Low-complexity transmission mode selection in MU-MIMO systems

Abstract

We propose a low-complexity transmission strategy in multi-user multiple-input multiple-output downlink systems. The adaptive strategy adjusts the precoding methods, denoted as the transmission mode, to improve the system sum rates while maintaining the number of simultaneously served users. Three linear precoding transmission modes are discussed, i.e., the block diagonalization zero-forcing, the cooperative zero-forcing (CZF), and the cooperative matched-filter (CMF). Considering both the number of data streams and the multiple-antenna configuration of users, we modify the common CZF and CMF modes by allocating data streams. Then, the transmission mode is selected between the modified ones according to the asymptotic sum rate analyses. As instantaneous channel state information is not needed for the mode selection, the computational complexity is significantly reduced. Numerical simulations confirm our analyses and demonstrate that the proposed scheme achieves substantial performance gains with very low computational complexity.

Low-complexity user scheduling in the downlink massive MU-MIMO system with linear precoding

“…To begin with, let us define D k = V k U 1/2 k , where V k ∈ C T ×N is constructed by the left singular vectors corresponding to non-zero singular values ofH k in (5). The diagonal power allocation matrix is expressed as U k = diag (P k,1 , ..., P k,N ), where tr (U k ) = P k .…”

“…In the DL MU-MIMO system, sophisticated precoding technique such as the dirty paper coding (DPC) [3] is shown to achieve the capacity region, which pre-cancels interference at the cost of high computational complexity. Recently, massive MU-MIMO system [4] has been attracting increasing attentions for its very high spectral and power efficiency [5]- [7]. The large number of antennas equipped at base station (BS) can provide sufficient transmit dimensions for linear precoders to eliminate interferences.…”

An ESPRIT-Based Approach for 2-D Localization of Incoherently Distributed Sources in Massive MIMO Systems

“…This is because they are capable of providing more degrees of freedom to significantly improve the system's data rate and link reliability [1]. Recently, the massive MIMO, which is also known as the large-scale MIMO, has been attracting increasing attentions owing to its unprecedented potential of high spectral efficiency [2]- [8]. In massive MIMO systems, the base station (BS) is equipped with a hundred or a few hundred antennas, and serves tens of user terminals (UTs) simultaneously.…”