Optimization of Training and Scheduling in the Non-Coherent SIMO Multiple Access Channel

  • Authors:
  • S. Murugesan;E. Uysal-Biyikoglu;P. Schniter

  • Affiliations:
  • Ohio State Univ., Columbus;-;-

  • Venue:
  • IEEE Journal on Selected Areas in Communications
  • Year:
  • 2007

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Abstract

Channel state information (CSI) is important for achieving large rates in MIMO channels. However, in time-varying MIMO channels, there is a tradeoff between the time/energy spent acquiring channel state information (CSI) and the time/energy remaining for data transmission. This tradeoff is accentuated in the MIMO multiple access channel (MAC), since the number of channel vectors to be estimated increases with the number of users. Furthermore, the problem of acquiring CSI is tightly coupled with the problem of exploiting CSI through multiuser scheduling. This paper considers a block-fading MAC with coherence time T, n uncoordinated users-each with one transmit antenna and the same average power constraint, and a base station with M receive antennas and no a priori CSI. For this scenario, a training-based communication scheme is proposed and the training and multiuser-scheduling aspects of the scheme are jointly optimized. In the high-SNR regime, the sum capacity of the non-coherent SIMO MAC is characterized and used to establish the SNR-scaling-law optimality of the proposed scheme. In the low-SNR regime, the sum-rate of the proposed scheme is found to decay linearly with vanishing SNR when flash signaling is incorporated. Furthermore, this linear decay is shown to be order-optimal through comparison to the low-SNR sum capacity of the non-coherent SIMO MAC. A by product of these SNR-asymptotic analyses is the observation that non-trivial scheduling (i.e., scheduling a strict subset of trained users) is advantageous at low SNR, but not at high SNR. The sum-rate and per-user throughput are also explored in the large-n and large-M regimes. Non-coherent capacity, training, multiple access channel, multiuser scheduling, opportunistic scheduling.