September 18, 2017

Machine learning using neural networks in digital signal processing for RF transceivers

  • Fischer G.
  • Jueschke P.

Communication is changing to a wireless world. Any wireless communication system needs radio frontends (transceivers) to link higher layer signals to the air interface. The increasing standard and technology diversity require transceivers with high flexibility supporting many of frequencies, standards and signal requirements. Design to cost, the demand for highest energy efficiency and MIMO systems don't ease the challenges for transceiver designs. A change in paradigm can be an opener to face these challenges for future systems. This work presents the use of neural networks for signal processing, impairment mitigation, control and optimization in transceiver systems.

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Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

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How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...