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Optimized Combination of Local Beams for Wireless Sensor Networks.

Semyoung Oh1, Young-Dam Kim2, Daejin Park3

  • 1Department of Electronic and Communication Engineering, Air Force Academy, Cheongju 28187, Korea. tpaud12345@airforce.mil.kr.

Sensors (Basel, Switzerland)
|February 6, 2019
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Summary

This study introduces an optimization algorithm using simulated annealing to reduce interference in wireless sensor networks. The method effectively lowers interference-to-noise ratio (INR) in millimeter wave channels, improving signal quality.

Keywords:
analog uniform linear arraycollaborative beamformingmillimeter wave channelsimulated annealing

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Area of Science:

  • Wireless communication networks
  • Signal processing
  • Optimization algorithms

Background:

  • Distributed wireless sensor networks (WSNs) often use analog uniform linear arrays for beam generation.
  • Irregular mounting surfaces cause beams to point in random directions, leading to unpredictable sidelobes.
  • Millimeter wave (mmWave) channels are susceptible to interference, impacting network performance.

Purpose of the Study:

  • To propose an optimization algorithm for optimal coherent combination of distributed local beams in WSNs.
  • To minimize the average interference-to-noise ratio (INR) in mmWave channels.
  • To reduce unwanted sidelobes in beam patterns.

Main Methods:

  • The proposed algorithm utilizes a single-objective simulated annealing meta-heuristic.
  • It optimizes the coherent combination of local beams from distributed nodes.
  • The objective function targets the minimization of average INR.

Main Results:

  • Simulations demonstrate that beam synthesis can create deterministic mainlobes but also unpredictable sidelobes.
  • The proposed algorithm significantly decreases the average INR.
  • Average INR improvements of 12.2 dB and 3.1 dB were observed at specific angles (π/6 and 2π/3) without substantial loss of signal-to-noise ratio (SNR).

Conclusions:

  • The simulated annealing-based optimization algorithm effectively reduces average INR in WSNs.
  • The method achieves significant interference reduction in mmWave channels.
  • This approach enhances signal quality by mitigating sidelobe interference without compromising desired signal strength.