This paper studies a downlink multi-user multiple-input multiple-output (MU-MIMO) system, where the precoding matrix is computed at a baseband unit (BBU) and then transmitted to the remote antenna array over a limited-capacity digital fronthaul. The limited bit resolution of the fronthaul introduces quantization effects that are explicitly modeled. We propose a novel sum rate maximization framework that directly incorporates the quantizer's constraints into the precoding design. The resulting maximization problem, a non-convex mixed-integer program, is addressed using a new iterative algorithm inspired by the weighted minimum mean square error (WMMSE) methodology. The precoding optimization subproblem is reformulated as an integer least-squares problem and solved using a novel sphere decoding (SD) algorithm. Additionally, a low-complexity expectation propagation (EP)-based method is introduced to enable the practical implementation of quantized precoding in MU-massive MIMO (MU-mMIMO) systems. Furthermore, numerical evaluations demonstrate that the proposed precoding schemes outperform conventional approaches that optimize infinite-resolution precoding followed by element-wise quantization. We also propose a heuristic quantization-aware precoding method with comparable complexity to the baseline but superior performance. In particular, the EP-based approach offers near-optimal performance with substantial complexity reduction, making it well-suited for real-time MU-mMIMO applications.

翻译：本文研究下行链路多用户多输入多输出（MU-MIMO）系统，其中预编码矩阵在基带单元（BBU）计算，随后通过有限容量的数字前传链路传输至远端天线阵列。前传链路的有限比特分辨率引入的量化效应被显式建模。我们提出一种新颖的和速率最大化框架，将量化器约束直接纳入预编码设计。由此产生的最大化问题——一个非凸混合整数规划——通过受加权最小均方误差（WMMSE）方法启发的新迭代算法求解。预编码优化子问题被重构为整数最小二乘问题，并采用一种新颖的球面解码（SD）算法求解。此外，引入一种基于期望传播（EP）的低复杂度方法，以实现量化预编码在MU大规模MIMO（MU-mMIMO）系统中的实际部署。数值评估进一步表明，所提出的预编码方案优于传统先优化无限分辨率预编码再进行逐元素量化的方法。我们还提出一种启发式的量化感知预编码方法，其复杂度与基线方案相当但性能更优。特别地，基于EP的方法能以显著降低的复杂度实现接近最优的性能，使其非常适合实时MU-mMIMO应用。