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Cognitive radio (CR) is a promising technology to address the challenge of spectrum scarcity due to the massive number of objects in the Internet of Things (IoT). Equipping IoT objects with CR capability can also alleviate interference situations and achieve seamless connectivity in IoT. This work deals with CR spectrum sensing and proposes a new eigenvalue-based detector by exploiting the summation of multiple large eigenvalues of the covariance matrix of received signals. By analyzing the distribution of the sum of the dependent large eigenvalues, we derive an approximate but explicit expression for the theoretical performance of the proposed detector. The theoretical analysis of the proposed detector is validated and its superior performance is demonstrated with real world signals. It is shown that the proposed detector outperforms the existing eigenvalue-based detectors and is more robust against noise uncertainty.

To exploit the potential of content caching and device-to-device (D2D) communication, we propose a user-centric joint device caching and channel assignment (DCA) policy to facilitate content exchanges between user equipments (UEs). The objective is to minimize the average content delivery delay by effectively leveraging D2D communications using as few channels as possible, subject to the UEs' cache capacities and availability of D2D links. This joint design problem is formulated as a nonlinear combinatorial optimization problem which is NP-hard. We first analyze the optimal DCA policy in two special cases. Then, a low-complexity heuristic algorithm is proposed for general cases which alternatively performs greedy device caching and graphcoloring based channel allocating. Simulation results show that the proposed DCA policy can reduce the average content delivery delay by more than half, in contrast to baseline schemes with locally popular caching. © 2018 IEEE.

This work focuses on exploiting both eigenvalues and eigenvectors for spectrum sensing in cognitive radio. First, we design a blind learning algorithm for obtaining the prior knowledge of the maximum eigenvalue of noises and the leading eigenvector of primary signals by using historical sensing data. Then, we propose a new detector for spectrum sensing by exploiting both the maximum eigenvalue and the leading eigenvector. A theoretical expression for the decision threshold of the proposed detector is derived. Numerical results are provided to validate the theoretical analysis and demonstrate the superior performance of the proposed detector.

The presence of malicious interferences in cognitive radio networks can severely degrade the performance of spectrum sensing. In this letter, we propose a cooperative spectrum sensing approach to tackle malicious interferences, where the local signal to interference plus noise power ratios at secondary users are transmitted to a fusion center for making decision. The theoretical performance of the proposed detector, in terms of false-alarm and detection probabilities, is analyzed, and simulation results are provided to validate the theoretical analyses and demonstrate the superior performance of the detector.

This paper concerns covariance matrix based spectrum sensing over frequency selective channels. The impact of frequency selective channels on the covariance matrix of received signals is analyzed in frequency domain and it is shown that the phases of channel spectra degrade the performance of covariance matrix based detectors. To overcome this problem, we propose a new detector which employs only the magnitude spectra of received signals and thereby achieves considerable performance gain. Theoretical performance, in terms of the false-alarm and detection probabilities of the proposed detector, is analyzed. Simulation results verify our theoretical analyses and demonstrate the superior performance of the proposed detector.

Cooperative spectrum sensing has been studied to combat the hidden terminal problem by exploiting the spatial diversity in cognitive radio (CR) networks. This paper concerns blind cooperative spectrum sensing with soft fusion, where the a priori knowledge of channels and primary signals is unavailable, and soft information is transmitted from each secondary user (SU) to a fusion center for detection. We first introduce the Quade test to design a blind detector. Then, a new detector with both lower computational complexity and lower overhead is derived, where only the estimated power and the variance of the instantaneous power at each SU are required at the fusion center. The analytical expressions for the detection performance, in terms of false-alarm probability and detection probability, are derived for the proposed detector. Simulation results are provided to validate the theoretical analyses and demonstrate the superior performance of proposed detector compared to the state-of-the-art detectors. It is also shown that, with the increase of the number of hidden terminals in the CR, the proposed detector can maintain high detection performance while the conventional detectors exhibit rapid performance degradation.

A multichannel selection strategy is proposed for RF energy harvesting cognitive radio networks, where multiple channels are available, and each secondary user (SU) harvests RF energy from the channel being occupied by active primary users and transmits data using other idle channels. To maximize the average throughput of SUs which contend for the use of idle channels, the multichannel selection strategy is formulated to a convex optimization problem. Further, we derive an analytical solution to the optimization problem. Numerical results are provided to demonstrate the superior performance of the proposed strategy and verify the analytical solution.

In underlay cognitive radios, the primary-channel gain from a primary transmitter (PU-Tx) to a primary receiver (PU-Rx) and the cross-channel gain from a cognitive transmitter (SU-Tx) to the PU-Rx are crucial for the SU-Tx in controlling its transmit power when sharing spectrum with primary users (PUs). However, the non-cooperation between PUs and secondary users (SUs) makes it difficult for the SU-Tx to acquire the primary-channel and cross-channel gains, in particular when the channel reciprocity is not applicable. Most existing works focus on cross-channel gain estimation and are often based on the channel reciprocity. In this work, we propose a scheme where the SU-Tx relays primary signals and transmits its own signals to the secondary receiver (SU-Rx) during the phase of channel gain estimation. Then, an estimation method is developed, which enables the SU-Tx to acquire the primary-channel and cross-channel gains simultaneously. The performance of the proposed estimator is analyzed. Numerical simulations are provided to validate the theoretical analysis and demonstrate the superior performance of the proposed method compared to state-of-the-art methods.