2,445 research outputs found
Software Defined Radio, a perspective from education
The evolution of communication systems has brought about a paradigm shift, particularly in radiocommunications, where software has increasingly taken precedence over hardware. This transition has not only reduced implementation costs but has also significantly enhanced the flexibility of equipment architecture. A prime example of this trend is the emergence and consolidation of software-defined radio (SDR) technology in recent decades. This study provides a comprehensive contextualization of SDR technology, offering insights into its current state in terms of development tools and market equipment. Additionally, two learning scenarios are presented that employ different teaching methodologies. In one of these scenarios, communication theory is exclusively approached from a theoretical perspective. In the second scenario, knowledge acquisition is encouraged through the implementation of low-cost laboratories that incorporate SDR technology. The study indicates that implementing SDR technology boosts student motivation and learning, with 73.13% believing it enhances engineering education and 96% showing increased motivation. Those using SDR in practical laboratories perform better on knowledge tests, but statistical analysis shows that the difference is not statistically significant
Power-Aperture Resource Allocation for a MPAR with Communications Capabilities
Multifunction phased array radars (MPARs) exploit the intrinsic flexibility of their active electronically steered array (ESA) to perform, at the same time, a multitude of operations, such as search, tracking, fire control, classification, and communications. This paper aims at addressing the MPAR resource allocation so as to satisfy the quality of service (QoS) demanded by both line of sight (LOS) and reflective intelligent surfaces (RIS)-aided non line of sight (NLOS) search operations along with communications tasks. To this end, the ranges at which the cumulative detection probability and the channel capacity per bandwidth reach a desired value are introduced as task quality metrics for the search and communication functions, respectively. Then, to quantify the satisfaction level of each task, for each of them a bespoke utility function is defined to map the associated quality metric into the corresponding perceived utility. Hence, assigning different priority weights to each task, the resource allocation problem, in terms of radar power aperture (PAP) specification, is formulated as a constrained optimization problem whose solution optimizes the global radar QoS. Several simulations are conducted in scenarios of practical interest to prove the effectiveness of the approach
Full Duplex Holographic MIMO for Near-Field Integrated Sensing and Communications
This paper presents an in-band Full Duplex (FD) integrated sensing and
communications system comprising a holographic Multiple-Input Multiple-Output
(MIMO) base station, which is capable to simultaneously communicate with
multiple users in the downlink direction, while sensing targets being randomly
distributed within its coverage area. Considering near-field wireless operation
at THz frequencies, the FD node adopts dynamic metasurface antenna panels for
both transmission and reception, which consist of massive numbers of
sub-wavelength-spaced metamaterials, enabling reduced cost and power
consumption analog precoding and combining. We devise an optimization framework
for the FD node's reconfigurable parameters with the dual objective of
maximizing the targets' parameters estimation accuracy and the downlink
communication performance. Our simulation results verify the integrated sensing
and communications capability of the proposed FD holographic MIMO system,
showcasing the interplays among its various design parameters.Comment: 5 pages, 3 figures, submitted for a conference
presentation/publicatio
Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion
Contact-free vital sign monitoring, which uses wireless signals for
recognizing human vital signs (i.e, breath and heartbeat), is an attractive
solution to health and security. However, the subject's body movement and the
change in actual environments can result in inaccurate frequency estimation of
heartbeat and respiratory. In this paper, we propose a robust mmWave radar and
camera fusion system for monitoring vital signs, which can perform consistently
well in dynamic scenarios, e.g., when some people move around the subject to be
tracked, or a subject waves his/her arms and marches on the spot. Three major
processing modules are developed in the system, to enable robust sensing.
Firstly, we utilize a camera to assist a mmWave radar to accurately localize
the subjects of interest. Secondly, we exploit the calculated subject position
to form transmitting and receiving beamformers, which can improve the reflected
power from the targets and weaken the impact of dynamic interference. Thirdly,
we propose a weighted multi-channel Variational Mode Decomposition (WMC-VMD)
algorithm to separate the weak vital sign signals from the dynamic ones due to
subject's body movement. Experimental results show that, the 90
percentile errors in respiration rate (RR) and heartbeat rate (HR) are less
than 0.5 RPM (respirations per minute) and 6 BPM (beats per minute),
respectively
Directional modulation design for multi-beam multiplexing based on hybrid antenna array structures
For integrated sensing and communication, one important research direction is to employ various beamforming techniques to avoid interference between the two functions. In this work, based on a hybrid beamforming antenna array structure, a physical layer security technique called directional modulation (DM) is studied for multi-beam multiplexing applications. The proposed design can form a more effective directional transmission through both beamforming and DM, while multiplexing multiple user beams through a common set of analog coefficients. In this hybrid beamforming structure, only one digital-to-analog converter (DAC) is connected to each subarray, and finite-precision phase shifters are further considered. Design examples for dual-beam multiplexing with an interleaved subarray structure and a localized subarray structure, respectively, are provided, which show that the interleaved subarray structure can form narrower mainlobe and a lower sidelobe level than the localized structure and has an overall better performance
Joint Location Sensing and Channel Estimation for IRS-Aided mmWave ISAC Systems
In this paper, we investigate a self-sensing intelligent reflecting surface
(IRS) aided millimeter wave (mmWave) integrated sensing and communication
(ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS
can effectively reduce the path loss of sensing-related links, thus rendering
it advantageous in ISAC systems. Aiming to jointly sense the
target/scatterer/user positions as well as estimate the sensing and
communication (SAC) channels in the considered system, we propose a two-phase
transmission scheme, where the coarse and refined sensing/channel estimation
(CE) results are respectively obtained in the first phase (using scanning-based
IRS reflection coefficients) and second phase (using optimized IRS reflection
coefficients). For each phase, an angle-based sensing turbo variational
Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging
passing and expectation-maximization (EM) methods, is developed to solve the
considered joint location sensing and CE problem. The proposed algorithm
effectively exploits the partial overlapping structured (POS) sparsity and
2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the
overall performance. Based on the estimation results from the first phase, we
formulate a Cram\'{e}r-Rao bound (CRB) minimization problem for optimizing IRS
reflection coefficients, and through proper reformulations, a low-complexity
manifold-based optimization algorithm is proposed to solve this problem.
Simulation results are provided to verify the superiority of the proposed
transmission scheme and associated algorithms
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
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