Big RF Data Assisted Cognitive Radio Network Coexistence in 3.5GHz Band

• Published in: 2017 26th International Conference on Computer Communication and Networks (ICCCN) pp. 1 - 8
• Main Authors: Omotere, Oluwaseyi, Lijun Qian, Jantti, Riku, Miao Pan, Zhu Han
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2017
• Subjects: Artificial neural networks; Cognitive radio; Interference; Radio frequency; Sensors
• DOI: 10.1109/ICCCN.2017.8038357

In this paper, big Radio Frequency (RF) data assisted optimization is considered for future wireless networks employing cognitive radio technology with machine learning capability. A cognitive radio network (CRN) with multiple Secondary Users (SUs) may coexist with other wireless systems such as Small Cells (SC) and Radar systems, both Primary Users (PUs) with different level of priorities. Traditional spectrum sensing typically only gives information about the presence or absence of a PU. However, when multiple heterogeneous systems coexist, it becomes imperative to acquire the knowledge of the systems operating in a specific band at a particular time so as to choose an appropriate transmission strategy. In this work, we take advantage of the learning capability of a Neural Network Predictor (NNP) to obtain the statistics of the coexisted wireless systems from the RF traces collected in our Universal Software Radio Peripheral (USRP) based test bed. The NNP is able to learn the features of the RF traces and make accurate prediction of the signals prevalent in the wireless environment. Because of the augmented information learned from the RF traces, a novel optimization problem incorporating the outputs from the NNP is formulated to maximize the throughput of the CRN. The solution is derived using Karush- Kuhn-Tucker (KKT) and extensive simulations using the real RF traces are carried out. It is demonstrated that the NNP can detect the type and number of coexisted users reliably and the proposed scheme will improve the performance of the coexisted CRN.