Joint decompression and decoding for cloud radio access networks

Seok Hwan Park, Osvaldo Simeone, Onur Sahin, Shlomo Shamai

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

In this work, joint decompression and decoding is studied for the uplink of multi-antenna cloud radio access networks. In this system, a set of multi-antenna mobile stations (MSs) wish to communicate with a 'cloud' decoder through a set of multi-antenna base stations (BSs), which are connected to the cloud decoder through digital backhaul links of limited capacity. The BSs compress the received signal and send it to the cloud decoder, which performs joint decoding of the signals from all MSs. While the conventional solution prescribes that the cloud decoder performs first decompression and then decoding, recent work has shown that potentially larger rates can be achieved with joint decompression and decoding (JDD) at the cloud decoder. The sum-rate maximization problem with JDD, under the assumption of Gaussian test channels, is shown here to be an instance of a class of non-convex problems known as Difference of Convex (DC) problems. Based on this observation, an iterative algorithm based on the Majorization Minimization (MM) approach is proposed that guarantees convergence to a stationary point of the sum-rate maximization problem. Numerical results demonstrate the advantage of the proposed algorithm compared to the conventional approach based on separate decompression and decoding.

Original languageEnglish (US)
Article number6480790
Pages (from-to)503-506
Number of pages4
JournalIEEE Signal Processing Letters
Volume20
Issue number5
DOIs
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Keywords

  • Cloud radio access networks
  • distributed source coding
  • multi-cell processing
  • noisy network coding

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