NeTS: Small: Enhanced Interference Alignment for Networks using Reconfigurable Antennas

点击翻译按钮获取中文摘要

Interference between wireless devices will become a serious bottleneck for future wireless networks asthey become more pervasive in our everyday lives. The traditional approach of dealing with interferenceis to share limited resources, such as spectrum, among many users such that a given resource is used byonly one user at a time. The recent notion of interference alignment (IA) proposes a new paradigm whereseveral users can simultaneously send information by sharing the same resource. This new approachresults in increased network capacity where every user gets half the interference-free throughput. In thisresearch, the project team will leverage reconfigurable antenna systems to design and enhanceinterference alignment techniques for multi-user wireless networks. Reconfigurable antennas are capableof electronically switching between different radiation patterns (or 'states') in response to the needs ofthe overlying communication link and network. In this regard, reconfigurable antenna systems represent asignificant new degree of freedom for developing interference management techniques. The project teamproposes new ways of using the antennas to enable more practical solutions for future multi-usernetworks and also provide a software defined radio platform to enable further research within thecommunity.While the vast majority of existing research in this area considers an antenna to be a monolithic 'blackbox', the project team will develop and demonstrate new compact form-factor electronicallyreconfigurable antenna technologies that are capable of providing pattern agility. This additional degreeof freedom allows for the dynamic selection of radiation patterns to seamlessly integrate with, and extendinterference alignment techniques. The first objective is to demonstrate how electrically reconfigurableantennas can greatly enhance the sum capacity achieved via distributed interference alignment, especiallyat low SNR, by improving the subspace design. The second objective is to demonstrate how blindinterference alignment can be practically achieved by combining antenna state selection algorithms withmultiple physical layer transmission schemes. The additional channel diversity provided byreconfigurable antennas comes with an overhead to acquire information about the state of all the channels,revealing a need for a strategy to select the optimal state and most importantly an ability to learn thechanges in the channel state in order to adapt. With these goals in mind, the project team will utilizeonline learning based on multi-armed bandit theory to design algorithms to control and adapt the state of areconfigurable antenna system. For the multi-user network, the team will analyze the cost of learningunder an unknown statistical model of the channel and compare it with the oracle with full priorknowledge. Finally, the team will focus on implementing the developed interference alignment algorithmson Drexel's Software Defined Communications (SDC) testbed. The SDC testbed is based upon a ScalableOFDM physical layer, which operates close to many different standards. The project team will developnew blocks for precoding and decoding, cross-transmitter symbol-timing synchronization, as well asimplement more efficient synchronization techniques that require less data overhead. The proposedcombination of the Drexel SDC testbed with IA algorithms will result in a product that has high relevanceto the industrial and academic research communities. Special effort will be made to not only disseminateresearch results through relevant conference and journal publication, but also through testbed technologydemonstrations. In order to extend the outreach of the proposed research beyond the research community,high school students and teachers, specifically under-represented populations, will be invited toparticipate in the PIs research laboratories through pre-existing educational programs. The developedtestbed will be used to develop demonstrations and laboratory-based course modules.