Cloud Assisted Two-Tier Wireless Networks

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This project will address a new concept for public wireless cellular networks based on the Two-Tier concept that we have been proposing to the industry. This concept will target beyond 5G networks and we utilize data driven neural network and machine learning methodologies for network configuration, interference management, and resource allocation. The Two-Tier concept involves an architecture with the regular base stations which we refer to as primary nodes, secondary nodes that are installed close to the user terminals which act in a sense like relays but are quite different from the relays studied in the current literature, and the user terminals. The link from the primary nodes to the secondary nodes is referred to as the primary link and it is the focus of this project. The secondary nodes will have the capability for a larger number of antenna elements than is possible in a regular user terminal, and the link from the primary nodes to the secondary nodes will be well-behaved in terms of channel propagation and prediction. The focus of the proposal is to devise data driven techniques to optimize this link. There are two main focus cases: 1) The secondary nodes are stationary, and 2) The secondary nodes are placed in a moving platform such as a car, truck, bus, streetcar, or train. The project will focus on novel techniques to associate a channel with a time and location so that we can do much more efficient physical layer adaptation than in normal cellular schemes. The techniques will involve the use of cloud databases to contain information about the propagation environment in relation to the location of the secondary node, either in a fixed installation such as a home or office, or in a moving platform along a street. The techniques utilized in the project will involve neural networks and machine learning in order to do power level adaptation, antenna beam configurations, and co-operation between primary nodes. The ultimate goal in this research is to reduce the radiation footprint of transmitters significantly so as to enhance network capacity, increase physical layer security, decrease energy consumption, and minimize radiation.  In our research we will develop an architecture that complements the traditional approaches of small-cells and massive MIMO on traditional base stations. The one-tier small cell approach has the drawback of requiring a back-haul network to interconnect the small cells, and the massive MIMO approach used on conventional base stations has the drawback that when we have a large number of terminals such as in IoT applications even the massive MIMO approach will have limitations due to excessive overhead. Our approach will complement these two classical approaches and allow for custom network configuration implementations that are specific to a City and a network operator.