51 research outputs found
On the Construction of Radio Environment Maps for Cognitive Radio Networks
The Radio Environment Map (REM) provides an effective approach to Dynamic
Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on
REM construction show that there exists a tradeoff between the number of
measurements (sensors) and REM accuracy. In this paper, we analyze this
tradeoff and determine that the REM error is a decreasing and convex function
of the number of measurements (sensors). The concept of geographic entropy is
introduced to quantify this relationship. And the influence of sensor
deployment on REM accuracy is examined using information theory techniques. The
results obtained in this paper are applicable not only for the REM, but also
for wireless sensor network deployment.Comment: 6 pages, 7 figures, IEEE WCNC conferenc
Seeing is Believing: Detecting Sybil Attack in FANET by Matching Visual and Auditory Domains
The flying ad hoc network (FANET) will play a crucial role in the B5G/6G era
since it provides wide coverage and on-demand deployment services in a
distributed manner. The detection of Sybil attacks is essential to ensure
trusted communication in FANET. Nevertheless, the conventional methods only
utilize the untrusted information that UAV nodes passively ``heard'' from the
``auditory" domain (AD), resulting in severe communication disruptions and even
collision accidents. In this paper, we present a novel VA-matching solution
that matches the neighbors observed from both the AD and the ``visual'' domain
(VD), which is the first solution that enables UAVs to accurately correlate
what they ``see'' from VD and ``hear'' from AD to detect the Sybil attacks.
Relative entropy is utilized to describe the similarity of observed
characteristics from dual domains. The dynamic weight algorithm is proposed to
distinguish neighbors according to the characteristics' popularity. The
matching model of neighbors observed from AD and VD is established and solved
by the vampire bat optimizer. Experiment results show that the proposed
VA-matching solution removes the unreliability of individual characteristics
and single domains. It significantly outperforms the conventional RSSI-based
method in detecting Sybil attacks. Furthermore, it has strong robustness and
achieves high precision and recall rates.Comment: 7 pages, 9 figures, 1 tabl
Joint Localization and Communication Enhancement in Uplink Integrated Sensing and Communications System with Clock Asynchronism
In this paper, we propose a joint single-base localization and communication
enhancement scheme for the uplink (UL) integrated sensing and communications
(ISAC) system with asynchronism, which can achieve accurate single-base
localization of user equipment (UE) and significantly improve the communication
reliability despite the existence of timing offset (TO) due to the clock
asynchronism between UE and base station (BS). Our proposed scheme integrates
the CSI enhancement into the multiple signal classification (MUSIC)-based AoA
estimation and thus imposes no extra complexity on the ISAC system. We further
exploit a MUSIC-based range estimation method and prove that it can suppress
the time-varying TO-related phase terms. Exploiting the AoA and range
estimation of UE, we can estimate the location of UE. Finally, we propose a
joint CSI and data signals-based localization scheme that can coherently
exploit the data and the CSI signals to improve the AoA and range estimation,
which further enhances the single-base localization of UE. The extensive
simulation results show that the enhanced CSI can achieve equivalent bit error
rate performance to the minimum mean square error (MMSE) CSI estimator. The
proposed joint CSI and data signals-based localization scheme can achieve
decimeter-level localization accuracy despite the existing clock asynchronism
and improve the localization mean square error (MSE) by about 8 dB compared
with the maximum likelihood (ML)-based benchmark method.Comment: 13 pages, 11 figures, submitted to JSAC special issue "Positioning
and Sensing Over Wireless Networks
Specific Beamforming for Multi-UAV Networks: A Dual Identity-based ISAC Approach
Beam alignment is essential to compensate for the high path loss in the
millimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network. The integrated
sensing and communication (ISAC) technology has been envisioned as a promising
solution to enable efficient beam alignment in the dynamic UAV network.
However, since the digital identity (D-ID) is not contained in the reflected
echoes, the conventional ISAC solution has to either periodically feed back the
D-ID to distinguish beams for multi-UAVs or suffer the beam errors induced by
the separation of D-ID and physical identity (P-ID). This paper presents a
novel dual identity association (DIA)-based ISAC approach, the first solution
that enables specific, fast, and accurate beamforming towards multiple UAVs. In
particular, the P-IDs extracted from echo signals are distinguished dynamically
by calculating the feature similarity according to their prevalence, and thus
the DIA is accurately achieved. We also present the extended Kalman filtering
scheme to track and predict P-IDs, and the specific beam is thereby effectively
aligned toward the intended UAVs in dynamic networks. Numerical results show
that the proposed DIA-based ISAC solution significantly outperforms the
conventional methods in association accuracy and communication performance.Comment: 7 pages, 8 figure
Dual Identities Enabled Low-Latency Visual Networking for UAV Emergency Communication
The Unmanned Aerial Vehicle (UAV) swarm networks will play a crucial role in
the B5G/6G network thanks to its appealing features, such as wide coverage and
on-demand deployment. Emergency communication (EC) is essential to promptly
inform UAVs of potential danger to avoid accidents, whereas the conventional
communication-only feedback-based methods, which separate the digital and
physical identities (DPI), bring intolerable latency and disturb the unintended
receivers. In this paper, we present a novel DPI-Mapping solution to match the
identities (IDs) of UAVs from dual domains for visual networking, which is the
first solution that enables UAVs to communicate promptly with what they see
without the tedious exchange of beacons. The IDs are distinguished dynamically
by defining feature similarity, and the asymmetric IDs from different domains
are matched via the proposed bio-inspired matching algorithm. We also consider
Kalman filtering to combine the IDs and predict the states for accurate
mapping. Experiment results show that the DPI-Mapping reduces individual
inaccuracy of features and significantly outperforms the conventional
broadcast-based and feedback-based methods in EC latency. Furthermore, it also
reduces the disturbing messages without sacrificing the hit rate.Comment: 6 pages, 6 figure
Communication-Assisted Sensing in 6G Networks
The exploration of coordination gain achieved through the synergy of sensing
and communication (S&C) functions plays a vital role in improving the
performance of integrated sensing and communication systems. This paper focuses
on the optimal waveform design for communication-assisted sensing (CAS) systems
within the context of 6G perceptive networks. In the CAS process, the base
station actively senses the targets through device-free wireless sensing and
simultaneously transmits the pertinent information to end-users. In our
research, we establish a CAS framework grounded in the principles of
rate-distortion theory and the source-channel separation theorem (SCT) in lossy
data transmission. This framework provides a comprehensive understanding of the
interplay between distortion, coding rate, and channel capacity. The purpose of
waveform design is to minimize the sensing distortion at the user end while
adhering to the SCT and power budget constraints. In the context of target
response matrix estimation, we propose two distinct waveform strategies: the
separated S&C and dual-functional waveform schemes. In the former strategy, we
develop a simple one-dimensional search algorithm, shedding light on a notable
power allocation tradeoff between the S&C waveform. In the latter scheme, we
conceive a heuristic mutual information optimization algorithm for the general
case, alongside a modified gradient projection algorithm tailored for the
scenarios with independent sensing sub-channels. Additionally, we identify the
presence of both subspace tradeoff and water-filling tradeoff. Finally, we
validate the effectiveness of the proposed algorithms through numerical
simulations
5G PRS-Based Sensing: A Sensing Reference Signal Approach for Joint Sensing and Communication System
The emerging joint sensing and communication (JSC) technology is expected to
support new applications and services, such as autonomous driving and extended
reality (XR), in the future wireless communication systems. Pilot (or
reference) signals in wireless communications usually have good passive
detection performance, strong anti-noise capability and good auto-correlation
characteristics, hence they bear the potential for applying in radar sensing.
In this paper, we investigate how to apply the positioning reference signal
(PRS) of the 5th generation (5G) mobile communications in radar sensing. This
approach has the unique benefit of compatibility with the most advanced mobile
communication system available so far. Thus, the PRS can be regarded as a
sensing reference signal to simultaneously realize the functions of radar
sensing, communication and positioning in a convenient manner. Firstly, we
propose a PRS based radar sensing scheme and analyze its range and velocity
estimation performance, based on which we propose a method that improves the
accuracy of velocity estimation by using multiple frames. Furthermore, the
Cramer-Rao lower bound (CRLB) of the range and velocity estimation for PRS
based radar sensing and the CRLB of the range estimation for PRS based
positioning are derived. Our analysis and simulation results demonstrate the
feasibility and superiority of PRS over other pilot signals in radar sensing.
Finally, some suggestions for the future 5G-Advanced and 6th generation (6G)
frame structure design containing the sensing reference signal are derived
based on our study
An Investigation of Decision Analytic Methodologies for Stress Identification
In modern society, more and more people are suffering from some type of stress. Monitoring and timely detecting of stress level will be very valuable for the person to take counter measures. In this paper, we investigate the use of decision analytics methodologies to detect stress. We present a new feature selection method based on the principal component analysis (PCA), compare three feature selection methods, and evaluate five information fusion methods for stress detection. A driving stress data set created by the MIT Media lab is used to evaluate the relative performance of these methods. Our study show that the PCA can not only reduce the needed number of features from 22 to five, but also the number of sensors used from five to two and it only uses one type of sensor, thus increasing the application usability. The selected features can be used to quickly detect stress level with good accuracy (78.94%), if support vector machine fusion method is used.EI041675-1699
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