Radio spectrum useable by mobile cellular networks is finite but cellular traffic continues to grow rampantly. This calls on scientific community to design networks that can push spectrum efficiency to its limits. On the other hand, making cellular networks energy efficient is a goal that is becoming more pressing than ever not only for operational cost reduction but also for minimizing the carbon foot print of the bulging information and communications technology industry. Recent studies show that unless new degrees of freedom and artificial intelligence based dynamic adaptability is added in the cellular architecture, any significant gain in spectral efficiency must come at cost of energy efficiency. The overarching goal of this project is to design, characterize, optimize and validate through a state-of-the-art testbed a new architecture that enables the additional degrees of freedom and intelligence to dynamically exploit these new degrees of freedom in the design and operation of the mobile network to yield substantial gains in both spectrum efficiency and energy efficiency, simultaneously. This proposed architecture is named as SpiderNET: Spectrally Efficient and Energy Efficient Data Aided Demand Driven Elastic Architecture for Future Networks. The key idea behind SpiderNET is to introduce additional degrees of freedom to relax the rigid spectrum efficiency-energy efficiency tradeoff and thus enable simultaneous enhancement of both. In wake of the internet of everything, as a key enabler of network resource efficiency, enhanced battery life and service level improvement, SpiderNET is bound to have a broad impact on nearly every aspect of evolving digital society that counts on wireless connectivity. In addition, as diminishing revenue per bit is already pushing cellular operators to reduce energy bills, huge energy savings enabled by SpiderNET can substantially reduce OPEX. Reducing the carbon foot print of cellular industry is also a key benefit of the proposed research. This project offers workforce training in a highly sought-after multi-disciplinary skill set while ensuring the participation of women and other underrepresented groups, and K-12 outreach. Compared to only theoretical or simulation-based research, a key distinction of the proposed research is the experimental research on a cutting-edge cellular testbed that is expected to cast a much broader impact. This project is collaborative undertaking with key national and international stakeholders in cellular eco-systems to ensure timely adaptation of the project outcomes by respective industry and government bodies.
The simultaneous enhancement of both spectrum efficiency and energy efficiency is achieved by shifting the pivot of operation from the rigid always-on base-station-centric cells to user-centric on-demand cells. To enable this, SpiderNET consists of a layer of low-density large footprint control base station underlaid by high-density switchable data base stations. The switching on/off the data base station, the size of user-centric cells (S-Zones) and other parameters are orchestrated proactively by a machine learning based self-organizing network (SON) engine that leverages a database of selected measurements at data and control base stations. Preliminary studies show that intelligent orchestration of the both, the size of the S-Zone and active data base station density, along with optimal design of the contents and spatiotemporal resolution of the database can substantially enhance both spectrum efficiency and energy efficiency without compromising quality of experience. The research will transform SpiderNET form an idea into a functional architecture by pursuing the following three research thrusts: 1) Developing analytical and simulation models to fully characterize the spectrum efficiency and energy efficiency of SpiderNET to determine the key design parameters that can be optimized to maximize its spectrum efficiency and energy efficiency gains. These models will then be leveraged to design algorithms for maximizing spectrum efficiency and energy efficiency in dynamic traffic conditions. 2) Designing the database of measurements and leveraging this data at control and data base stations to develop algorithms for proactive cell discovery and selection and radio resource allocation for jointly maximizing both spectrum efficiency and energy efficiency without compromising quality of experience. 3) Proof of concept of SpiderNET on TurboRAN (an NSF-funded end-to-end programmable testbed). This research leverages tools from domains of fluid modelling, stochastic geometry, game theory, machine learning and stochastic and multi-objective optimization to achieve its goals.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
