Fatima Salahdine

A survey on compressive sensing techniques for cognitive radio networks

In cognitive radio, one of the main challenges is wideband spectrum sensing. Existing spectrum sensing techniques are based on a set of observations sampled by an analog/digital converter (ADC) at the Nyquist rate. However, those techniques can sense only one band at a time because of the hardware limitations on sampling rate. In addition, in order to sense a wideband spectrum, the band is divided into narrow bands or multiple frequency bands. Secondary users (SU) have to sense each band using multiple RF frontends simultaneously, which results in a very high processing time, hardware cost, and computational complexity. In order to overcome this problem, the signal sampling should be as fast as possible, even with high dimensional signals. Compressive sensing has been proposed as one of the solutions to reduce the processing time and accelerate the scanning process. It allows reducing the number of samples required for high dimensional signal acquisition while keeping the important information. Over the last decade, a number of papers related to compressive sensing techniques have been published. However, most of these papers describe techniques corresponding to one process either sparse representation, sensing matrix, or recovery. This paper provides an in depth survey on compressive sensing techniques and classifies these techniques according to which process they target, namely, sparse representation, sensing matrix, or recovery algorithms. It also discusses examples of potential applications of these techniques including in spectrum sensing, channel estimation, and multiple-input multiple-output (MIMO) based cognitive radio. Metrics to evaluate the efficiencies of existing compressive sensing techniques are provided as well as the benefits and challenges in the context of cognitive radio networks.

Matched filter detection with dynamic threshold for cognitive radio networks

In cognitive radio networks, spectrum sensing aims to detect the unused spectrum channels in order to use the radio spectrum more efficiently. Various methods have been proposed in the past, such as energy, feature detection, and matched filter. These methods are characterized by a sensing threshold, which plays an important role in the sensing performance. Most of the existing techniques used a static threshold. However, the noise is random, and, thus the threshold should be dynamic. In this paper, we suggest an approach with an estimated and dynamic sensing threshold to increase the efficiency of the sensing detection. The matched filter method with dynamic threshold is simulated and its results are compared to those of other existing techniques.

Bayesian compressive sensing with circulant matrix for spectrum sensing in cognitive radio networks

For wideband spectrum sensing, compressive sensing has been proposed as a solution to speed up the high dimensional signals sensing and reduce the computational complexity. Compressive sensing consists of acquiring the essential information from a sparse signal and recovering it at the receiver based on an efficient sampling matrix and a reconstruction technique. In order to deal with the uncertainty, improve the signal acquisition performance, and reduce the randomness during the sensing and reconstruction processes, compressive sensing requires a robust sampling matrix and an efficient reconstruction technique. In this paper, we propose an approach that combines the advantages of a Circulant matrix with Bayesian models. This approach is implemented, extensively tested, and using several metrics its results are compared to those of L1 norm minimization with Circulant and random matrices. These metrics are Mean Square Error, reconstruction error, correlation, recovery time, sampling time, and processing time. The results show that our technique is faster and more efficient in compressing and recovering signals.

A survey on decentralized random access MAC protocols for cognitive radio networks

The scarcity of spectrum radio and the immense growth of mobile applications that demand real-time data communications have raised several challenges in the level of medium access control (MAC) design which impose the necessity for a real review and enhancement of MAC protocols. The MAC layer has gained new capabilities with cognitive radio (CR) and opportunistic spectrum access. It is one of the challenging problems in cognitive radio systems. In this paper, we present a survey of some decentralized MAC protocols for CR networks with analysis and comparative assessment of CR-MAC protocols using some Key Performance Indicators.

Techniques for dealing with uncertainty in cognitive radio networks

A cognitive radio system has the ability to observe and learn from the environment, adapt to the environmental conditions, and use the radio spectrum more efficiently. However, due to multipath fading, shadowing, and varying channel conditions, uncertainty affects the cognitive cycle processes, measurements, decisions, and actions. In the observing process, measurements (i.e., information) taken by the secondary users are uncertain. In the next step, the secondary users make decisions based on what has already been observed using their knowledge bases, which may have been impacted by uncertainty. This can lead to the wrong decisions, and, thus the cognitive radio system can take the wrong actions. Hence, uncertainty propagation influences the cognitive radio performance. Therefore, mitigating uncertainty in the cognitive cycle is a necessity. This paper provides a deep overview of techniques that handle uncertainty in cognitive radio networks.

A Bayesian recovery technique with Toeplitz matrix for compressive spectrum sensing in cognitive radio networks

Summary Compressive sensing has been proposed as a low-cost solution for dynamic wideband spectrum sensing in cognitive radio networks. It aims to accelerate the acquisition process and minimize the hardware cost. It consists of directly acquiring a sparse signal in its compressed form that includes the maximum information using a minimum number of measurements and then recovering the original signal at the receiver. Over the last decade, a number of compressive sensing techniques have been proposed to enable scanning the wideband radio spectrum at or below the Nyquist rate. However, these techniques suffer from uncertainty due to random measurements, which degrades their performances. To enhance the compressive sensing efficiency, reduce the level of randomness, and handle uncertainty, signal sampling requires a fast, structured, and robust sampling matrix; and signal recovery requires an accurate and efficient reconstruction algorithm. In this paper, we proposed a method that addresses the previously mentioned problems by exploiting the Bayesian model strengths and the Toeplitz matrix structure. The proposed method was implemented and extensively tested. The simulation results were analyzed and compared to those of the 2 techniques: basis pursuit and orthogonal matching pursuit algorithms with Toeplitz and random matrix. To evaluate the efficiency of the proposed method, several metrics were used, namely, sampling time, sparsity, required number of measurements, recovery time, processing time, recovery error, signal-to-noise ratio, and mean square error. The results demonstrate the superiority of our proposed method over the 2 other techniques in speed, robustness, recovery success, and handling uncertainty.