Search Results (3)

The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML-driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML-based algorithms for the sensing and perception of waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to a high chip area and power consumption. We address this issue by exploiting the recently proposed approximate discrete Fourier transform (ADFT), which has its own sparse factorization for real-time implementation at a low complexity and power consumption. The ADFT is used to create a wideband multibeam RF digital beamformer and temporal spectrum-based attention unit that monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm with low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high resolution. This two-step process allows the fine-gain FFT to be applied only to directions and bands of interest as determined by the ADFT-based low-complexity 2D spacetime attention unit. The fine-grain FFT provides a spectral signature that can find future use cases in neural network engines for achieving modulation recognition, IoT device identification, and RFI identification. Beamforming and spectral channelization algorithms, a digital computer architecture, and early prototypes using a 32-element fully digital multichannel receiver and field programmable gate array (FPGA)-based high-speed software-defined radio (SDR) are presented. Full article

The visible and infrared wavelengths are the most frequently used electromagnetic (EM) waves in the frequency spectrum; able to penetrate the atmosphere and reach Earth’s surface. These wavelengths have attracted much attention in solar energy harvesting; thermography; and infrared imaging applications for the detection of electrical failures; faults; or thermal leakage hot spots and inspection of tapped live energized components. This paper presents a numerical analysis of a compact cubic cross-shaped four-layer metamaterial absorber (MA) structure by using a simple metal-dielectric-metal-dielectric configuration for wideband visible and infrared applications. The proposed MA achieved above 80% absorption in both visible and near-infrared regions of the spectrum from 350 to 1250 nm wavelength with an overall unit cell size of 0.57λ × 0.57λ × 0.59λ. The SiO₂ based anti-reflection coating of sandwiched tungsten facilitates to achieve the wide high absorption bandwidth. The perceptible novelty of the proposed metamaterial is to achieve an average absorptivity of 95.3% for both visible and infrared wavelengths with a maximum absorptivity of 98% from 400 nm to 900 nm. Furthermore, the proposed structure provides polarization insensitivity with a higher oblique incidence angle tolerance up to 45°. Full article

With the development of wireless communication technology, cognitive radio needs to solve the spectrum sensing problem of wideband wireless signals. Due to performance limitation of electronic components, it is difficult to complete spectrum sensing of wideband wireless signals at once. Therefore, it is required that the wideband wireless signal has to be split into a set of sub-bands before the further signal processing. However, the sequence of sub-band perception has become one of the important factors, which deeply-impact wideband spectrum sensing performance. In this paper, we develop a novel approach for sub-band selection through the non-stationary multi-arm bandit (NS-MAB) model. This approach is based on a well-known order optimal policy for NS-MAB mode called discounted upper confidence bound (D-UCB) policy. In this paper, according to different application requirements, various discount functions and exploration bonuses of D-UCB are designed, which are taken as the parameters of the policy proposed in this paper. Our simulation result demonstrates that the proposed policy can provide lower cumulative regret than other existing state-of-the-art policies for sub-band selection of wideband spectrum sensing. Full article