AI 中文总结
针对多用户大规模 MIMO 系统下行链路，提出多比特量化预编码方法（MQP），通过 l0 范数惩罚等手段扩展量化预编码。经差异原则选正则化参数，有高效迭代算法。纳入 GaBP 降复杂度，仿真表明该方法在误码率性能上有优势。
AI 中文摘要
我们为具有低分辨率数模转换器的多用户大规模 MIMO 系统的下行链路提出了一种新颖的多比特量化预编码方法。新方法称为多比特量化预编码（MQP），通过由平滑替代函数近似的 l0 范数惩罚来强制有限字母约束，从而产生一个重新表述的问题，然后通过分式规划将其凸化，最终将量化预编码扩展到超过 1 比特字母表。所提出方法的正则化参数通过与渐进非凸连续相结合的差异原则来选择，从而产生一种有原则且可重复的超参数调整方法以及一种高效的迭代算法，每次迭代具有闭式、最小二乘型更新。为了进一步降低该方法的计算复杂度，我们纳入了高斯置信传播（GaBP）步骤以将最小二乘更新转化为线性时间。对不同规模系统进行的仿真表明，与各种信道条件下的现有量化预编码算法相比，带和不带 GaBP 的 MQP 方法均实现了具有竞争力或更优的误码率性能。
英文摘要
We propose a novel multibit quantized precoding method for the downlink of multi-user massive MIMO systems with low-resolution digital-to-analog converters. The new method, termed multibit quantized precoding (MQP), enforces the finite-alphabet constraint through an l0-norm penalty, approximated by a smooth surrogate so as to yield a reformulated problem, which is then convexized via fractional programming, ultimately extending quantized precoding beyond 1-bit alphabets. The regularization parameter of the proposed method is selected via a discrepancy principle integrated with graduated non-convexity continuation, resulting in a principled and reproducible hyperparameter tuning method and an efficient iterative algorithm with a closed-form, least-squares-type update per iteration. In order to further reduce the computational complexity of the method, we include a Gaussian belief propagation (GaBP) step for turning the least-squares update in linear-time. Simulations performed for systems with different sizes demonstrate that both methods, namely the MQP with and without GaBP, achieve competitive or superior error-rate performance compared to state-of-the-art quantized precoding algorithms under various channel conditions.
CommentsSubmitted to Transactions on Signal Processing
