SpecEES: Collaborative Research: CoSeC-RAN: Cognitive Secure Cloud RAN for Efficient Spectrum Sharing

Supported by NSF CNS #1731833 #1731698 


Next generation wireless networks will be characterized by larger volume, faster information transfer, and diversity. Wireless industry has been altering conventional license-based spectrum access policies through approaches utilizing unlicensed spectrum. This leads to dynamic spectrum access (DSA), where unlicensed use of a spectrum should avoid harm to licensed users, or should ensure a fair share of spectrum with other unlicensed users. DSA places an additional burden on business operations because revenue needs to be generated over dynamically changing resources, while providing expected quality of service to potential users. Yet, existing high-level approaches for spectrum sharing are not well poised to solve the core problem. Instead, finer-scale spectrum sharing in time, space, and spectrum dimensions is required. Furthermore, at such finer scales, it is essential to consider human behaviors and integrate economics into the tool-set of spectrum sharing. Finally, recent cyber-security concerns necessitate that such a system should incorporate security and privacy in its core. This project designs and develops advanced spectrum sharing techniques at the nexus of spectrum, pricing, and privacy, for next-generation DSA solutions. The project is conducted by an interdisciplinary team of experts in security, operations management, decision science, wireless communication, networking, and optimization. The fundamental results emerging from this research can enable transformative cognitive radio network management and operation solutions. The project supports multiple graduate students. Insights from the proposed collaborative research project between Ohio State and University of Nebraska Lincoln will educate both industry and academia across rural Nebraska and urban Ohio regions.


The project explores four main goals:

(1) Secure spectrum resource metering solutions with a Cognitive Secure Cloud Radio Access Networks (CoSeC-RAN) architecture. Preserving the digital base-band data within the network leads to more accurate resource assignment and pricing decisions.

(2) Advanced security and privacy-preserving solutions. The novel CoSeC-RAN architecture enables new directions to provide security to and by the network while preserving user privacy.

(3) Dynamic pricing algorithms, where prices are updated in real-time based on available capacity and customer load. These pricing algorithms reflect non-stationary and stochastic nature of both available capacity and demand. Customer-specific bandwidth requirements and mechanisms for ensuring customer privacy are incorporated into algorithm design.

(4) Spectrum sensing methods with incomplete information. CoSeC-RAN estimates primary signals at secondary user locations, with incomplete information from the network, to ensure minimum interference. Developed solutions are rigorously tested through large-scale simulations and experimentally in a city-wide testbed.

This study is a continuation of our previous research on Cog-TV.



PI: Mehmet C. Vuran (UNL)

PI: Eylem Ekici (OSU)

Co PI: Demet Batur (UNL)

Co PI: Jennifer Ryan (UNL)

Co PI: Qiben Yan (UNL)

Students: Fujuan Guo (UNL),

                Boyang Hu (UNL),

                Mohammad Lunar (UNL),

                Baofeng Zhou (UNL)


Intellectual Merit Outcomes 2017-2018

CoSeC-RAN Architecture and Testbed Deployment: To establish a city-wide experimental testbed and evaluate the results of this research, we have designed, implemented, and deployed a pilot urban testbed, which features the CoSeC-RAN architecture with common public radio interface over Ethernet (eCPRI) FPGA implementation. The testbed has been deployed on UNL building rooftops as well as a small cell location on a traffic light owned by the City of Lincoln.

Spectrum Sensing under Incomplete Information: Spectrum sensing performance depends on the relative locations of sensing platforms and the RF emitters, as well as other factors pertaining to propagation. To this end, the optimal solutions of the sensor selection and channel access decision have been extensively studied. However, the presence of shadow fading poses a challenge as the channels between RF emitters and receivers may not be known to the network controller a priori and can only be learned through sensing feedback. The feedback information is also limited as only a subset of sensors can be selected for sensing at a time (due to energy constraints) and the sensing results are contaminated by noise. Hence, we investigate methods for spectrum sensing and channel access in the presence of limited information.

Aggregate Interference Modeling: A stochastic geometry-based aggregate interference model is developed for unlicensed spectrum shared by heterogeneous secondary users that have various transmit powers and multi-antenna capabilities. Moreover, an efficient computation approach is presented to capture network dynamics in real-time via a down-sampling that preserves high-quantile precision of the model. The stochastic geometry-based model is verified experimentally in ISM band.

Spectrum Capacity Sharing: We focus on large geographical areas, such as university or industrial campuses, where the use of Wi-Fi exclusively belongs to the occupant because of land and building ownership in that area. Our objective is to determine how much of the Wi-Fi spectrum capacity a primary user in a campus area (e.g., a university) can dynamically share with a secondary user (e.g., a commercial internet service provider) without reducing the quality of the internet service to its own customers (e.g., university students and personnel). We developed an optimal dynamic capacity sharing decision strategy based on the current state of the system, i.e., the number of Wi-Fi channels that are used by the primary user's customers and the number of Wi-Fi channels that are shared with the other service providers. We have also started working on machine learning tools to analyze an actual campus data set to determine usage patterns of Wi-Fi customers. This will help us develop techniques to accurately predict the online activity of an incoming customer based on that customer's observable characteristics so that capacity sharing decisions can be made properly in real application settings.

Neural Network-based Channel Estimation: As a first step towards incorporating neural network models into spectrum sensing, we analyzed MIMO channel estimation using machine learning. We analyze the performance of machine learning-based channel estimation approaches to enhance channel estimation performance in high noise environments. More specifically, bit error rate (BER) performance of 2x2 and 4x4 MIMO communication systems with space-time block coding model (STBC) and two neural network-based channel estimation algorithms is analyzed.

MU-MIMO Privacy: We investigated the privacy leakage issue in 802.11ac MU-MIMO system. We explored the possibility of CSI forgery attack to reach both high eavesdropping opportunity and high eavesdropping quality simultaneously. We developed a novel forgery scheme, namely User Selective Eavesdropping (USE) Attack. USE Attack operates in two stages that construct an orthogonal CSI to guarantee eavesdropping opportunity and then search for a CSI direction with the best overhearing quality. We also proposed AngleSec to protect existing MU-MIMO networks from USE Attack. AngleSec exploits the characteristics of channel reciprocity in which a downlink angle-of-departure (AoD) resembles an uplink angle-of-arrival (AoA) in terms of normalized angular spectrum at the AP. 

Broader Impact Outcomes 2017-2018

Course design: CoSeC-RAN testbed is being used in CSCE 465/865 Wireless Communication Networks class of 25 senior and graduate students, for education in wireless communication principles, network experimentation, and spectrum sensing, during their course projects and labs. 

Training and professional development: The project informed the project scope of a capstone course. A prototype of a CPRI over Ethernet (e-CPRI) protocol is developed as a capstone project by a group of 4 undergraduate students. It offered them opportunities to solve practical problems using a wide-range of skills in software, hardware, embedded systems, and integration, as well as learning experience in a highly interdisciplinary deployment task. In addition, 3 graduate students were supported in UNL and OSU. 

Intellectual Merit Outcomes 2018-2019

Complex-Valued Deep Neural Network-based Wireless OFDM Receiver: Complex-Valued Deep Neural Network-based Wireless OFDM Receiveris developed based on a new architecture composed of fully-connected and convolutional layers. This receiver can perform the physical layer functionalities of OFDM system with explicit DFT/IDTF transformation. Moreover, it can enhance the performance of receiver by exploiting the redundancy of the waveform and learning the channel statistics.

Security of interference management protocol in LTE network: we used Vienna LTE-A Downlink System Level Simulator for our simulation. Vienna LTE is one of the most advanced suite of Matlab based simulators, including link and system level simulators for the 4G and 5G mobile communication network. In order to validate our attack, we set up a macro base station and let 10 users to connect to it. We manipulate the CQI from one of the users and verify that several users’ throughput decreases significantly. The simulation results show the feasibility of the CSI/CQI manipulation attacks.

Downlink non-orthogonal multiple access (NOMA): we implemented a practical downlink NOMA scheme for WLANs and evaluated its performance in real-world wireless environments. Our NOMA scheme has three key components: precoder design, user grouping, and successive interference cancellation (SIC). On the transmitter side, we first formulate the precoding problem as an optimization problem by developing a set of affine constraints to characterize the relation of SINR and achievable data rate, and then develop an efficient algorithm to solve this problem. We also proposed a lightweight user grouping algorithm to ensure the success of SIC at the receivers. On the receiver side, we proposed a new SIC-based signal detection algorithm to decode the desired signal in the face of strong interference. In contrast to existing SIC methods, it does not require channel estimation to decode the signals, thereby improving the resilience to interference. We have implemented the NOMA scheme on a GNURadio-USRP2 testbed. Experimental results show that our proposed NOMA scheme can significantly improve the weak users’ data rate.

Beam discovery in mmWave networks: This work provides a solution for the mmWave channel estimation problem by exploiting its sparse nature in the angular domain. The proposed solution is a beam discovery technique that is similar to error discovery in channel coding. We show that our proposed technique can significantly reduce the number of measurements required for reliable channel estimation. Our solution takes into account the size of TX/RX arrays and the sparsity level of the channel. We determine the number of measurements and the design of each measurement in a deterministic way; based on parity check matrices of appropriately selected LBCs. Under no measurement errors, our solution is guaranteed to find all available beams (paths) between TX and RX. We also assess the performance of the proposed scheme under different levels of SNR and ADC resolutions. We further provide a technique for error correction that is also inspired by channel coding.

Beam alignment and user scheduling in mmWave networks: The problem of minimizing beam alignment overhead under the minimum rate constraints is formulated as CMDP. From the structural result derived from the Lagrangian formulation of the MDP, it is shown that the complexity of the CMDP can be reduced from O(LN) to O(NK). The structural properties of the problem lead to optimal solutions based on the decision of not relinquishing wireless resources unless the user is forced out by beam blocking. In addition, a round-robin-based heuristic deterministic algorithm is proposed and shown to achieve (1 + ε) approximation of the optimal solution.

Predictive rate allocation over multiple wireless interfaces: We propose an online predictive algorithm that exploits the predictability of wireless connectivity for a small look-ahead window w. We show that this algorithm has a competitive ration of 1-(1/(w+1)). We also propose another predictive algorithm based on the “Receding Horizon Control” principle from control theory that performs very well in practice. Numerical simulations serve to validate our formulation, by showing that under the DRUM framework, the more delay-tolerant the flow, the less it uses the cellular network, preferring to transmit in high rate bursts over the secondary interfaces. Conversely, delay-sensitive flows consistently transmit irrespective of different interfaces’ availability. Simulations also show that the proposed online predictive algorithms have a near-optimal performance compared to the offline prescient solution under all considered scenario.

Broader Impact Outcomes 2018-2019

Course design: The CoSeC-RAN testbed has been used by a class of undergraduate and master students in their course projects. It offers them opportunities to solve practical problems in software, hardware, cloud-computing, and radio signals processing.

Training and professional development: One PhD student participated in this project in the past year. The project offered the opportunity for the student to solve challenges in building LTE simulation systems, and perform security analysis on complex LTE protocols. The project also enabled a course project for undergraduate course CSCE 422 “Wireless Communication Networks”. Around 30 undergraduate/graduate students had the opportunity to remotely operate the testbed of USRP devices to learn wireless communication fundamentals. They have created several innovative projects, such as: using the testbed to collect aircraft transponder signals; identifying mobile device locations; collection of weather broadcast data; etc.

Our findings on mmWave beam discovery and user scheduling and predictive rate allocation over heterogeneous wireless interfaces have been incorporated into ECE6102 “Wireless Networks” course material. Students presented and discussed these work in the context of emerging wireless networks.

Outreach: At the LPS Science Connectors events, we demonstrated our research on dynamic spectrum sharing and presented the testbed. To make the demonstrations more interpretable, we showed demos from the testbed so that the teachers can understand the application and outcome of our study. As a part of our outreach plans, we actively participate in the organization of local outreach events to share our research and education experience with students and teachers. In this investigation period, we presented our research activities at Science Connectors showcases with Lincoln Public Schools (LPS) on August 7th, 2019 through easy-to-understand demonstrations. The main goal of these events is to inform the public-school teachers about state-of-the-art scientific research at the University of Nebraska and enable teachers to engage in building creative K-12 curricula. Co-coordinated by the LPS science curriculum specialist, this annual event shares STEM activities with over 150 science teachers at UNL.

Testbed Development



Pictures from outreach events.

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Zhao, Z., M. C. Vuran, Z. Aref, W. Humphrey, S. Goddard, G. Attebury, B. France, B. Zhou, and M. M. R. Lunar, "A City-Wide Experimental Testbed for Next Generation Wireless Networks", IEEE Int. Balkan Conference on Communications and Networking (BalkanCom’19), Skopje, North Macedonia, June 2019.  Download: Balkan19_Testbed.pdf (895.41 KB)
Batur, D., J. Ryan, F. Guo, and M. C. Vuran, "Dynamic spectrum capacity sharing", Manufacturing & Service Operations Management Conference, Singapore, July 2019.
Batur, D., J. Ryan, Z. Zhao, and M. C. Vuran, "Dynamic Pricing for Wireless Internet Based on Changing Capacity and Usage,", Manufacturing and Service Operations Management Journal, 2019.
Zhao, Z., and M. C. Vuran, "Modeling Aggregate Interference with Heterogeneous Secondary Users and Passive Primary Users for Dynamic Admission and Power Control in TV Spectrum", in Proc. Int. Balkan Conference on Communications and Networking (BalkanCom’18), Podgorica, Montenegro, June 2018.
Yang, K., M. C. Vuran, S. Scott, F. Guo, and C. R. Ahn, "Neural Network-based Channel Estimation for 2x2 and 4x4 MIMO Communication in Noisy Channels", in Proc. Balkan Conference on Communications and Networking (BalkanCom’18), Podgorica, Montenegro, June 2018.