Quantized Precoding for Massive MU-MIMO

Abstract

Abstract-Massive multiuser (MU) multiple-input multipleoutput (MIMO) is foreseen to be one of the key technologies in fifth-generation wireless communication systems. In this paper, we investigate the problem of downlink precoding for a narrowband massive MU-MIMO system with low-resolution digital-toanalog converters (DACs) at the base station (BS). We analyze the performance of linear precoders, such as maximal-ratio transmission and zero-forcing, subject to coarse quantization. Using Bussgang's theorem, we derive a closed-form approximation on the rate achievable under such coarse quantization. Our results reveal that the performance attainable with infinite-resolution DACs can be approached using DACs having only 3 to 4 bits of resolution, depending on the number of BS antennas and the number of user equipments (UEs). For the case of 1-bit DACs, we also propose novel nonlinear precoding algorithms that significantly outperform linear precoders at the cost of an increased computational complexity. Specifically, we show that nonlinear precoding incurs only a 3 dB penalty compared to the infinite-resolution case for an uncoded bit error rate of 10 −3 , in a system with 128 BS antennas that uses 1-bit DACs and serves 16 single-antenna UEs. In contrast, the penalty for linear precoders is about 8 dB.Index Terms-Massive multi-user multiple-input multipleoutput, digital-to-analog converter, Bussgang's theorem, minimum mean-square error precoding, convex optimization, semidefinite relaxation, Douglas-Rachford splitting, sphere precoding.

Near-Optimal Interference Exploitation 1-Bit Massive MIMO Precoding Via Partial Branch-and-Bound

“…We focus on a generic downlink massive MIMO system, where the BS with each RF chain equipped with a pair of 1-bit DACs communicates with multiple single-antenna users in the same timefrequency resource simultaneously. We denote the total number of transmit antennas at the BS by Nt and the total number of users by K, where Nt ≫ K. Since we focus on the effect of 1-bit DACs on the data transmission, we assume ideal ADCs are adopted at each receiver and perfect CSI is available at the BS [15]- [20]. We denote the intended data symbol for user k by s k , which is assumed to be drawn from a unit-norm M-PSK constellation, and we express the data symbol vector as s = [s1, s2, · · · , sK] T ∈ C K×1 .…”

Spectral Efficiency of Mixed-ADC Massive MIMO

“…This assumption implies that the quantizer output levels are identical for the real and imaginary parts, and thus we use α m to represent both α m,r and α m,i . The expression in (14) has been defined in the literature as the equivalent gain of a non-linear device [42], [43].…”

“…sin 2 (2πxδn) n and in (a) we have used Eq. (14) of [47]. Equation (46) states that, for standard onebit quantization, increasing the spatial oversampling in a large antenna array (d/λ → 0) increases the quantization noise power proportional to (d/λ) −1 .…”

On Performance of Quantized Transceiver in Multiuser Massive MIMO Downlinks

“…While for DAC, the Bussang Theorem can also be applied for making the operation linearly approximated as exemplified in [7] and [12]. Similarly, we have the following representation:…”