Sensing and Wireless Communications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Wireless Technologies".

Deadline for manuscript submissions: 28 December 2025 | Viewed by 3500

Special Issue Editor


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Guest Editor
Center of Computational and Applied Mathematics, California State University in Fullerton, Fullerton, CA 92831, USA
Interests: SATCOM; wireless communications and networking; ground tracking systems; TT&C, modulation and coding; information theory; waveform design; optical communications; tracking and synchronization; radar systems and synthetic aperture radar (SAR); space-based sensing; mobile adhoc network (MANET); cyber physical network (CPN); ML-AI; traditional position, navigation, and timing (PNT); non-traditional PNT; game theory; war gaming for acquisition; data science; decision support systems; program management; complex systems-of-systems design; modeling; simulation and analysis (MS&A); modular open system approach (MOSA).

Special Issue Information

Dear Colleagues,

The current trends in sensing and wireless communications fields are generally difficult to predict in terms of what technologies will reshape the market associated with these two fields. Sensing technologies are dependent on sensor technology enablers (TEs) that provide sensing applications for both commercial and defense applications. Some emerging applications include (i) Earth monitoring for weather and crop prediction, earthquake detection and tracking, flood prediction and response, and wildfire prevention and response; (ii) Global Position Satellite System (GNSS) environment sensing for the prediction of soil moisture content, ice and snow thickness, ocean heights, and wind speed and direction, etc; (iii) sensing for autonomous driving, emergency braking, adaptive cruise control, and self-parking; (iv) sensing for internet of things (IoT) applications using a wide range of IoT sensors, including biomedical, proximity, humidity, chemical, image, and accelerometer sensors; (v) missile detection and tracking for human protection against missile threats; and (vi) intelligence surveillance reconnaissance (ISR). Likewise, wireless communications technologies are dependent on communication TEs, covering a wide range of commercial and defense communications applications. Recent applications include (i) space-based wireless communications providing both line-of-sight (LOS) and beyond line-of-sight (BLOS) video, audio, text, data communications capabilities; (ii) advanced terrestrial-based wireless communications using 4G, 5G, and 6G (to be deployed by 2030) technologies that provide mobile web access, IP telephony, high-definition mobile TV, video conferencing, 3D television, gaming services, cable internet, internet-of-things (IoT) with machine-to-machine (M2M), human-to-machine (H2M), and machine-to-human (M2H) services, and low-latency advanced communication service for artificial intelligence-of-things (ACS-AIoT) with edge computing capabilities; (iii) wideband global satellite communication (SATCOM) providing both commercial and defense wideband data comnnunication capabilities; (iv) protected satellite communications (SATCOM) providing anti-jammed communication capabilities for both tactical (edge) and strategic users; (v) 5G/6G satellite integration (SATis5/6) providing advanced communication capabilities for communications-on-the-move (COTM) for moving user terminals over a large area such as ships at sea or cars driving across the US, and potential backup of the terrestrial network to improve 5G/6G network resiliency; and (vi) mobile adhoc networking (MANET) technology providing adhoc communication capabilities for airbornes, vehicles, and vessels.           

With the advancement of data sciences along with machine learning and artificial intelligence (ML-AI), the current trend is to incorporate ML-AI into the design and operations of sensing and communications systems, making them much smarter with new autonomous capabilities; these include self-reconfiguration and adjustment to adapt to the operational environment, and self-scaling to accommodate new end users. In addition, ML-AI technology enablers can also facilitate the seamless integration between sensing and communication systems, leading to new capabilities for M2M, H2M, and M2H interfaces associated with IoT, ACS-AIoT, and cyber-physical networking (CPN) services.

The aim of this Special Issue is to seek original and unpublished research contributions, tutorials, and review papers that address a wide range of topics regarding Sensing and Wireless Communications (SAC), including new and advanced sensor TEs, sensing techniques, sensing applications, new and innovative wireless communication TEs, advanced communication techniques, emerging wireless communication applications, integrated SAC (ISAC) technology enablers, and ICS techniques and applications. Some of the indicative SAC and ISAC topics for this Special Issue are given below:

  • Sensor TEs: Advancements in sensor TEs related to (i) electro optical (EO) technology associated with photoconductive, photovoltaics, phototransistors, photodiodes sensors; (ii) infrared (IR) connected with pyroelectric, Golay cells, bolometers, and thermopiles IR sensors, etc.; (iii) lasers such as light detection and ranging (LIDAR); (iv) radar such as pulse radar, Doppler radar, continuous wave (CW) radar, frequency-modulated continuous wave (FMCW) radar, deep dive CW (DD-CW) radar, short-wave radar (SRR), mid-wave radar (MWR), and long-wave radar (LWR); (v) acceleration sensors such as piezoelectric, strain gauges, MEMS, and capacitive sensors; and (vi) position, navigation, and timing (PNT) sensors such as GPS, Glonass, BeiDou, Galileo, and the Quasi-Zennith Satellite System (QZSS). Other topics of interest related to this subject area are advanced sensors and smart sensor design and photonic quantum sensors for health monitoring, intelligence surveillance and reconnaissance (ISR), smart cities, autonomous driving, and IoT.  
  • Sensing Techniques: Some examples of sensing techniques include (i) remote sensing using synthetic aperture radar (SAR), interferometric SAR, Doppler radar, LIDAR, radiometers, photometers, etc; (ii) ISR techniques using spectropolarimetric imaging, SAR, spotlight SAR, sliding spotlight SAR, scan SAR and stripmap SAR; (iii) high-resolution wide swath (HRWS SAR), HRWS sliding spotlight-SAR, HRWS scan-SAR, HRWS stripmap-SAR; and (iv) digital cartography or digital mapping techniques using LIDAR, SAR, and HRWS SAR, interferometric SAR, etc. Other related topics of interest in this subject area are (i) advanced sensing techniques using bi-static SAR/HRWS-SAR and multi-static SAR/HRWS-SAR, (ii) active phase array antenna (APAA) design and small, low-weight, low-power, and low-cost (SWAP-Cost) passive slot array antenna (PSAA) design for conventional SAR and HRWS-SAR applications, and (iii) profilerated low Earth orbiter (pLEO) small satellite constellations using low SWAP-cost HRWS-SAR payload with distributed coherent beamforming (DisCoBeam) capabilities for future global ISR.   
  • Sensing Applications: Global ISR; 2-D/3-D digital mapping; object detection, acquisition, and tracking; weather monitoring prediction; earthquake detection and tracking; flood prediction and response; wildfire prevention and response; crop prediction; high-precision platform position, navigation, and timing; high-resolution imaging; electromagnetic radiation detection; autonomous driving; robotics; smart cities; and defense applications such as force protection. Topics related to the application of energy-efficient embedded intelligent sensor systems, ML-AI for automotive perception and autonomous driving, and quantum sensors are also of interest.
  • Wireless Communication TEs: Advanced digital communication waveforms and modulation TEs at the physical-layer level (some existing TEs include BPSK, QPSK/OQPSK, MPSK, DPSK, MSK, GMSK, FSK, MFSK, QAM/OQAM, ASK, MASK, etc); innovative channel coding (CC) TEs at the data link layer (some existing CC TEs are linear and BCH, Reed–Solomon (RS), convolutional, concatenated RS-convolutional, turbo, turbo product, low-density parity check (LDPC), polar and rate-compatible polar (RC-Polar) codes, etc.); advanced channel coding and coded modulation techniques using trellis-coded modulation (TCM), multilevel TCM and unequal error protection (UEP), bit-interleaved coded modulation (BICM), turbo TCM, and hybrid multidimensional coded modulation schemes. Other related topics of interest in this subject area are as follows: (i) Advanced adaptive coded modulation, adaptive nonbinary LDPC-coded multidimensional modulation suitable for high-speed optical communications, (ii) adaptive hybrid free-space optical (FSO)-RF-coded modulation, (iii) new radio (NR) waveforms for existing 4G/5G wireless communication systems, (iv) advanced modulation and coding for 4G/5G/6G wireless communication systems, (vi) software-defined radio (SDR), (vii) software-defined networking (SDN), (viii) network function virtualization (NFV), and (ix) cloud communication technology concepts.   
  • Communications Techniques: Advancements in (i) single-carrier modulation (SCM) techniques using amplitude, phase, frequency, TCM, Turbo-TCM and advanced adaptive coded modulation (ACM), etc; (ii) multiple carrier modulation (MCM) techniques using coherent adaptive subcarrier modulation (CASM), PCM/PSK/PM-Sinewave Subcarriers, the OFDM-multicarrier code (OFDM-MC), and intrinsic mode functions with a unique and sparse frequency band generated via variational mode decomposition (IMFs-VMD), etc.; (iii) innovative multiple access techniques using time division multiplexing access (TDMA), frequency division multiplexing access (FDMA), code division multiplexing access (CDMA), orthogonal frequency division multiplexing access (OFDMA) and hybrid division multiplexing access (H-DMA); and (iv) protected communications using anti-jam, low probability of interception, and low probability of detection (AJ/LPI/LPD) techniques. Related topics of interest for this subject area include smart antenna, adaptive antenna array, distributed coherent beamforming technology for the extended wireless communication range of a low-power communications system-of-systems, pulse communications, ultra-wideband communications, advanced propagation modeling and the sounding of communications channels, and advanced ML-AI for wireless communications.        
  • Wireless Communications Applications: Voice calls (VOIP), audio transmission, live video streaming, image data dissemination, text messages, internet access, internet of things (IoT) devices, etc; SATCOM, Mobile SATCOM, and SATCOM on-the-move using satellites that send and receive required information and mission data such as weather forecasting, TV broadcasting, EO/IR and SAR imaging data for situational awareness, platform tracking, telemetry, and command (TT&C) data, etc.; IoT and wireless sensor networks (WSN) for smart homes, autonomous driving, traffic management, environmental monitoring, etc; wireless local area networks (WLAN) for connecting devices to the internet without wires; and wireless power transfer (WPT) for sending electrical power to the desired devices and systems without cords. Other topics of interest related to advanced wireless communications applications using ML-AI technology include ML-AI-Driven Distributed Communication Systems; Advanced Communication Systems for AI of Things (ACS-AIoT); AI-Based Collaborative Computing for Smart Communications and Networking Systems; communication framework design for AIoT; passive and active target recognition communication technologies for AIoT; communication task scheduling for AIoT; cloud, edge, and-fog communication architecture for AIoT; AIoT-based innovative communication systems and services for smart homes, smart cities, smart forests, and smart industries; communication architecture for federated learning-based AIoT environments; smart communication and networking using a shared long short-term memory (S-LSTM) generative adversarial network (GAN) and recurrent neural networks (RNN) (i.e., RNN with adversarial training); and commercial and defense 5G/6G Satellite Integration (SATis5/6).
  • Integrated Sensing and Communication (ISAC) TEs: TEs for commercial-off-the shelf radar-common data link (COTS-R-CDL), full duplex radar and radio communication TEs using multi-function RF (MFRF) technology, multibeam TEs using steerable analog antenna arrays, MFRF APAA for ISAC applications, and MIMO radars. Related topics of interest in this subject area are: (i) 5G/6G TEs allowing for ISAC applications, and (ii) new and advanced ISAC TEs for radar-and-communication waveform selection and sensor and communication resource management (SCRM). Other topics related to waveform selection include optimization of the (i) MFRF radar system performance, which will be the signal-to-noise ratio (SNR) at the radar receiver, RX, range/velocity resolution, mainlobe width, and integrated or peak side-lobe level of the autocorrelation function, detection probability, and estimation performance, and (ii) the MFRF communication system channel capacity and/or spectral efficiency at the communication user terminal.
  • ISAC Techniques and Applications: Innovative COTS-R-CDL and multiple beamforming techniques for antenna aperture sharing that efficiently perform both communications and sensing functions, and advanced ISAC with incorporated ML-AI techniques for optimizing radar and communications resources for (i) space-based remote sensing and near real-time mission data dissemination that can be used for environmental monitoring and ISR missions, (ii) space-based systems networking, (iii) vehicular and terrestrial networking, (iv) IoT and combined AIoT and IoT, and (v) smart city and indoor services such as human activity and gesture recognition.  

Dr. Tien M. Nguyen
Guest Editor

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Keywords

  • sensing and wireless communications
  • sensor technology, EO, IR, radar, LIDAR, synthetic aperture radar (SAR), quantum sensor
  • position, navigation, and timing (PNT)
  • intelligence surveillance and reconnaissance (ISR)
  • artificial intelligence-of-things (AIoT)
  • high resolution wide swath (HRWS SAR)
  • interferometric SAR, bi-static SAR, multi-static SAR
  • distributed coherent beamforming
  • active phase array antenna (APAA)
  • passive slot array antenna
  • embedded intelligent sensor
  • digital communication waveforms
  • advanced channel coding and coded modulation
  • optical communications
  • multidimensional modulation
  • low power communications system
  • hybrid multidimensional coded modulation
  • adaptive coded modulation (ACM)
  • pulse communications
  • ultra-wideband communications
  • software-defined radio (SDR)
  • software-defined networking (SDN)
  • network function virtualization (NFV)
  • cloud communication technology
  • single carrier modulation
  • multi-carrier modulation
  • division multiplexing access, hybrid division multiplexing access (H-DMA)
  • anti-jam, low probability of interception, and low probability of detection (AJ/LPI/LPD)
  • SATCOM
  • tracking, telemetry, and command (TT&C)
  • wireless sensor networks (WSN)
  • internet of things (IoT)
  • wireless local area networks (WLAN)
  • smart communication and networking
  • 5G/6G Satellite Integration (SATis5, SATis6)
  • generative adversarial network (GAN)
  • recurrent neural networks (RNN)
  • 4G, 5G, and 6G
  • wireless communication technology
  • machine learning and artificial intelligence (ML-AI)
  • edge computing
  • space-based remote sensing
  • integrated sensing and communication (ISAC)
  • multi-function RF (MFRF)
  • MIMO radars
  • communication resources management (SCRM)
  • commercial-off-the shelf radar-common data link (COTS-R-CDL)

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Published Papers (4 papers)

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Research

16 pages, 1978 KiB  
Article
Learning-Assisted Multi-IMU Proprioceptive State Estimation for Quadruped Robots
by Xuanning Liu, Yajie Bao, Peng Cheng, Dan Shen, Zhengyang Fan, Hao Xu and Genshe Chen
Information 2025, 16(6), 479; https://doi.org/10.3390/info16060479 - 9 Jun 2025
Abstract
This paper presents a learning-assisted approach for state estimation of quadruped robots using observations of proprioceptive sensors, including multiple inertial measurement units (IMUs). Specifically, one body IMU and four additional IMUs attached to each calf link of the robot are used for sensing [...] Read more.
This paper presents a learning-assisted approach for state estimation of quadruped robots using observations of proprioceptive sensors, including multiple inertial measurement units (IMUs). Specifically, one body IMU and four additional IMUs attached to each calf link of the robot are used for sensing the dynamics of the body and legs, in addition to joint encoders. The extended Kalman filter (KF) is employed to fuse sensor data to estimate the robot’s states in the world frame and enhance the convergence of the extended KF (EKF). To circumvent the requirements for the measurements from the motion capture (mocap) system or other vision systems, the right-invariant EKF (RI-EKF) is extended to employ the foot IMU measurements for enhanced state estimation, and a learning-based approach is presented to estimate the vision system measurements for the EKF. One-dimensional convolutional neural networks (CNN) are leveraged to estimate required measurements using only the available proprioception data. Experiments on real data from a quadruped robot demonstrate that proprioception can be sufficient for state estimation. The proposed learning-assisted approach, which does not rely on data from vision systems, achieves competitive accuracy compared to EKF using mocap measurements and lower estimation errors than RI-EKF using multi-IMU measurements. Full article
(This article belongs to the Special Issue Sensing and Wireless Communications)
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21 pages, 1755 KiB  
Article
Wi-Fi Sensing and Passenger Counting: A Statistical Analysis of Local Factors and Error Patterns
by Cristina Pronello, Deepan Anbarasan and Alessandra Boggio Marzet
Information 2025, 16(6), 459; https://doi.org/10.3390/info16060459 - 29 May 2025
Viewed by 207
Abstract
Automatic passenger counting (APC) systems are an important asset for public transport operators, allowing them to optimise networks by monitoring lines’ utilisation. However, the cost of these systems is high and the development of alternative devices, cheaper than the most widely used optical [...] Read more.
Automatic passenger counting (APC) systems are an important asset for public transport operators, allowing them to optimise networks by monitoring lines’ utilisation. However, the cost of these systems is high and the development of alternative devices, cheaper than the most widely used optical systems, seems promising. This paper aims at understanding the influence of local factors on the accuracy of a Wi-Fi APC system by analysing error patterns in a real-world scenario. The APC system was installed on a bus operating regularly within the public transport network and, in the meantime, ground truth data were collected through manual counting. The collected data were then analysed to calculate accuracy and, finally, multilevel modelling was used to identify error patterns due to local factors. This study challenges traditional assumptions, revealing that factors like high pedestrian traffic or intense vehicular movement around the bus have minimal impact on accuracy, if effective received signal strength indicator filters are used. Instead, the number of passengers within the bus affects Wi-Fi systems significantly, especially when the bus is carrying more than 10 passengers, which leads to undercounting due to signal obstruction. This research lays the foundation for strategic error correction to improve accuracy in real-world scenarios. Full article
(This article belongs to the Special Issue Sensing and Wireless Communications)
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18 pages, 811 KiB  
Article
RL-BMAC: An RL-Based MAC Protocol for Performance Optimization in Wireless Sensor Networks
by Owais Khan, Sana Ullah, Muzammil Khan and Han-Chieh Chao
Information 2025, 16(5), 369; https://doi.org/10.3390/info16050369 - 30 Apr 2025
Viewed by 302
Abstract
Applications of wireless sensor networks have significantly increased in the modern era. These networks operate on a limited power supply in the form of batteries, which are normally difficult to replace on a frequent basis. In wireless sensor networks, sensor nodes alternate between [...] Read more.
Applications of wireless sensor networks have significantly increased in the modern era. These networks operate on a limited power supply in the form of batteries, which are normally difficult to replace on a frequent basis. In wireless sensor networks, sensor nodes alternate between sleep and active states to conserve energy through different methods. Duty cycling is among the most commonly used methods. However, it suffers from problems like unnecessary idle listening, extra energy consumption, and packet drop rate. A Deep Reinforcement Learning-based B-MAC protocol called (RL-BMAC) has been proposed to address this issue. The proposed protocol deploys a deep reinforcement learning agent with fixed hyperparameters to optimize the duty cycling of the nodes. The reinforcement learning agent monitors essential parameters such as energy level, packet drop rate, neighboring nodes’ status, and preamble sampling. The agent stores the information as a representative state and adjusts the duty cycling of all nodes. The performance of RL-BMAC is compared to that of conventional B-MAC through extensive simulations. The results obtained from the simulations indicate that RL-BMAC outperforms B-MAC in terms of throughput by 58.5%, packet drop rate by 44.8%, energy efficiency by 35%, and latency by 26.93% Full article
(This article belongs to the Special Issue Sensing and Wireless Communications)
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32 pages, 1019 KiB  
Article
Time Scale in Alternative Positioning, Navigation, and Timing: New Dynamic Radio Resource Assignments and Clock Steering Strategies
by Khanh Pham
Information 2025, 16(3), 210; https://doi.org/10.3390/info16030210 - 9 Mar 2025
Viewed by 681
Abstract
Terrestrial and satellite communications, tactical data links, positioning, navigation, and timing (PNT), as well as distributed sensing will continue to require precise timing and the ability to synchronize and disseminate time effectively. However, the supply of space-qualified clocks that meet Global Navigation Satellite [...] Read more.
Terrestrial and satellite communications, tactical data links, positioning, navigation, and timing (PNT), as well as distributed sensing will continue to require precise timing and the ability to synchronize and disseminate time effectively. However, the supply of space-qualified clocks that meet Global Navigation Satellite Systems (GNSS)-level performance standards is limited. As the awareness of potential disruptions to GNSS due to adversarial actions grows, the current reliance on GNSS-level timing appears costly and outdated. This is especially relevant given the benefits of developing robust and stable time scale references in orbit, especially as various alternatives to GNSS are being explored. The onboard realization of clock ensembles is particularly promising for applications such as those providing the on-demand dissemination of a reference time scale for navigation services via a proliferated Low-Earth Orbit (pLEO) constellation. This article investigates potential inter-satellite network architectures for coordinating time and frequency across pLEO platforms. These architectures dynamically allocate radio resources for clock data transport based on the requirements for pLEO time scale formations. Additionally, this work proposes a model-based control system for wireless networked timekeeping systems. It envisions the optimal placement of critical information concerning the implicit ensemble mean (IEM) estimation across a multi-platform clock ensemble, which can offer better stability than relying on any single ensemble member. This approach aims to reduce data traffic flexibly. By making the IEM estimation sensor more intelligent and running it on the anchor platform while also optimizing the steering of remote frequency standards on participating platforms, the networked control system can better predict the future behavior of local reference clocks paired with low-noise oscillators. This system would then send precise IEM estimation information at critical moments to ensure a common pLEO time scale is realized across all participating platforms. Clock steering is essential for establishing these time scales, and the effectiveness of the realization depends on the selected control intervals and steering techniques. To enhance performance reliability beyond what the existing Linear Quadratic Gaussian (LQG) control technique can provide, the minimal-cost-variance (MCV) control theory is proposed for clock steering operations. The steering process enabled by the MCV control technique significantly impacts the overall performance reliability of the time scale, which is generated by the onboard ensemble of compact, lightweight, and low-power clocks. This is achieved by minimizing the variance of the chi-squared random performance of LQG control while maintaining a constraint on its mean. Full article
(This article belongs to the Special Issue Sensing and Wireless Communications)
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