This paper addresses channel estimation and data equalization on frequency-selective 1-bit quantized Multiple Input-Multiple Output (MIMO) systems. No joint processing or Channel State Information is assumed at the transmitter, and therefore our findings are also applicable to the uplink of Multi-User MIMO systems. System models for both Orthogonal Division Frequency Multiplexing (OFDM) and single-carrier schemes are developed. A Cram\'er-Rao Lower Bound for the estimation problems is derived. The two nonlinear algorithms Expectation Maximization (EM) and Generalized Approximate Message Passing (GAMP) are adapted to the problems, and a linear method based on the Bussgang theorem is proposed. In the OFDM case, the linear method enables subcarrier-wise estimation, greatly reducing computational complexity. Simulations are carried out to compare the algorithms with different settings. The results turn out to be close to the Cram\'er-Rao bound in the low Signal to Noise Ratio (SNR) region. The OFDM setting is more suitable for the nonlinear algorithms, and that the linear methods incur a performance loss with respect to the nonlinear approaches. In the relevant low and medium SNR regions, the loss amounts to 2-3 dB and might well be justified in exchange for the reduced computational effort, especially in Massive MIMO settings.

翻译：本文论述频率选择 1 位数多输入多输出(MIIMO)系统的频道估计和数据均匀性。 发射机不假定联合处理或频道国家信息,因此,我们的调查结果也适用于多用户MIMO系统的上链接。 开发了Orthodal Division 频率多重化(OFDM)和单载机系统的系统模型。 生成了用于估算问题的Cram\'er- Rao 低频值。 两种非线性算法“ 期望最大化” 和“ 通用消息传递( GAMP) ” 都适应了问题, 并提出了基于 Busgang 的线性方法。 在OrdM 案中, 线性方法可以进行亚性估算, 大大降低计算复杂性。 模拟是为了比较不同环境的算法。 结果与Cram\'er-Rao 相近于“ 信号最大化” (EM) 和“ 通用消息传递( GAMMP ) 区域。 在非线性、 不线性成本的M 中, 的计算方法可能与非线性成本损失。