March 20, 2017

Virtual Massive MIMO Beamforming Gains for 5G User Terminals

  • Amin M.
  • Haardt M.
  • Zirwas W.

For future 5G systems significant performance benefits are expected from massive MIMO, especially in combination with tight inter-cell cooperation including joint-transmission using cooperative multi-point transmission. Most massive MIMO evaluations concentrate on the base station side with the goal to achieve high spectral efficiency by MU-MIMO or large coverage by strong beamforming gains. Especially for the, here interesting, below 6 GHz RF-bands user equipment (UE) sided analysis is typically limited to four or mostly eight antenna elements per UE, which can be justified by the limited space to place more antenna elements as well as the related UE complexity. At the same time, there would be many benefits from UE-sided beamforming, ranging from improved channel estimation and prediction accuracy, effective interference suppression up to coverage and spectral efficiency gains on the system level. In our previous work, we have already proposed virtual beamforming for channel estimation and prediction, while here we extend the concept to user data transmission over virtually generated beams. Virtual beamforming directly applied to user data can be very inefficient, a challenge we overcome by parallel transmission over a set of coded virtual beams.

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5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

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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 ...