Optimization of an optically implemented on-board FDMA demultiplexer

Performance of a 30 GHz frequency division multiple access (FDMA) uplink to a processing satellite is modelled for the case where the onboard demultiplexer is implemented optically. Included in the performance model are the effects of adjacent channel interference, intersymbol interference, and spurious signals associated with the optical implementation. Demultiplexer parameters are optimized to provide the minimum bit error probability at a given bandwidth efficiency when filtered QPSK modulation is employed

Hybrid computer Monte-Carlo techniques

Hybrid analog-digital computer systems for Monte Carlo method application

Maximizing the Bandwidth Efficiency of the CMS Tracker Analog Optical Links

The feasibility of achieving faster data transmission using advanced digital modulation techniques over the current CMS Tracker analog optical link is explored. The spectral efficiency of Quadrature Amplitude Modulation -Orthogonal Frequency Division Multiplexing (QAM-OFDM) makes it an attractive option for a future implementation of the readout link. An analytical method for estimating the data-rate that can be achieved using OFDM over the current optical links is described and the first theoretical results are presented

Reliable and Repeatable Power Measurements in DVB-T Systems

Non

High performance photonic microwave filters based on a 50GHz optical soliton crystal Kerr micro-comb

We demonstrate a photonic radio frequency (RF) transversal filter based on an integrated optical micro-comb source featuring a record low free spectral range of 49 GHz yielding 80 micro-comb lines across the C-band. This record-high number of taps, or wavelengths for the transversal filter results in significantly increased performance including a QRF factor more than four times higher than previous results. Further, by employing both positive and negative taps, an improved out-of-band rejection of up to 48.9 dB is demonstrated using Gaussian apodization, together with a tunable centre frequency covering the RF spectra range, with a widely tunable 3-dB bandwidth and versatile dynamically adjustable filter shapes. Our experimental results match well with theory, showing that our transversal filter is a competitive solution to implement advanced adaptive RF filters with broad operational bandwidths, high frequency selectivity, high reconfigurability, and potentially reduced cost and footprint. This approach is promising for applications in modern radar and communications systems.Comment: 19 pages, 12 figures, 107 reference

RF channel characterization for cognitive radio using support vector machines

Cognitive Radio promises to revolutionize the ways in which a user interfaces with a communications device. In addition to connecting a user with the rest of the world, a Cognitive Radio will know how the user wants to connect to the rest of the world as well as how to best take advantage of unused spectrum, commonly called white space\u27. Through the concept of Dynamic Spectrum Acccess a Cognitive Radio will be able to take advantage of the white space in the spectrum by first identifying where the white space is located and designing a transmit plan for a particular white space. In general a Cognitive Radio melds the capabilities of a Software Defined Radio and a Cognition Engine. The Cognition Engine is responsible for learning how the user interfaces with the device and how to use the available radio resources while the SDR is the interface to the RF world. At the heart of a Cognition Engine are Machine Learning Algorithms that decide how best to use the available radio resources and can learn how the user interfaces to the CR. To decide how best to use the available radio resources, we can group Machine Learning Algorithms into three general categories which are, in order of computational cost: 1.) Linear Least Squares Type Algorithms, e.g. Discrete Fourier Transform (DFT) and their kernel versions, 2.) Linear Support Vector Machines (SVMs) and their kernel versions, and 3.) Neural Networks and/or Genetic Algorithms. Before deciding on what to transmit, a Cognitive Radio must decide where the white space is located. This research is focused on the task of identifying where the white space resides in the spectrum, herein called RF Channel Characterization. Since previous research into the use of Machine Learning Algorithms for this task has focused on Neural Networks and Genetic Algorithms, this research will focus on the use of Machine Learning Algorithms that follow the Support Vector optimization criterion for this task. These Machine Learning Algorithms are commonly called Support Vector Machines. Results obtained using Support Vector Machines for this task are compared with results obtained from using Least Squares Algorithms, most notably, implementations of the Fast Fourier Transform. After a thorough theoretical investigation of the ability of Support Vector Machines to perform the RF Channel Characterization task, we present results of using Support Vector Machines for this task on experimental data collected at the University of New Mexico.\u2

Design and Validation of a Software Defined Radio Testbed for DVB-T Transmission

This paper describes the design and validation of a Software Defined Radio (SDR) testbed, which can be used for Digital Television transmission using the Digital Video Broadcasting - Terrestrial (DVB-T) standard. In order to generate a DVB-T-compliant signal with low computational complexity, we design an SDR architecture that uses the C/C++ language and exploits multithreading and vectorized instructions. Then, we transmit the generated DVB-T signal in real time, using a common PC equipped with multicore central processing units (CPUs) and a commercially available SDR modem board. The proposed SDR architecture has been validated using fixed TV sets, and portable receivers. Our results show that the proposed SDR architecture for DVB-T transmission is a low-cost low-complexity solution that, in the worst case, only requires less than 22% of CPU load and less than 170 MB of memory usage, on a 3.0 GHz Core i7 processor. In addition, using the same SDR modem board, we design an off-line software receiver that also performs time synchronization and carrier frequency offset estimation and compensation