An Efficient Wideband Spectrum Sensing Algorithm for Unmanned Aerial Vehicle Communication Networks

With increasingly smaller size, more powerful sensing capabilities and higher level of autonomy, multiple unmanned aerial vehicles (UAVs) can form UAV networks to collaboratively complete missions more reliably, efficiently and economically. While UAV networks are promising for many applications, there are many outstanding issues to be resolved before large scale UAV networks are practically used. In this paper we study the application of cognitive radio technology for UAV communication networks, to provide high capacity and reliable communication with opportunistic and timely spectrum access. Compressive sensing is applied in the cognitive radio to boost the performance of spectrum sensing. However, the performance of existing compressive spectrum sensing schemes is constrained with non-strictly sparse spectrum. In addition, the reconstruction process applied in existing schemes has unnecessarily high computational complexity and low energy efficiency. We proposed a new compressive signal processing algorithm, called Iterative Compressive Filtering, to improve the UAV network communication performance. The key idea is using orthogonal projection as a bandstop filter in compressive domain. The components of primary users (PUs) in the recognized subchannels are adaptively eliminated in compressive domain, which can directly update the measurement for further detection of other active users. Experiment results showed increased efficiency of the proposed algorithm over existing compressive spectrum sensing algorithms. The proposed algorithm achieved higher detection probability in identifying the occupied subchannels under the condition of non-strictly sparse spectrum with large computational complexity reduction, which can provide strong support of reliable and timely communication for UAV networks

Sparse Distributed Learning Based on Diffusion Adaptation

This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to enhance the detection of sparsity via a diffusive process over the network. The resulting algorithms endow networks with learning abilities and allow them to learn the sparse structure from the incoming data in real-time, and also to track variations in the sparsity of the model. We provide convergence and mean-square performance analysis of the proposed method and show under what conditions it outperforms the unregularized diffusion version. We also show how to adaptively select the regularization parameter. Simulation results illustrate the advantage of the proposed filters for sparse data recovery.Comment: to appear in IEEE Trans. on Signal Processing, 201

Iteratively reweighted compressive sensing based algorithm for spectrum cartography in cognitive radio networks

© 2014 IEEE. Spectrum cartography is the process of constructing a map showing Radio Frequency signal strength over a finite geographical area. In our previous work we formulated spectrum cartography as a compressive sensing problem and we illustrated how cartography can be used in the context of discovering spectrum holes in space that can be exploited locally in cognitive radio networks. This paper investigates the performance of compressive sensing based approach to cartography in a fading environment where realtime channel estimation is not feasible. To accommodate for lack of channel information we take an iterative approach. We extend the well-known iteratively reweighted ℓ1 minimisation approach by exploiting spatial correlation between two points in space. We evaluate the performance in an urban environment where Rayleigh fading is prominent. Our numerical results show a significant improvement in the probability of accurately making a spectrum sensing decision, in comparison to the well-known weighted approach and the traditional compressive sensing based method

Compressed Sensing based Dynamic PSD Map Construction in Cognitive Radio Networks

In the context of spectrum sensing in cognitive radio networks, collaborative spectrum sensing has been proposed as a way to overcome multipath and shadowing, and hence increasing the reliability of the sensing. Due to the high amount of information to be transmitted, a dynamic compressive sensing approach is proposed to map the PSD estimate to a sparse domain which is then transmitted to the fusion center. In this regard, CRs send a compressed version of their estimated PSD to the fusion center, whose job is to reconstruct the PSD estimates of the CRs, fuse them, and make a global decision on the availability of the spectrum in space and frequency domains at a given time. The proposed compressive sensing based method considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors’ and the fusion center. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 20 % of full data transmission between sensors and master node. Also, simulation results show the robustness of the proposed method against the channel variations, diverse compression ratios and processing times in comparison with static methods