Some results on the sum-rate capacity of MIMO fading broadcast channels

Abstract

We study the ergodic sum-rate capacity of the fading MIMO broadcast channel which is used to model the downlink of a cellular system with Nt transmit antennas at the base and K mobile users each having Nr receive antennas. Assuming perfect channel state information (CSI) for all users is available at the transmitter and the receivers, we evaluate the sum-rate capacity numerically using the duality between uplink and downlink. Assuming Nt K, we also derive both upper and lower bounds on the sum-rate capacity to study its increase rate due to multi-user diversity. Finally, we compare three transmission schemes which use the single-user-MIMO scheme (SU-MIMO), ranked known interference (RKI) and zeroforcing beamforming (ZFB), respectively, to transmit to a selected set of users in order to approach the sum-rate capacity. We show that both ZFB and RKI outperform SU-MIMO in a cellular downlink scenario when many mobile users are present.

Delay Considerations for Opportunistic Scheduling in Broadcast Fading Channels

“…However, beamforming has long been proposed as a heuristic method to mitigate the interference in the transmitter and to send multiple beams to different users. Although, beamforming is not optimal in achieving the sum rate capacity, its throughput does scale the same as that of dirty paper coding for a system with many users and has much less complexity than that of dirty paper coding [36], [37]. In this paper, for a system with M transmit antennas, we assume a simple model in which the base station transmits to M different receivers at each channel use.…”

A Comparison of Time-Sharing, DPC, and Beamforming for MIMO Broadcast Channels With Many Users

“…Furthermore, in [16], the asymptotic behavior of the throughput for DPC and time sharing are obtained for large SNRs and large when the other parameters of the system are fixed. However, motivated by a cellular system with a large number of users (say 100), and having , which is about , we consider a different region in which is large and is either fixed or growing to infinity at a much slower pace, i.e., logarithmically with (see also [15]). This letter also generalizes a result in [17], where the scaling laws of the sum rate of DPC is derived for the case where is fixed and .…”

Multi-user diversity for the multiple antenna broadcast channel with linear receivers: asymptotic analysis

“…While in the single antenna scenario the attenuation of the channel plays a role, in the MIMO case the authors showed that the singular values of the channel better characterize performance. In [10], [11], [12] capacity scaling laws are considered under different information-theoretic scheduling disciplines. A common result in all these analyses is the rather slow log log N increase in average capacity with increasing size of the user population, N .…”