Simulating dynamic communication systems using the core functional dataflow model

The latest communication technologies invariably consist of modules with dynamic behavior. There exists a number of design tools for communication system design with their foundation in dataflow modeling semantics. These tools must not only support the functional specification of dynamic communication modules and subsystems but also provide accurate estimation of resource requirements for efficient simulation and implementation. We explore this trade-off - between flexible specification of dynamic behavior and accurate estimation of resource requirements - using a representative application employing an adaptive modulation scheme. We propose an approach for precise modeling of such applications based on a recently-introduced form of dynamic dataflow called core functional dataflow. From our proposed modeling approach, we show how parameterized looped schedules can be generated and analyzed to simulate applications with low run-time overhead as well as guaranteed bounded memory execution. We demonstrate our approach using the Advanced Design System from Agilent Technologies, Inc., which is a commercial tool for design and simulation of communication systems.

[1]  Thomas Martyn Parks,et al.  Bounded scheduling of process networks , 1996 .

[2]  Shuvra S. Bhattacharyya,et al.  Efficient simulation of critical synchronous dataflow graphs , 2006, 2006 43rd ACM/IEEE Design Automation Conference.

[3]  Ed F. Deprettere,et al.  Parameterized Looped Schedules for Compact Representation of Execution Sequences in DSP Hardware and Software Implementation , 2007, IEEE Transactions on Signal Processing.

[4]  W. Marsden.  I and J , 2012 .

[5]  Jeffrey G. Andrews,et al.  Fundamentals of WiMAX: Understanding Broadband Wireless Networking , 2007 .

[6]  K. Kalbasi,et al.  Cosimulating synchronous DSP applications with analog RF circuits , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[7]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[8]  J. Shynk.  Frequency-domain and multirate adaptive filtering , 1992, IEEE Signal Processing Magazine.

[9]  Edward A. Lee,et al.  Scheduling dynamic dataflow graphs with bounded memory using the token flow model , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Christian Haubelt,et al.  A SystemC-Based Design Methodology for Digital Signal Processing Systems , 2007, EURASIP J. Embed. Syst..

[11]  Shuvra S. Bhattacharyya,et al.  A generalized scheduling approach for dynamic dataflow applications , 2009, 2009 Design, Automation & Test in Europe Conference & Exhibition.

[12]  Jeffrey G. Andrews,et al.  Fundamentals of WiMAX: Understanding Broadband Wireless Networking (Prentice Hall Communications Engineering and Emerging Technologies Series) , 2007 .

[13]  Edward A. Lee,et al.  Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing , 1989, IEEE Transactions on Computers.

[14]  Soonhoi Ha,et al.  H.264 Decoder Algorithm Specification and Simulation in Simulink and PeaCE , 2004 .

[15]  Shuvra S. Bhattacharyya,et al.  Parameterized dataflow modeling of DSP systems , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[16]  Shuvra S. Bhattacharyya,et al.  Functional DIF for Rapid Prototyping , 2008, 2008 The 19th IEEE/IFIP International Symposium on Rapid System Prototyping.

[17]  Edward A. Lee,et al.  Software Synthesis from Dataflow Graphs , 1996 .

[18]  Alexandru Turjan,et al.  System design using Khan process networks: the Compaan/Laura approach , 2004, Proceedings Design, Automation and Test in Europe Conference and Exhibition.