Multiband Spectrum Sensing with Non-exponential Channel Occupancy Times
Multiband Spectrum Sensing with Non-exponential Channel Occupancy Times
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DOI：
10.1109/icc42927.2021.9500620
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Hanke Cheng;B. L. Mark;Y. Ephraim;Chun-Hung Chen
中科院分区：
文献类型：
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作者：
Hanke Cheng;B. L. Mark;Y. Ephraim;Chun-Hung Chen
在动态频谱共享的无线网络中，跟踪宽带上的时间频谱空洞是一项具有挑战性的任务。我们考虑这样一种场景，其中频谱被划分为大量的频带或信道，每个频带或信道都具有提供动态频谱接入机会的潜力。主用户对每个频段的占用时间通常是非指数分布的。我们开发了一种方法，在噪声测量下使用有限的计算资源来确定和参数化具有良好频谱访问机会的一小部分选定的频段。我们将接收信号在每个频带上的噪声测量建模为一个二变量马尔可夫调制的高斯过程，该过程可以看作是一个通过高斯噪声观测的连续时间的双变量马尔可夫链。基本的双变量马尔可夫过程允许非指数分布的状态逗留时间的表征。该方案结合了在线期望最大化参数估计算法和计算预算分配算法。跨频带分配观测时间，以确定具有最大动态频谱接入平均空闲时间的G个频带中的G*的子集，同时获得该频带子集的准确参数估计。仿真结果表明，当信道保持时间为非指数时，与基于(单变量)马尔可夫调制高斯过程模型的方法相比，所提出的方法在正确选择最佳频带子集的概率方面取得了显著的改善。
In a wireless network with dynamic spectrum sharing, tracking temporal spectrum holes across a wide spectrum band is a challenging task. We consider a scenario in which the spectrum is divided into a large number of bands or channels, each of which has the potential to provide dynamic spectrum access opportunities. The occupancy times of each band by primary users are generally non-exponentially distributed. We develop an approach to determine and parameterize a small selected subset of the bands with good spectrum access opportunities, using limited computational resources under noisy measurements. We model the noisy measurements of the received signal in each band as a bivariate Markov modulated Gaussian process, which can be viewed as a continuous-time bivariate Markov chain observed through Gaussian noise. The underlying bivariate Markov process allows for the characterization of non-exponentially distributed state sojourn times. The proposed scheme combines an online expectation-maximization algorithm for parameter estimation with a computing budget allocation algorithm. Observation time is allocated across the bands to determine the subset of G* out of G frequency bands with the largest mean idle times for dynamic spectrum access and at the same time to obtain accurate parameter estimates for this subset of bands. Our simulation results show that when channel holding times are non-exponential, the proposed scheme achieves a substantial improvement in the probability of correct selection of the best subset of bands compared to an approach based on a (univariate) Markov modulated Gaussian process model.