Building Robust Wireless LAN for Industrial Control with DSSS-CDMA Cellphone Network Paradigm

  • Authors:
  • Qixin Wang;Xue Liu;Weiqun Chen;Wenbo He;Marco Caccamo

  • Affiliations:
  • University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign;University of Cincinnati;University of Illinois at Urbana-Champaign;University of Illinois at Urbana-Champaign

  • Venue:
  • RTSS '05 Proceedings of the 26th IEEE International Real-Time Systems Symposium
  • Year:
  • 2005

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Abstract

Deploying Wireless LAN for Industrial Control (IC-WLAN) has many benefits, such as mobility, low deployment cost and ease of reconfiguration. However, the top concern is robustness of wireless communications. Wireless control loops must be maintained under persistent adverse channel conditions, such as noise, large-scale path loss and fading. Many electro-magnetic interference sources in industrial environments, e.g. electric motor and welding, make wireless communication more challenging. The conventional IEEE 802.11 WLANs, which are designed for providing high bandwidth instead of high robustness, are therefore inappropriate for IC-WLAN. On the other hand, if the low data rate feature of industrial control is fully exploited by the state-of-the-art Direct Sequence Spread Spectrum (DSSS) technology, much higher robustness can be achieved. We hereby propose using DSSS-CDMA to build IC-WLAN, and exploiting the low data rate feature of industrial control loops for enhanced robustness. We carried out fine-grained physical layer simulations and Monte Carlo comparisons. The results show that DSSS-CDMA IC-WLAN provides much higher robustness than IEEE 802.11 WLAN, so that reliable wireless industrial control loops are made feasible. The DSSS-CDMA IC-WLAN scheme also opens up a new problem space for interdisciplinary study, involving real-time scheduling and resource management, communication, networking and control. In this paper, we study the resource management problems on maximizing robustness and minimizing control utility loss. Analytical resource optimization solutions are given.