Joint detection and localization of users and scatterers in multipath-rich channels on multiple bands is critical for integrated sensing and communication (ISAC) in 6G. Existing multiband sensing methods are limited by classical beamforming or computationally expensive approaches. This paper introduces alternating direction method of multipliers (ADMM)-assisted compressed multiband sensing (CMS), hereafter referred to as ADMM-CMS, which is a novel framework for multiband sensing using uplink quadrature amplitude modulation-modulated pilot symbols. To solve the CMS problem, we develop an adaptive ADMM algorithm that adjusts to noise and ensures automatic stopping if converged. ADMM combines the decomposability of dual ascent with the robustness of augmented Lagrangian methods, making it suitable for large-scale structured optimization. Simulations show that ADMM-CMS achieves higher spatial resolution and improved denoising compared to Bartlett-type beamforming, yielding a 34 dB gain in per-antenna transmit power for achieving a 0.9 successful recovery probability (SRP). Moreover, compared to performing compressed sensing separately on the constituent 7 GHz and 10 GHz sub-bands, ADMM-CMS achieves reductions in delay root mean squared error of 34.46% and 40.76%, respectively, at -41 dBm per-antenna transmit power, while also yielding improved SRP. Our findings demonstrate ADMM-CMS as an efficient enabler of ISAC in frequency range 3 (FR3, 7-24 GHz) for 6G systems.

This paper introduces two novel algorithms designed to address the challenge of super-resolution sensing parameter estimation in bistatic configurations within communication-centric integrated sensing and communication (ISAC) systems. Our approach leverages the estimated channel state information derived from reference symbols originally intended for communication to achieve super-resolution sensing parameter estimation. The first algorithm, IFFT-C2VNN, employs complex-valued convolutional neural networks to estimate the parameters of different targets, achieving significant reductions in computational complexity compared to traditional methods. The second algorithm, PARAMING, utilizes a parametric method that capitalizes on the knowledge of the system model, including the transmit and receive array geometries, to extract the sensing parameters accurately. Through a comprehensive performance analysis, we demonstrate the effectiveness and robustness of both algorithms across a range of signal-to-noise ratios, underscoring their applicability in realistic ISAC scenarios.

In this study, we perform statistical radar cross section (RCS) analysis for various test targets in an indoor factory at 25-28 GHz, with the goal of determining the best-fit parametric distributions that characterize the target scattering properties to be used in integrated sensing and communication channel modeling standardization. The analysis is conducted based on measurements in quasi-monostatic and bistatic configurations with bistatic angles of 20°, 40°, and 60°. The test targets include unmanned aerial vehicles, an autonomous mobile robot, and a robotic arm. Goodness-of-fit tests validate that the RCS of these targets is best modeled by lognormal and gamma distributions with high statistical confidence. Additionally, we provide a framework for evaluating the near-field (NF), specular-dominant effective bistatic RCS of a rectangular sheet under controlled bistatic geometries. Novel deterministic RCS models are evaluated, incorporating dependencies on the bistatic angle, transmitter-target separation ( 2 m to 10 m ). The results demonstrate that some proposed deterministic RCS models accurately fit the measured data, highlighting their applicability in deterministic RCS characterization in NF bistatic configurations.

This paper proposes a comprehensive framework for a geometry-based statistical model for integrated sensing and communication (ISAC) tailored for bistatic systems. Our dualcomponent model decomposes the ISAC channel into a target channel encompassing all multipath components produced by a sensing target parameterized by the target’s radar cross-section and scattering points, and a background channel comprising all other propagation paths that do not interact with the sensing target. The framework extends TR38.901 via a hybrid clustering approach, integrating spatiotemporally consistent deterministic clusters with stochastic clusters to preserve channel reciprocity and absolute delay alignment for sensing parameter estimation. Extensive simulations across urban macro, urban micro, and indoor factory scenarios demonstrate that the model maintains communication performance parity with the standard TR38.901, validated through bit-error rate analysis obtained via simulated and measured ISAC channels and channel capacity assessment, while enabling sensing performance evaluation, such as target ranging error for localization and receiver operating characteristic curves for detection probability.

Integrated sensing and communication (ISAC) has recently emerged as a viable technique for establishing sensing and communication using the same resources. Nonetheless, the operation of ISAC networks is often challenged by the absence of a direct link between the sensing node and the targets, and by the risk of disclosing confidential data to malicious targets when using the same signal for both tasks. In this paper, a robust reconfigurable intelligent surface (RIS)-aided scheme for securing a full-duplex (FD) ISAC network is proposed. The considered network consists of uplink and downlink users served in FD through a multi-antenna dual-functional radar communication base station (BS), which employs co-located multi-antenna communication-radar arrays to detect multiple malicious targets while preserving communication secrecy in their presence. Additionally, the BS utilizes an optimized artificial noise (AN) that serves to disrupt the malicious targets’ reception and increase the sensing illumination power. By optimally designing the RIS phase shifts, transmit beamforming matrix, AN covariance matrix, and uplink users’ transmit power and combining vectors using an alternating optimization-based algorithm, the network’s sensing performance is maximized under secrecy and total power constraints. Numerical results present the proposed scheme’s efficacy, particularly when a direct link between the BS and the various nodes/targets is absent.

The following paper presents a systematic 3rd Generation Partnership Project (3GPP)-compliant characterization of radar cross section (RCS) for indoor factory (InF) objects, including small and mid-sized unmanned aerial vehicles (UAVs), robotic arms, and automated guided vehicles (AGVs). Through measurements in the 25GHz to 28GHz range, we validate the 3GPP standardized log-normal distribution model for RCS for above-mentioned target objects. The 3GPP-complaint RCS parameters obtained for the small-sized UAV are in close agreement (< 1 dB deviation) with 3GPP agreed values. The mid-sized UAVs exhibit higher reflectivity compared to the small-sized UAV due to enhanced specular components attributed to material and lithium-ion battery packs. On the other hand, the robotic arm exhibits dynamic RCS behavior due to mechanical articulation, whereas AGVs show height and motion-dependent reflectivity patterns. Our findings provide empirical validation for RCS characterization for integrated sensing and communication channel modeling in InF environments.

Integrated Sensing and Communication (ISAC) systems vulnerability to information interception has become more dramatic with the usage of unified signaling techniques. This paper introduces a secure full-duplex (FD) multi-eavesdroppers (Eve) ISAC system, where malicious users, termed as Eve, aim at intercepting the downlink (DL) as well as the uplink (UL) information interchanged between legitimate DL/UL users and the dual functional radar and communication (DFRC) base station (BS). Following the model, we design a suitable secure ISAC method that invests minimal power in the FD system in order to satisfy desired UL/DL secrecy rates, while illuminating radar beams at Eves. The motivation being that the FD system should expose minimal useful information towards the Eves, while simultaneously sensing their physical parameters. To this end, a suitable optimization problem is formed taking into account the aforementioned requirements, and a successive convex approximation approach is taken to iteratively solve the problem. Simulation results also reveal secrecy-sensing trade-offs and demonstrate the effectiveness of the method for different system parameters and requirements.

The following paper presents a multi-band sensing channel quality analysis in the upper mid-band, also known as frequency range 3 (FR3). Measurements were conducted at 6.5 GHz, 8.75 GHz, 10 GHz, and 15 GHz, using a setup designed for integrated sensing and communication (ISAC). The sensing channel quality is evaluated using the estimation reliability metric, based on the iterative Levenberg–Marquardt (LM) algorithm. Given the static environment, we also validate a method to handle time-invariant dense multipath components (DMCs). Results show that lower bands enable the detection of more specular components due to lower path loss, but stronger DMC leads to lower estimation SNR. Higher bands provide cleaner estimates despite detecting fewer components. The trade-offs inherent to upper and lower FR3 bands highlight the potential of multi-band ISAC in the FR3 spectrum.

Recent advancements have underscored the relevance of low-resolution analog-to-digital converters (ADCs) in integrated sensing and communication (ISAC) systems. Nevertheless, their specific impact on hybrid radar fusion (HRF) remains largely unexplored. In HRF systems, where uplink (UL) paths carry direct and reflected signals in the same frequency band, the reflected signal is often significantly weaker, making HRF performance particularly sensitive to ADC resolution. To study this effect, we use the quantized Cramér-Rao bound (CRB) to measure sensing accuracy. This work derives an upper bound on the quantized CRB for angle of arrival (AoA) estimation and explores CRB-rate trade-offs through two formulated optimization problems. Simulation results indicate that HRF becomes infeasible when the dynamic range of the received signal exceeds the dynamic range supported by the ADC, which is inherently limited by its resolution. Furthermore, the UL communication rate does not increase significantly when the ADC resolution is raised beyond a certain threshold. These observations highlight a fundamental trade-off between sensing and communication performance: while HRF performance benefits from higher ADC resolutions, the corresponding gains in communication rate plateau. This trade-off is effectively characterized using CRB-rate boundaries derived through simulation.

Incorporating integrated sensing and communication capabilities into forthcoming 6G wireless networks is crucial for achieving seamless synchronization between the digital and physical worlds. The following paper focuses on a scenario where a passive radar (PR) is subject to weak line-of-sight signals of opportunity, emanating from an access point and subsequently reflecting off targets, ultimately reaching the PR. Furthermore, a normalized least mean squares method is presented for jointly detecting the number of targets and estimating target angles of arrival (AoAs). The algorithm iteratively adjusts the steering vector estimates to minimize a suitable error cost function, while the target AoAs are identified via a peak-finding search conducted on the resulted power spectrum. Simulation results show the capabilities of the proposed localization method, as well as a 14 dB dynamic range reduction that can be achieved at the PR.

This paper presents a new optimization framework dedicated for integrated sensing and communication (ISAC) waveform design. In particular, the problem aims at maximizing the total achievable sum-rate, through multi-user interference minimization, while preserving a certain level of similarity to a given desired radar waveform. Aiming towards feasible and practical PHY architectures, we also offer the flexibility of tuning the peak-to-average power ratio to a desired level. Towards this design, a non-convex optimization problem is formulated, and an alternating direction method of multipliers based solution is derived to converge towards the superiority of the final ISAC waveform. Finally, simulation results validate the proposed ISAC waveform design, as compared to state-of-the-art solutions.

Global allocations in the upper mid-band spectrum (4-24 GHz) necessitate a comprehensive exploration of the propagation behavior to meet the promise of coverage and capacity. This paper presents an extensive Urban Microcell (UMi) outdoor propagation measurement campaign at 6.75 GHz and 16.95 GHz conducted in Downtown Brooklyn, USA, using a 1 GHz bandwidth sliding correlation channel sounder over 40-880 m propagation distance, encompassing seven Line of Sight (LOS) and 13 Non-Line of Sight (NLOS) locations. Analysis of the path loss (PL) reveals lower directional and omnidirectional PL exponents compared to mmWave and sub-THz frequencies in the UMi environment, using the close-in (CI) free space PL (FSPL) model with a 1 m reference distance. Additionally, a decreasing trend in root mean square (RMS) delay spread (DS) and angular spread (AS) with increasing frequency was observed. The measured NLOS RMS DS and RMS AS mean values (as computed by 3GPP methods) are found to be consistently lower compared to 3GPP model predictions. Point-data tables with corresponding site-specific environmental information for all measured statistics at each TX-RX location are provided to support the models and results. The spatio-temporal statistics presented here offer valuable insights for the design of nextgeneration wireless systems and networks.

The following paper presents a framework for DC-biased optical OFDM (DCO-OFDM) in visible light communication (VLC) systems, addressing critical implementation challenges in timing synchronization and channel estimation. We propose a novel preamble design based on modified Zadoff-Chu (ZC) sequences that preserve constant amplitude zero autocorrelation (CAZAC) properties in the frequency-domain while satisfying intensity modulation/direct detection (IM/DD) constraints. The frequency-domain cross-correlation of the modified ZC sequence enables robust timing offset estimation, particularly in low-SNR scenarios. We evaluate the probability of correct timing offset estimation and the normalized mean square error (NMSE) for the proposed channel estimation scheme. Furthermore, we characterize the system-level trade-offs between clipping distortion, quantization resolution, and DC biasing, deriving optimal operating points for power efficiency under the proposed framework. Simulation results demonstrate significant improvements in synchronization accuracy compared to other alternatives, with the proposed method achieving a correct timing estimation probability of ≥ 0.99 even at low SNR values. The channel estimation NMSE performance confirms the scheme’s efficiency, outperforming conventional approaches. The approach presented hereby establishes practical design guidelines for robust timing synchronization and channel estimation for IM/DD-based DCO-OFDM implementations in VLC systems.

The following paper presents a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system model scenario, where a base station communicates with a user, and a bi-static sensing unit, i.e. the passive radar (PR), senses targets using downlink signals. Given that the RIS aids with communication and sensing tasks, this paper introduces new interfering paths that can overwhelm the PR with unnecessarily high power, namely the path interference (PI), which is itself a combination of two interfering paths, the direct path interference (DPI) and the reflected path interference (RPI). For this, we formulate an optimization framework that allows the system to carry on with its ISAC tasks, through analog space-time beamforming at the sensing unit, in collaboration with RIS phase shift and statistical transmit covariance matrix optimization, while minimizing the PI power. As the proposed optimization problem is non-convex, we tailor a block-cyclic coordinate descent (BCCD) method to decouple the non-convex sub-problem from the convex one. A Riemannian conjugate gradient method is devised to generate the RIS and PR space-time beamforming phase shifts per BCCD iteration, while the convex sub-problem is solved via off-the-shelf solvers. Simulation results demonstrate the effectiveness of the proposed solver when compared with benchmarking ones.

The following paper presents a novel orthogonal pilot design dedicated for integrated sensing and communications (ISAC) systems performing multi-user communications and target detection. After careful characterization of both sensing and communication metrics based on mutual information (MI), we propose a multi-objective optimization problem (MOOP) tailored for pilot design, dedicated for simultaneously maximizing both sensing and communication MIs. Moreover, the MOOP is further simplified to a single-objective optimization problem, which characterizes trade-offs between sensing and communication performances. Due to the non-convex nature of the optimization problem, we propose to solve it via the projected gradient descent method on the Stiefel manifold. Closed-form gradient expressions are derived, which enable execution of the projected gradient descent algorithm. Furthermore, we prove convergence to a fixed orthogonal pilot matrix. Finally, we demonstrate the capabilities and superiority of the proposed pilot design, and corroborate relevant trade-offs between sensing MI and communication MI. In particular, significant signal-to-noise ratio (SNR) gains for communication are reported, while re-using the same pilots for target detection with significant gains in terms of probability of detection for fixed false-alarm probability. Other interesting findings are reported through simulations, such as an information overlap phenomenon, whereby the fruitful ISAC integration can be fully exploited.

The following paper proposes a new target localization system design using an architecture based on reconfigurable intelligent surfaces (RISs) and passive radars (PRs) for integrated sensing and communications systems. The preamble of the communication signal is exploited in order to perform target sensing tasks, which involve detection and localization. The RIS in this case can aid the PR in sensing targets that are otherwise not seen by the PR itself, due to the many obstacles encountered within the propagation channel. Therefore, this work proposes a localization algorithm tailored for the integrated sensing and communications RIS-aided architecture, which is capable of uniquely positioning targets within the scene. The algorithm is capable of detecting the number of targets along with estimating the position of targets via angles and times of arrival. Our simulation results demonstrate the performance of the localization method in terms of different localization and detection metrics and for increasing RIS sizes.

This article examines two chirp spread spectrum techniques specifically devised for low-power wide-area networks (LPWANs) to optimize energy and spectral efficiency (SE). These methods referred to as layered CSS (LCSS) and layered dual-mode CSS (LDMCSS), involves utilizing multiple layers for multiplexing symbols with varying chirp rates. These waveform designs exemplify a high degree of SE compared to existing schemes. Additionally, LDMCSS necessitates a lesser number of layers than LCSS to attain comparable SE, thereby reducing computational complexity. These proposed techniques can employ coherent and non-coherent detection and can be adjusted to achieve various spectral efficiencies by altering the number of multiplexed layers. Unlike our proposed LCSS and LDMCSS, other CSS alternatives for LPWANs cannot provide the same level of flexibility and SE. The performance of these techniques is evaluated in terms of bit error rate under different channel conditions, as well as with phase and frequency offsets.

The following paper models a secure full duplex (FD) integrated sensing and communication (ISAC) scenario, where malicious eavesdroppers aim at intercepting the downlink (DL) as well as the uplink (UL) information exchanged between the dual functional radar and communication (DFRC) base station (BS) and a set of communication users. The DFRC BS, on the other hand, aims at illuminating radar beams at the eavesdroppers in order to sense their physical parameters, while maintaining high UL/DL secrecy rates. Based on the proposed model, we formulate a power efficient secure ISAC optimization framework design, which is intended to guarantee both UL and DL secrecy rates requirements, while illuminating radar beams towards eavesdroppers. The framework exploits artificial noise (AN) generation at the DFRC BS, along with UL/DL beamforming design and UL power allocation. We propose a beamforming design solution to the secure ISAC optimization problem. Finally, we corroborate our findings via simulation results and demonstrate the feasibility, as well as the superiority of the proposed algorithm, under different situations. We also reveal insightful trade-offs achieved by our approach.

The following paper introduces a novel integrated sensing and communication (ISAC) scenario termed hybrid radar fusion. In this setting, the dual-functional radar and communications (DFRC) base station (BS) acts as a mono-static radar in the downlink (DL), for sensing purposes, while performing its DL communication tasks. Meanwhile, the communication users act as distributed bi-static radar nodes in the uplink (UL) following a frequency-division duplex protocol. The DFRC BS fuses the information available at different DL and UL resource bands to estimate the angles-of-arrival (AoAs) of the multiple targets existing in the scene. In this work, we derive the maximum likelihood (ML) criterion for the hybrid radar fusion problem at hand. Additionally, we design efficient estimators; the first algorithm is based on an alternating optimization approach to solve the ML criterion, while the second one designs an optimization framework that leads to an alternating subspace approach to estimate AoAs for both the target and users. Finally, we demonstrate the superior performance of both algorithms in different scenarios, and the gains offered by these proposed methods through numerical simulations.

Target detection and recognition is a very challenging task in a wireless environment where a multitude of objects are located, whether to effectively determine their positions or to identify them and predict their moves. In this work, we propose a new method based on a convolutional neural network (CNN) to estimate the range and velocity of moving targets directly from the range-Doppler map of the detected signals. We compare the obtained results to the two dimensional (2D) periodogram, and to the similar state of the art methods, 2DResFreq and VGG-19 network and show that the estimation process performed with our model provides better estimation accuracy of range and velocity index in different signal to noise ratio (SNR) regimes along with a reduced prediction time. Afterwards, we assess the performance of our proposed algorithm using the peak signal to noise ratio (PSNR) which is a relevant metric to analyse the quality of an output image obtained from compression or noise reduction. Compared to the 2D-periodogram, 2DResFreq and VGG-19, we gain 33 dB, 21 dB and 10 dB, respectively, in terms of PSNR when SNR = 30 dB.

This article studies and derives beamforming design in a dual-functional radar-communication (DFRC) multiple-input-multiple-output system. We focus on a scenario, where the DFRC base station communicates with downlink communication users, with imperfect channel state information knowledge, and performs target detection, all via the same transmit signal. Through careful relaxation procedures, we arrive at a suitable and novel optimization problem, which maximizes the radar output power in the Bartlett sense, under probabilistic outage signal-to-interference-and-noise ratio constraints. Theoretical analysis proves optimality of the solution given by the relaxed version of the problem, as well as closed-form solutions in certain scenarios. Finally, the achieved performances and trade-offs of the proposed beamforming design are demonstrated through numerical simulations.

We present a novel approach to the problem of dual-functional radar and communication (DFRC) waveform design with adjustable peak-to-average power ratio (PAPR), while minimizing the multi-user communication interference and maintaining a similarity constraint towards a radar chirp signal. The approach is applicable to generic radar chirp signals and for different constellation sizes. We formulate the waveform design problem as a non convex optimization problem. As a solution, we adopt the alternating direction method of multipliers (ADMM), hence iterating towards a stable waveform for both radar and communication purposes. Additionally, we prove convergence of the proposed method and analyze its computational complexity. Moreover, we offer an extended version of the method to cope with imperfect channel state information (CSI). Finally, we demonstrate its superior performance through simulations, in comparison to state-of-the-art radar-communication waveform designs.

This paper addresses the problem of bearing estimation with partial knowledge on the array response. In a real life scenario, it may seem fictitious to assume a closed-form steering vector as a function of the direction of the received signal. Indeed, an antenna array is subject to many perturbations, such as calibration errors, imperfect positioning of array sensors, mutual coupling, and so on. We introduce a MUSIC estimator, hereby referred to as Robust-MUSIC, that is capable of estimating AoAs of multiple signals, when the antenna array's response is subject to uncertainties, due to the aforementioned reasons. Simulation results are also presented to demonstrate the robustness of the proposed MUSIC estimator.

We present a novel and efficient approach for estimating the maximum likelihood (ML) estimates of the angles-of-arrival (AoAs) of multiple sources. The approach is iterative and is based on orthogonal projections in order to optimise the ML cost function, thus the name OPML. As will be shown, the advantage of using an orthogonal basis of the signal manifold would allow solving the ML cost function in an iterative manner. In fact, we propose two algorithms based on OPML, i.e. OPML-1 and OPML-2, which exhibit lower computational complexity and faster convergence than existing ML algorithms. In this paper, we discuss the idea of OPML and its two implementations, followed by simulation results to demonstrate their performance.

The problem of joint Angle-of-Arrival (AoA) and calibration parameters in a wideband scenario is addressed. The system consists of multiple sources transmitting from different directions and in certain subcarriers. Sources in the same beam are regarded as transmitted from the same AoA and their signals are allocated to different subcarriers while users in different beams may transmit on the same subcarrier, i.e. the signals are multiplexed in space and frequency. Then, signals from a given beam not necessarily occupy the full bandwidth but only specific subcarriers. In addition, due to the wideband of the signal, each RF chain introduces frequency dependent gain and phase shift that need to be calibrated to perform a subsequent demultiplexing in a digital beamforming matrix. We propose a novel blind algorithm that (i) estimates the AoAs of all present sources in the bandwidth of interest and (ii) estimates the different gains/phases at each antenna per frequency up to an unknown impairment at a reference antenna. We provide identifiability conditions that ensure a successful parameter estimation. Finally, the potential of the proposed algorithm compared to the case of known calibration parameters is assessed by simulations.

The problem of Angle-of-Arrival estimation of multiple sources in the presence of mutual coupling is addressed. In this paper, we derive a Mean-Squared-Error (MSE) expression of a recently proposed algorithm that could estimate the Angles-of-Arrival of multiple sources in the presence of more mutual coupling parameters, compared to traditional methods. The MSE expression is compared with the MSE of MUSIC with known mutual coupling parameters and to the Cramer-Rao bound (CRB) of any unbiased estimator that estimates the Angles-of-Arrival in the presence of mutual coupling. It is shown that the proposed method is asymptotically unbiased. In addition, it is also shown that the method attains CRB for large number of antennas with fixed coupling parameters and uncorrelated sources. For high SNR, the CRB is not necessarily attained, however, we study the gap between the derived MSE and the CRB.

The problem of Angle-of-Arrival estimation of multiple sources in the presence of mutual coupling is addressed. The presence of unknown mutual coupling between antenna array elements is known to degrade the performance of direction-finding algorithms. We first present a result explaining why some traditional methods, that estimate Angles-of-Arrival (AoAs) of multiple sources in the presence of mutual coupling, suffer from an identifiability issue, when the number of coupling parameters exceeds a certain level. Then, we present a first method that estimates AoAs of sources when more coupling parameters are present, namely when the number of coupling parameters exceeds that certain level. Finally, we propose a refinement of the proposed algorithm, which could further enhance the AoA estimates. Simulation results have demonstrated the potential of the proposed method and its refined version, for different scenarios, as it enjoys better performance than existing methods. A better description of the paper could be found in the Conclusions section.

A natural extension of the "Spatial" smoothing preprocessing technique is presented and analysed. It is well known that subspace methods do not work properly in the presence of coherent sources. In this paper, a "Spatio-Frequential" smoothing technique is described when the transmit OFDM symbol is received through multiple coherent signals using a uniform linear antenna array. After this preprocessing technique, one could efficiently apply any 2-dimensional subspace method to jointly estimate the angles and times of arrival of the incoming coherent signals. Simulation results demonstrate the potential of the proposed 2D smoothing method over existing separate spatial or frequential smoothing techniques.

The problem of estimating the number of superimposed signals using noisy observations from N antennas is addressed. In particular, we are interested in the case where a low number of snapshots L = O(N) is available. We focus on the Minimum Description Length (MDL) estimator, which is revised herein. Furthermore, we propose a modified MDL estimator, with the help of random matrix tools, which improves the estimation of the number of sources. Simulation results demonstrate the potential of the modified MDL estimator over the traditional one, in the case where L = O(N).

Two algorithms for the problem of joint angles and delays of arrival (JADE) of multiple paths are presented. The algorithms are based on a generalisation of the Matrix Pencil algorithm to the two dimensional case, i.e. 2D Matrix Pencils. Matrix pencil algorithms offer estimation of signal parameters, i.e. angles of arrival (AoA) or times of arrival (ToA), of multiple sources using a single snapshot. We focus on a scenario, where the OFDM symbol is transmitted in a rich multipath channel, which is the case of an indoor environment, and received through multiple antennas. The first algorithm seems more interesting than the second one, since it's motivated from an idea that most Wi-Fi systems use a large number of subcarriers compared to the number of antennas. Simulation results demonstrate the potential of the first algorithm and its performance as a function of Signal-to-Noise ratio (SNR).

Zero-day exploits against servers pose one of the most challenging problems faced by system and security administrators. Current solutions rely mainly on signature databases of known attacks and are not efficient at detecting new attacks not covered by their attack signature database. We propose using a dummy server, i.e. A mirror of the server to be protected but without the real data. Consequently, any incoming network packet is first tested against the dummy server and once it is ensured that the packet is benign, it is delivered to the real server. This would prevent all types of attacks, including those based on zero-day exploits, from reaching the protected server.