Multicell Massive MIMO Multicast Transmission With Finite-Alphabet Inputs
Multicell Massive MIMO Multicast Transmission With Finite-Alphabet Inputs
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DOI：
10.1109/tvt.2019.2917069
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发表时间：
2019-05
影响因子：
6.8
通讯作者：
Wenqian Wu;Chengshan Xiao;Xiqi Gao
中科院分区：
文献类型：
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作者：
Wenqian Wu;Chengshan Xiao;Xiqi Gao
研究了有限字母输入的多小区大规模多输入多输出（MIMO）系统中组播传输的预编码设计问题。每个小区内的用户对公共信息感兴趣，而不同的小区提供不同的信息。针对基站仅具有统计信道状态信息的加权最大最小公平性（MMF）问题，给出了最小加权可达遍历速率最大化的最优预编码向量的必要条件，并提出了一种迭代算法来优化预编码向量.为了实现较低的计算复杂度，然后，我们推导出有限字母表输入的可实现的遍历速率的下限。考虑最小加权率下界最大化问题，我们利用凹凸过程（CCCP）开发了一个基于CCCP的算法，该算法被证明是收敛到局部最优。进一步，利用大规模MIMO系统的信道特性，证明了使最小加权速率下界最大化的最优预编码向量是发射相关矩阵特征向量的线性组合，从而将原问题转移到低维空间中。基于这种认识，我们设计了一个基于关系的算法，利用MMF问题和服务质量问题之间的对偶性来获得加权MMF问题的最优解。数值结果表明，可达到的遍历率下界的紧密性和设计的算法的显着性能。
This paper investigates the precoding design for multicast transmission in multicell massive multiple-input multiple-output (MIMO) systems with finite-alphabet inputs. The users within each cell are interested in common information, and different cells provide distinct information. Focusing on the weighted max–min fairness (MMF) problem with only statistical channel state information at the base station, we provide the necessary conditions of the optimal precoding vectors to maximize the minimum weighted achievable ergodic rate, and an iterative algorithm is proposed to optimize the precoding vectors. To achieve lower computational complexity, we then derive a lower bound on the achievable ergodic rate for finite-alphabet inputs. Considering the problem of the minimum weighted rate lower bound maximization, we utilize the concave–convex procedure (CCCP) to develop a CCCP-based algorithm, which is proven to converge to a local optimum. Furthermore, exploiting the channel characteristic in massive MIMO systems, we prove that the optimal precoding vectors, maximizing the minimum weighted rate lower bound, are linear combinations of eigenvectors of transmit correlation matrices, and the original problem can be shifted into a lower dimensional space. Motivated by this insight, a relation-based algorithm is devised to obtain the optimal solution of the weighted MMF problem by using the duality between the MMF problem and the quality of service problem. Numerical results illustrate the tightness of the achievable ergodic rate lower bound and the significant performance of the devised algorithms.