On the capacity of MIMO broadcast channels with partial side information

Abstract

Abstract-In multiple-antenna broadcast channels, unlike point-to-point multiple-antenna channels, the multiuser capacity depends heavily on whether the transmitter knows the channel coefficients to each user. For instance, in a Gaussian broadcast channel with transmit antennas and single-antenna users, the sum rate capacity scales like log log for large if perfect channel state information (CSI) is available at the transmitter, yet only logarithmically with if it is not. In systems with large , obtaining full CSI from all users may not be feasible. Since lack of CSI does not lead to multiuser gains, it is therefore of interest to investigate transmission schemes that employ only partial CSI. In this paper, we propose a scheme that constructs random beams and that transmits information to the users with the highest signal-to-noise-plus-interference ratios (SINRs), which can be made available to the transmitter with very little feedback. For fixed and increasing, the throughput of our scheme scales as log log , where is the number of receive antennas of each user. This is precisely the same scaling obtained with perfect CSI using dirty paper coding. We furthermore show that a linear increase in throughput with can be obtained provided that does not not grow faster than log .We also study the fairness of our scheduling in a heterogeneous network and show that, when is large enough, the system becomes interference dominated and the probability of transmitting to any user converges to 1 , irrespective of its path loss. In fact, using = log transmit antennas emerges as a desirable operating point, both in terms of providing linear scaling of the throughput with as well as in guaranteeing fairness.Index Terms-Broadcast channel, channel state information (CSI), multiuser diversity, wireless communications.

Impact of Interference on the Performance of Selection Based Parallel Multiuser Scheduling

“…Therefore, the individual average sum rate capacity [11][12][13] can be obtained by averaging (11) with respect to p γSINR m (γ) as C m = ∞ 0 log 2 (1 + γ) p γSINR m (γ)dγ. By adding the individual average sum rate capacities of the K s scheduled users, we can obtain the total average sum rate capacity as …”

Performance of Layered Steered Space–Time Codes in Wireless Systems

“…Some work has been done in extending the opportunistic beamforming idea to the case where base station and mobile stations all have multiple antennas [9], but this has not been yet studied extensively. Multiple random beams are used in [64] with limited feedback to communicate with many users. Further work on feedback reduction has been conducted in [65], where it is shown that the feedback rate can be minimized without losing the gains due to adaptive modulation and multi-user diversity.…”

Fundamental Limits in MIMO Broadcast Channels

“…The idea is based on transmitting M random beams and exploiting multi-user diversity. The details are described in [18] (a similar construction, albeit with little analysis, appears in the appendix of [38]). …”

“…In this way, the multi-user diversity of the system can be exploited. (This is the jist of the idea-for more details see [18]. )…”