Non-Orthogonal Downlink Transmissions Using Module Multilevel Codes

  • Claussen H.
  • Fang D.
  • Zhang M.

In this paper, a new approach to non-orthogonal multiuser downlink transmission, referred to as modulo multilevel codes based multiuser superposition transmissions (MMC-MUST), is proposed, which addresses the main issues of existing solutions by 1) overcoming the challenge faced by NOMA when users have the similar channel conditions; 2) adopting the modulo multilevel codes for multiuser superposition transmissions which forms a non-uniform composite constellation, offering unequal error protection for near and far users; and 3) supporting arbitrary number of users for superposition transmissions. The simulation results show that the proposed MMC-MUST outperforms NOMA in MIMO systems.

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 ...