully realise the gains that direct (device-to-device) communication promises, idle user equipment can be exploited to serve as relays to enforce device-to-device communication. The availability of potential relay user equipment creates a problem: a way to select the relay user equipment. Moreover, unlike infrastructure relays, user equipment are carried around by people and these users are self-interested. Thus the problem of relay selection goes beyond choosing which device to assist in relayed communication but catering for user self-interest. Another problem in wireless communication is the unavailability of perfect channel state information. This reality creates uncertainty in the channel and so in designing selection algorithms, channel uncertainty awareness needs to be a consideration. Therefore the work in this thesis considers the design of relay user equipment selection algorithms that are not only device centric but that are relay user equipment centric. Furthermore, the designed algorithms are channel uncertainty aware. Firstly, a stable matching based relay user equipment selection algorithm is put forward for underlay device-to-device communication. A channel uncertainty aware approach is proposed to cater to imperfect channel state information at the devices. The algorithm is combined with a rate based mode selection algorithm. Next, to cater to the queue state at the relay user equipment, a cross-layer selection algorithm is proposed for a twoway decode and forward relay set up. The algorithm proposed employs deterministic uncertainty constraint in the interference channel, solving the selection algorithm in a heuristic fashion. Then a cluster head selection algorithm is proposed for device-to-device group communication constrained by channel uncertainty in the interference channel. The formulated rate maximization problem is solved for deterministic and probabilistic constraint scenarios, and the problem extended to a multiple-input single-out scenario for which robust beamforming was designed. Finally, relay utility and social distance based selection algorithms are proposed for full duplex decode and forward device-to-device communication set up. A worst-case approach is proposed for a full channel uncertainty scenario. The results from computer simulations indicate that the proposed algorithms offer spectral efficiency, fairness and energy efficiency gains. The results also showed clearly the deterioration in the performance of networks when perfect channel state information is assumed

Novel Processing and Transmission Techniques Leveraging Edge Computing for Smart Health Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Visible Light Communication Cyber Security Vulnerabilities For Indoor And Outdoor Vehicle-To-Vehicle Communication

Light fidelity (Li-Fi), developed from the approach of Visible Light Communication (VLC), is a great replacement or complement to existing radio frequency-based (RF) networks. Li-Fi is expected to be deployed in various environments were, due to Wi-Fi congestion and health limitations, RF should not be used. Moreover, VLC can provide the future fifth generation (5G) wireless technology with higher data rates for device connectivity which will alleviate the traffic demand. 5G is playing a vital role in encouraging the modern applications. In 2023, the deployment of all the cellular networks will reach more than 5 billion users globally. As a result, the security and privacy of 5G wireless networks is an essential problem as those modern applications are in people\u27s life everywhere. VLC security is as one of the core physical-layer security (PLS) solutions for 5G networks. Due to the fact that light does not penetrate through solid objects or walls, VLC naturally has higher security and privacy for indoor wireless networks compared to RF networks. However, the broadcasting nature of VLC caused concerns, e.g., eavesdropping, have created serious attention as it is a crucial step to validate the success of VLC in wild. The aim of this thesis is to properly address the security issues of VLC and further enhance the VLC nature security. We analyzed the secrecy performance of a VLC model by studying the characteristics of the transmitter, receiver and the visible light channel. Moreover, we mitigated the security threats in the VLC model for the legitimate user, by 1) implementing more access points (APs) in a multiuser VLC network that are cooperated, 2) reducing the semi-angle of LED to help improve the directivity and secrecy and, 3) using the protected zone strategy around the AP where eavesdroppers are restricted. According to the model\u27s parameters, the results showed that the secrecy performance in the proposed indoor VLC model and the vehicle-to-vehicle (V2V) VLC outdoor model using a combination of multiple PLS techniques as beamforming, secure communication zones, and friendly jamming is enhanced. The proposed model security performance was measured with respect to the signal to noise ratio (SNR), received optical power, and bit error rate (BER) Matlab simulation results

Cooperative Communications inWireless Local Area Networks: MAC Protocol Design and Multi-layer Solutions

This dissertation addresses cooperative communications and proposes multi-layer solu- tions for wireless local area networks, focusing on cooperative MAC design. The coop- erative MAC design starts from CSMA/CA based wireless networks. Three key issues of cooperation from the MAC layer are dealt with: i.e., when to cooperate (opportunistic cooperation), whom to cooperate with (relay selection), and how to protect cooperative transmissions (message procedure design). In addition, a cooperative MAC protocol that addresses these three issues is proposed. The relay selection scheme is further optimized in a clustered network to solve the problem of high collision probability in a dense network. The performance of the proposed schemes is evaluated in terms of through- put, packet delivery rate and energy efficiency. Furthermore, the proposed protocol is verified through formal model checking using SPIN. Moreover, a cooperative code allo- cation scheme is proposed targeting at a clustered network where multiple relay nodes can transmit simultaneously. The cooperative communication design is then extended to the routing layer through cross layer routing metrics. Another part of the work aims at enabling concurrent transmissions using cooperative carrier sensing to improve the per- formance in a WLAN network with multiple access points sharing the same channel

User mobility prediction and management using machine learning

The next generation mobile networks (NGMNs) are envisioned to overcome current user mobility limitations while improving the network performance. Some of the limitations envisioned for mobility management in the future mobile networks are: addressing the massive traffic growth bottlenecks; providing better quality and experience to end users; supporting ultra high data rates; ensuring ultra low latency, seamless handover (HOs) from one base station (BS) to another, etc. Thus, in order for future networks to manage users mobility through all of the stringent limitations mentioned, artificial intelligence (AI) is deemed to play a key role automating end-to-end process through machine learning (ML). The objectives of this thesis are to explore user mobility predictions and management use-cases using ML. First, background and literature review is presented which covers, current mobile networks overview, and ML-driven applications to enable user’s mobility and management. Followed by the use-cases of mobility prediction in dense mobile networks are analysed and optimised with the use of ML algorithms. The overall framework test accuracy of 91.17% was obtained in comparison to all other mobility prediction algorithms through artificial neural network (ANN). Furthermore, a concept of mobility prediction-based energy consumption is discussed to automate and classify user’s mobility and reduce carbon emissions under smart city transportation achieving 98.82% with k-nearest neighbour (KNN) classifier as an optimal result along with 31.83% energy savings gain. Finally, context-aware handover (HO) skipping scenario is analysed in order to improve over all quality of service (QoS) as a framework of mobility management in next generation networks (NGNs). The framework relies on passenger mobility, trains trajectory, travelling time and frequency, network load and signal ratio data in cardinal directions i.e, North, East, West, and South (NEWS) achieving optimum result of 94.51% through support vector machine (SVM) classifier. These results were fed into HO skipping techniques to analyse, coverage probability, throughput, and HO cost. This work is extended by blockchain-enabled privacy preservation mechanism to provide end-to-end secure platform throughout train passengers mobility

Strategic Latency Unleashed: The Role of Technology in a Revisionist Global Order and the Implications for Special Operations Forces

The article of record may be found at https://cgsr.llnl.govThis work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-59693This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory in part under Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344. The views and opinions of the author expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC. ISBN-978-1-952565-07-6 LCCN-2021901137 LLNL-BOOK-818513 TID-5969

Resource Allocation in Relay Networks

Demand for high data rates is increasing rapidly, due to the rapid rise of mobile data traffic volume. In order to meet the demands, the future generation of wireless communication systems has to support higher data rates and quality of service. The inherent unreliable and unpredictable nature of wireless medium provides a challenge for increasing the data rate. Cooperative communications, is a prominent technique to combat the detrimental fading effect in wireless communications. Adding relay nodes to the network, and creating s virtual multiple-input multiple-output (MIMO) antenna array is proven to be an efficient method to mitigate the multipath fading and expand the network coverage. Therefore, cooperative relaying is considered as a fundamental element in the Long Term Evolution (LTE)-Advanced standard. In this thesis, we address the problem of resource allocation in cooperative networks. We provide a detailed review on the resource allocation problem. We look at the joint subcarrier-relay assignment and power allocation. The objective of this optimization problem is to allocate the resources fairly, so even the cell-edge users with weakest communication links receive a fair share of resources. We propose a simple and practical algorithm to find the optimal solution. We assess the performance of the proposed algorithm by providing simulations. Furthermore, we investigate the optimality and complexity of the proposed algorithm. Due to the layered architecture of the wireless networks, to achieve the optimal performance it is necessary that the design of the algorithms be based on the underlying physical and link layers. For a cooperative network with correlated channels, we propose a cross-layer algorithm for relay selection, based on both the physical and link-layer characteristics, in order to maximize the linklayer throughput. The performance of the proposed algorithm is studied in different network models. Furthermore, we investigate the optimum number of relays required for cooperation in order to achieve maximum throughput. Buffering has proven to improve the performance of the cooperative network. In light of this, we study the performance of buffer-aided relay selection. In order to move one step closer to the practical applications, we consider a system with coded transmissions. We study three different coding schemes: convolutional code, Turbo code, and distributed Turbo code (DTC). For each scheme, the performance of the system is simulated and assessed analytically. We derive a closed form expression of the average throughput. Using the analysis results, we investigate the diversity gain of the system in asymptotic conditions. Further, we investigate the average transmission delay for different schemes

A PARADIGM SHIFTING APPROACH IN SON FOR FUTURE CELLULAR NETWORKS

The race to next generation cellular networks is on with a general consensus in academia and industry that massive densification orchestrated by self-organizing networks (SONs) is the cost-effective solution to the impending mobile capacity crunch. While the research on SON commenced a decade ago and is still ongoing, the current form (i.e., the reactive mode of operation, conflict-prone design, limited degree of freedom and lack of intelligence) hinders the current SON paradigm from meeting the requirements of 5G. The ambitious quality of experience (QoE) requirements and the emerging multifarious vision of 5G, along with the associated scale of complexity and cost, demand a significantly different, if not totally new, approach to SONs in order to make 5G technically as well as financially feasible. This dissertation addresses these limitations of state-of-the-art SONs. It first presents a generic low-complexity optimization framework to allow for the agile, on-line, multi-objective optimization of future mobile cellular networks (MCNs) through only top-level policy input that prioritizes otherwise conflicting key performance indicators (KPIs) such as capacity, QoE, and power consumption. The hybrid, semi-analytical approach can be used for a wide range of cellular optimization scenarios with low complexity. The dissertation then presents two novel, user-mobility, prediction-based, proactive self-optimization frameworks (AURORA and OPERA) to transform mobility from a challenge into an advantage. The proposed frameworks leverage mobility to overcome the inherent reactiveness of state-of-the-art self-optimization schemes to meet the extremely low latency and high QoE expected from future cellular networks vis-à-vis 5G and beyond. The proactiveness stems from the proposed frameworks’ novel capability of utilizing past hand-over (HO) traces to determine future cell loads instead of observing changes in cell loads passively and then reacting to them. A semi-Markov renewal process is leveraged to build a model that can predict the cell of the next HO and the time of the HO for the users. A low-complexity algorithm has been developed to transform the predicted mobility attributes to a user-coordinate level resolution. The learned knowledge base is used to predict the user distribution among cells. This prediction is then used to formulate a novel (i) proactive energy saving (ES) optimization problem (AURORA) that proactively schedules cell sleep cycles and (ii) proactive load balancing (LB) optimization problem (OPERA). The proposed frameworks also incorporate the effect of cell individual offset (CIO) for balancing the load among cells, and they thus exploit an additional ultra-dense network (UDN)-specific mechanism to ensure QoE while maximizing ES and/or LB. The frameworks also incorporates capacity and coverage constraints and a load-aware association strategy for ensuring the conflict-free operation of ES, LB, and coverage and capacity optimization (CCO) SON functions. Although the resulting optimization problems are combinatorial and NP-hard, proactive prediction of cell loads instead of reactive measurement allows ample time for combination of heuristics such as genetic programming and pattern search to find solutions with high ES and LB yields compared to the state of the art. To address the challenge of significantly higher cell outage rates in anticipated in 5G and beyond due to higher operational complexity and cell density than legacy networks, the dissertation’s fourth key contribution is a stochastic analytical model to analyze the effects of the arrival of faults on the reliability behavior of a cellular network. Assuming exponential distributions for failures and recovery, a reliability model is developed using the continuous-time Markov chains (CTMC) process. Unlike previous studies on network reliability, the proposed model is not limited to structural aspects of base stations (BSs), and it takes into account diverse potential fault scenarios; it is also capable of predicting the expected time of the first occurrence of the fault and the long-term reliability behavior of the BS. The contributions of this dissertation mark a paradigm shift from the reactive, semi-manual, sub-optimal SON towards a conflict-free, agile, proactive SON. By paving the way for future MCN’s commercial and technical viability, the new SON paradigm presented in this dissertation can act as a key enabler for next-generation MCNs

Industrial Policy and Global Value Chains: The experience of Guangdong, China and Malaysia in the Electronics Industry

Research on Global Value Chains (GVC) and their potential for encouraging industrial upgrading in developing economies has proliferated during the last two decades, but an in-depth understanding of what the scope for implementing industrial policy is in the GVC context is lacking in the literature. This thesis aims to fill this gap by creating a theoretical framework to link the theory of industrial policy with that of GVCs with insights from the innovation economics literature. Using a mixed methods approach (interviews to key actors, archival research, analyses of official documents and descriptive statistical analyses), the framework is then used to examine the empirical case studies of industrial policy in the electronics industry in Guangdong province of China and in Malaysia. While integration into GVCs can provide firms with some opportunities for upgrading, developing country firms often find it difficult to accumulate enough capabilities to operate in the technological frontier, even after decades of supplying lead firms. Drawing on three literatures (industrial policy, GVCs and technological capabilities), this thesis argues that in the absence of industrial policy to provide incentives for investing the accumulation of capabilities and to build a domestic system of innovation, it is difficult for firms to overcome the market failures associated with industrialization and technological learning. The two case studies provide further evidence that industrial policy remains relevant for contemporary industrial development. Both Guangdong province and Malaysia attempted to leverage integration into GVCs to promote the development of an electronics industry, but while Guangdong has developed indigenous firms with high-tech capabilities, in Malaysia the industry remains technologically weak and lacks indigenous firms with advanced capabilities. This thesis argues that Guangdong followed a mixed strategy, where both integration at the lower end of export-oriented GVCs and engaging in import-substitution were leveraged to improve the technological capabilities of firms. However, the lack of access to credit for the private sector and the lack of research capabilities in the public sector may constrain the future development of the industry. In Malaysia, industrial policy focused on attracting export-oriented foreign direct investments (FDI) and increasing the sophistication of foreign subsidiaries. Efforts to encourage indigenous firm development lacked coherence, consistence and adequate resources, leading to poor results. This meant that the few indigenous firms developed capabilities to become GVC suppliers and those that did, lacked sufficient incentives and resources to scale up and develop more sophisticated capabilities. At the same time, Transnational Corporations (TNCs) continued to be reluctant to shift frontier activities to the country. As a result, the industry in Malaysia has struggled to enhance its position in GVCs and has faced pressures from emerging, low-cost locations in the region.Cambridge University, Clare Hall, the Vergottis Foundation, the Karelia Foundation, the Centre of Development Studies and the Faculty of Department of Politics and International Studies (POLIS) provided me with generous financial support to undertake this Ph.D. This work was also supported by the Smuts Memorial Fund, managed by the University of Cambridge in memory of Jan Christiaan Smuts

State of the Art and Future Perspectives in Smart and Sustainable Urban Development

This book contributes to the conceptual and practical knowledge pools in order to improve the research and practice on smart and sustainable urban development by presenting an informed understanding of the subject to scholars, policymakers, and practitioners. This book presents contributions—in the form of research articles, literature reviews, case reports, and short communications—offering insights into the smart and sustainable urban development by conducting in-depth conceptual debates, detailed case study descriptions, thorough empirical investigations, systematic literature reviews, or forecasting analyses. This way, the book forms a repository of relevant information, material, and knowledge to support research, policymaking, practice, and the transferability of experiences to address urbanization and other planetary challenges