73 research outputs found
Exploring the Synergy: A Review of Dual-Functional Radar Communication Systems
This review paper examines the concept and advancements in the evolving
landscape of Dual-functional Radar Communication (DFRC) systems. Traditionally,
radar and communication systems have functioned independently, but current
research is actively investigating the integration of these functionalities
into a unified platform. This paper discusses the motivations behind the
development of DFRC systems, the challenges involved, and the potential
benefits they offer. A discussion on the performance bounds for DFRC systems is
also presented. The paper encompasses a comprehensive analysis of various
techniques, architectures, and technologies used in the design and optimization
of DFRC systems, along with their performance and trade-offs. Additionally, we
explore potential application scenarios for these joint communication and
sensing systems, offering a comprehensive perspective on the multifaceted
landscape of DFRC technology.Comment: 17 pages, 7 figure
Mathematical optimization and game theoretic methods for radar networks
Radar systems are undoubtedly included in the hall of the most momentous discoveries of the previous century. Although radars were initially used for ship and aircraft detection, nowadays these systems are used in highly diverse fields, expanding from civil aviation, marine navigation and air-defence to ocean surveillance, meteorology and medicine. Recent advances in signal processing and the constant development of computational capabilities led to radar systems with impressive surveillance and tracking characteristics but on the other hand the continuous growth of distributed networks made them susceptible to multisource interference. This thesis aims at addressing vulnerabilities of modern radar networks and further improving their characteristics through the design of signal processing algorithms and by utilizing convex optimization and game theoretic methods. In particular, the problems of beamforming, power allocation, jammer avoidance and uncertainty within the context of multiple-input multiple-output (MIMO) radar networks are addressed.
In order to improve the beamforming performance of phased-array and MIMO radars employing two-dimensional arrays of antennas, a hybrid two-dimensional Phased-MIMO radar with fully overlapped subarrays is proposed. The work considers both adaptive (convex optimization, CAPON beamformer) and non-adaptive (conventional) beamforming techniques. The transmit, receive and overall beampatterns of the Phased-MIMO model are compared with the respective beampatterns of the phased-array and the MIMO schemes, proving that the hybrid model provides superior capabilities in beamforming.
By incorporating game theoretic techniques in the radar field, various vulnerabilities and problems can be investigated. Hence, a game theoretic power allocation scheme is proposed and a Nash equilibrium analysis for a multistatic MIMO network is performed. A network of radars is considered, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since no communication between the clusters is assumed, non-cooperative game theoretic techniques and convex optimization methods are utilized to tackle the power adaptation problem. During the proof of the existence and the uniqueness of the solution, which is also presented, important contributions on the SINR performance and the transmission power of the radars have been derived.
Game theory can also been applied to mitigate jammer interference in a radar network. Hence, a competitive power allocation problem for a MIMO radar system in the presence of multiple jammers is investigated. The main objective of the radar network is to minimize the total power emitted by the radars while achieving a specific detection criterion for each of the targets-jammers, while the intelligent jammers have the ability to observe the radar transmission power and consequently decide its jamming power to maximize the interference to the radar system. In this context, convex optimization methods, noncooperative game theoretic techniques and hypothesis testing are incorporated to identify the jammers and to determine the optimal power allocation. Furthermore, a proof of the existence and the uniqueness of the solution is presented.
Apart from resource allocation applications, game theory can also address distributed beamforming problems. More specifically, a distributed beamforming and power allocation technique for a radar system in the presence of multiple targets is considered. The primary goal of each radar is to minimize its transmission power while attaining an optimal beamforming strategy and satisfying a certain detection criterion for each of the targets. Initially, a strategic noncooperative game (SNG) is used, where there is no communication between the various radars of the system. Subsequently, a more coordinated game theoretic approach incorporating a pricing mechanism is adopted. Furthermore, a Stackelberg game is formulated by adding a surveillance radar to the system model, which will play the role of the leader, and thus the remaining radars will be the followers. For each one of these games, a proof of the existence and uniqueness of the solution is presented.
In the aforementioned game theoretic applications, the radars are considered to know the exact radar cross section (RCS) parameters of the targets and thus the exact channel gains of all players, which may not be feasible in a real system. Therefore, in the last part of this thesis, uncertainty regarding the channel gains among the radars and the targets is introduced, which originates from the RCS fluctuations of the targets. Bayesian game theory provides a framework to address such problems of incomplete information. Hence, a Bayesian game is proposed, where each radar egotistically maximizes its SINR, under a predefined power constraint
Robust Joint Active-Passive Beamforming Design for IRS-Assisted ISAC Systems
The idea of Integrated Sensing and Communication (ISAC) offers a promising
solution to the problem of spectrum congestion in future wireless networks.
This paper studies the integration of intelligent reflective surfaces (IRS)
with ISAC systems to improve the performance of radar and communication
services. Specifically, an IRS-assisted ISAC system is investigated where a
multi-antenna base station (BS) performs multi-target detection and multi-user
communication. A low complexity and efficient joint optimization of transmit
beamforming at the BS and reflective beamforming at the IRS is proposed. This
is done by jointly optimizing the BS beamformers and IRS reflection
coefficients to minimize the Frobenius distance between the covariance matrices
of the transmitted signal and the desired radar beam pattern. This optimization
aims to satisfy the signal-to-interference-and-noise ratio (SINR) constraints
of the communication users, the total transmit power limit at the BS, and the
unit modulus constraints of the IRS reflection coefficients. To address the
resulting complex non-convex optimization problem, an efficient alternating
optimization (AO) algorithm combining fractional programming (FP),
semi-definite programming (SDP), and second order cone programming (SOCP)
methods is proposed. Furthermore, we propose robust beamforming optimization
for IRS-ISAC systems by adapting the proposed optimization algorithm to the IRS
channel uncertainties that may exist in practical systems. Using advanced tools
from convex optimization theory, the constraints containing uncertainty are
transformed to their equivalent linear matrix inequalities (LMIs) to account
for the channels' uncertainty radius. The results presented quantify the
benefits of IRS-ISAC systems under various conditions and demonstrate the
effectiveness of the proposed algorithm
End-to-End Learning for Integrated Sensing and Communication
Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments
Integrated Sensing and Communications with Reconfigurable Intelligent Surfaces
Integrated sensing and communications (ISAC) are envisioned to be an integral
part of future wireless networks, especially when operating at the
millimeter-wave (mmWave) and terahertz (THz) frequency bands. However,
establishing wireless connections at these high frequencies is quite
challenging, mainly due to the penetrating pathloss that prevents reliable
communication and sensing. Another emerging technology for next-generation
wireless systems is reconfigurable intelligent surfaces (RISs), which are
capable of modifying harsh propagation environments. RISs are the focus of
growing research and industrial attention, bringing forth the vision of smart
and programmable signal propagation environments. In this article, we provide a
tutorial-style overview of the applications and benefits of RISs for sensing
functionalities in general, and for ISAC systems in particular. We highlight
the potential advantages when fusing these two emerging technologies, and
identify for the first time that: i) joint sensing and communications designs
are most beneficial when the channels referring to these operations are
coupled, and that ii) RISs offer means for controlling this beneficial
coupling. The usefulness of RIS-aided ISAC goes beyond the individual obvious
gains of each of these technologies in both performance and power efficiency.
We also discuss the main signal processing challenges and future research
directions which arise from the fusion of these two emerging technologies.Comment: 37 pages, 9 figure
Joint waveform and precoding design for coexistence of MIMO radar and MU-MISO communication
peer reviewedThe joint design problem for the coexistence of multiple-input multiple-output (MIMO) radar and multi-user multiple-input-single-output (MU-MISO) communication is investigated. Different from the conventional design schemes, which require defining the primary function, we consider designing the transmit waveform, precoding matrix and receive filter to maximize the radar SINR and the minimal SINR of communication users, simultaneously. By doing so, the promising overall performance for both sensing and communication is achieved without requiring parameter tuning for the threshold of communication or radar. However, the resulting optimization problem which contains the maximin objective function and the unit sphere constraint, is highly nonconvex and hence difficult to attain the optimal solution directly. Towards this end, the epigraph-form reformulation is first adopted, and then an alternating maximisation (AM) method is devised, in which the Dinkelbach’s algorithm is used to tackle the nonconvex fractional-programing subproblem. Simulation results indicate that the proposed method can achieve improved performance compared with the benchmarks
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