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Feedback power control strategies in wireless sensor networks with joint channel decoding.
Andrea Abrardo1, Gianluigi Ferrari, Marco Martalò
1Department of Information Engineering, University of Siena, Italy; E-Mails: abrardo@dii.unisi.it ; perna@unisi.it.
Sensors (Basel, Switzerland)
|February 1, 2012
Summary
This study introduces feedback power control strategies for multiple access schemes, optimizing signal-to-noise ratios (SNRs) for efficient communication. The methods balance SNR or minimize energy consumption, enhancing system performance.
Area of Science:
- Wireless communication systems
- Information theory
- Signal processing
Background:
- Multiple access schemes are crucial for wireless networks.
- Correlated sources and joint channel decoding (JCD) present unique challenges.
- Efficient power control is vital for optimizing performance and energy consumption.
Purpose of the Study:
- To derive feedback power control strategies for block-faded multiple access schemes.
- To define the feasible signal-to-noise ratio (SNR) region for these schemes.
- To propose strategies that minimize energy consumption while ensuring reliable communication.
Main Methods:
- Derivation of the feasible SNR region for multiple access schemes.
- Proposal of two feedback power control strategies: balanced SNR and optimized (unbalanced) SNR.
- Analysis of practical strategy versions with limited power control range.
- Investigation of robustness to interference and noisy feedback channels.
Main Results:
- Two feedback power control strategies, "balanced SNR" and "unbalanced SNR," are proposed.
- The study considers both unlimited and limited power control ranges.
- Robustness analysis confirms effectiveness under non-ideal conditions.
- Successful application to a central estimating officer (CEO) scenario.
Conclusions:
- The proposed feedback power control strategies effectively manage SNRs in complex wireless environments.
- Optimized strategies offer reduced energy consumption while maintaining communication reliability.
- The framework is applicable to advanced scenarios like the CEO problem.
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