Performance of distributed beamforming for dense relay deployments in the presence of limited feedback information

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This paper studies the impact of quantized channel feedback on the performance of a (coherent) distributed beamforming (DBF) scheme. The analysis is done in the context of a wireless access network, and the goal is to provide an adequate broadband coverage for users located inside buildings. In the examined scenario, instead of trying to reach the serving base station (BS) directly, we assume that each mobile user equipment (UE) receives assistance from a cooperative group of network elements that is placed in close proximity (e.g., in the same room or office). This cluster of cooperative network elements is formed by a large number of low-cost relaying stations (RSs), which have fixed locations and are equipped with only one antenna. To simplify the analysis, communication in the first hop (i.e., from the mobile UE to the elements of the cluster) is assumed practically costless, making the bottleneck lie in the second hop of the system (i.e., from the elements of the cluster to the serving BS). Closed-form approximations for three different performance measures are derived (i.e., outage probability, ergodic capacity, bit error probability), providing accurate predictions of the fundamental limits that proposed system architecture is able to provide. Our analysis reveals that the achievable end-to-end performance when using a small amount of phase feedback information (per RS in the second hop) is very close to the full phase information upper bound, paving the way to the use of massive DBF architectures as a practical way to cope with high data rate demands of future wireless systems.
Original languageEnglish
Article number88
Pages (from-to)1-14
JournalEurasip Journal on Advances in Signal Processing
Publication statusPublished - 2013
MoE publication typeA1 Journal article-refereed


  • Cooperative communications
  • Distributed beamforming
  • Decode-and-forward relays
  • Heterogeneous networks
  • Limited feedback information
  • Massive network element deployments
  • Non-perfect channel knowledge
  • Performance prediction


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