Fundamental Limits of Training-Based Uplink Multiuser MIMO Systems

Abstract

In this paper, we endeavour to seek a fundamental understanding of the potentials and limitations of training-based multiuser multiple-input multiple-output (MIMO) systems. In a multiuser MIMO system, users are geographically separated. So, the near-far effect plays an indispensable role in channel fading. The existing optimal training design for convenitional MIMO does not take the near-far effect into account, and thus is not applicable to a multiuser MIMO system. In this work, we use the majorization theory as a basic tool to study the tradeoff between the channel estimation quality and the information throughput. We establish tight upper and lower bounds of the throughput, and prove that the derived lower bound is asymptotically optimal for throughput maximization at high signal-to-noise ratio. Our analysis shows that the optimal training sequences for throughput maximization in a multiuser MIMO system are in general not orthogonal to each other. Futhermore, due to the near-far effect, the optimal training design for throughput maximization is to deactivate a portion of users with the weakest channels in transmission. These observations shed light on the practical design of training-based multiuser MIMO systems. Index Terms-Training-based multiuser MIMO, throughput maximization, massive MIMO

Semi-Blind Channel-and-Signal Estimation for Uplink Massive MIMO With Channel Sparsity

“…In principle, the estimated data can be used to further refine the channel estimate and hence improve the system performance. To this end, the authors in [9] proposed a joint channel and signal estimation method which involves approximate message passing over the factor graph obtained by factorizing the probability distribution p H,XD|Y,XP in (8).…”

Structured Turbo Compressed Sensing for Downlink Massive MIMO-OFDM Channel Estimation

“…We next transform the channel response matrix from the angle-frequency domain to the angledelay domain with an inverse Fourier transform F * , i.e., H f F * = H d in (5). Then the received signal in the delay domain can be represented as…”

“…where y 5 The STCS algorithms in this paper can be extended to the system model such as (13) in [21]. However, this involves more complicated signal processing since then the path delay taps are mixed in the channel output.…”

“…The maximum delay length L is 16. Once H d is generated, H f can be obtained from (5). The training length is M = 103 ≈ 0.4N .…”

“…In massive MIMO-OFDM, the acquisition of accurate channel state information (CSI) is essential for harvesting the capacity and reliability enhancement promised by the system. However, conventional channel estimation approaches require that the pilot length should be at least the same as the number of transmit antennas [5], [6]. This may cause a significant pilot overhead in the downlink of a massive MIMO-OFDM system, where a large number of antennas are deployed at base station (BS).…”