A Dual-Band MAC Protocol for Indoor Cognitive Radio Networks: An e-Health Case Study

The importance of wireless technology in modern medicine has increased in the last years. It is anticipated that a large number of wireless communication devices for e-health will operate in unlicensed frequency bands in indoor environments. This represents a coexistence problem, which will be particularly challenging in confined areas of hospitals. Electromagnetic interference (EMI) from wireless devices can disrupt the performance of non-communication electronic medical equipment. Cognitive radio is a technology that can ease the coexistence by protecting non-communication electronic medical equipment. In this work we improved a cognitive radio EMI-aware protocol for e-health applications. The original protocol protects medical equipment from harmful interference by preventing wireless transmissions when interference immunity levels are exceeded. However, this leads to high outage probability in areas where protected medical apparatuses are located. In order to maintain a low outage probability under this scheme, we propose the use of an additional channel in a different frequency band for control/data transmission from potential interference sources. We considered the recently allocated 2360--2400 MHz for medical body area networks and the 902--928 MHz band for allocation of the additional control/data channel. Simulation results demonstrated that the use of the proposed dual-band EMI-aware protocol using the 902--928 MHz band significantly reduces the outage probability.

[1]  J. Seybold Introduction to RF Propagation , 2005 .

[2]  Cem Ersoy,et al.  Wireless sensor networks for healthcare: A survey , 2010, Comput. Networks.

[3]  Ilangko Balasingham,et al.  Applications of software-defined radio (SDR) technology in hospital environments , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[4]  Vineet R. Kamat,et al.  Evaluation of position tracking technologies for user localization in indoor construction environments , 2009 .

[5]  T. Ulversoy,et al.  Software Defined Radio: Challenges and Opportunities , 2010, IEEE Communications Surveys & Tutorials.

[6]  Erik G. Larsson,et al.  Sensor networks for cognitive radio : theory and system design , 2008 .

[7]  R. Van der Togt,et al.  Electromagnetic interference from radio frequency identification inducing potentially hazardous incidents in critical care medical equipment. , 2008, JAMA.

[8]  Yunghsiang Sam Han,et al.  Can multiple subchannels improve the delay performance of RTS/CTS-based MAC schemes? , 2009, IEEE Transactions on Wireless Communications.

[9]  Ilangko Balasingham,et al.  Cognitive radio for medical body area networks using ultra wideband , 2012, IEEE Wireless Communications.

[10]  Kaushik R. Chowdhury,et al.  Transforming healthcare and medical telemetry through cognitive radio networks , 2012, IEEE Wireless Communications.

[11]  Maria-Gabriella Di Benedetto,et al.  A Survey on MAC Strategies for Cognitive Radio Networks , 2012, IEEE Communications Surveys & Tutorials.

[12]  Dusit Niyato,et al.  A cognitive radio system for e-health applications in a hospital environment , 2010, IEEE Wireless Communications.

[13]  Yunghsiang Sam Han,et al.  Analyzing multi-channel medium access control schemes with ALOHA reservation , 2006, IEEE Transactions on Wireless Communications.