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Marouan Mizmizi

Publications and source records attributed to Marouan Mizmizi.

At least 19 recordsLinked to original sources

Bistatic ISAC under Data Payload Uncertainty

We investigate the problem of data payload uncertainty (DPU) in bistatic integrated sensing and communication (ISAC) systems, which refers to imperfect knowledge of transmit data symbols at the sensing receiver. DPU may result from limited fronthaul capacity in base station (BS)-to-BS bistatic sensing or data demodulation errors in BS-to-user equipment (UE) bistatic sensing, and can impair target detection through elevated sidelobe levels in the delay-Doppler domain. To characterize this effect, we derive closed-form expressions for the peak-to-sidelobe level ratio (PSLR) of two common OFDM receivers, namely reciprocal filtering (RF) and matched filtering (MF), under DPU. The analysis shows that the DPU-induced sidelobe degradation of RF depends on the inverse second-, fourth-, and sixth-order constellation moments, whereas that of MF depends only on the direct fourth-order moment, making RF considerably more vulnerable to DPU. To mitigate the resulting sidelobes, we further propose a novel, DPU-aware minimum mean-squared-error (MMSE) receiver. Simulation results validate the PSLR analysis and demonstrate superior detection performance of MMSE over RF and MF while revealing the extent to which it can recover DPU-induced performance loss relative to a genie-aided receiver with perfect knowledge of data payload.

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From Focusing to Con-Focusing: Optimal Power Transfer in Line-of-Sight Near-Field MIMO

Beamfocusing is the established near-field strategy for a large array serving a single-antenna user. We consider the single-user line-of-sight MIMO link, free of multipath, in which the user, too, carries an extended aperture, and show that the focusing prescription inverts: beyond a modest Fresnel number, focusing on the user is outperformed by far-field steering. Under fully analog, unit-modulus beamforming, we derive closed-form power gains for focusing (each aperture phase-matched to the other's center) and for steering (a planar phase ramp) in the Fresnel regime, and prove that their comparison is governed by two dimensionless quantities: the link Fresnel number, the product of the two aperture lengths normalized by wavelength and link distance, and the aperture ratio, irrespective of how many elements discretize the apertures. For equal apertures the two gains cross exactly once, at the universal value 1.947; beyond it, focusing loses ten dB per decade of Fresnel number, and the advantage celebrated in the MISO literature survives only as the receive aperture vanishes. We then derive the strategy that is order-optimal at every Fresnel number, con-focusing: both apertures aim at the common point from which they subtend equal angles. It attains the rank-one eigenbound in leading constant, needs no channel knowledge, degenerates to plain steering for equal apertures, and is acquirable within one beam-refinement round with no geometry exchange between the terminals.

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Device-Centric ISAC for Exposure Control via Opportunistic Virtual Aperture Sensing

Regulatory limits on Maximum Permissible Exposure (MPE) require handheld devices to reduce transmit power when operated near the user's body. Current proximity sensors provide only binary detection, triggering conservative power back-off that degrades link quality. If the device could measure its distance from the body, transmit power could be adjusted proportionally, improving throughput while maintaining compliance. This paper develops a device-centric integrated sensing and communication (ISAC) method for the device to measure this distance. The uplink communication waveform is exploited for sensing, and the natural motion of the user's hand creates a virtual aperture that provides the angular resolution necessary for localization. Virtual aperture processing requires precise knowledge of the device trajectory, which in this scenario is opportunistic and unknown. One can exploit onboard inertial sensors to estimate the device trajectory; however, the inertial sensors accuracy is not sufficient. To address this, we develop an autofocus algorithm based on extended Kalman filtering that jointly tracks the trajectory and compensates residual errors using phase observations from strong scatterers. The Bayesian Cramér-Rao bound for localization is derived under correlated inertial errors. Numerical results at 28GHz demonstrate centimeter-level accuracy with realistic sensor parameters.

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Echo-Side Integrated Sensing and Communication via Space-Time Reconfigurable Intelligent Surfaces

This paper presents an echo-side modulation framework for integrated sensing and communication (ISAC) systems. A space-time reconfigurable intelligent surface (ST-RIS) impresses a continuous-phase modulation onto the radar echo, enabling uplink data transmission with a phase modulation of the transmitted radar-like waveform. The received signal is a multiplicative composition of the sensing waveform and the phase for communication. Both functionalities share the same physical signal and perceive each other as impairments. The achievable communication rate is expressed as a function of a coupling parameter that links sensing accuracy to phase error accumulation. Under a fixed bandwidth constraint, the sensing and communication figures of merit define a convex Pareto frontier. The optimal bandwidth allocation satisfying a minimum sensing requirement is derived in closed form. The modified Cramer-Rao bound (MCRB) for range estimation is derived in closed form; this parameter must be estimated to compensate for the frequency offset before data demodulation. Frame synchronization is formulated as a generalized likelihood ratio test (GLRT), and the detection probability is obtained through characteristic function inversion, accounting for residual frequency errors from imperfect range estimation. Numerical results validate the theoretical bounds and characterize the trade-off across the operating range.

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Towards Channel Charting Enhancement with Non-Reconfigurable Intelligent Surfaces

We investigate how fully-passive electromagnetic skins (EMSs) can be engineered to enhance channel charting (CC) in dense urban environments. We employ two complementary state-of-the-art CC techniques, semi-supervised t-distributed stochastic neighbor embedding (t-SNE) and a semi-supervised Autoencoder (AE), to verify the consistency of results across nonparametric and parametric mappings. We show that the accuracy of CC hinges on a balance between signal-to-noise ratio (SNR) and spatial dissimilarity: EMS codebooks that only maximize gain, as in conventional Reconfigurable Intelligent Surface (RIS) optimization, suppress location fingerprints and degrade CC, while randomized phases increase diversity but reduce SNR. To address this trade-off, we design static EMS phase profiles via a quantile-driven criterion that targets worst-case users and improves both trustworthiness and continuity. In a 3D ray-traced city at 30 GHz, the proposed EMS reduces the 90th-percentile localization error from > 50 m to < 25 m for both t-SNE and AE-based CC, and decreases severe trajectory dropouts by over 4x under 15% supervision. The improvements hold consistently across the evaluated configurations, establishing static, pre-configured EMS as a practical enabler of CC without reconfiguration overheads.

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Channel Charting in Smart Radio Environments

This paper introduces the use of static electromagnetic skins (EMSs) to enable robust device localization via channel charting (CC) in realistic urban environments. We develop a rigorous optimization framework that leverages EMS to enhance channel dissimilarity and spatial fingerprinting, formulating EMS phase profile design as a codebook-based problem targeting the upper quantiles of key embedding metric, localization error, trustworthiness, and continuity. Through 3D ray-traced simulations of a representative city scenario, we demonstrate that optimized EMS configurations, in addition to significant improvement of the average positioning error, reduce the 90th-percentile localization error from over 60 m (no EMS) to less than 25 m, while drastically improving trustworthiness and continuity. To the best of our knowledge, this is the first work to exploit Smart Radio Environment (SRE) with static EMS for enhancing CC, achieving substantial gains in localization performance under challenging None-Line-of-Sight (NLoS) conditions.

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Bridging the Gap via Data-Aided Sensing: Can Bistatic ISAC Converge to Genie Performance?

We investigate data-aided iterative sensing in bistatic OFDM ISAC systems, focusing on scenarios with co-located sensing and communication receivers. To enhance target detection beyond pilot-only sensing methods, we propose a multi-stage bistatic OFDM receiver, performing iterative sensing and data demodulation to progressively refine ISAC channel and data estimates. Simulation results demonstrate that the proposed data-aided scheme significantly outperforms pilot-only benchmarks, particularly in multi-target scenarios, substantially narrowing the performance gap compared to a genie-aided system with perfect data knowledge. Moreover, the proposed approach considerably expands the bistatic ISAC trade-off region, closely approaching the probability of detection-achievable rate boundary established by its genie-aided counterpart.

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Joint Optimization of Uplink and Downlink Power in Full-Duplex Integrated Access and Backhaul

We examine the performance of an Integrated Access and Backhaul (IAB) node as a range extender for beyond-5G networks, focusing on the significant challenges of effective power allocation and beamforming strategies, which are vital for maximizing users' spectral efficiency (SE). We present both max-sum SE and max-min fairness power allocation strategies, to assess their effects on system performance. The results underscore the necessity of power optimization, particularly as the number of users served by the IAB node increases, demonstrating how efficient power allocation enhances service quality in high-load scenarios. The results also show that the typical line-of-sight link between the IAB donor and the IAB node has rank one, posing a limitation on the effective SEs that the IAB node can support.

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Hybrid MIMO in the Upper Mid-Band: Architectures, Processing, and Energy Efficiency

As 6G networks evolve, the upper mid-band spectrum (7 GHz to 24 GHz), or frequency range 3 (FR3), is emerging as a promising balance between the coverage offered by sub-6 GHz bands and the high-capacity of millimeter wave (mmWave) frequencies. This paper explores the structure of FR3 hybrid MIMO systems and proposes two architectural classes: Frequency Integrated (FI) and Frequency Partitioned (FP). FI architectures enhance spectral efficiency by exploiting multiple sub-bands parallelism, while FP architectures dynamically allocate sub-band access according to specific application requirements. Additionally, two approaches, fully digital (FD) and hybrid analog-digital (HAD), are considered, comparing shared (SRF) versus dedicated RF (DRF) chain configurations. Herein signal processing solutions are investigated, particularly for an uplink multi-user scenario with power control optimization. Results demonstrate that SRF and DRF architectures achieve comparable spectral efficiency; however, SRF structures consume nearly half the power of DRF in the considered setup. While FD architectures provide higher spectral efficiency, they do so at the cost of increased power consumption compared to HAD. Additionally, FI architectures show slightly greater power consumption compared to FP; however, they provide a significant benefit in spectral efficiency (over 4 x), emphasizing an important trade-off in FR3 engineering.

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Bayesian EM Digital Twins Channel Estimation

This letter proposes a Bayesian channel estimation method that leverages on the a priori information provided by the Electromagnetic Digital Twin's (EM-DT) representation of the environment. The proposed approach is compared with several conventional techniques in terms of Normalized Mean Square Error (NMSE), spectral efficiency, and number of pilots. Simulations prove more than $10\,$dB gain in NMSE and a spectral efficiency comparable to that of the ideal channel state information, for different signal-to-noise ratio (SNR) values. Additionally, the Bayesian EM-DT-empowered channel estimation enables a remarkable pilot reduction compared to maximum likelihood methods at low SNR.

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Optimal Planning for Heterogeneous Smart Radio Environments

Smart Radio Environment (SRE) is a central paradigms in 6G and beyond, where integrating SRE components into the network planning process enables optimized performance for high-frequency Radio Access Network (RAN). This paper presents a comprehensive planning framework utilizing realistic urban scenarios and precise channel models to analyze diverse SRE components, including Reconfigurable Intelligent Surface (RIS), Network-Controlled Repeater (NCR), and advanced technologies like Simultaneous transmitting and reflecting RIS (STAR RIS) and trisectoral NCR (3SNCR). We propose two optimization methods, full coverage minimum cost (FCMC) and maximum budget-constrained coverage (MBCC), that address key cost and coverage objectives by considering both physical characteristics and scalable costs of each component, influenced by factors such as NCR amplification gain and RIS dimensions. Extensive numerical results demonstrate the significant impact of these models in enhancing network planning efficiency for high-density urban environments.

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Advanced Network Planning in 6G Smart Radio Environments

The growing demand for high-speed, reliable wireless connectivity in 6G networks necessitates innovative approaches to overcome the limitations of traditional Radio Access Network (RAN). Reconfigurable Intelligent Surface (RIS) and Network-Controlled Repeater (NCR) have emerged as promising technologies to address coverage challenges in high-frequency millimeter wave (mmW) bands by enhancing signal reach in environments susceptible to blockage and severe propagation losses. In this paper, we propose an optimized deployment framework aimed at minimizing infrastructure costs while ensuring full area coverage using only RIS and NCR. We formulate a cost-minimization optimization problem that integrates the deployment and configuration of these devices to achieve seamless coverage, particularly in dense urban scenarios. Simulation results confirm that this framework significantly reduces the network planning costs while guaranteeing full coverage, demonstrating RIS and NCR's viability as cost-effective solutions for next-generation network infrastructure.

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Multi-View Integrated Imaging and Communication

Non-line-of-sight (NLOS) operation is one of the open issues to be solved for integrated sensing and communication (ISAC) systems to become a pillar of the future wireless infrastructure above 10 GHz. Existing NLOS countermeasures use either metallic mirrors, that are limited in coverage, or reconfigurable metasurfaces, that are limited in size due to cost. This paper focuses on integrated imaging and communication (IIAC) systems for NLOS exploration, where a base station (BS) serves the users while gathering a high-resolution image of the area. We exploit a large reflection plane, that is phase-configured in space and time jointly with a proper BS beam sweeping to provide a multi-view observation of the area and maximizing the image resolution. Remarkably, we achieve a near-field imaging through successive far-field acquisitions, limiting the design complexity and cost. Numerical results prove the benefits of our proposal.

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Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems

In Integrated Sensing and Communication (ISAC) systems, matching the radar targets with communication user equipments (UEs) is functional to several communication tasks, such as proactive handover and beam prediction. In this paper, we consider a radar-assisted communication system where a base station (BS) is equipped with a multiple-input-multiple-output (MIMO) radar that has a double aim: (i) associate vehicular radar targets to vehicular equipments (VEs) in the communication beamspace and (ii) predict the beamforming vector for each VE from radar data. The proposed target-to-user (T2U) association consists of two stages. First, vehicular radar targets are detected from range-angle images, and, for each, a beamforming vector is estimated. Then, the inferred per-target beamforming vectors are matched with the ones utilized at the BS for communication to perform target-to-user (T2U) association. Joint multi-target detection and beam inference is obtained by modifying the you only look once (YOLO) model, which is trained over simulated range-angle radar images. Simulation results over different urban vehicular mobility scenarios show that the proposed T2U method provides a probability of correct association that increases with the size of the BS antenna array, highlighting the respective increase of the separability of the VEs in the beamspace. Moreover, we show that the modified YOLO architecture can effectively perform both beam prediction and radar target detection, with similar performance in mean average precision on the latter over different antenna array sizes.

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Sensing in NLOS: a Stroboscopic Approach

Sensing in non-line-of-sight (NLOS) is a well-known issue that limits the effective range of radar-like sensors. Existing approaches for NLOS sensing consider the usage of either metallic mirrors, that only work under specular reflection, or dynamically-reconfigurable metasurfaces that steer the signal to cover a desired area in NLOS, with the drawback of cost and control signaling. This paper proposes a novel sensing system, that allows a source to image a desired region of interest (ROI) in NLOS, using the combination of a proper beam sweeping (by the source) as well as a passive reflection plane configured as a periodic angular deflecting function (that allows illuminating the ROI). \textit{Stroboscopic sensing} is obtained by sweeping over a sufficiently large portion of the reflection plane, the source covers the ROI \textit{and} enhance the spatial resolution of the image, thanks to multiple diverse observation angles of ROI. Remarkably, the proposed system achieves a near-field imaging with a sequence of far-field acquisitions, thus limiting the implementation complexity. We detail the system design criteria and trade-offs, demonstrating the remarkable benefits of such a stroboscopic sensing system, where a possibly moving source can observe a ROI through multiple points of view as if it were static.

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Optimized Waveform Design for OFDM-based ISAC Systems Under Limited Resource Occupancy

The sixth generation (6G) of wireless networks introduces integrated sensing and communication (ISAC), a technology in which communication and sensing functionalities are inextricably linked, sharing resources across time, frequency, space, and energy. Despite its popularity in communication, the orthogonal frequency division multiplexing (OFDM) waveform, while advantageous for communication, has limitations in sensing performance within an ISAC network. This paper delves into OFDM waveform design through optimal resource allocation over time, frequency, and energy, maximizing sensing performance while preserving communication quality. During quasi-normal operation, the Base Station (BS) does not utilize all available time-frequency resources, resulting in high sidelobes in the OFDM waveform's ambiguity function, as well as decreased sensing accuracy. To address these latter issues, the paper proposes a novel interpolation technique using matrix completion through the Schatten p quasi-normal approximation, which requires fewer samples than the traditional nuclear norm for effective matrix completion and interpolation. This approach effectively suppresses the sidelobes, enhancing the sensing performance. Numerical simulations confirm that the proposed method outperforms state-of-the-art frameworks, such as standard complaint resource scheduling and interpolation, particularly in scenarios with limited resource occupancy.

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Optimizing Curved EM Skins for Opportunistic Relaying in Vehicular Networks

Electromagnetic skins (EMSs) are recognized for enhancing communication performance, spanning from coverage to capacity. While much of the scientific literature focuses on reconfigurable intelligent surfaces that dynamically adjust phase configurations over time, this study takes a different approach by considering low-cost static passive curved EMS (CEMS)s. These are pre-configured during manufacturing to conform to the shape of irregular surfaces, e.g., car doors, effectively transforming them into anomalous mirrors. This design allows vehicles to serve as opportunistic passive relays, mitigating blockage issues in vehicular networks. This paper delves into a novel design method for the phase profile of CEMS based on coarse a-priori distributions of incident and reflection angles onto the surface, influenced by vehicular traffic patterns. A penalty-based method is employed to optimize both the average spectral efficiency (SE) and average coverage probability, and it is compared against a lower-complexity and physically intuitive modular architecture, utilizing a codebook-based discrete optimization technique. Numerical results demonstrate that properly designed CEMS lead to a remarkable improvements in average SE and coverage probability, namely when the direct path is blocked.

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A Thorough Analysis of Radio Resource Assignment for UAV-Enhanced Vehicular Sidelink Communications

The rapid expansion of connected and autonomous vehicles (CAVs) and the shift towards millimiter-wave (mmWave) frequencies offer unprecedented opportunities to enhance road safety and traffic efficiency. Sidelink communication, enabling direct Vehicle-to-Vehicle (V2V) communications, play a pivotal role in this transformation. As communication technologies transit to higher frequencies, the associated increase in bandwidth comes at the cost of a severe path and penetration loss. In response to these challenges, we investigate a network configuration that deploys beamforming-capable Unmanned Aerial Vehicles (UAVs) as relay nodes. In this work, we present a comprehensive analytical framework with a groundbreaking performance metric, i.e. average access probability, that quantifies user satisfaction, considering factors across different protocol stack layers. Additionally, we introduce two Radio Resources Assignment (RRA) methods tailored for UAVs. These methods consider parameters such as resource availability, vehicle distribution, and latency requirements. Through our analytical approach, we optimize the average access probability by controlling UAV altitude based on traffic density. Our numerical findings validate the proposed model and strategy, which ensures that Quality of Service (QoS) standards are met in the domain of Vehicle-to-Anything (V2X) sidelink communications.

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