Secure Intelligent Radio for Trains (SIRT)

2017 ◽  
Author(s):  
K. R. Damindra S. Bandara ◽  
Satish Kolli ◽  
Duminda Wijesekara
Keyword(s):  
Critical Infrastructure ◽  
Adaptive Modulation ◽  
Radio Spectrum ◽  
Spectrum Management ◽  
Worker Safety ◽  
Radio Noise ◽  
Radio Communication ◽  
Radio System ◽  
Cognitive Engine ◽  
Prototype Development

American Railroads are planning to complete implementing their Positive Train Control (PTC) systems by 2020. Safety objectives of PTC are to avoid inter-train collisions, train derailments and ensuring railroad worker safety. Under published specifications of I-ETMS (the PTC system developed by Class I freight railroads), the on-board PTC controller communicates with two networks; namely, the Signaling network and the Wayside Interface Unit network to gather navigational information such as the positions of other trains, the status of critical infrastructure (such as switches) and any hazardous conditions that may affect the train path. By design, PTC systems are predicated on having a reliable radio network operating in reserved radio spectrum, although the PTC system itself is designed to be a real-time fail safe distributed control systems. Secure Intelligent Radio for Trains (SIRT) is an intelligent radio that is customized to train operations with the aim of improving the reliability and security of the radio communication network. SIRT has two tiers. The upper tier has the Master Cognitive Engine (MCE) which communicates with other SIRT nodes to obtain signaling and wayside device information. To do so, the MCE communicates with cognitive engines at the lower tier of SIRT; namely the Cryptographic Cognitive Engine (CCE) (that provide cryptographic security and threat detection) and the Spectrum Management Cognitive Engine (SCE) (that uses spectrum monitoring, frequency hopping and adaptive modulation to ensure the reliability of the radio communication medium). We presented the architecture and the prototype development of the CCE in [1]. This paper presents the design of the MCE and the SCE. We are currently developing a prototype of the SCE and the MCE and testing the performance of our cognitive radio system under varying radio noise conditions. Our experiments show that SIRT dynamically switches modulation schemes in response to radio noise and switches channels in response to channel jamming.

Multi-Tiered Cognitive Radio Network for Positive Train Control Operations

2016 ◽  
Author(s):  
K. R. Damindra S. Bandara ◽  
Anthony P. Melaragno ◽  
Duminda Wijesekara ◽  
Paulo Costa
Keyword(s):  
Cognitive Radio ◽  
Software Defined Radio ◽  
Cognitive Radio Network ◽  
Spectrum Management ◽  
Base Station ◽  
Worker Safety ◽  
Radio Network ◽  
Operational Environment ◽  
Cognitive Engine ◽  
Lower Tier

Positive Train Controller (PTC) is a communication based system designed to enforce PTC safety objectives for trains such as train-to-train collisions, train derailments, and ensure railroad worker safety. Existing PTC designs consider risks due to operational environment such as location of other trains, switches, and speed limits. We propose to enhance PTC by using a multi-tiered cognitive radio network that considers multiple risks such as those due to bandwidth congestion, packet length limitations, propagation losses, detectable exploitation of Software Defined Radio vulnerabilities, and protocol vulnerabilities. Radios operating at PTC nodes (such as train, WIU and Base station) is equipped with a cognitive layer, which communicates with other nodes to create a cognitive radio network. The proposed network as a whole strives to provide spectrum management and security for the radio communication system, which can enhance the PTC functionality. Each cognitive radio in our proposed network consists of multiple tiers. The upper tier consists of a master cognitive engine that holistically evaluates the operational risks of the network and acts to mitigate them using the lower tiers. The lower tier (immediate slave tier to the master) consists of sub cognitive engines for cryptographic operations and spectrum management. The traditional PTC protocol is implemented at a lower tier module that interface with the master Cognitive Engine (CE). The master-slave communications within one radio is implemented using middleware. The proposed cognitive radio network can be modeled as a cyber-physical system by incorporating train movement dynamics, radio transmission characteristics and cryptographical computations, thereby constituting a distributed system of communicating hybrid automatons. This design enables us to verify safety and the security of the system using formal methods, which constitutes our ongoing work. We also discuss potential issues such as FRA mandated safety cases that needs to be addressed if the proposed features are to be added to the PTC systems.

Flexible Spectrum Management: Approaches for India

2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Arturo Basaure ◽  
Heikki Kokkinen ◽  
Heikki Hämmäinen ◽  
V. Sridhar
Keyword(s):  
Large Scale ◽  
Mobile Services ◽  
Radio Spectrum ◽  
Spectrum Management ◽  
Management Approach ◽  
Unlicensed Spectrum ◽  
Spectrum Band ◽  
Management Policies ◽  
Management Approaches ◽  
Lock In

Radio spectrum for commercial mobile services continues to be scarce. Countries around the world have recognized the importance of efficient utilization of this scarce resource and have initiated regulatory and policy steps towards flexible approaches to spectrum management, including sharing of licensed spectrum, and releasing unlicensed spectrum for mobile services. Technologies for shared access and the associated standardization activities have also progressed towards possible large scale deployments. In this paper, we analyze the evolution of spectrum management policies using a causal model and indicate how the markets can lock in to either centralized or flexible approach. We also cite a use case of a flexible spectrum management approach using spectrum band fill option and indicate its suitability to the Indian context.

scholarly journals Radio Spectrum Management and RFI in the United States

1991 ◽  
Vol 112 ◽  
pp. 174-175
Author(s):  
R. Marcus Price
Keyword(s):  
United States ◽  
Department Of Defense ◽  
Government Regulation ◽  
The United States ◽  
Radio Spectrum ◽  
Spectrum Management ◽  
Federal Communications Commission ◽  
Private Companies ◽  
The Government ◽  
Government Body

ABSTRACTIn the United States, civil common carrier telecommunications are provided by private companies, not by any agency of the government. Regulation of these services and spectrum management oversight is provided by the Federal Communications Commission (FCC), an agency of the government. Government telecommunications are operated by individual agencies, e.g. the Department of Defense, under the overall regulation of the Office of Spectrum Management of the National Telecommunications and Information Administration (NTIA), a government body separate from the FCC. In bands shared by the civil and government sectors, liaison and coordination is effected between the FCC and the NTIA.

The New Research of Spectrum Sensing Technology for Cognitive Radio

2014 ◽  
Vol 945-949 ◽  
pp. 2301-2305
Author(s):  
Yi Peng ◽  
Yan Jun Wang
Keyword(s):  
Cognitive Radio ◽  
Wireless Communication ◽  
Spectrum Sensing ◽  
Communication Technology ◽  
Spectrum Sharing ◽  
Rapid Development ◽  
Current Model ◽  
Radio Spectrum ◽  
Spectrum Management ◽  
Sensing Technology

With the rapid development of wireless communication technology, the shortage of spectrum resources is becoming more and more serious, and may even become a bottleneck restricting of the development wireless communication technology in the future. Now, Spectrum sensing technology, spectrum sharing technology and spectrum management technology is the three core technologies of cognitive radio spectrum,and sensing technology is to implement the follow-up of spectrum sharing and the premise of spectrum management.So mainly to the current model of the cognitive radio spectrum sensing technology,to make a classification and comparison, finally it is concluded that cognitive users under the environment of higher signal-to-noise ratio, the better results of the perceived performance.

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