D. Raychaudhuri, N. B. Mandayam, J. B. Evans, B. J. Ewy, S. Seshan, P. Steenkiste, "CogNet - An Architectural Foundation for Experimental Cognitive Radio Networks within the Future Internet,"
In Proc. of MobiArch’06 December 2006.
 X. Jing and D. Raychaudhuri, "Global Control Plane Architecture for Cognitive Radio Networks,"
In Proc. IEEE CogNet’07 Workshop (with IEEE ICC)
 Z. Miljanic, I. Seskar, K. Le and D. Raychaudhuri, "The WINLAB Network Centric Cognitive Radio Platform – WiNC2R”, in Proc. CrownComm 2007
 D. Raychaudhuri, X. Jing, I. Seskar, K. Le and J. B. Evans, "Cognitive Radio Technology: From Distributed Spectrum Coordination to Adaptive Network Collaboration," Accepted for publication in Pervasive and Mobile Computing (PMC) Journal, 2008.
 Vijayant Bhatnagar, “Performance and Hardware Complexity Analysis of Programmable Radio Platform for MIMO OFDM based WLAN Systems”, M.S. Thesis, WINLAB TR-329, Rutgers University, 2008.
 Y. Hur, J. Park, W. Woo, K. Lim, C.-H. Lee, H. S. Kim, and J. Laskar, ``A wideband analog multi-resolution spectrum sensing (MRSS) technique for cognitive radio (CR) systems,'' in Proc. IEEE Int. Symp. Circuit and System, May 21-24, 2006, pp. 4090-4093.
 J . Laskar, R. Mukhopadhyay, Y. Hur, C. –H. Lee, and K. Lim, ” Reconfigurable RFICs and Modules for Cognitive Radio”, Proc IEEE SiRF 2006.
 Youngsik Hur, Jongmin Park, W. Woo, J. S. Lee, Kyutae Lim, Chang-Ho Lee, Hyoungsoo Kim, Joy Laskar, “A Cognitive Radio (CR) System Employing A Dual-Stage Spectrum Sensing Technique : A Multi-Resolution Spectrum Sensing (MRSS) and A Temporal Signature Detection (TSD) Technique”, Proc. IEEE GLOBECOM 2006.
 Youngsik Hur, G. Park, C.-H. Lee, H. S. Kim, Joy Laskar: A Cognitive Radio (CR)-Based Mobile Interactive Digital Broadcasting Application adopting a Multi-Resolution Spectrum-Sensing (MRSS) Technique. VTC Fall 2007: 1912-1916.
This project was started in September 2004 as a 4-year collaborative research effort (with Lucent Bell-Labs and GA Tech) aimed at design and prototyping of a high-performance cognitive radio platform with integrated physical and network layer capabilities. Key design objectives for the cognitive radio platform include:
• multi-band operation, fast spectrum scanning and frequency agility;
• software-defined modem capable of operating at speeds up to 50 Mbps with OFDM and QPSK/DSSS class waveforms;
• network processor capable of ad-hoc packet routing with throughput ~100 Mbps;
• spectrum policy processor for dynamic spectrum sharing algorithms and etiquette protocols
Expected outcomes of the project include: (a) high-performance cognitive radio architecture suitable for low-cost ASIC implementation; (b) performance comparisons and benchmarks for example cognitive radio usage scenarios; (c) board level proof-of-concept prototypes demonstrating key technical features;
“Moore’s law” advances in programmable silicon integrated circuits have created an opportunity to develop intelligent or “cognitive” radios which can adapt to a wide variety of radio interference conditions and multiple protocol standards. Such a cognitive radio would be capable of dynamic physical layer adaptation via scanning of available spectrum, selection from a wide range of operating frequencies (possibly non-contiguous), rapid adjustment of modulation waveforms and adaptive power control. In addition, a suitably designed cognitive radio with a software-defined physical layer would be capable of collaborating with neighboring radios to ameliorate interference using higher-layer protocols. These higher layer coordination protocols could range from etiquette mechanisms all the way to fully collaborative multi-hop forwarding between radio nodes. Such cognitive radios with both physical and network layer capabilities are expected to improve the prospects for both spectrum compatibility and interoperability between proliferating wireless data standards.
The network-centric cognitive radio architecture under consideration in this project is aimed at providing a high-performance platform for experimentation with various adaptive wireless network protocols ranging from simple etiquettes to more complex ad-hoc collaboration. The basic design provides for fast RF scanning capability, an agile RF transceiver working over a range of frequency bands, a software-defined radio modem capable of supporting a variety of waveforms including OFDM and DSSS/QPSK, a packet processing engine for protocol and routing functionality, and a general purpose processor for implementation of spectrum etiquette policies and algorithms.
The architecture of our network centric WiNC2R cognitive radio platform (see figure above for high-level hardware diagram) is based on the recognition of the workload characteristic of multi-layer wireless communication protocol processing, which are quite different from the embedded computing applications, and even more so from the ones of the general purpose computing applications. To name a few, the context switching is very frequent (it is needed for every packet, or even for every packet processing unit), there is no spatial and temporal locality of data, workload size is small, processing time constraints are very stringent, and the data input and output is very intensive. Most importantly the processing flow is driven by the events rather than by the program counter as in the stored program paradigm. The events driving the flow can be time or data based, and both can be generated by the environment, or as the result of internal processing. Given these sharp differences of the SDR applications and the traditional application workload, we need a different architectural framework to address both functional requirements and workload characteristics of programmable radio applications.
Architecture of Nework-Centric Cognitive Radio Platform
The WiNC2R architecture framework addresses the workload characteristics of wireless communication protocols with programmable control mechanisms that engage both hardware and software modules in a uniform manner in order to satisfy both functional and performance requirements. The proposed design is based on the virtual flow pipeline (VFP) concept. The traditional hardware pipeline based approach has a fixed sequence of operations, a fixed operation at each stage of the selected operating mode, and a fixed timing of operations, i.e., end to end processing latency. Furthermore there is no provision for multiplexing functional units among the flows provisioned in the system which is the basic requirement for enabling the processing platform virtualization. The VFP approach adds the flexibility with respect to each design dimension described above and, in addition, allows software defined functions to be incorporated into the VFP based program control framework.
Virtual flow consists of a set of functions and their scheduling requirements associated with a higher protocol entity (application, session, IP, or MAC address). In a VFP scheme, the sequence of operations is organized by a flow control data structure which specifies, for each function completed, the follow up candidate functions. The actual sequence is selected at run time, for each packet processing unit, based on the result of processing by each functional unit. Thus, the potential sequence space is defined during the flow provisioning time, but the actual operation sequence is determined at run time. Furthermore, the synchronization mechanisms of a VFP scheme ensure that the functional unit does not start the processing until all of the previous units in the flow have completed processing.
Results To Date and Future Work Plan:
WINLAB has developed a prototype “network centric” cognitive radio board (called “WiNC2R”) during this reporting period. This board (shown in the figure below) was completed in Nov 2007 and is now being used for architecture evaluation and software development. The prototype system demonstrates the virtual flow pipeline architecture and can be used to support multiple (virtual) OFDM-based radio standards operating at speeds ~10 Mbps. As of 2008-09, the project team is focused on completing the WiNC2R prototype implementation and demonstrating functionality and performance for selected cognitive radio scenarios. For further details see references.
Baseband Diagram RF Module Diagram
(Model and Networking Modules)
Prof. D. Raychaudhuri
732-932-6857 Ext. 638
732-932-6857 Ext. 640
Prof. Zoran Miljanic
732-932-6857 Ext. 647