The relentless growth of wireless applications and data traffic continues to accentuate the long felt need for decentralized, self-managed, and cooperative network architectures. Enlightened by the power of blockchain technology, we propose a blockchain radio access network (B-RAN) architecture and develop decentralized, secure, and efficient mechanisms to manage network access and authentication among inherently trustless network entities. We further identify promising advanced functions made possible by adopting blockchain for open radio access networks. Our test results demonstrate the benefits of the BRAN architecture. We also present a number of challenges and future research directions. INDEX TERMS Blockchain, communication networks, decentralization, radio access network, wireless application protocol.
Learning and exploring connectivity of unknown networks represent an important problem in practical applications of communication networks and social-media networks. Modeling large scale networks as connected graphs, it is highly desirable to extract their connectivity information among nodes to visualize network topology, disseminate data, and improve routing efficiency. This work investigates a simple measurement model in which a small subset of source nodes collect hop distance information from networked nodes in order to generate a virtual coordinate system (VCS) for networks of unknown topology. We establish a VCS to define logical distance among nodes based on principal component analysis (PCA) and to determine connectivity relationship and effective routing methods. More importantly, we present robust analytical algorithm to derive VCS against practical issues of missing and corrupted measurements. We also develop a connectivity inference method which classifies nodes into layers based on the hop distances and derives partial information on network connectivity.
Abstract-In multiple-input multiple-output radar systems, it is usually desirable to steer transmitted power in the regionof-interest. To do this, conventional methods optimize the waveform covariance matrix, R, for the desired beampattern, which is then used to generate actual transmitted waveforms. Both steps require constrained optimization, therefore, use iterative algorithms. The main challenges encountered in the existing approaches are the computational complexity and the design of waveforms to use in practice. In this paper, we provide a closed-form solution to design covariance matrix for the given beampattern using the planar array, which is then used to derive a novel closed-form algorithm to directly design the finitealphabet constant-envelope (FACE) waveforms. The proposed algorithm exploits the two-dimensional fast-Fourier-transform. The performance of our proposed algorithm is compared with existing methods that are based on semi-definite quadratic programming with the advantage of a considerably reduced complexity.Index Terms-Multiple-input multiple-output radars, beampattern design, closed-form solution, waveform design, twodimensional fast-Fourier-transform.
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