论文标题
通过$ \ ell_3 $ -norm最大化在大规模MIMO中的盲数据检测
Blind Data Detection in Massive MIMO via $\ell_3$-norm Maximization over the Stiefel Manifold
论文作者
论文摘要
大量的MIMO被视为5G和超越网络的关键促成技术。然而，其性能受到获得高维渠道信息所需的大开销的限制。为了减少与常规试验辅助设计相关的巨大训练开销，我们通过利用通道的稀疏性和数据浓度属性提出了一种新型的盲目数据检测方法。具体来说，我们提出了一种基于新颖的$ \ ell_3 $ -norm公式，以恢复数据，而无需估计通道。我们证明，可以任意将所提出的公式的全局最佳解决方案与传输的数据接近，直到阶段性的歧义。然后，我们提出了一种有效的无参数算法来解决$ \ ell_3 $ - norm问题并解决相位排列歧义。我们还根据关键系统参数（例如发射机和接收器的数量，通道噪声功率和通道稀疏级别）得出收敛速率。数值实验将表明，所提出的方案具有出色的性能，计算复杂性低。
Massive MIMO has been regarded as a key enabling technique for 5G and beyond networks. Nevertheless, its performance is limited by the large overhead needed to obtain the high-dimensional channel information. To reduce the huge training overhead associated with conventional pilot-aided designs, we propose a novel blind data detection method by leveraging the channel sparsity and data concentration properties. Specifically, we propose a novel $\ell_3$-norm-based formulation to recover the data without channel estimation. We prove that the global optimal solution to the proposed formulation can be made arbitrarily close to the transmitted data up to a phase-permutation ambiguity. We then propose an efficient parameter-free algorithm to solve the $\ell_3$-norm problem and resolve the phase permutation ambiguity. We also derive the convergence rate in terms of key system parameters such as the number of transmitters and receivers, the channel noise power, and the channel sparsity level. Numerical experiments will show that the proposed scheme has superior performance with low computational complexity.
