In wireless healthcare monitoring systems, bandwidth allocation is an efficient solution to the problem of scarce wireless bandwidth for the monitoring of patients. However, when the central unit cannot access the exact channel state information (CSI), the efficiency of bandwidth allocation decreases, and the system performance also decreases. In this paper, we propose an algorithm to reduce the negative effects of imperfect CSI on system performance. In this algorithm, the central unit can predict the current CSI by previous CSI when the current CSI is not available. We analyze the reliability of the proposed algorithm by deducing the standard error of estimated CSI with this algorithm. In addition, we analyze the efficiency of the proposed algorithm by discussing the system performance with this algorithm. 1. Introduction The increasing number of cases on waiting room death, which refers to the death of patients while staying in a hospital’s waiting room to be given a medical examination, underscores the significance of improving healthcare quality [1]. Most of these cases occur when patients are left alone in waiting rooms, such as when healthcare staff are taking a break or being busy performing other clinical and non-clinical functions. As a potential way of improving healthcare quality, a wireless healthcare monitoring system (illustrated in Figure 1 and detailed later) could help healthcare staff monitor the condition of patients by automatically collecting patient’s data, making some initial decisions on patient condition, and transmitting these decisions and medical data to a doctor’s office via wireless local area network (WLAN). Once emergent condition of a particular patient occurs, healthcare staff would be alerted. Figure 1: Architecture of our healthcare monitoring system. From a network design perspective, a wireless healthcare monitoring system should be capable of supporting the number of patients that will be using the system; being able to assess the network’s capability to serve a given number of patients is a critical factor in promoting adoption of such systems. Therefore, the network patient capacity, which we define as the number of patients that one WLAN deployment can support, is a critical design criterion and performance metric for wireless healthcare monitoring systems. From a practical standpoint, if the hospital’s patient capacity exceeds the network patient capacity, then another WLAN will need to be deployed in parallel within the hospital. Beyond the cost of deploying several networks in parallel, their co-existence
References
[1]
A. B. Debra I and J. M. Gail, “Waiting: the experience of persons in a critical care waiting room,” Research in Nursing and Health, vol. 25, no. 1, pp. 58–67, 2002.
[2]
M. Patzold and K. Yang, “An exact solution for the levelcrossing rate of shadow fading processes modelled by using the sumof-sinusoids principle,” in Proceedings of the 9th International Symposium on Wireless Personal Multimedia Communications, pp. 188–193, 2006.
[3]
B. Mielczarek and W. A. Krzymień, “Vector quantized CSI prediction in linear multi-user MIMO systems,” in Proceedings of the IEEE Vehicular Technology Conference, pp. 852–857, May 2008.
[4]
B. Mielczarek and W. A. Krzymień, “Adaptive CSI prediction in linear multi-user MIMO systems,” in Proceedings of the IEEE 70th Vehicular Technology Conference Fall (VTC '09), September 2009.
[5]
A. E. Ekpenyong and Y. F. Huang, “Feedback constraints for adaptive transmission,” IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 69–78, 2007.
[6]
T. R. Ramya and S. Bhashyam, “Using delayed feedback for antenna selection in MIMO systems,” IEEE Transactions on Wireless Communications, vol. 8, no. 12, Article ID 5351724, pp. 6059–6067, 2009.
[7]
N. Golmie, D. Cypher, and O. Rebala, “Performance analysis of low rate wireless technologies for medical applications,” Computer Communications, vol. 28, no. 10, pp. 1266–1275, 2005.
[8]
U. Varshney and S. Sneha, “Patient monitoring using ad hoc wireless networks: reliability and power management,” IEEE Communications Magazine, vol. 44, no. 4, pp. 49–55, 2006.
[9]
S. Agarwal, A. Joshi, T. Finin, Y. Yesha, and T. Ganous, “A pervasive computing system for the operating room of the future,” Mobile Networks and Applications, vol. 12, no. 2-3, pp. 215–228, 2007.
[10]
U. Varshney, “A framework for supporting emergency messages in wireless patient monitoring,” Decision Support Systems, vol. 45, no. 4, pp. 981–996, 2008.
[11]
H. F. Rashvand, V. Traver Salcedo, E. Montón Sánchez, and D. Iliescu, “Ubiquitous wireless telemedicine,” IET Communications, vol. 2, no. 2, pp. 237–254, 2008.
[12]
S. Sneha and U. Varshney, “Enabling ubiquitous patient monitoring: model, decision protocols, opportunities and challenges,” Decision Support Systems, vol. 46, no. 3, pp. 606–619, 2009.
[13]
D. Cypher, N. Chevrollier, N. Montavont, and N. Golmie, “Prevailing over wires in healthcare environments: benefits and challenges,” IEEE Communications Magazine, vol. 44, no. 4, pp. 56–63, 2006.
[14]
S. Garawi, R. S. H. Istepanian, and M. A. Abu-Rgheff, “3G wireless communications for mobile robotic tele-ultrasonography systems,” IEEE Communications Magazine, vol. 44, no. 4, pp. 91–96, 2006.
[15]
D. J. Love, R. W. Heath, V. K. N. Lau, D. Gesbert, B. D. Rao, and M. Andrews, “An overview of limited feedback in wireless communication systems,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 8, Article ID 4641946, pp. 1341–1365, 2008.
[16]
W. K. M. Ahmed and P. J. McLane, “Random coding error exponents for flat fading channels with realistic channel estimation,” IEEE Journal on Selected Areas in Communications, vol. 18, no. 3, pp. 369–379, 2000.
[17]
T. Eriksson and T. Ottosson, “Compression of feedback for adaptive transmission and scheduling,” Proceedings of the IEEE, vol. 95, no. 12, Article ID 4389755, pp. 2314–2321, 2007.
[18]
A. E. Ekpenyong and Y. F. Huang, “Feedback-detection strategies for adaptive modulation systems,” IEEE Transactions on Communications, vol. 54, no. 10, pp. 1735–1740, 2006.
[19]
L. Huang, R. de Francisco, and G. Dolmans, “Channel measurement and modeling in medical environments,” in Proceedings of the 3rd International Symposium on Medical Information and Communication Technology, Montreal, Canada, 2009.
[20]
M. Kuhn, A. Ettefagh, I. Hammerstrom, and A. Wittneben, “Twoway Com- munication for IEEE 802.11n WLANs Using Decode and Forward Relays,” in Proceedings of the 4th Asilomar Conference on Signals, Systems and Computers, 2006.
[21]
E. Hanada, Y. Watanabe, and Y. Antoku, “Hospital construction materials: poor shielding capacity with respect to signals transmitted by mobile telephones,” Biomedical Instrumentation and Technology, vol. 32, no. 5, pp. 489–496, 1998.
[22]
L. Hentila, A. Taparungssanagorn, H. Viittala, and M. Hamalainen, “Measurement and modelling of an UWB channel at hospital,” in Proceedings of the IEEE International Conference on Ultra-Wideband (ICUWB '05), Zurich, Switzerland, 2005.
[23]
D. J. Vergados, D. D. Vergados, and I. Maglogiannis, “Applying wireless DiffServ for QoS provisioning in mobile emergency telemedicine,” in Proceedings of the IEEE Global Telecommunications Conference, December 2006.
[24]
L. Di and L. Fabrice, “A scheme of bandwidth allocation for the transmission of medical data,” in Proceedings of the 44th IEEE Asilomar Conference on Signals, Systems, and Computers, 2010.
[25]
N. Ghevondian and H. Nguyen, “Using fuzzy logic reasoning for monitoring hypoglycaemia in diabetic patients,” in Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1108–1111, November 1997.
[26]
A. Maaref and S. A？ssa, “Closed-form expressions for the outage and ergodic shannon capacity of MIMO MRC systems,” IEEE Transactions on Communications, vol. 53, no. 7, pp. 1092–1095, 2005.
[27]
M. Nakagami, Statistical Methods in Radio Wave Propagation, Perga-mon Press, Oxford, UK, 1960.
[28]
L. C. Wang, W. C. Liu, A. Chen, and K. N. Yen, “Joint rate and power adaptation for wireless local area networks in generalized Nakagami fading channels,” IEEE Transactions on Vehicular Technology, vol. 58, no. 3, pp. 1375–1386, 2009.
[29]
L. C. Wang, Y. W. Lin, and W. C. Liu, “Cross-layer goodput analysis for rate adaptive IEEE 802.11a WLAN in the generalized Nakagami fading,” in Proceedings of the IEEE International Conference on Communications, pp. 2312–2316, June 2004.
[30]
C. D. Iskander and P. T. Mathiopoulos, “Fast simulation of diversity Nakagami fading channels using finite-state Markov models,” IEEE Transactions on Broadcasting, vol. 49, no. 3, pp. 269–277, 2003.
[31]
Y. L. Guan and L. F. Turner, “Generalised FSMC model for radio channels with correlated fading,” IEE Proceedings, vol. 146, no. 2, pp. 133–137, 1999.
