In this paper, we study the problem of downlink (DL) sum rate maximization in codebook based multiuser (MU) multiple input multiple output (MIMO) systems. The user equipments (UEs) estimate the DL channels using pilot symbols sent by the access point (AP) and feedback the estimates to the AP over a control channel. We present a closed form expression for the achievable sum rate of the MU-MIMO broadcast system with codebook constrained precoding based on the estimated channels, where multiple data streams are simultaneously transmitted to all users. Next, we present novel, computationally efficient, minorization-maximization (MM) based algorithms to determine the selection of beamforming vectors and power allocation to each beam that maximizes the achievable sum rate. Our solution involves multiple uses of MM in a nested fashion. Based on this approach, we propose and contrast two algorithms, which we call the square-root-MM (SMM) and inverse-MM (IMM) algorithms. The algorithms are iterative and converge to a locally optimal beamforming vector selection and power allocation solution from any initialization. We evaluate the performance and complexity of the algorithms for various values of the system parameters, compare them with existing solutions, and provide further insights into how they can be used in system design.

中文翻译：

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用于 MU-MIMO 系统的基于码本的预编码和功率分配以实现和速率最大化

在本文中，我们研究了基于码本的多用户 (MU) 多输入多输出 (MIMO) 系统中下行链路 (DL) 和速率最大化问题。用户设备（UE）使用由接入点（AP）发送的导频符号来估计DL信道并且通过控制信道将估计反馈给AP。我们提出了基于估计信道的码本约束预编码的 MU-MIMO 广播系统的可实现总速率的封闭形式表达式，其中多个数据流同时传输到所有用户。接下来，我们提出了新颖的、计算效率高的、基于最小化最大化 (MM) 的算法，以确定波束成形向量的选择和每个波束的功率分配，以最大化可实现的总速率。我们的解决方案涉及以嵌套方式多次使用 MM。基于这种方法，我们提出并对比了两种算法，我们称之为平方根 MM (SMM) 和逆 MM (IMM) 算法。这些算法是迭代的，并且会从任何初始化收敛到局部最优的波束成形矢量选择和功率分配解决方案。我们针对系统参数的各种值评估算法的性能和复杂性，将它们与现有解决方案进行比较，并提供有关如何在系统设计中使用它们的进一步见解。