arXiv subjects

Christos Masouros

Publications and source records attributed to Christos Masouros.

At least 127 records · Page 7Linked to original sources

arXiv2023

Securing the Sensing Functionality in ISAC Networks: An Artificial Noise Design

Integrated sensing and communications (ISAC) systems employ dual-functional signals to simultaneously accomplish radar sensing and wireless communication tasks. However, ISAC systems open up new sensing security vulnerabilities to malicious illegitimate eavesdroppers (Eves) that can also exploit the transmitted waveform to extract sensing information from the environment. In this paper, we investigate the beamforming design to enhance the sensing security of an ISAC system, where the communication user (CU) serves as a sensing Eve. Our objective is to maximize the mutual information (MI) for the legitimate radar sensing receiver while considering the constraint of the MI for the Eve and the quality of service to the CUs. Then, we consider the artificial noise (AN)-aided beamforming to further enhance the sensing security. Simulation results demonstrate that our proposed methods achieve MI improvement of the legitimate receiver while limiting the sensing MI of the Eve, compared with the baseline scheme, and that the utilization of AN further contributes to sensing security.

eess.SP↗

arXiv2023

Intelligent Surface Empowered Integrated Sensing and Communication: From Coexistence to Reciprocity

Integrated sensing and communication (ISAC) has attracted growing interests for sixth-generation (6G) and beyond wireless networks. The primary challenges faced by highly efficient ISAC include limited sensing and communication (S&C) coverage, constrained integration gain between S&C under weak channel correlations, and unknown performance boundary. Intelligent reflecting/refracting surfaces (IRSs) can effectively expand S&C coverage and control the degree of freedom of channels between the transmitters and receivers, thereby realizing increasing integration gains. In this work, we first delve into the fundamental characteristics of IRS-empowered ISAC and innovative IRS-assisted sensing architectures. Then, we discuss various objectives for IRS channel control and deployment optimization in ISAC systems. Furthermore, the interplay between S&C in different deployment strategies is investigated and some promising directions for IRS enhanced ISAC are outlined.

eess.SP↗

arXiv2023

Robust NOMA-assisted OTFS-ISAC Network Design with 3D Motion Prediction Topology

This paper proposes a novel non-orthogonal multiple access (NOMA)-assisted orthogonal time-frequency space (OTFS)-integrated sensing and communication (ISAC) network, which uses unmanned aerial vehicles (UAVs) as air base stations to support multiple users. By employing ISAC, the UAV extracts position and velocity information from the user's echo signals, and non-orthogonal power allocation is conducted to achieve a superior achievable rate. A 3D motion prediction topology is used to guide the NOMA transmission for multiple users, and a robust power allocation solution is proposed under perfect and imperfect channel estimation for Maxi-min Fairness (MMF) and Maximum sum-Rate (SR) problems. Simulation results demonstrate the superiority of the proposed NOMA-assisted OTFS-ISAC system over other systems in terms of achievable rate under both perfect and imperfect channel conditions with the aid of 3D motion prediction topology.

eess.SP↗

arXiv2023

Towards Structural Sparse Precoding: Dynamic Time, Frequency, Space, and Power Multistage Resource Programming

In last decades, dynamic resource programming in partial resource domains has been extensively investigated for single time slot optimizations. However, with the emerging real-time media applications in fifth-generation communications, their new quality of service requirements are often measured in temporal dimension. This requires multistage optimization for full resource domain dynamic programming. Taking experience rate as a typical temporal multistage metric, we jointly optimize time, frequency, space and power domains resource for multistage optimization. To strike a good tradeoff between system performance and computational complexity, we first transform the formulated mixed integer non-linear constraints into equivalent convex second order cone constraints, by exploiting the coupling effect among the resources. Leveraging the concept of structural sparsity, the objective of max-min experience rate is given as a weighted 1-norm term associated with the precoding matrix. Finally, a low-complexity iterative algorithm is proposed for full resource domain programming, aided by another simple conic optimization for obtaining its feasible initial result. Simulation verifies that our design significantly outperform the benchmarks while maintaining a fast convergence rate, shedding light on full domain dynamic resource programming of multistage optimizations.

eess.SP↗

arXiv2023

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.

eess.SP↗

arXiv2023

Block-Level Interference Exploitation Precoding for MU-MISO: An ADMM Approach

We study constructive interference based block-level precoding (CI-BLP) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. Specifically, our aim is to extend the analysis on CI-BLP to the case where the considered number of symbol slots is smaller than that of the users. To this end, we mathematically prove the feasibility of using the pseudo-inverse to obtain the optimal CI-BLP precoding matrix in a closed form. Similar to the case when the number of users is small, we show that a quadratic programming (QP) optimization on simplex can be constructed. We also design a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) framework, which can efficiently solve large-scale QP problems. We further analyze the convergence and complexity of the proposed algorithm. Numerical results validate our analysis and the optimality of the proposed algorithm, and further show that the proposed algorithm offers a flexible performance-complexity tradeoff by limiting the maximum number of iterations, which motivates the use of CI-BLP in practical wireless systems.

cs.IT↗

arXiv2023

Rethinking Dense Cells for Integrated Sensing and Communications: A Stochastic Geometric View

The inclusion of the sensing functionality in the coming generations of cellular networks necessitates a rethink of dense cell deployments. In this paper, we analyze and optimize dense cell topologies for dual-functional radar-communication (DFRC) cellular networks. With the aid of tools from stochastic geometry, we derive new analytical expressions of the potential area spectral efficiencies in (bit/sec/m2) of radar and communication systems. Based on the new formulations of the potential area spectral efficiencies, the energy efficiency (bit/Joule) of DFRC systems is provided in a closed-form formula. Then, an optimization problem to obtain the optimal base station (BS) density that maximizes the network-level energy efficiency is formulated and investigated. In this regard, the mathematical expression of the energy efficiency is shown to be a uni-modal and pseudo-concave function in the density of the BSs. Therefore, the optimal density of the BSs that maximizes the energy efficiency can be obtained. Our analytical and numerical results demonstrate that the inclusion of the sensing functionality clearly differentiates the optimal BS topologies for the DFRC systems against classical communication-only systems.

cs.IT↗

arXiv2023

Energy-Efficient Beamforming Design for Integrated Sensing and Communications Systems

In this paper, we investigate the design of energy-efficient beamforming for an ISAC system, where the transmitted waveform is optimized for joint multi-user communication and target estimation simultaneously. We aim to maximize the system energy efficiency (EE), taking into account the constraints of a maximum transmit power budget, a minimum required signal-to-interference-plus-noise ratio (SINR) for communication, and a maximum tolerable Cramer-Rao bound (CRB) for target estimation. We first consider communication-centric EE maximization. To handle the non-convex fractional objective function, we propose an iterative quadratic-transform-Dinkelbach method, where Schur complement and semi-definite relaxation (SDR) techniques are leveraged to solve the subproblem in each iteration. For the scenarios where sensing is critical, we propose a novel performance metric for characterizing the sensing-centric EE and optimize the metric adopted in the scenario of sensing a point-like target and an extended target. To handle the nonconvexity, we employ the successive convex approximation (SCA) technique to develop an efficient algorithm for approximating the nonconvex problem as a sequence of convex ones. Furthermore, we adopt a Pareto optimization mechanism to articulate the tradeoff between the communication-centric EE and sensing-centric EE. We formulate the search of the Pareto boundary as a constrained optimization problem and propose a computationally efficient algorithm to handle it. Numerical results validate the effectiveness of our proposed algorithms compared with the baseline schemes and the obtained approximate Pareto boundary shows that there is a non-trivial tradeoff between communication-centric EE and sensing-centric EE, where the number of communication users and EE requirements have serious effects on the achievable tradeoff.

eess.SP↗

arXiv2023

Faster-Than-Nyquist Symbol-Level Precoding for Wideband Integrated Sensing and Communications

In this paper, we present an innovative symbol-level precoding (SLP) approach for a wideband multi-user multi-input multi-output (MU-MIMO) downlink Integrated Sensing and Communications (ISAC) system employing faster-than-Nyquist (FTN) signaling. Our proposed technique minimizes the minimum mean squared error (MMSE) for the sensed parameter estimation while ensuring the communication per-user quality-of-service through the utilization of constructive interference (CI) methodologies. While the formulated problem is non-convex in general, we tackle this issue using proficient minorization and successive convex approximation (SCA) strategies. Numerical results substantiate that our FTN-ISAC-SLP framework significantly enhances communication throughput while preserving satisfactory sensing performance.

eess.SP↗

arXiv2023

Towards ISAC-Empowered Vehicular Networks: Framework, Advances, and Opportunities

Connected and autonomous vehicle (CAV) networks face several challenges, such as low throughput, high latency, and poor localization accuracy. These challenges severely impede the implementation of CAV networks for immersive metaverse applications and driving safety in future 6G wireless networks. To alleviate these issues, integrated sensing and communications (ISAC) is envisioned as a game-changing technology for future CAV networks. This article presents a comprehensive overview on the application of ISAC techniques in vehicle-to-infrastructure (V2I) networks. We cover the general system framework, representative advances, and a detailed case study on using the 5G New Radio (NR) waveform for sensing-assisted communications in V2I networks. Finally, we highlight open problems and opportunities in the field.

eess.SP↗

arXiv2023

Seventy Years of Radar and Communications: The Road from Separation to Integration

Radar and communications (R&C) as key utilities of electromagnetic (EM) waves have fundamentally shaped human society and triggered the modern information age. Although R&C have been historically progressing separately, in recent decades they have been converging towards integration, forming integrated sensing and communication (ISAC) systems, giving rise to new, highly desirable capabilities in next-generation wireless networks and future radars. To better understand the essence of ISAC, this paper provides a systematic overview on the historical development of R&C from a signal processing (SP) perspective. We first interpret the duality between R&C as signals and systems, followed by an introduction of their fundamental principles. We then elaborate on the two main trends in their technological evolution, namely, the increase of frequencies and bandwidths, and the expansion of antenna arrays. We then show how the intertwined narratives of R&C evolved into ISAC, and discuss the resultant SP framework. Finally, we overview future research directions in this field.

eess.SP↗

arXiv2023

Robust Symbol Level Precoding for Overlay Cognitive Radio Networks

This paper focuses on designing robust symbol-level precoding (SLP) in an overlay cognitive radio (CR) network, where the primary and secondary networks transmit signals concurrently. When the primary base station (PBS) shares data and perfect channel state information (CSI) with the cognitive base station (CBS), we derive an SLP approach that minimizes the CR transmission power and satisfies symbol-wise Safety Margin (SM) constraints of both primary users (PUs) and cognitive users (CUs). The resulting optimization has a quadratic objective and linear inequality (LI) constraints, which can be solved by standard convex methods. For the case of imperfect CSI from the PBS, we propose robust SLP schemes. First, with a norm-bounded CSI error model to approximate the uncertain channels, we adopt a max-min philosophy to conservatively achieve robust SLP constraints. Second, we use the additive quantization noise model (AQNM) to describe the quantized PBS CSI and employ a stochastic constraint to formulate the problem. Both robust approaches also result in a quadratic objective with LI constraints. Simulation results show that, rather than simply trying to eliminate the network's cross-interference, the proposed robust SLP schemes enable the primary and secondary networks to aid each other in meeting their quality of service constraints.

eess.SP↗

arXiv2023

ISAC-Enabled V2I Networks Based on 5G NR: How Much Can the Overhead Be Reduced?

The emergence of the fifth-generation (5G) New Radio (NR) brings additional possibilities to vehicle-to-everything (V2X) network with improved quality of services. In order to obtain accurate channel state information (CSI) in high-mobility V2X networks, pilot signals and frequent handover between vehicles and infrastructures are required to establish and maintain the communication link, which increases the overheads and reduces the communication throughput. To address this issue, integrated sensing and communications (ISAC) was employed at the base station (BS) in the vehicle-to-infrastructure (V2I) network to reduce a certain amount of overheads, thus improve the spectral efficiency. Nevertheless, the exact amount of overheads reduction remains unclear, particularly for practical NR based V2X networks. In this paper, we study a link-level NR based V2I system employing ISAC signaling to facilitate the communication beam management, where the Extended Kalman filtering (EKF) algorithm is performed to realize the functions of tracking and predicting the motion of the vehicle. We provide detailed analysis on the overheads reduction with the aid of ISAC, and show that up to 43.24% overheads can be reduced under assigned NR frame structure. In addition, numerical results are provided to validate the improved performance on the beam tracking and communication throughput.

eess.SP↗

arXiv2022

Speeding-up Symbol-Level Precoding Using Separable and Dual Optimizations

Symbol-level precoding (SLP) manipulates the transmitted signals to accurately exploit the multi-user interference (MUI) in the multi-user downlink. This enables that all the resultant interference contributes to correct detection, which is the so-called constructive interference (CI). Its performance superiority comes at the cost of solving a nonlinear optimization problem on a symbol-by-symbol basis, for which the resulting complexity becomes prohibitive in realistic wireless communication systems. In this paper, we investigate low-complexity SLP algorithms for both phase-shift keying (PSK) and quadrature amplitude modulation (QAM). Specifically, we first prove that the max-min SINR balancing (SB) SLP problem for PSK signaling is not separable, which is contrary to the power minimization (PM) SLP problem, and accordingly, existing decomposition methods are not applicable. Next, we establish an explicit duality between the PM-SLP and SB-SLP problems for PSK modulation. The proposed duality facilitates obtaining the solution to the SB-SLP given the solution to the PM-SLP without the need for one-dimension search, and vice versa. We then propose a closed-form power scaling algorithm to solve the SB-SLP via PM-SLP to take advantage of the separability of the PM-SLP. As for QAM modulation, we convert the PM-SLP problem into a separable equivalent optimization problem, and decompose the new problem into several simple parallel subproblems with closed-form solutions, leveraging the proximal Jacobian alternating direction method of multipliers (PJ-ADMM). We further prove that the proposed duality can be generalized to the multi-level modulation case, based on which a power scaling parallel inverse-free algorithm is also proposed to solve the SB-SLP for QAM signaling. Numerical results show that the proposed algorithms offer optimal performance with lower complexity than the state-of-the-art.

cs.IT↗

arXiv2022

RIS-assisted Integrated Sensing and Communications: A Subspace Rotation Approach

In this paper, we propose a novel joint active and passive beamforming approach for integrated sensing and communication (ISAC) transmission with assistance of reconfigurable intelligent surfaces (RISs) to simultaneously detect a target and communicate with a communication user. We first show that the sensing and communication (S&C) performance can be jointly improved due to the capability of the RISs to control the ISAC channel. In particular, we show that RISs can favourably enhance both the channel gain and the coupling degree of S&C channels by modifying the underlying subspaces. In light of this, we develop a heuristic algorithm that expands and rotates the S&C subspaces that is able to attain significantly improved ISAC performance. To verify the effectiveness of the subspace rotation scheme, we further provide a benchmark scheme which maximizes the signal-to-noise ratio (SNR) at the sensing receiver while guaranteeing the SNR at the communication user. Finally, numerical simulations are provided to validate the proposed approaches.

eess.SP↗

arXiv2022

Sensing-Assisted Eavesdropper Estimation: An ISAC Breakthrough in Physical Layer Security

In this paper, we investigate the sensing-aided physical layer security (PLS) towards Integrated Sensing and Communication (ISAC) systems. A well-known limitation of PLS is the need to have information about potential eavesdroppers (Eves). The sensing functionality of ISAC offers an enabling role here, by estimating the directions of potential Eves to inform PLS. In our approach, the ISAC base station (BS) firstly emits an omni-directional waveform to search for potential Eves' directions by employing the combined Capon and approximate maximum likelihood (CAML) technique. Using the resulting information about potential Eves, we formulate secrecy rate expressions, that are a function of the Eves' estimation accuracy. We then formulate a weighted optimization problem to simultaneously maximize the secrecy rate and minimize the CRB with the aid of the artificial noise (AN), and minimize the CRB of targets'/Eves' estimation. By taking the possible estimation errors into account, we enforce a beampattern constraint with a wide main beam covering all possible directions of Eves. This implicates that security needs to be enforced in all these directions. By improving estimation accuracy, the sensing and security functionalities provide mutual benefits, resulting in improvement of the mutual performances with every iteration of the optimization, until convergence. Our results avail of these mutual benefits and reveal the usefulness of sensing as an enabler for practical PLS.

eess.SP↗

arXiv2022

NOMA Made Practical: Removing the Receive SIC Processing through Interference Exploitation

Non-orthogonal multiple access (NOMA) is a powerful transmission technique that enhances the spectral efficiency of communication links, and is being investigated for 5G standards and beyond. A major drawback of NOMA is the need to apply successive interference cancellation (SIC) at the receiver on a symbol-by-symbol basis, which limits its practicality. To circumvent this, in this paper a novel constructive multiple access (CoMA) scheme is proposed and investigated. CoMA aligns the superimposed signals to the different users constructively to the signal of interest. Since the superimposed signal aligns with the data signal, there is no need to remove it at the receiver using SIC. Accordingly, SIC component can be removed at the receiver side. In this regard and in order to provide a comprehensive investigation and comparison, different optimization problems for user paring NOMA multiple-input-single-output (MISO) systems are considered. Firstly, an optimal precoder to minimize the total transmission power for CoMA subject to a quality-of-service constraint is obtained, and compared to conventional NOMA. Then, a precoder that minimizes the CoMA symbol error rate (SER) subject to power constraint is investigated. Further, the computational complexity of CoMA is considered and compared with conventional NOMA scheme in terms of total number of complex operations. The results in this paper prove the superiority of the proposed CoMA scheme over the conventional NOMA technique, and demonstrate that CoMA is an attractive solution for user paring NOMA MISO systems with low number of BS antennas, while circumventing the receive SIC complexity.

cs.IT↗

arXiv2022

Low Complexity SLP: An Inversion-Free, Parallelizable ADMM Approach

We propose a parallel constructive interference (CI)-based symbol-level precoding (SLP) approach for massive connectivity in the downlink of multiuser multiple-input single-output (MU-MISO) systems, with only local channel state information (CSI) used at each processor unit and limited information exchange between processor units. By reformulating the power minimization (PM) SLP problem and exploiting the separability of the corresponding reformulation, the original problem is decomposed into several parallel subproblems via the ADMM framework with closed-form solutions, leading to a substantial reduction in computational complexity. The sufficient condition for guaranteeing the convergence of the proposed approach is derived, based on which an adaptive parameter tuning strategy is proposed to accelerate the convergence rate. To avoid the large-dimension matrix inverse operation, an efficient algorithm is proposed by employing the standard proximal term and by leveraging the singular value decomposition (SVD). Furthermore, a prox-linear proximal term is adopted to fully eliminate the matrix inversion, and a parallel inverse-free SLP (PIF-SLP) algorithm is finally obtained. Numerical results validate our derivations above, and demonstrate that the proposed PIF-SLP algorithm can significantly reduce the computational complexity compared to the state-of-the-arts.

cs.IT↗