Solving the Near-Far Problem: Exploitation of Spatial and Spectral Diversity in Wireless Personal Communication Networks

  • Brian G. Agee
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 262)


A general approach is presented for overcoming the near-far power management problem in wireless communication networks, by exploiting the spatial or spectral diversity inherent to the communication network. It is shown that the stability and efficiency of near-far power management strategies used in CDMA, TDMA, or FDMA communication networks are greatly enhanced by exploiting the spatial diversity of the communication network. It is also shown that the same improvements in stability and efficiency can be obtained by exploiting the spectral diversity of CDMA networks. In particular, it is shown that use of on M-element multiport antenna array at the base station of any communication network can increase the frequency reuse of the network by a factor of M and greatly broaden the range of input SINRs required for adequate demodulation of the subscriber signals in the network. A similar result is obtained for CDMA networks employing an M-chip modulation on symbol (MOS) DSSS spreading formats where the code sequence repeats once per message symbol, even if a single antenna is used at the base stations in the network. These results are supported via computer simulations for an FDMA network employing a 3-element antenna array to separate 3 AMPS-type FM signals received at the same frequency, and for a CDMA communication network employing a single-antenna optimal linear despreader to separate 32 users with 64-chip MOS-DSSS modulation formats.


Antenna Array Power Management Subscriber Signal Spreading Code Power Management Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    W. A. Gardner, “On the Meanings and Uses of Coherence in Signal Processing,” Tech. Rep. No. SIPL-89-2, Dept. of EECS, University of California, Davis, December 1988Google Scholar
  2. [2]
    W. A. Gardner, “Exploitation of Spectral Correlation as Inherent Frequency Diversity for Signal Correction in Digital Communication Systems,” Tech. Rep. No. SIPL-89-5, Department of EECS, University of California, Davis, 1989Google Scholar
  3. [3]
    B. Agee, “The Property Restoral Approach to Blind Adaptive Signal Extraction,” Ph.D. Dissertation, Department of EECS, University of California, Davis, June 1989Google Scholar
  4. [4]
    B. G. Agee, S. V. Schell, W. A. Gardner, “Spectral Self-Coherence Restoral: A New Approach to Blind Adaptive Signal Extraction Using Antenna Arrays,” Proc. IEEE, vol. 78, no. 4, pp. 753–767, April 1990Google Scholar
  5. [5]
    B. Agee, “Blind Separation and Capture of Communication Signals Using a Multitarget Constant Modulus Beamformer,” in Proc. 1989 IEEE Military Comm. Conf., 1989Google Scholar
  6. [6]
    K. Bakhru, D. J. Torrieri, “The Maximin Algorithm for Adaptive Arrays and Frequency-Hopping Communications,” IEEE Trails. Ant. and Prop., vol. AP-32, Sept. 1984Google Scholar
  7. [7]
    D. Dlugos, R. Scholtz, “Acquisition of Spread Spectrum Signals by an Adaptive Array,” IEEE Trans. ASSP, vol. 37, no. 8, pp. 1253–1270, Aug. 1989CrossRefGoogle Scholar
  8. [8]
    B. Agee, “Fast Acquisition of Burst and Transient Signals Using a Predictive Adaptive Beamformer,” in Proc. 1989 IEEE Military Comm. Conf., 1989Google Scholar

Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Brian G. Agee
    • 1
  1. 1.Radix Technologies, Inc.CaliforniaUSA

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