Symbol-level precoding (SLP), which converts the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol-error-rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol precoder design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop efficient design algorithms for typical max-min fairness and power minimization problems. In particular, after dividing all users into several groups, the precoders for each group are separately designed on a symbol-by-symbol basis by only utilizing the symbol information of the users in that group, in which the intra-group MUI is exploited using the concept of constructive interference (CI) and the inter-group MUI is also effectively suppressed. In order to further reduce the computational complexity, we utilize the Lagrangian dual, Karush-Kuhn-Tucker (KKT) conditions and the majorization-minimization (MM) method to transform the resulting problems into more tractable forms, and develop efficient algorithms for obtaining closed-form solutions to them. Extensive simulation results illustrate that the proposed G-SLP strategy and design algorithms dramatically reduce the computational complexity without causing significant performance loss compared with the traditional SLP schemes.

中文翻译：

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MU-MISO系统符号级预编码的低复杂度设计

符号级预编码 (SLP) 将有害的多用户干扰 (MUI) 转换为有益信号，可以显着提高多用户通信系统中的符号错误率 (SER) 性能。然而，在享受符号增益的同时，复杂的非线性逐符号预编码器设计的计算复杂度随用户数量呈指数增长，这在现实系统中是无法承受的。在本文中，我们提出了一种新颖的低复杂度分组 SLP (G-SLP) 方法，并为典型的最大最小公平性和功率最小化问题开发了有效的设计算法。特别是，在将所有用户分成若干组后，仅利用该组中用户的符号信息，逐个符号地单独设计每个组的预编码器，其中使用建设性干扰（CI）的概念利用了组内MUI，并且还有效地抑制了组间MUI。为了进一步降低计算复杂度，我们利用拉格朗日对偶、Karush-Kuhn-Tucker (KKT) 条件和majorization-minimization (MM) 方法将产生的问题转化为更易于处理的形式，并开发有效的算法来获得封闭- 形成他们的解决方案。大量的仿真结果表明，与传统的 SLP 方案相比，所提出的 G-SLP 策略和设计算法显着降低了计算复杂度，而不会造成显着的性能损失。为了进一步降低计算复杂度，我们利用拉格朗日对偶、Karush-Kuhn-Tucker (KKT) 条件和majorization-minimization (MM) 方法将产生的问题转化为更易于处理的形式，并开发有效的算法来获得封闭- 形成他们的解决方案。大量的仿真结果表明，与传统的 SLP 方案相比，所提出的 G-SLP 策略和设计算法显着降低了计算复杂度，而不会造成显着的性能损失。为了进一步降低计算复杂度，我们利用拉格朗日对偶、Karush-Kuhn-Tucker (KKT) 条件和majorization-minimization (MM) 方法将产生的问题转化为更易于处理的形式，并开发有效的算法来获得封闭- 形成他们的解决方案。大量的仿真结果表明，与传统的 SLP 方案相比，所提出的 G-SLP 策略和设计算法显着降低了计算复杂度，而不会造成显着的性能损失。