Mimo Scheme(Mimo 计划)研究综述
Mimo Scheme Mimo 计划 - In this paper, Robust transmit beamforming method for interference mitigation in MIMO scheme is studied in the presence of array amplitude and phase error. [1] The simulation results show that the SNR performance enhancement for the UEP- MIMO scheme is approximately 9. [2] We show experimentally that in the case of no or low oversampling of 1 or 2 samples per symbol (Sps), the MIMO scheme can only be applied if all channels are fully symbol aligned, whereas by oversampling at 3 Sps or higher, operation is possible without symbol alignment. [3] In the following, we start with the SISO channel capacity for a real-valued signal, extend it to complex signals, and finally derive the channel capacity of a MIMO scheme. [4] Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO schemes, and propose a novel DNN-based multi-label classifier for TAS and PA parameter evaluation. [5] The performance degradation effect of atmospheric turbulence and pointing errors can be reduced by using MIMO scheme, that is by increasing the number of transmit and/or receive apertures. [6] However, the performance advantages combined with the possibility of parallel receiver implementations, make those techniques particularly interesting for m-MIMO schemes, either employing OFDM or SC-FDE modulations. [7] Simulation results show that the proposed method has significant improvement in spectral efficiency and bit-error-rate (BER) performance as compared to other MIMO schemes. [8] Based on the framework of SPIM-mmWave, a fundamental study is conducted towards a theoretic condition under which SPIM-mmWave guarantees to outperform conventional mmWave-MIMO schemes. [9] The MIMO schemes considered for this paper are repetition coding (RC), space-time block codes (STBCs), and spatial multiplexing (SMP). [10] In this paper, we develop a joint blind identification algorithm to determine the number of transmit antennas and MIMO schemes simultaneously. [11]本文研究了存在阵列幅度和相位误差的情况下，MIMO方案中用于抑制干扰的鲁棒发射波束形成方法。 [1] 仿真结果表明，UEP-MIMO 方案的 SNR 性能提升约为 9。 [2] 我们通过实验表明，在每个符号 (Sps) 没有或仅过采样 1 或 2 个样本的情况下，MIMO 方案只能在所有信道完全符号对齐的情况下应用，而通过 3 Sps 或更高的过采样，操作是可能的没有符号对齐。 [3] 下面，我们从实值信号的 SISO 信道容量开始，将其扩展到复信号，最后推导出 MIMO 方案的信道容量。 [4] 此外，为了进一步比较，我们将深度神经网络 (DNN) 与这些自适应 SM-MIMO 方案相结合，并提出了一种新的基于 DNN 的多标签分类器，用于 TAS 和 PA 参数评估。 [5] 通过使用 MIMO 方案，即通过增加发射和/或接收孔径的数量，可以减少大气湍流和指向误差的性能下降影响。 [6] 然而，性能优势与并行接收器实现的可能性相结合，使得这些技术对于使用 OFDM 或 SC-FDE 调制的 m-MIMO 方案特别有趣。 [7] 仿真结果表明，与其他 MIMO 方案相比，所提出的方法在频谱效率和误码率 (BER) 性能方面有显着提高。 [8] 基于SPIM-mmWave的框架，对SPIM-mmWave保证优于传统mmWave-MIMO方案的理论条件进行了基础研究。 [9] 本文考虑的 MIMO 方案是重复编码 (RC)、空时分组码 (STBC) 和空间复用 (SMP)。 [10] 在本文中，我们开发了一种联合盲识别算法来同时确定发射天线的数量和 MIMO 方案。 [11]
Conventional Mimo Scheme 传统的Mimo方案
QSM is a recently proposed propitious MIMO technique that promises significant advantages over conventional MIMO schemes including high spectral efficiency with single RF-chain transmitter and very low receiver complexity. [1] Numerical simulation results demonstrate that the proposed scheme outperforms the conventional MIMO scheme. [2]QSM 是最近提出的一种有利的 MIMO 技术，与传统的 MIMO 方案相比，它具有显着的优势，包括具有单射频链发射器的高频谱效率和非常低的接收器复杂性。 [1] 数值模拟结果表明，所提出的方案优于传统的 MIMO 方案。 [2]
Massive Mimo Scheme 大规模 Mimo 计划
Since the required overheads in massive MIMO schemes can be too high, leading to spectral degradation, the use of superimposed pilots (i. [1] Compared to conventional massive MIMO, the proposed cache-aided massive MIMO scheme achieves a significantly higher ergodic rate especially when the number of users approaches the number of transmit antennas. [2]由于大规模 MIMO 方案所需的开销可能太高，导致频谱退化，因此使用叠加导频（即 [1] 与传统的大规模 MIMO 相比，所提出的缓存辅助大规模 MIMO 方案实现了显着更高的遍历速率，尤其是当用户数量接近发射天线数量时。 [2]