Overlay Cognitive Radio Based on OFDM with Channel Estimation Issues
Overlay Cognitive Radio Based on OFDM with Channel Estimation Issues
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基于 OFDM 的叠加认知无线电，存在信道估计问题
DOI：
10.1007/s11277-019-06455-2
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发表时间：
2019
影响因子：
2.2
通讯作者：
Ali Jamoos
中科院分区：
文献类型：
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作者：
A. Abdou;A. Abdo;Ali Jamoos
认知无线电（CR）已被提出作为一种技术，以提高频谱效率，通过给予一个机会的许可用户频谱的非授权用户的访问。我们考虑一个覆盖CR由一个主宏小区和认知小小区的合作次级基站（SBS）。我们建议研究一种CR，其中正交频分复用用于主用户（PU）和次用户（SU）。为了消除干扰，在SBS处需要预编码。因此，我们首先推导出由于SU在PU接收机的干扰表达式。然后，迫零波束形成（ZFBF）被认为是消除干扰。然而，应用ZFBF取决于SBS和PU之间的通道。因此，信道估计是必要的。为此，我们建议近似信道的自回归过程（AR），并考虑使用训练序列的信道估计问题。所接收的信号（也称为观测值）被认为受到加性白色测量噪声的干扰。在这种情况下，AR参数和信道可以通过使用递归方法从接收到的噪声信号中联合估计。然而，系统的相应状态空间表示是非线性的。然后，我们建议通过比较基于非线性卡尔曼滤波器的方法进行补充研究。
Cognitive radio (CR) has been proposed as a technology to improve the spectrum efficiency by giving an opportunistic access of the licensed-user spectra to unlicensed users. We consider an overlay CR consisting of a primary macro-cell and cognitive small cells of cooperative secondary base stations (SBS). We suggest studying a CR where an orthogonal frequency division multiplexing is used for both the primary users (PU) and the secondary users (SU). In order to cancel the interferences, a precoding is required at the SBS. Therefore, we first derive the interferences expression due to SU at the PU receiver. Then, zero forcing beamforming (ZFBF) is considered to cancel the interferences. However, applying ZFBF depends on the channels between the SBS and the PU. A channel estimation is hence necessary. For this purpose, we propose to approximate the channel by an autoregressive process (AR) and to consider the channel estimation issue by using a training sequence. The received signals, also called the observations, are considered to be disturbed by an additive white measurement noise. In that case, the AR parameters and the channel can be jointly estimated from the received noisy signal by using a recursive approach. Nevertheless, the corresponding state space representation of the system is non-linear. Then, we propose to carry out a complementary study by compare non-linear Kalman filter based approaches.