scholarly journals GSM-RF Channel Characterization Using a Wideband Subspace Sensing Mechanism for Cognitive Radio Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Soumaya El Barrak ◽  
Amina El Gonnouni ◽  
Salvatore Serrano ◽  
Antonio Puliafito ◽  
Abdelouahid Lyhyaoui
Keyword(s):  
Software Defined Radio ◽  
Spectrum Sharing ◽  
Spectral Estimation ◽  
Energy Detection ◽  
Minimum Variance ◽  
Detection Accuracy ◽  
Sensing Mechanism ◽  
Wideband Spectrum Sensing ◽  
Density Peaks ◽  
Minimum Variance Distortionless Response

In this paper, we examine a spectrum sharing opportunities over the existing Global System of Mobile Communication (GSM) networks, by identifying the unused channels at a specific time and location. For this purpose, we propose a wideband spectrum sensing mechanism to analyze the status of 51 channels at once, belonging to the 10  MHz bandwidth centered at the frequency 945  MHz, in four different areas. We propose a subspace based spectral estimation mechanism, adapted to deal with real measurements. The process begins with data collection using Secondary User (SU) device enabled with Software Defined Radio (SDR) technology, configured to operate in the GSM band. Obtained samples are used then to feed the sensing mechanism. Spectral analysis is delivered to estimate power density peaks and corresponding frequencies. Decision making phase brings together power thresholding technique and GSM control channel decoding to identify idle and busy channels. Experiments are evaluated using detection and false alarm probabilities emulated via Receiver Operating Characteristic (ROC) curves. Obtained performances show better detection accuracy and robustness against variant noise/fading effects, when using our mechanism compared to Energy Detection (ED) based ones as Welch method, and Beamforming based ones as Minimum Variance Distortionless Response (MVDR) method. Occupancy results exhibit considerable potential of secondary use in GSM based primary network.

scholarly journals New Features Using Robust MVDR Spectrum of Filtered Autocorrelation Sequence for Robust Speech Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Sanaz Seyedin ◽  
Seyed Mohammad Ahadi ◽  
Saeed Gazor
Keyword(s):  
Speech Recognition ◽  
Spectral Estimation ◽  
Estimation Method ◽  
Minimum Variance ◽  
Mel Frequency Cepstral Coefficients ◽  
Feature Extraction Method ◽  
Minimum Variance Distortionless Response ◽  
Robust Feature Extraction ◽  
Noise Robust ◽  
Short Time

This paper presents a novel noise-robust feature extraction method for speech recognition using the robust perceptual minimum variance distortionless response (MVDR) spectrum of temporally filtered autocorrelation sequence. The perceptual MVDR spectrum of the filtered short-time autocorrelation sequence can reduce the effects of residue of the nonstationary additive noise which remains after filtering the autocorrelation. To achieve a more robust front-end, we also modify the robust distortionless constraint of the MVDR spectral estimation method via revised weighting of the subband power spectrum values based on the sub-band signal to noise ratios (SNRs), which adjusts it to the new proposed approach. This new function allows the components of the input signal at the frequencies least affected by noise to pass with larger weights and attenuates more effectively the noisy and undesired components. This modification results in reduction of the noise residuals of the estimated spectrum from the filtered autocorrelation sequence, thereby leading to a more robust algorithm. Our proposed method, when evaluated on Aurora 2 task for recognition purposes, outperformed all Mel frequency cepstral coefficients (MFCC) as the baseline, relative autocorrelation sequence MFCC (RAS-MFCC), and the MVDR-based features in several different noisy conditions.

PARALLEL ALGORITHMS FOR ADAPTIVE MATCHED-FIELD PROCESSING ON DISTRIBUTED ARRAY SYSTEMS

2004 ◽  
Vol 12 (02) ◽  
pp. 149-174 ◽  
Author(s):  
KILSEOK CHO ◽  
ALAN D. GEORGE ◽  
RAJ SUBRAMANIYAN ◽  
KEONWOOK KIM
Keyword(s):  
Parallel Algorithms ◽  
Real Time ◽  
Digital Signal Processor ◽  
High Performance ◽  
Digital Signal ◽  
Minimum Variance ◽  
Adaptive Beamforming ◽  
Matched Field Processing ◽  
Minimum Variance Distortionless Response ◽  
Distributed Array

Matched-field processing (MFP) localizes sources more accurately than plane-wave beamforming by employing full-wave acoustic propagation models for the cluttered ocean environment. The minimum variance distortionless response MFP (MVDR–MFP) algorithm incorporates the MVDR technique into the MFP algorithm to enhance beamforming performance. Such an adaptive MFP algorithm involves intensive computational and memory requirements due to its complex acoustic model and environmental adaptation. The real-time implementation of adaptive MFP algorithms for large surveillance areas presents a serious computational challenge where high-performance embedded computing and parallel processing may be required to meet real-time constraints. In this paper, three parallel algorithms based on domain decomposition techniques are presented for the MVDR–MFP algorithm on distributed array systems. The parallel performance factors in terms of execution times, communication times, parallel efficiencies, and memory capacities are examined on three potential distributed systems including two types of digital signal processor arrays and a cluster of personal computers. The performance results demonstrate that these parallel algorithms provide a feasible solution for real-time, scalable, and cost-effective adaptive beamforming on embedded, distributed array systems.

scholarly journals Differences Amplitude Based Improvement of Minimum Variance Distortionless Response Filter

2020 ◽  
Vol 8 (5) ◽  
pp. 1635-1637
Keyword(s):  
A Priori ◽  
Minimum Variance ◽  
Coherent Noise ◽  
Noise Interference ◽  
Power Spectral ◽  
Minimum Variance Distortionless Response ◽  
Power Spectral Densities ◽  
Noisy Signals ◽  
A New Technique ◽  
Priori Information

In this work, the author introduces a new technique for improving the performance of minimum variance distortionless response filter in condition of coherent noise. The proposal algorithm exploits a priori information of differences amplitude to balance power spectral densities of observed noisy signals. The output signal of MVDR filter is then processed by an additional post-filtering, which based speech presence probability to suppress more noise interference and increase quality speech. In experiments using two noisy signal recordings in anechoeic room, the modified MVDR-filter results provides that the suggested algorithm increases speech quality compared to the conventional MVDR filter.

Efficient Wideband Spectrum Sensing Using Mems Acoustic Resonators

2022 ◽  
Vol 25 (3) ◽  
pp. 23-27
Author(s):  
Junfeng Junfeng Guan ◽  
Jitian Zhang ◽  
Ruochen Lu ◽  
Hyungjoo Seo ◽  
Jin Zhou ◽  
...  
Keyword(s):  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Dynamic Spectrum ◽  
Frequency Bands ◽  
Acoustic Resonators ◽  
Wireless Applications ◽  
Dynamic Spectrum Sharing ◽  
Wideband Spectrum Sensing ◽  
Broadband Radio ◽  
Increasing Demand

The ever-increasing demand for wireless applications has resulted in an unprecedented radio frequency (RF) spectrum shortage. Ironically, at the same time, actual utilization of the spectrum is sparse in practice [1]. To exploit previously underutilized frequency bands to accommodate new unlicensed applications and achieve highly efficient usage of the spectrum, the Federal Communications Committee (FCC) has repurposed many frequency bands for dynamic spectrum sharing. This includes the 6 GHz band to be shared between Wi-Fi 6 and the incumbent users [2] as well as the 3.5 GHz Citizens Broadband Radio Service (CBRS) band [3].

DISTRIBUTED PARALLEL PROCESSING TECHNIQUES FOR ADAPTIVE SONAR BEAMFORMING

2002 ◽  
Vol 10 (01) ◽  
pp. 1-23 ◽  
Author(s):  
ALAN D. GEORGE ◽  
JESUS GARCIA ◽  
KEONWOOK KIM ◽  
PRIYABRATA SINHA
Keyword(s):  
Parallel Processing ◽  
Minimum Variance ◽  
Adaptive Beamforming ◽  
Autonomous Operation ◽  
Minimum Variance Distortionless Response ◽  
Efficiency And Effectiveness ◽  
Execution Speed ◽  
Processing Techniques ◽  
Performance Results ◽  
Communication Demands

Quiet submarine threats and high clutter in the littoral environment increase computation and communication demands on beamforming arrays, particularly for applications that require in-array autonomous operation. By coupling each transducer node in a distributed array with a microprocessor, and networking them together, embedded parallel processing for adaptive beamformers can glean advantages in execution speed, fault tolerance, scalability, power, and cost. In this paper, a novel set of techniques for the parallelization of adaptive beamforming algorithms is introduced for in-array sonar signal processing. A narrowband, unconstrained, Minimum Variance Distortionless Response (MVDR) beamformer is used as a baseline to investigate the efficiency and effectiveness of this method in an experimental fashion. Performance results are also included, among them execution times, parallel efficiencies, and memory requirements, using a distributed system testbed comprised of a cluster of workstations connected by a conventional network.

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