On Channel Estimation Performance in Multi-Cell TDD Systems

  • Gacanin H.
  • Ligata A.

In this paper, we analyze the bit error rate (BER) and mean square error (MSE) performance for data uplink of a multi-cell TDD system with imperfect knowledge of channel state information (CSI). We investigate the impact of imperfect CSI, channel tracking methods and pilot contamination on the achievable performance, and derive the closed-form MSE and BER expressions. Performance degradation due to channel aging is analyzed in the context of different channel tracking methods and to what extent we can control it by proper system design. Our results indicate that the channel estimator coherence time should be properly selected given the impact of channel estimation over different blocks on achievable performance. Moreover, decision-feedback channel tracking can be used to optimize transmission efficiency given the target performance.

Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

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 ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

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 ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

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 ...