arXiv subjects

S. Mohammad Razavizadeh

Publications and source records attributed to S. Mohammad Razavizadeh.

At least 19 recordsLinked to original sources

arXiv2026

Latency-Aware Uplink NOMA-MEC with PASS: A Feasibility-First Resource Allocation Framework

Meeting hard task-completion deadlines for many simultaneously offloading users is the binding challenge in mobile-edge computing (MEC). As the user count grows, the transmit power required to satisfy every deadline within per-user budgets rises until no feasible allocation exists. We take a \emph{feasibility-first} view of this $K$-user problem in an uplink non-orthogonal multiple access (NOMA) MEC system served by a pinching-antenna system (PASS), whose radiating points establish strong line-of-sight (LoS) links that lower the received-power requirement. We formulate a latency-constrained joint optimization of transmit powers, CPU frequencies, and antenna configuration. We then derive a \emph{closed-form least-power feasibility test}: the componentwise-least allocation meeting every deadline. It certifies feasibility with the largest attainable margin to the power budgets and defines a practical \emph{quality-of-service (QoS)-feasibility outage} metric, the probability that no allocation meets all deadlines within budget. We solve the problem with a three-block alternating-optimization algorithm that is provably monotone-convergent and has polynomial complexity. Numerical results show that the proposed scheme sustains deadline feasibility at user loads where equal-power, orthogonal multiple access (OMA), and single-antenna baselines fail or degrade sharply, each by a mechanism predicted directly from the system model, whereas the proposed design degrades gracefully. Stress tests further show that feasibility is sustained under order-of-magnitude budget reductions, and that the pinching-antenna array itself enables constrained-budget operation. The result is a scalable, closed-form-initialized design for deadline-feasible offloading.

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arXiv2026

CKM-Based Predictive Beamforming for Energy-Efficient Mobile Tracking in ISCAP System

This paper proposes a channel-knowledge-map (CKM)-based predictive beamforming framework for integrated sensing, communication, and powering (ISCAP) of a mobile energy-constrained node in non-line-of-sight (NLoS) multipath environments. The CKM provides location-dependent channel information for both predictive beamforming and EKF-based tracking, while resolved path parameters estimated from the sensing echoes are used as observations. Based on the predicted node location, the transmit covariance and power-splitting ratio are jointly optimized to maximize wireless power-transfer efficiency subject to communication, sensing, transmit-power, and tracking-quality constraints. A finite-battery model and an adaptive inter-frame energy-management policy are further introduced to balance decoding energy consumption and battery preservation. The resulting non-convex fractional problem is solved using Dinkelbach's method, alternating optimization, and semidefinite relaxation. Numerical results show that the proposed framework maintains reliable tracking, satisfies the prescribed position-uncertainty requirement, achieves near-oracle wireless power-transfer performance after the initial tracking transient, and preserves more battery energy while meeting the required communication quality of service.

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arXiv2026

Doubly Adaptive Channel and Spatial Attention for Semantic Image Communication by IoT Devices

Internet of Things (IoT) networks face significant challenges such as limited communication bandwidth, constrained computational and energy resources, and highly dynamic wireless channel conditions. Utilization of deep neural networks (DNNs) combined with semantic communication has emerged as a promising paradigm to address these limitations. Deep joint source-channel coding (DJSCC) has recently been proposed to enable semantic communication of images. Building upon the original DJSCC formulation, low-complexity attention-style architectures has been added to the DNNs for further performance enhancement. As a main hurdle, training these DNNs separately for various signal-to-noise ratios (SNRs) will amount to excessive storage or communication overhead, which can not be maintained by small IoT devices. SNR Adaptive DJSCC (ADJSCC), has been proposed to train the DNNs once but feed the current SNR as part of the data to the channel-wise attention mechanism. We improve upon ADJSCC by a simultaneous utilization of doubly adaptive channel-wise and spatial attention modules at both transmitter and receiver. These modules dynamically adjust to varying channel conditions and spatial feature importance, enabling robust and efficient feature extraction and semantic information recovery. Simulation results corroborate that our proposed doubly adaptive DJSCC (DA-DJSCC) significantly improves upon ADJSCC in several performance criteria, while incurring a mild increase in complexity. These facts render DA-DJSCC a desirable choice for semantic communication in performance demanding but low-complexity IoT networks.

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arXiv2025

Enhancing Connectivity for Emergency Vehicles Through UAV Trajectory and Resource Allocation Optimization

Effective communication for emergency vehicles - such as ambulances and fire trucks - is essential to support their operations in various traffic and environmental conditions. In this context, this paper investigates a vehicular communication system assisted by an Unmanned Aerial Vehicle (UAV), which adjusts its trajectory and resource allocation according to communication needs. The system classifies vehicles into two groups to address their varying service requirements: emergency vehicles, which require a minimum instantaneous data rate to access critical information timely, and normal vehicles. To support both categories effectively, this paper proposes a joint optimization approach that coordinates UAV trajectory planning and Dynamic Bandwidth Allocation (DBA). The objective is to maximize the minimum average data rate for normal vehicles while ensuring that emergency vehicles maintain an instantaneous rate above a predefined threshold. This approach takes into account some system constraints, including UAV propulsion power consumption, mobility limitations, and backhaul capacity. To tackle the resulting non-convex problem, an iterative optimization method is employed, where the original problem is decomposed into two subproblems: bandwidth allocation and UAV trajectory design. In each iteration, the trajectory subproblem is solved using the Successive Convex Approximation (SCA) method. Numerical results confirm that the proposed solution achieves superior performance in meeting service requirements compared to baseline methods.

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arXiv2025

Capacity Achieving Design for Hybrid Beamforming in Millimeter Wave Massive MIMO Systems

Hybrid digital and analog beamforming is a highly effective technique for implementing beamforming methods in millimeter wave (mmWave) systems. It provides a viable solution to replace the complex fully digital beamforming techniques. However, the current design of precoding and combining matrices in hybrid beamforming solely relies on the channel information, neglecting the crucial consideration of the structure of covariance matrices of the transmit signals. In this paper, we present a novel approach for the joint design of hybrid beamforming matrices at the transmitter and receiver. This approach is centered around the optimization of the covariance matrix of the transmitted signals. Our goal is to maximize the downlink sum rate capacity of the system by achieving an optimal design of the transmit covariance matrix. We tackle the non-convex nature of this problem by leveraging the dual relationship between the broadcast channel (BC) and the multiple access channel (MAC). Through extensive simulations in various scenarios, including point-to-point multi-input multi-output (MIMO), multi-user (MU) multi-input single-output (MISO), and MU-MIMO, we demonstrate the superiority of our proposed method over traditional designs. These results highlight the effectiveness and versatility of our approach in optimizing beamforming for mmWave systems.

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arXiv2025

Secure Beamforming in Multi-User Multi-IRS Millimeter Wave Systems

We study the secrecy rate maximization problem in a millimeter wave (mmWave) network, consisting of a base station (BS), multiple intelligent reflecting surfaces (IRSs) (or reconfigurable intelligent surfaces (RISs)), multiple users, and a single eavesdropper. To ensure a fair secrecy rate among all the users, we adopt a max-min fairness criterion which results in a mixed integer problem. We first relax discrete IRSs phase shifts to the continuous ones. To cope with the non-convexity of the relaxed optimization problem, we leverage the penalty method and block coordinate descent approach to divide it into two sub-problems, which are solved by successive convex approximation (SCA). Then, we propose a low-complexity mapping algorithm where feasible IRSs phase shifts are obtained. Mathematical evaluation shows the convergence of sub-problems to a Karush-Kuhn-Tucker (KKT) point of the original ones. Furthermore, the convergence guarantee of the overall proposed algorithm and computational complexity are investigated. Finally, simulation results show our proposed algorithm outweighs the conventional solutions based on the semi-definite programming (SDP) in terms of convergence and secrecy rate, especially in a larger number of IRSs and phase shifts where SDP suffers from rank-one approximation. Maximum ratio transmission (MRT) and IRS-free systems are also considered as other benchmarks.

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arXiv2025

Integrated Sensing, Communication, and Powering (ISCAP) for IoT: A Joint Beamforming Design

This paper studies Integrated Sensing, Communication, and Powering (ISCAP) as a novel framework designed to enhance Internet of Things (IoT) applications within sixth-generation wireless networks. In these applications, in addition to IoT devices requiring an energy supply and receiving information or control data to perform their tasks, the base station serving them must sense the devices and their environment to localize them, thereby improving data transmission and enabling simultaneous power delivery. In our multi-node ISCAP IoT system, we optimize base station beamforming alongside the receiver's power-splitting factor to maximize energy harvesting while adhering to strict communication and sensing constraints. To effectively tackle this non-convex optimization problem, we decompose it into three manageable subproblems and employ several techniques such as semidefinite relaxation and Rayleigh quotient methods to find an efficient solution. Simulation results demonstrate the effectiveness of the proposed design, highlighting performance trade-offs among sensing accuracy, communication reliability, and power transfer efficiency.

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arXiv2025

DRL-Based Secure Spectrum-Reuse D2D Communications with RIS Assistance

This study examines the secrecy performance of an uplink device-to-device (D2D) communication system enhanced by reconfigurable intelligent surfaces (RIS) while considering the presence of multiple eavesdroppers. RIS technology is employed to improve wireless communication environment by intelligently reflecting signals, thereby improving both capacity and security. We employ deep reinforcement learning (DRL) to optimize resource allocation dynamically, addressing challenges in D2D pairs and optimizing RIS positioning and phase shifts in a changing wireless environment. Our simulations demonstrate that the developed DRL-based framework significantly maximizes the sum secrecy capacity of both D2D and cellular communications, achieving higher transmission secrecy rates compared to existing benchmarks. The results highlight the effectiveness of integrating RIS with D2D communications for improved security performance.

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arXiv2024

Securing RIS-Aided Wireless Networks Against Full Duplex Active Eavesdropping

This paper investigates the physical layer security of a Reconfigurable Intelligent Surface (RIS)-aided wireless network in the presence of full-duplex active eavesdropping. In this scenario, the RIS cooperates with the Base Station (BS) to transfer information to the intended user while an active attacker attempts to intercept the information through a wiretap channel. In addition, the attacker sends jamming signals to interfere with the legitimate user's reception of the signal and increase the eavesdropping rate. Our objective is to maximize the secrecy rate by jointly optimizing the active and passive beamformers at the BS and RIS, respectively. To solve the resulting non-convex optimization problem, we propose a solution that decomposes it into two disjoint beamforming design sub-problems solved iteratively using Alternating Optimization (AO) techniques. Numerical analysis is conducted to evaluate the impacts of varying the number of active attacking antennas and elements of the RIS on the secrecy performance of the considered systems under the presence of jamming signals sent by the attacker. The results demonstrate the importance of considering the impact of jamming signals on physical layer security in RIS-aided wireless networks. Overall, our work contributes to the growing body of literature on RIS-aided wireless networks and highlights the need to address the effects of jamming and active eavesdropping signals in such systems.

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arXiv2024

Integrated Access and Backhaul (IAB) in Low Altitude Platforms

In this paper, we explore the problem of utilizing Integrated Access and Backhaul (IAB) technology in Non-Terrestrial Networks (NTN), with a particular focus on aerial access networks. We consider an Uncrewed Aerial Vehicle (UAV)-based wireless network comprised of two layers of UAVs: (a) a lower layer consisting a number of flying users and a UAV Base Station (BS) that provides coverage for terrestrial users and, (b) an upper layer designated to provide both wireless access for flying users and backhaul connectivity for UAV BS. By adopting IAB technology, the backhaul and access links collaboratively share their resources, enabling aerial backhauling and the utilization of the same infrastructure and frequency resources for access links. A sum-rate maximization problem is formulated by considering aerial backhaul constraints to optimally allocate the frequency spectrum between aerial and terrestrial networks. We decompose the resulting non-convex optimization problem into two sub-problems of beamforming and spectrum allocation and then propose efficient solutions for each. Numerical results in different scenarios yield insightful findings about the effectiveness of using the IAB technique in aerial networks.

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arXiv2022

Integrated Access and Backhaul in Cell-free Massive MIMO Systems

One of the major challenges with cell-free (CF) massive multiple-input multiple-output (MIMO) networks is providing backhaul links for a large number of distributed access points (APs). In general, providing fiber optics backhaul for these APs is not cost-effective and also reduces network scalability. Wireless backhauling can be a promising solution that can be integrated with wireless access links to increase spectrum efficiency. In this paper, the application of integrated access and backhaul (IAB) technique in millimeter-wave (mmWave) CF massive MIMO systems is investigated. The access and backhaul links share a frequency spectrum in the mmWave bands, and in both, hybrid beamforming techniques are adopted for signal transmission. The bandwidth allocation (division) parameter between the two link types as well as the beamforming matrices are optimized to maximize the end-to-end data-rate. This leads to a non-convex optimization problem for which an efficient solution method is proposed. The simulation results show the effectiveness of the IAB technique and our proposed scheme in CF massive MIMO systems. These simulations also compare the proposed hybrid beamforming method with a fully digital solution in terms of the number of radio frequency (RF) chains and the volume of backhaul traffic. Finally, the effect of increasing the number of APs on the users data rates in terms of wireless access and backhaul links constraints is also examined.

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arXiv2021

Secrecy Rate Maximization in Multi-IRS Millimeter Wave Networks

This paper investigates the problem of increasing the security at the physical layer of a Millimeter Wave (mmWave) network equipped with several Intelligent Reflecting Surfaces (IRSs). In this network, multiple IRSs help the Base Station (BS) to reach the signal to the desired user and at the same time maintain the security of the network i.e. securing the signal from receiving by the unallowable eavesdropper. The target of the proposed scheme is to maximize the secrecy rate by jointly optimizing the active beamforming at the BS and passive beamforming at the IRSs. This leads to a non-convex optimization problem which we solve by decomposing into two sub-problems. The sub-problems alternatively solve the active and passive beamforming design problems using the Semi-Definite Relaxation (SDR) technique. Finally, simulations are done to assess the performance of the proposed algorithm. These results show the superiority of using multiple IRSs in the enhancement of the secrecy rate in the wireless networks that operate in the mmWave frequency bands.

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arXiv2021

Direction-Based Jamming Detection and Suppression in mmWave Massive MIMO Networks

In this paper, we study the problem of physical layer security in the uplink of millimeter-wave massive multiple-input multiple-output (MIMO) networks and propose a jamming detection and suppression method. The proposed method is based on directional information of the received signals at the base station antenna array. The proposed jamming detection method can accurately detect both the existence and direction of the jammer using the received pilot signals in the training phase. The obtained information is then exploited to develop a channel estimator that excludes the jammer's angular subspace from received training signals. The estimated channel information is then used for designing a combiner at the base station that is able to effectively cancel out the deliberate interference of the jammer. By numerical simulations, we evaluate the performance of the proposed jamming detection method in terms of correct detection probability and false alarm probability and show its effectiveness when the jammer's power is substantially lower than the user's power. Also, our results show that the proposed jamming suppression method can achieve a very close spectral efficiency as the case of no jamming in the network

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arXiv2021

Vertical Beamforming in Reconfigurable Intelligent Surface-aided Cognitive Radio Networks

In this letter, we investigate joint application of reconfigurable intelligent surface (RIS) and vertical beamforming in cognitive radio networks (CRN). After properly modeling the network, an optimization problem is formed to jointly design the beamforming vector and tilt angle at the secondary base station (BS) as well as the phase shifts at the RIS with the objective of maximizing spectral efficiency of the secondary network. The optimization problem is non-convex; thus, we propose an efficient solution method for it. Numerical results show that adding a RIS and optimizing the radiation orientation, can significantly improve performance of the CRNs.

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arXiv2020

Spectral Efficiency of Dense Multicell Massive MIMO Networks in Spatially Correlated Channels

This paper is on the spectral efficiency (SE) of a dense multi-cell massive multiple-input multiple-output (MIMO). The channels are spatially correlated and the multi-slope path loss model is considered. In our framework, the channel state information is obtained by using pilot sequences and the BSs are deployed randomly. First, we study the channel estimation accuracy and its impact on the SE as the BS density increases and the network becomes densified. Second, we consider the special case of uncorrelated channels for which the stochastic geometry framework helps us to simplify the SE expressions and obtain the minimum value of antenna-UE ratio over which the pilot contamination is dominant rather than the inter- and intra-cell interference. Finally, we provide some insights into the obtained SE for the spatially correlated channels, from a multi-cell processing scheme as well as the single-cell ones in terms of the BS density. Our results show that while all the detectors result in non-increasing SE in terms of the BS density, their area SE increases exponentially as the network becomes densified. Moreover, we conclude that in order to achieve a given SE, the required value of antenna-UE ratio decreases as the level of channel correlation increases.

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arXiv2020

Secure Simultaneous Information and Power Transfer for Downlink Multi-user Massive MIMO

In this paper, downlink secure transmission in simultaneous information and power transfer (SWIPT) system enabled with massive multiple-input multiple-output (MIMO) is studied. A base station (BS) with a large number of antennas transmits energy and information signals to its intended users, but these signals are also received by an active eavesdropper. The users and eavesdropper employ a power splitting technique to simultaneously decode information and harvest energy. Massive MIMO helps the BS to focus energy to the users and prevent information leakage to the eavesdropper. The harvested energy by each user is employed for decoding information and transmitting uplink pilot signals for channel estimation. It is assumed that the active eavesdropper also harvests energy in the downlink and then contributes during the uplink training phase. Achievable secrecy rate is considered as the performance criterion and a closed-form lower bound for it is derived. To provide secure transmission, the achievable secrecy rate is then maximized through an optimization problem with constraints on the minimum harvested energy by the user and the maximum harvested energy by the eavesdropper. Numerical results show the effectiveness of using massive MIMO in providing physical layer security in SWIPT systems and also show that our closed-form expressions for the secrecy rate are accurate.

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arXiv2020

Jamming-Robust Uplink Transmission for Spatially Correlated Massive MIMO Systems

In this paper, we consider how the uplink transmission of a spatially correlated massive multiple-input multiple-output (MIMO) system can be protected from a jamming attack. To suppress the jamming, we propose a novel framework including a new optimal linear estimator in the training phase and a bilinear equalizer in the data phase. The proposed estimator is optimal in the sense of maximizing the spectral efficiency of the legitimate system attacked by a jammer, and its implementation needs statistical knowledge about the jammer's channel. We derive an efficient algorithm to estimate the jamming information needed for the implementation of the proposed framework. Furthermore, we demonstrate that optimized power allocation at the legitimate users can improve the performance of the proposed framework regardless of the jamming power optimization. Our proposed framework can be exploited to combat jamming in scenarios with either ideal or non-ideal hardware at the legitimate users and the jammer. Numerical results reveal that using the proposed framework, the jammer cannot dramatically affect the performance of the legitimate system.

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arXiv2020

Price of Anarchy in Multiuser Massive MIMO: Coordinated versus Uncoordinated Channel Tracking for High-Rate Internet of Things

Incorporation of high-rate internet of things (IoT) service into a massive MIMO framework is investigated. It is revealed that massive MIMO possess the inherent potential to offer such service provided it knows the channels for all devices. Our proposed method is to jointly estimate and track the channels of all devices irrespective of their current activity. Using the dynamical model for devices' channels evolution over time, optimal and sub-optimal trackers are developed for coordinated scenario. Furthermore, we introduce a new paradigm where the BS need not know the pilot access patterns of devices in advance which we refer to as uncoordinated setup. After motivating this scenario, we derive the optimal tracker which is intractable. Then, target tracking approaches are applied to address uncertainties in the measurements and derive sub-optimal trackers. Our proposed approaches explicitly address the channel aging problem and will not require downlink paging and uplink access request control channels which can become bottlenecks in crowded scenarios. The fundamental minimum mean square error (MMSE) gap between optimal coordinated and uncoordinated trackers which is defined as price of anarchy is evaluated and upper-bounded. Stability of optimal trackers is also investigated. Finally, performance of various proposed trackers are numerically compared.

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