Mimo Cognitive(Mimo认知)研究综述
Mimo Cognitive Mimo认知 - In this paper, we develop a new robust spectrum sensing method for MIMO cognitive radios in the presence of heavy-tailed noise. [1] We considered the detection and estimation framework for MIMO cognitive network where the noise covariance matrix is unknown with perfect channel state information. [2] In this paper, we develop a new robust spectrum sensing method for MIMO cognitive radios in the presence of heavy-tailed noise. [3] We study power control and frequency band selection policies for multi-band underlay MIMO cognitive radio with the objective of maximizing the rate of a secondary user (SU) link while limiting the interference leakage toward primary users (PUs) below a threshold. [4] Simulation results also presents that the massive MIMO cognitive femtocell network increases signal to interference plus noise ratio and spectral efficiency ~ 13% and ~ 20% respectively than the existing CRN and only MIMO CRN based approaches. [5] Beamforming has been considered as a potential candidate for throughput maximization in MIMO cognitive radio networks. [6]在本文中，我们为存在重尾噪声的 MIMO 认知无线电开发了一种新的鲁棒频谱感知方法。 [1] 我们考虑了 MIMO 认知网络的检测和估计框架，其中噪声协方差矩阵未知且具有完美的信道状态信息。 [2] 在本文中，我们为存在重尾噪声的 MIMO 认知无线电开发了一种新的鲁棒频谱感知方法。 [3] 我们研究了多频段底层 MIMO 认知无线电的功率控制和频段选择策略，目的是最大化次要用户 (SU) 链路的速率，同时将对主要用户 (PU) 的干扰泄漏限制在阈值以下。 [4] 仿真结果还表明，与现有 CRN 和仅基于 MIMO CRN 的方法相比，大规模 MIMO 认知毫微微蜂窝网络分别提高了信干噪比和频谱效率~ 13% 和~ 20%。 [5] 波束成形已被认为是 MIMO 认知无线电网络中吞吐量最大化的潜在候选者。 [6]
mimo cognitive radio Mimo认知收音机
In this paper, we develop a new robust spectrum sensing method for MIMO cognitive radios in the presence of heavy-tailed noise. [1] In this paper, we develop a new robust spectrum sensing method for MIMO cognitive radios in the presence of heavy-tailed noise. [2] We study power control and frequency band selection policies for multi-band underlay MIMO cognitive radio with the objective of maximizing the rate of a secondary user (SU) link while limiting the interference leakage toward primary users (PUs) below a threshold. [3] Beamforming has been considered as a potential candidate for throughput maximization in MIMO cognitive radio networks. [4]在本文中，我们为存在重尾噪声的 MIMO 认知无线电开发了一种新的鲁棒频谱感知方法。 [1] 在本文中，我们为存在重尾噪声的 MIMO 认知无线电开发了一种新的鲁棒频谱感知方法。 [2] 我们研究了多频段底层 MIMO 认知无线电的功率控制和频段选择策略，目的是最大化次要用户 (SU) 链路的速率，同时将对主要用户 (PU) 的干扰泄漏限制在阈值以下。 [3] 波束成形已被认为是 MIMO 认知无线电网络中吞吐量最大化的潜在候选者。 [4]