Context Information for Fast Cell Discovery in mm-wave 5G Networks

The exploitation of the mm-wave bands is one of the most promising solutions for 5G mobile radio networks. However, the use of mm-wave technologies in cellular networks is not straightforward due to mm-wave harsh propagation conditions that limit access availability. In order to overcome this obstacle, hybrid network architectures are being considered where mm-wave small cells can exploit an overlay coverage layer based on legacy technology. The additional mm-wave layer can also take advantage of a functional split between control and user plane, that allows to delegate most of the signaling functions to legacy base stations and to gather context information from users for resource optimization. However, mm-wave technology requires high gain antenna systems to compensate for high path loss and limited power, e.g., through the use of multiple antennas for high directivity. Directional transmissions must be also used for the cell discovery and synchronization process, and this can lead to a non-negligible delay due to the need to scan the cell area with multiple transmissions at different directions. In this paper, we propose to exploit the context information related to user position, provided by the separated control plane, to improve the cell discovery procedure and minimize delay. We investigate the fundamental trade-offs of the cell discovery process with directional antennas and the effects of the context information accuracy on its performance. Numerical results are provided to validate our observations.Comment: 6 pages, 8 figures, in Proceedings of European Wireless 201

Fast Cell Discovery in mm-wave 5G Networks with Context Information

The exploitation of mm-wave bands is one of the key-enabler for 5G mobile radio networks. However, the introduction of mm-wave technologies in cellular networks is not straightforward due to harsh propagation conditions that limit the mm-wave access availability. Mm-wave technologies require high-gain antenna systems to compensate for high path loss and limited power. As a consequence, directional transmissions must be used for cell discovery and synchronization processes: this can lead to a non-negligible access delay caused by the exploration of the cell area with multiple transmissions along different directions. The integration of mm-wave technologies and conventional wireless access networks with the objective of speeding up the cell search process requires new 5G network architectural solutions. Such architectures introduce a functional split between C-plane and U-plane, thereby guaranteeing the availability of a reliable signaling channel through conventional wireless technologies that provides the opportunity to collect useful context information from the network edge. In this article, we leverage the context information related to user positions to improve the directional cell discovery process. We investigate fundamental trade-offs of this process and the effects of the context information accuracy on the overall system performance. We also cope with obstacle obstructions in the cell area and propose an approach based on a geo-located context database where information gathered over time is stored to guide future searches. Analytic models and numerical results are provided to validate proposed strategies.Comment: 14 pages, submitted to IEEE Transaction on Mobile Computin

D2D-Based Grouped Random Access to Mitigate Mobile Access Congestion in 5G Sensor Networks

The Fifth Generation (5G) wireless service of sensor networks involves significant challenges when dealing with the coordination of ever-increasing number of devices accessing shared resources. This has drawn major interest from the research community as many existing works focus on the radio access network congestion control to efficiently manage resources in the context of device-to-device (D2D) interaction in huge sensor networks. In this context, this paper pioneers a study on the impact of D2D link reliability in group-assisted random access protocols, by shedding the light on beneficial performance and potential limitations of approaches of this kind against tunable parameters such as group size, number of sensors and reliability of D2D links. Additionally, we leverage on the association with a Geolocation Database (GDB) capability to assist the grouping decisions by drawing parallels with recent regulatory-driven initiatives around GDBs and arguing benefits of the suggested proposal. Finally, the proposed method is approved to significantly reduce the delay over random access channels, by means of an exhaustive simulation campaign.Comment: First submission to IEEE Communications Magazine on Oct.28.2017. Accepted on Aug.18.2019. This is the camera-ready versio

Enhancements in spectrum management techniques for heterogeneous 5G future networks

Mención Internacional en el título de doctorIn the last decade, cellular networks are undergoing with a radical change in their basic design foundations. The huge increase in traffic demand requires a novel design of future cellular networks. Driven by this increase, a network densification phenomena is occurring thereby, which in turns requires to devise efficient and reliable mechanisms to deal with the interference problems resulting from such densification. The architecture and mechanisms resulting from such drastic re-design of the network are commonly referred under the term ’5G network’. In this context, this work unveils that current networking solutions are no longer sufficient to (i) provide the required network spectral efficiency, and (ii) guarantee the desired level of quality of experience from the user side. In order to address this problem, in this thesis we propose a novel SDN-like framework that incorporates the needed mechanisms to improve spectral efficiency while delivering the desired quality of experience to users. In particular, our architecture includes the following two approaches: Our first approach addresses the intercell interference issues resulting from high network densification. To this end, we propose novel mechanisms to mitigate the inter-cell interference problem. We address the design of such schemes from two angles: (i) a controller-aided mechanism, which gathers all the information of the network at a centralized point and, based on this information, optimally schedules the transmission from different users, and (ii) a semi-distributed mechanism, which limits the signaling overhead involved in sending the information to a centralized point while providing close to optimal performance. One of the key novelties of our scheduling algorithms is that they are based on the Almost Blank SubFrame (ABSF) scheme; indeed, this scheme has been standardized only recently and very little work has addressed the design of algorithm to use it. Our second approach addresses spectral efficiency from a complementary angle: cellular traffic offloading for content update applications. This approach leverages high user mobility to offload the cellular downlink traffic through a device-to-device communication. In this context, we propose an adaptive algorithm to decide how to optimally transmit content to base stations in order to maximize traffic offload. By relying on control theory techniques, our approach delivers near optimally performance. A third key contribution of this thesis is the design of a solution that combines the above two approaches. In particular, our solution takes into account that traffic offload is taking place in the network and addresses the design of an optimal scheduling algorithm that leverages on the Almost Blank SubFrame (ABSF) scheme. Indeed, the combination of these kind of approaches has received little attention from the literature. The feasibility and performance of the approaches described above are thoroughly evaluated and compared against state-of-the-art solutions through an exhaustive simulation campaign. Our results show that the proposed approaches outperform conventional eICIC techniques as well as standard offloading mechanisms, respectively, and confirm their feasibility in terms of overhead and computational complexity. To the best of our knowledge, this thesis is the first attempt to design an unified framework which is able to optimally perform offloading for content-update distribution applications while boosting the network performance in terms of spectral efficiency.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Pablo Serrano Yáñez-Mingot.- Secretario: Juan José Alacaraz Espín.- Vocal: Matteo Cesan

Passive and Privacy-preserving Human Localization via mmWave Access Points for Social Distancing

The pandemic outbreak has profoundly changed our life, especially our social habits and communication behaviors. While this dramatic shock has heavily impacted human interaction rules, novel localization techniques are emerging to help society in complying with new policies, such as social distancing. Wireless sensing and machine learning are well suited to alleviate viruses propagation in a privacy-preserving manner. However, its wide deployment requires cost-effective installation and operational solutions. In public environments, individual localization information-such as social distancing-needs to be monitored to avoid safety threats when not properly observed. To this end, the high penetration of wireless devices can be exploited to continuously analyze-and-learn the propagation environment, thereby passively detecting breaches and triggering alerts if required. In this paper, we describe a novel passive and privacy-preserving human localization solution that relies on the directive transmission properties of mmWave communications to monitor social distancing and notify people in the area in case of violations. Thus, addressing the social distancing challenge in a privacy-preserving and cost-efficient manner. Our solution provides an overall accuracy of about 99% in the tested scenarios