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Power management for energy-aware communication systems

Published:01 August 2003Publication History
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Abstract

System-level power management has become a key technique to render modern wireless communication devices economically viable. Despite their relatively large impact on the system energy consumption, power management for radios has been limited to shutdown-based schemes, while processors have benefited from superior techniques based on dynamic voltage scaling (DVS). However, similar scaling approaches that trade-off energy versus performance are also available for radios. To utilize these in radio power management, existing packet scheduling policies have to be thoroughly rethought to make them energy-aware, essentially opening a whole new set of challenges the same way the introduction of DVS did to CPU task scheduling. We use one specific scaling technique, dynamic modulation scaling (DMS), as a vehicle to outline these challenges, and to introduce the intricacies caused by the nonpreemptive nature of packet scheduling and the time-varying wireless channel.

References

  1. Balachandran, K., Kadaba, S. R., and Nanda, S. 1999. Channel quality estimation and rate adaptation for cellular mobile radio. IEEE Journal on Selected Areas in Communications 17, 7, 1244--1256.]]Google ScholarGoogle Scholar
  2. Benini, L. and de Micheli, G. 1997. Dynamic Power Management: Design Techniques and CAD Tools. Kluwer, Norwell, MA.]] Google ScholarGoogle Scholar
  3. Benini, L., Bogliolo, A., and de Micheli, G. 1999. A survey of design techniques for system-level dynamic power management. IEEE Transactions on VLSI Systems 8, 3, 813--833.]] Google ScholarGoogle Scholar
  4. Burd, T., Pering, A., Stratakos, A., and Brodersen, R. 2000. A dynamic voltage scaled microprocessor system. IEEE Journal of Solid-State Circuits 35, 11, 1571--1580.]]Google ScholarGoogle Scholar
  5. Chandrakasan, A., Sheng, S., and Brodersen, R. 1992. Low-power CMOS digital Design. IEEE Journal of Solid-State Circuits 27, 4, 473--484.]]Google ScholarGoogle Scholar
  6. Cho, K. and Samueli, H. 2000. A 8.75-MBaud single-chip digital QAM modulator with frequency-agility and beamforming diversity. In Proceedings CICC'00. Orlando, FL (May), 27--30.]]Google ScholarGoogle Scholar
  7. Chow, P., Cioffi, J., and Bingham, J. 1995. A practical discrete multitone transceiver loading algorithm for data transmission over spectrally shaped channels. IEEE Trans. on Communications 43, 2, 773--775.]]Google ScholarGoogle Scholar
  8. Demers, A., Keshav, S., and Shenker, S. 1989. Analysis and simulation of a fair queueing algorithm. Computer Communication Review 19, 4, 1--12.]] Google ScholarGoogle Scholar
  9. Govil, K., Chan, E., and Wasserman, H. 1995. Comparing algorithms for dynamic speed-Setting of a low-power CPU. In Proceedings MobiCom'95. Berkeley, CA (Nov.), 13--25.]] Google ScholarGoogle Scholar
  10. Gruian, F. 2001. Hard real-time scheduling for low energy using stochastic data and DVS processor. In Proceedings ISLPED'01. Huntington Beach, CA (Aug.), 46--51.]] Google ScholarGoogle Scholar
  11. Gutnik, V. and Chandrakasan, A. 1997. Embedded power supply for low-power DSP. IEEE Transactions on VLSI Systems 5, 4, 425--435.]] Google ScholarGoogle Scholar
  12. Hughes-Hartogs, D. 1987. Ensemble modem structure for imperfect transmission media. U.S. Patents, nos. 4,679,227 (July 1987), 4,731,816 (March 1988), 4,833,796 (May 1989).]]Google ScholarGoogle Scholar
  13. Jakes, W. 1994. Microwave Mobile Communication. John Wiley, New York.]] Google ScholarGoogle Scholar
  14. Jeffay, K., Stanat, D., and Martel, C. 1991. On non-preemptive scheduling of periodic and sporadic tasks. In Proceedings IEEE RTSS'91. San Antonio, TX (Dec.), 129--139.]]Google ScholarGoogle Scholar
  15. Krishna, C. and Lee, Y. 2000. Voltage-clock-scaling adaptive scheduling techniques for low power in hard real-time systems. In Proceedings RTAS'00. Washington, DC (June), 156--165.]] Google ScholarGoogle Scholar
  16. Lettieri, P., Schurgers, C., and Srivastava, M. 1999. Adaptive link layer strategies for energy efficient wireless networking. Wireless Networks 5, 5, 339--355.]] Google ScholarGoogle Scholar
  17. Lorch, J. and Smith, A. 1998. Software strategies for portable computer energy management. IEEE Personal Communications 5, 3, 60--73.]]Google ScholarGoogle Scholar
  18. Lu, S., Bharghavan, V., and Srikant, R. 1997. Fair scheduling in wireless packet networks. Computer Communication Review 27, 4, 63--74.]] Google ScholarGoogle Scholar
  19. Manzak, A. and Chakrabarty, C. 2000. Variable voltage task scheduling for minimizing energy or minimizing power. In Proceedings ICASSP'00. Istanbul, Turkey (June), 3239--3242.]] Google ScholarGoogle Scholar
  20. Nielsen, L., Niessen, C., Sparsø, J., and van Berkel, K. 1994. Low power operation using self-timed circuits and adaptive scaling of the supply voltage. IEEE Transactions on VLSI Systems 2, 4, 391--397.]] Google ScholarGoogle Scholar
  21. Parekh, A. and Gallager, R. 1994. A generalized processor sharing approach to flow control in integrated services networks: The multiple node case. IEEE/ACM Transactions on Networking 2, 2, 137--150.]] Google ScholarGoogle Scholar
  22. Pedram, M. 2001. Power optimization and management in embedded systems. In Proceedings ASP-DAC 2001. Yokohama, Japan, 239--244.]] Google ScholarGoogle Scholar
  23. Prabhakar, B., Biyikoglu, E., and el Gamal, A. 2001. Energy-efficient transmission over a wireless link via lazy packet scheduling. In Proceedings Infocom'01. Anchorage, AK (April), 386--394.]]Google ScholarGoogle Scholar
  24. Proakis, J. 1995. Digital Communications. McGraw-Hill Series in Electrical and Computer Engineering. McGraw-Hill New York.]]Google ScholarGoogle Scholar
  25. Raghunathan, V., Schurgers, C., Park, S., and Srivastava, M. 2002. Energy-aware wireless sensor networks. IEEE Signal Processing Magazine 19, 2, 40--50.]]Google ScholarGoogle Scholar
  26. Raghunathan, V., Spanos, P., and Srivastava, M. 2001. Adaptive power-fidelity in energy aware wireless systems. In Proceedings RTSS'01. London, UK (Dec.), 106--115.]] Google ScholarGoogle Scholar
  27. Schurgers, C. 2002. Energy-aware wireless communications. Ph.D. dissertation, University of California at Los Angeles.]]Google ScholarGoogle Scholar
  28. Schurgers, C., Aberthorne, O., and Srivastava, M. 2001a. Modulation scaling for energy aware communication systems. In Proceedings ISLPED'01. Huntington Beach, CA (Aug.), 96--99.]] Google ScholarGoogle Scholar
  29. Schurgers, C., Raghunathan, V., and Srivastava, M. 2001b. Modulation scaling for real-time energy aware packet scheduling. In Proceedings Globecom'01. San Antonio, TX (Nov.), 3653--3657.]]Google ScholarGoogle Scholar
  30. Schurgers, C. and Srivastava, M. 2002. Energy efficient wireless scheduling: Adaptive loading in time. In Proceedings WCNC'02. Orlando, FL (Mar.), 706--711.]]Google ScholarGoogle Scholar
  31. Shin, Y. and Choi, K. 1999. Power conscious fixed priority scheduling for hard real-time systems. In Proceedings DAC'99. New Orleans, LA (June), 134--139.]] Google ScholarGoogle Scholar
  32. Sinha, A. and Chandrakasan, A. 2000. Energy aware software. In Proceedings VLSI Design 2000. Calcutta, India (Jan.), 50--55.]] Google ScholarGoogle Scholar
  33. Srivastava, M., Chandrakasan, A., and Brodersen, R. 1996. Predictive system shutdown and other architectural techniques for energy efficient programmable computation. IEEE Transactions. on VLSI Systems 4, 1, 42--55.]] Google ScholarGoogle Scholar
  34. Tenenbaum, A. 1990. Computer Networks. Prentice-Hall, Englewood Cliffs, NJ.]]Google ScholarGoogle Scholar
  35. Thoen, S., van der Perre, L., Gyselinckx, B., Engels, M., and de Man, H. 2000. Predictive adaptive loading for HIPERLAN II. In Proceedings VTC'00 Fall. Boston, MA (Sept.), 2166--2172.]]Google ScholarGoogle Scholar
  36. Ue, T., Sampei, S., Morinaga, N., and Hamaguchi, K. 1998. Symbol rate and modulation level-controlled adaptive modulation/TDMA/TDD System for High-Bit Rate Wireless Data Transmission. IEEE Transactions on Vehicular Technology 47, 4, 1134--1147.]]Google ScholarGoogle Scholar
  37. Wang, H. and Mandayam, N. 2001. Delay and energy constrained dynamic power control. In Proceedings Globecom'01. San Antonio, TX (Nov.), 1287--1291.]]Google ScholarGoogle Scholar
  38. Webb, W. and Steele, R. 1995. Variable rate QAM for mobile radio. IEEE Transactions on Communications 43, 7, 2223--2230.]]Google ScholarGoogle Scholar
  39. Weiser, M., Welch, B., Demers, A., and Shenker, B. 1994. Scheduling for reduced CPU energy. In Proceedings USENIX Symposium on Operating Systems Design and Implementation. Monterey, CA (Nov.), 13--23.]] Google ScholarGoogle Scholar
  40. Yao, F., Demers, A., and Shenker, S. 1995. A scheduling model for reduced CPU energy. In Proceedings 36th Annual Symposium on Foundations of Computer Science. Milwaukee, WI (Oct.), 374--385.]] Google ScholarGoogle Scholar

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