Sunday, November 4, 2012

Multiuser MIMO: A brief introduction

  • Simultaneous transmissions to multiple stations. Extends the idea of MIMO where multiple streams are sent to a single stations. Here the multiple streams are sent to different stations.
  • Other cases where the MU-MIMO model applies- "The downlink of a DSL system with crosstalk between the wires for each user is one scenario where the transmitter terminals can cooperate, but the far end of the MIMO channel cannot."
  • Capacity of MU-MIMO channel in accordance with the dirty paper approach: It has been shown that  in the case where the interference on the channel is known before hand, the achievable capacity is similar to that which can be achieved without interference.
  • Two approaches to MU-MIMO: (1) Signal processing  and (2) Dirty paper approach.
  • MIMO channels are represented by the standard equation: y = Hx + w, where H is the transfer function of the channel whose dimensions are determined by the receivers(nr) x transmitters(nt).
  • MU-MIMO is more suited to WLANs rather than cellular because the channel is rich with multipath and is quasi-static. Cellular is difficult because of cost constraints, mobility,and small cell size.
  • Most recent MU-MIMO works assumes that the channel state information (CSI) is available at the transmitter.
  • SU-MIMO benefit from CSI only when nt > nr or when operating with low SNRs.
  • MU-MIMO systems always benefit from CSI.
  • Obtaining CSI: For TDD systems, use of training or pilot data UL (because same channel is used - assuming reciprocity). For FDD systems, explicit feedback from the receiver is used based on the training data sent downlink.
  • Multiple access interference (MAI) is the interference caused to one user because of simultaneous transmission to other users. Techniques like multiuser detection (MUD) could be used to detect signals.
  • Ideally, using CSI, MAI should be mitigated at the transmitter. 
  • Capacity of MU-MIMO channel is based on the fraction of power allocated to each of the users of the system.
  • Channel inversion is a linear processing technique used at the transmitter to mitigate MAI. x = H†d = H* (HH*)–1d
  • Expected capacity improvement with MU-MIMO is min(nT,nR). However, if the channel matrix H is ill conditioned, then the gains with linear processing are not achieved.


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