179 research outputs found
Optimal Transmit Beamforming for Integrated Sensing and Communication
This paper studies the transmit beamforming in a downlink integrated sensing
and communication (ISAC) system, where a base station (BS) equipped with a
uniform linear array (ULA) sends combined information-bearing and dedicated
radar signals to simultaneously perform downlink multiuser communication and
radar target sensing. Under this setup, we maximize the radar sensing
performance (in terms of minimizing the beampattern matching errors or
maximizing the minimum weighted beampattern gains), subject to the
communication users' minimum signal-to-interference-plus-noise ratio (SINR)
requirements and the BS's transmit power constraints. In particular, we
consider two types of communication receivers, namely Type-I and Type-II
receivers, which do not have and do have the capability of cancelling the
interference from the {\emph{a-priori}} known dedicated radar signals,
respectively. Under both Type-I and Type-II receivers, the beampattern matching
and minimum weighted beampattern gain maximization problems are globally
optimally solved via applying the semidefinite relaxation (SDR) technique
together with the rigorous proof of the tightness of SDR for both Type-I and
Type-II receivers under the two design criteria. It is shown that at the
optimality, radar signals are not required with Type-I receivers under some
specific conditions, while radar signals are always needed to enhance the
performance with Type-II receivers. Numerical results show that the minimum
weighted beampattern gain maximization leads to significantly higher
beampattern gains at the worst-case sensing angles with a much lower
computational complexity than the beampattern matching design. We show that by
exploiting the capability of canceling the interference caused by the radar
signals, the case with Type-II receivers results in better sensing performance
than that with Type-I receivers and other conventional designs.Comment: submitted for possible journal publicatio
Capacity-CRB Tradeoff in OFDM Integrated Sensing and Communication Systems
Integrated sensing and communication (ISAC) has emerged as a key technology
for future communication systems. In this paper, we provide a general framework
to reveal the fundamental tradeoff between sensing and communication in OFDM
systems, where a unified ISAC waveform is exploited to perform both tasks. In
particular, we define the Capacity-Bayesian Cramer Rao Bound (BCRB) region in
the asymptotically case when the number of subcarriers is large. Specifically,
we show that the asymptotically optimal input distribution that achieves the
Pareto boundary point of the Capacity-BCRB region is Gaussian and the entire
Pareto boundary can be obtained by solving a convex power allocation problem.
Moreover, we characterize the structure of the sensing-optimal power allocation
in the asymptotically case. Finally, numerical simulations are conducted to
verify the theoretical analysis and provide useful insights
Information-Theoretic Limits of Integrated Sensing and Communication with Correlated Sensing and Channel States for Vehicular Networks
In connected vehicular networks, it is vital to have vehicular nodes that are
capable of sensing about surrounding environments and exchanging messages with
each other for automating and coordinating purpose. Towards this end,
integrated sensing and communication (ISAC), combining both sensing and
communication systems to jointly utilize their resources and to pursue mutual
benefits, emerges as a new cost-effective solution. In ISAC, the hardware and
spectrum co-sharing leads to a fundamental tradeoff between sensing and
communication performance, which is not well understood except for very simple
cases with the same sensing and channel states, and perfect channel state
information at the receiver (CSIR). In this paper, a general point-to-point
ISAC model is proposed to account for the scenarios that the sensing state is
different from but correlated with the channel state, and the CSIR is not
necessarily perfect. For the model considered, the optimal tradeoff is
characterized by a capacity-distortion function that quantifies the best
communication rate for a given sensing distortion constraint requirement. An
iterative algorithm is proposed to compute such tradeoff, and a few non-trivial
examples are constructed to demonstrate the benefits of ISAC as compared to the
separation-based approach
A Two-stage Multiband Radar Sensing Scheme via Stochastic Particle-Based Variational Bayesian Inference
Multiband fusion is an important technique for radar sensing, which jointly
utilizes measurements from multiple non-contiguous frequency bands to improve
the sensing performance. In the multi-band radar sensing signal model, there
are many local optimums in the associated likelihood function due to the
existence of high frequency component, which makes it difficult to obtain
high-accuracy parameter estimation. To cope with this challenge, we divide the
radar target parameter estimation into two stages equipped with different but
equivalent signal models, where the first-stage coarse estimation is used to
narrow down the search range for the next stage, and the second-stage refined
estimation is based on the Bayesian approach to avoid the convergence to a bad
local optimum of the likelihood function. Specifically, in the coarse
estimation stage, we employ a weighted root MUSIC algorithm to achieve initial
estimation. Then, we apply the block stochastic successive convex approximation
(SSCA) approach to derive a novel stochastic particle-based variational
Bayesian inference (SPVBI) algorithm for the Bayesian estimation of the radar
target parameters in the refined stage. Unlike the conventional particle-based
VBI (PVBI) in which only the probability of each particle is optimized and the
per-iteration computational complexity increases exponentially with the number
of particles, the proposed SPVBI optimizes both the position and probability of
each particle, and it adopts the block SSCA to significantly improve the
sampling efficiency by averaging over iterations. As such, it is shown that the
proposed SPVBI can achieve a better performance than the conventional PVBI with
a much smaller number of particles and per-iteration complexity. Finally,
extensive simulations verify the advantage of the proposed algorithm over
various baseline algorithms
Intelligent Reflecting Surface Enabled Sensing: Cram\'er-Rao Bound Optimization
This paper investigates intelligent reflecting surface (IRS) enabled
non-line-of-sight (NLoS) wireless sensing, in which an IRS is dedicatedly
deployed to assist an access point (AP) to sense a target at its NLoS region.
It is assumed that the AP is equipped with multiple antennas and the IRS is
equipped with a uniform linear array. We consider two types of target models,
namely the point and extended targets, for which the AP aims to estimate the
target's direction-of-arrival (DoA) and the target response matrix with respect
to the IRS, respectively, based on the echo signals from the
AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit
beamforming at the AP and the reflective beamforming at the IRS to minimize the
Cram\'er-Rao bound (CRB) on the estimation error. Towards this end, we first
obtain the CRB expressions for the two target models in closed form. It is
shown that in the point target case, the CRB for estimating the DoA depends on
both the transmit and reflective beamformers; while in the extended target
case, the CRB for estimating the target response matrix only depends on the
transmit beamformers. Next, for the point target case, we optimize the joint
beamforming design to minimize the CRB, via alternating optimization,
semi-definite relaxation, and successive convex approximation. For the extended
target case, we obtain the optimal transmit beamforming solution to minimize
the CRB in closed form. Finally, numerical results show that for both cases,
the proposed designs based on CRB minimization achieve improved sensing
performance in terms of mean squared error, as compared to other traditional
schemes.Comment: 14 pages, 7 figures. arXiv admin note: substantial text overlap with
arXiv:2204.1107
Fully-Passive versus Semi-Passive IRS-Enabled Sensing: SNR and CRB Comparison
This paper investigates the sensing performance of two intelligent reflecting
surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with
fully-passive and semi-passive IRSs, respectively. In particular, we consider a
fundamental setup with one base station (BS), one uniform linear array (ULA)
IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze
the sensing signal-to-noise ratio (SNR) performance for a target detection
scenario and the estimation Cram\'er-Rao bound (CRB) performance for a target's
direction-of-arrival (DoA) estimation scenario, in cases where the transmit
beamforming at the BS and the reflective beamforming at the IRS are jointly
optimized. First, for the target detection scenario, we characterize the
maximum sensing SNR when the BS-IRS channels are line-of-sight (LoS) and
Rayleigh fading, respectively. It is revealed that when the number of
reflecting elements equipped at the IRS becomes sufficiently large, the
maximum sensing SNR increases proportionally to for the semi-passive-IRS
sensing system, but proportionally to for the fully-passive-IRS
counterpart. Then, for the target's DoA estimation scenario, we analyze the
minimum CRB performance when the BS-IRS channel follows Rayleigh fading.
Specifically, when grows, the minimum CRB decreases inversely
proportionally to and for the semi-passive and fully-passive-IRS
sensing systems, respectively. Finally, numerical results are presented to
corroborate our analysis across various transmit and reflective beamforming
design schemes under general channel setups. It is shown that the
fully-passive-IRS sensing system outperforms the semi-passive counterpart when
exceeds a certain threshold. This advantage is attributed to the additional
reflective beamforming gain in the IRS-BS path, which efficiently compensates
for the path loss for a large .Comment: 13 pages,7 figure
Reference-free amplitude-based WiFi passive sensing
The parasitic exploitation of WiFi signals for passive sensing purposes is a topic that is attracting considerable interest in the scientific community. In an attempt at meeting the requirements for sensor compactness, easy deployment, and low cost, we resort to a non-coherent signal processing scheme that does not rely on the availability of a reference signal and relaxes the constraints on the sensor hardware implementation. Specifically, with the proposed strategy, the presence of a moving target echo is determined by detecting the amplitude modulation that it produces on the direct signal transmitted from the WiFi access point. We investigate the target discrimination capability of the resulting sensor against the competing interference background and we theoretically characterize the impact of undesired amplitude fluctuations in the received signal that are determined by causes other than the superposition of the target echo, thereby including the waveform properties. Hence, we propose different solutions to address the limitations identified, characterized by different complexities, and we investigate their advantages and drawbacks. The conceived signal processing schemes are thoroughly validated on both simulated and experimental data, collected in different operational scenarios
Guest Editorial Special Issue on Integrated Sensing and Communication-Part I
Driving a gradual integration of the physical and digital worlds is perceived to become a reality in the 6G era, from vehicles to drones, from surveillance facilities in cities to agricultural tools in the countryside. Jointly motivated by recent advances in communication and signal processing, radio sensing functionality can be integrated into a 6G radio access network (RAN) in a low-cost and fast manner. That is, future networks have the ability to “see” the physical world through imaging and measuring the surrounding environment, which enables advanced location-aware services, ranging from the physical to application layers. In essence, a radio emission could simultaneously convey communication data from the transmitter to the receiver and deliver environmental information from the scattered echoes. Therefore, sensing and communication (S&C) functionalities are possible to be co-designed to utilize resources efficiently and to assist each other for mutual benefits. This type of research is typically referred to as integrated sensing and communication (ISAC)
Rethinking the Tradeoff in Integrated Sensing and Communication: Recognition Accuracy versus Communication Rate
Integrated sensing and communication (ISAC) is a promising technology to
improve the band-utilization efficiency via spectrum sharing or hardware
sharing between radar and communication systems. Since a common radio resource
budget is shared by both functionalities, there exists a tradeoff between the
sensing and communication performance. However, this tradeoff curve is
currently unknown in ISAC systems with human motion recognition tasks based on
deep learning. To fill this gap, this paper formulates and solves a
multi-objective optimization problem which simultaneously maximizes the
recognition accuracy and the communication data rate. The key ingredient of
this new formulation is a nonlinear recognition accuracy model with respect to
the wireless resources, where the model is derived from power function
regression of the system performance of the deep spectrogram network. To avoid
cost-expensive data collection procedures, a primitive-based autoregressive
hybrid (PBAH) channel model is developed, which facilitates efficient training
and testing dataset generation for human motion recognition in a virtual
environment. Extensive results demonstrate that the proposed wireless
recognition accuracy and PBAH channel models match the actual experimental data
very well. Moreover, it is found that the accuracy-rate region consists of a
communication saturation zone, a sensing saturation zone, and a
communication-sensing adversarial zone, of which the third zone achieves the
desirable balanced performance for ISAC systems.Comment: arXiv admin note: text overlap with arXiv:2104.1037
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