Blind interference alignment for cellular networks

Mención Internacional en el título de doctorManaging the interference is the main challenge in cellular networks. Multiple-Input Multiple-Output (MIMO) schemes have emerged as a means of achieving high-capacity in wireless communications. The most efficient MIMO techniques are based on managing the interference instead of avoiding it by employing orthogonal resource allocation schemes. These transmission schemes require the knowledge of the Channel State Information at the Transmitter (CSIT) to achieve the optimal Degrees of Freedom (DoF), also known as multiplexing gain. Providing an accurate CSIT in cellular environments involves high-capacity backhaul links and accurate synchronization, which imply the use of a large amount of network resources. Recently, a Blind Interference Alignment (BIA) scheme was devised as a means of achieving a growth in DoF regarding the amount of users served without the need for CSIT in the Multiple-Input Single-Output (MISO) Broadcast Channel (BC). It is demonstrated that BIA achieves the optimal DoF in the BC without CSIT. However, the implementation of BIA in cellular networks is not straightforward. This dissertation investigates the DoF and the corresponding sum-rate of cellular networks in absence of CSIT and their achievability by using BIA schemes. First, this dissertation derives the DoF-region of homogenous cellular networks with partial connectivity. Assuming that all the Base Stations (BSs) cooperate in order to transmit to all users in the network, we proposed an extension of the BIA scheme for the MISO BC where the set of BSs transmits as in a network MIMO. It is shown that the cooperation between BSs results futile because of the lack of full connectivity in cellular networks. After that, this dissertation presents several transmission schemes based on the network topology. By differentiating between users that can treat this interference optimally as noise and those who need to manage the interference from neighbouring BSs, a network BIA scheme is devised to achieve the optimal DoF in homogeneous cellular networks. Second, the use of BIA schemes is analyzed for heterogeneous cellular networks. It is demonstrated that the previous BIA schemes based on the network topology result nonoptimal in DoF because of the particular features of the heterogenous cellular networks. More specifically, assuming a macro-femto network, cooperation between both tiers leads to a penalty for macro users while femto users do not exploit the particular topology of this kind of network. In this dissertation, the optimal linear DoF (lDoF) in a two-tier network are derived subject to optimality in DoF for the upper tier. It is demonstrated that, without CSIT or any cooperation between tiers, the lower tier can achieve nonzero DoF while the upper tier attains the optimal DoF by transmitting independently of the lower tier deployment. After that, a cognitive BIA scheme that achieves this outer bound is devised for macro-femto cellular networks. The third part of this dissertation is focused on the implementation of BIA in practical scenarios. It is shown that transmission at limited SNR and coherence time are the main hurdles to overcome for practical implementations of BIA. With aim of managing both constraints, the use of BIA together with orthogonal approaches is proposed in this work. An improvement on the inherent noise increase of BIA and the required coherence time is achieved at expenses of losing DoF. Therefore, there exists a trade-off between multiplexing gain, sum-rate at finite SNR and coherence time in practical scenarios. The optimal resource allocation for orthogonal transmission is obtained after solving a very specific optimization problem. To complete the characterization of the performance of BIA in realistic scenarios a experimental evaluation based on a hardware implementation is presented at the end of this work. It is shown that BIA outperforms the sum-rate of schemes based on CSIT such as LZFB because of the hardware impairments and the costs of providing CSIT in a realist implementation.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Luc Vandendorpe.- Secretario: María Julia Fernández-Getino García.- Vocal: Ignacio Santamaría Caballer

Optical Wireless Data Center Networks

Bandwidth and computation-intensive Big Data applications in disciplines like social media, bio- and nano-informatics, Internet-of-Things (IoT), and real-time analytics, are pushing existing access and core (backbone) networks as well as Data Center Networks (DCNs) to their limits. Next generation DCNs must support continuously increasing network traffic while satisfying minimum performance requirements of latency, reliability, flexibility and scalability. Therefore, a larger number of cables (i.e., copper-cables and fiber optics) may be required in conventional wired DCNs. In addition to limiting the possible topologies, large number of cables may result into design and development problems related to wire ducting and maintenance, heat dissipation, and power consumption. To address the cabling complexity in wired DCNs, we propose OWCells, a class of optical wireless cellular data center network architectures in which fixed line of sight (LOS) optical wireless communication (OWC) links are used to connect the racks arranged in regular polygonal topologies. We present the OWCell DCN architecture, develop its theoretical underpinnings, and investigate routing protocols and OWC transceiver design. To realize a fully wireless DCN, servers in racks must also be connected using OWC links. There is, however, a difficulty of connecting multiple adjacent network components, such as servers in a rack, using point-to-point LOS links. To overcome this problem, we propose and validate the feasibility of an FSO-Bus to connect multiple adjacent network components using NLOS point-to-point OWC links. Finally, to complete the design of the OWC transceiver, we develop a new class of strictly and rearrangeably non-blocking multicast optical switches in which multicast is performed efficiently at the physical optical (lower) layer rather than upper layers (e.g., application layer). Advisors: Jitender S. Deogun and Dennis R. Alexande

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks