Projects 2016

Data-guided Resource Management for Dense Heterogeneous Networks

Prof. Zhi Ding, University of California-Davis, USA
Prof. Jyri Hämäläinen, Aalto University, Finland
Assoc. Prof. Xiaojun Lin, Purdue University, USA
Assoc. Prof. Xin Liu, University of California-Davis, USA
Prof. Risto Wichman, Aalto University, Finland

Device-to-Device Communications at Millimeter-Wave Frequencies

Prof. Katsuyuki Haneda, Aalto University, Finland
Prof. Andreas Molisch, University of Southern California, USA

The project investigates device-to-device (D2D) communications at millimeter-wave (mm-wave) frequencies. These systems have the promise of extremely high data rates because of the large available bandwidth in the mm-wave bands, and high spectral efficiency because of the high spatial frequency reuse in D2D networks. However, such systems also face challenges because of the inherent properties of the propagation channels that govern the behavior of such systems. In this project we aim to develop insights into the channel behavior through new measurements and models, and use those to develop improved systems that actively exploit the channel properties. We will also assess the system capacity and reliability of setups that combine communications at cellular/WiFi frequencies with D2D links at mm-wave frequencies. Our work will concentrate on 1) channel measurements and models, where we develop a new dynamic directional channel sounder, novel deterministic channel prediction method, and channel models that incorporate the correlations between mm-wave and microwave frequencies; 2) neighbor discovery, where we exploit the sparsity of the neighbor graph and multipath channels for efficient neighbor discovery. We also improve Zig-Zag schemes by exploiting correlation between channels at the different frequency bands; 3) beamforming and beam tracking, where we focus on combined analogue and digital beam forming; 4) performance of holistic systems, where we investigate the system capacity and outage probability of systems that combine mm-wave D2D and microwave D2D and cellular links.

Exploiting Social Structure for Cooperative Mobile Networking

Dr. Ulrico Celentano, University of Oulu, Finland
Dr. Tao Chen, VTT Technical Research Centre of Finland, Finland
Dr. Natalia Ermolova, Aalto University, Finland
Dr. Lei Yang, Arizona State University, USA
Prof. Junshan Zhang, Arizona State University, USA

In this project, we will investigate social-aware approaches to enable shared spectrum access, cooperative spectrum sensing, data mining, information dissemination, and intelligent device-to-device (D2D) communications, via exploiting the social structure among mobile users. By combining theoretical studies with practical applications, the project aims to integrate social elements into the design of cooperative mobile networks, so as to enrich the evolution of future mobile networks. Such social-structure-based cooperation among mobile devices will enable self-organizing networking, and be promising to improve spectral efficiency and network capacity of mobile networks. The outcomes from the project will be new knowledge, protocol and algorithm design, and performance analysis on social information integrated mobile communications.

Future Uncoordinated Small-Cell Networks Using Reconfigurable Antennas

Assoc. Prof. Kapil Dandekar, Drexel University, USA
Prof. Aarne Mämmelä, VTT Technical Research Centre of Finland, Finland
Dr. Harri Saarnisaari, University of Oulu, Finland
Mikko Valkama, Tampere University of Technology, Finland
Assoc. Prof. Steven Weber, Drexel University, USA
Assoc. Prof. Alexander Wyglinski, Worchester Polytechnic Institute, USA

In a dense wireless network, like small cells, performance is constrained heavily by the interference. The interference can be mitigated using directional antenna like reconfigurable antenna systems whose radiation characteristics can be adapted in response to the needs of the overlying small-cellular system. We will demonstrate how intelligent deployment of reconfigurable antenna systems in small-cell base stations can improve cellular network capacity, while coexisting with the overlaying macro network, by mitigating interference and allowing for densification of mobile users in a given spatial deployment. The research for this includes three parts. Algorithm and System Design – A vital component of this overall system design is the development of efficient and effective analytical tools and algorithms for downlink transmission, focusing on directional network design, algorithms for directionality selection, and base station user association. Antenna and Transceiver Design – We will adapt reconfigurable metamaterial and Alford loop antenna designs to provide new, compact reconfigurable antenna architectures with both beam steering along with DOA estimation and variable beam width capabilities. Testbed Implementation – The cornerstone of the proposed research is a fully implemented, programmable across-the-stack SDR platform with integrated reconfigurable antennas.

Heterogeneous Resource Allocation for Hierarchical Software-defined Radio Access Networks at the Edge

Dr. Mehdi Bennis, University of Oulu, Finland
Dr. Xianfu Chen, VTT Technical Research Centre of Finland, Finland
Assoc. Prof. Zhu Han, University of Houston, USA
Prof. Guoliang Xue, Arizona State University, USA

This project targets the development of a feasible and effective SDN-at-the-Edge type of architecture for large-scale RANs. The high centralization as well as the limited-capacity backhauls makes it difficult to perform centralized control plane functions on a large network scale. For a cellular network with very dense BS deployment, a number of clusters (i.e., multiple controllers) have to be dynamically set up or configured. In this way, BSs in each cluster are managed by a local controller in a programmable way, such that the communications within a single cluster are well coordinated. However, due to the frequency reuse, control plane decisions in different clusters are still coupled. Beyond that, future cellular network designs should be user-centered. That is, wireless resources are adaptively distributed across the network according to terminal users’ traffic characteristics, which requires a highly flexible software-defined architecture that is fitted to a dynamic networking scenario. The proposed research addresses many of the fundamental problems and challenges in creating a HSDRAN, with emphasis on a novel architecture design and network-wide resource allocation strategies. The project involves a complementary mix of network architecture design, theoretical modeling and analysis, and experimental simulations quantifying performance benefits of the developed protocols. In particular, the diverse expertise of our collaborative institutions in several research areas including network science, wireless communications, and machine learning allows us to investigate the interplay of these elements at different network layers from multiple perspectives.

Joint Network and Market Design for Content and Spectrum Sharing in Future 5G Networks

Adj. Prof. Petri Ahokangas, University of Oulu, USA
Assoc. Prof. Abouzeid, Alhussein, Rensselaer Polytechnic Institute, USA
Prof. Hesham El Gamal, Ohio State University, USA
Assoc. Prof. Atilla Eryilmaz, Ohio State University, USA
Prof. Matti Latva-aho, University of Oulu, USA

Future wireless networks, as represented by the 5G concept and associated set of future standards, are expected to meet a diverse range of new requirements, leverage technological and regulatory advancements, and overcome the spectrum scarcity challenges. However, the success of a new technology is not only determined by its technical strengths but also by an intricate interplay between the economic considerations of the consumers/users, competing service and content providers, and governing/regulating public agencies. This project explores new wireless spectrum and content sharing concepts from both technological and business perspectives for future 5G networks. The overall objective of this project is to investigate and develop fundamental technological and business aspects of new spectrum/content sharing for 5G networks, that potentially could lead to significant technical performance
improvements as well as revolutionize future wireless markets and operators business.
The intellectual merits of the proposed work can be described around its four research thrusts: (i) new and potentially transformative business models for future 5G markets, and in particular new business models for mobile operators, (ii) in-network dynamic spectrum and content sharing and pricing mechanisms under various possible future architectures that take into account the new business models and bridge the gap between technological and economic considerations, (iii) collaborative content distribution that could lead to win-win relationships for wireless stakeholders, and (iv) intelligent content caching for improved performance for different network and business scenarios. The unique aspects of the research plan are that it views content and spectrum as two resource dimensions that can be explored for future wireless networks, and that it stresses business and economic implications of various architectural choices, with formal models that capture technology performance as well as business/economic considerations.

Message and CSI Sharing for Cellular Interference Management with Backhaul Constraints

Prof. Markku Juntti, University of Oulu, Finland
Prof. Venugopal V. Veeravalli, University of Illinois at Urbana-Champaign, USA

The focus of this proposed project is to explore the potential benefits of exploiting these developments through the lens of information theoretic models of single hop wireless networks. The proposed project involves three research thrusts. The first is to study the fundamental limits of the rate of communication in large interference networks with a rate-limited backhaul that allows for sharing messages between base stations in both downlink and uplink scenarios. The second is to relax the perfect global channel knowledge assumption, and consider the achievable gains in settings with imperfect, local, or no channel knowledge at base stations and mobile users. Finally, we plan to study dynamic network models.

Sequential Inference and Learning for Agile Spectrum Use

Prof. Visa Koivunen, Aalto University, Finland
Prof. H. Vincent Poor, Princeton University, USA
Prof. Lifeng Lai, Worcester Polytechnic Institute, USA

Lack of availability of radio spectrum for wireless communication purposes is becoming a serious problem. Instead of the spectrum being actually fully in use, the scarcity of useful radio spectrum is mainly due to static allocation and rigid regulation of the spectrum use. Flexible spectrum use aims at exploiting under-utilized spectrum resources. Available spectrum opportunities may be non-contiguous, scattered over a large bandwidth, and are available locally and for a limited period of time due to the highly dynamic nature of wireless transmissions. There is a pressing need to understand how to discover, assess and utilize the time-frequency-location varying spectrum resources efficiently and with a minimal delay. It is critical to access identified idle spectrum in an agile manner.
In this project, we propose to design sequential inference and learning algorithms for the agile spectrum access when the state of the spectrum varies rapidly. The key advantage of sequential algorithms, as compared to block-wise algorithms, is that these sequential algorithms typically lead to significantly reduced decision delays. Although sequential analysis techniques have found great success in other areas, other than sequential detection, sequential analysis tools’ applications in wireless communications have been limited. Motivated by our initial success in designing sequential algorithms for spectrum sensing and cognitive radio networks, the overarching goal of this project is to design sequential inference and learning algorithms for agile spectrum utilization. In particular, we propose to employ advanced sequential inference and learning methods for the following three interconnected yet increasingly sophisticated and demanding tasks: 1) to employ sequential reinforcement learning and sequential inference algorithms to design sensing policies for rapid spectrum opportunities discovery; 2) to design sequential algorithms for fast and accurate spectrum quality assessment; and 3) to build, maintain and exploit an interference map of the area where our network operates and represent it as a spatial potential field.
This project is done in co-operation with Princeton University, NJ, USA, Worcester Polytechnic Institute, MA, USA and Aalto University, Finland.

Ubiquitous Video over Dynamic Spectrum

Prof. Mung Chiang, Princeton University, USA
Assist. Prof. Mario Di Francesco, Aalto University, Finland
Prof. Jussi Kangasharju, University of Helsinki, Finland

This project makes cognitive radio networks (CRNs) suitable as a platform to deliver ubiquitous wireless video. It takes a flexible approach that is applicable to diverse regulations across national boundaries and specifically targets mobile devices. The research addresses the major challenges affecting video delivery over CRNs: the highly-varying nature of the channel; the presence of misbehaving users; the dynamic availability of heterogeneous resources. It finally creates a cognitive phone, i.e., a smartphone with cognitive radio capabilities, to bridge the gap between the technologies behind CRNs and real applications. The project builds on two major research thrusts. First, it adopts spectrum crowdsensing as a means to model the availability of whitespace at multiple scales and support long-lived communications. Such an approach enables novel solutions to accurately characterize the channel and enforce the policies established by the communication authorities. Second, it leverages adaptive mechanisms as foundations to efficiently deliver video streams to end users. These mechanisms are implemented through streaming protocols and components in the network infrastructure that can deliver video content with a target quality of experience. The research addresses the challenges generated by the explosive growth of mobile devices and its consequences on both content distribution and load on the wireless infrastructure. The project also integrates synergistic activities between the United States and Finland in education, industry collaboration and entrepreneurship. In the long term, the research will allow the creation of novel value-added services related to streaming data for mobile wireless devices. For more information on the project, please visit the project website.