arXiv subjects

Mohammad Javad Omidi

Publications and source records attributed to Mohammad Javad Omidi.

15 recordsLinked to original sources

arXiv2026

QoS Improvement in Multi User Cellular-Symbiotic Radio Network Assisted by Active-STAR-RIS

In this article, we employ active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (ASRIS) to enhance the quality of 6G cellular network services. The network integrates commensal symbiotic radio (CSR) subsystems to facilitate communication between passive Internet of Things (IoT) users and active users, referred to as symbiotic backscatter devices (SBDs) and symbiotic user equipments (SUEs), respectively. Since the SBDs are passive, transmitting information to the SUEs poses significant challenges. To overcome this challenge, we harness the capabilities of massive multiple input multiple output (MIMO) antennas within the base station (BS) to relay the information transmitted by SBDs with greater power. This scheme uses the non-orthogonal multiple access (NOMA) technique for multiple access among all users, and potential interferences are eliminated using successive interference cancellation (SIC). The primary objective is to maximize the throughput between SBDs and SUEs. To achieve this, we formulate an optimization problem involving variables such as active beamforming coefficients at the BS and ASRIS, phase adjustments of ASRIS, and scheduling parameters between CSR and cellular networks. To solve this optimization problem, we used three deep reinforcement learning (DRL) methods: proximal policy optimization (PPO), twin delayed deep deterministic policy gradient (TD3), and asynchronous advantage actor critic (A3C). These methods were simulated, and the results demonstrate that A3C, TD3, and PPO have the best convergence speeds and achieve the highest increases in network throughput, respectively. Finally, the proposed scheme was evaluated using passive simultaneously transmitting and reflecting RIS (STAR-RIS), which demonstrated poorer performance compared to ASRIS.

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arXiv2026

Enhancing Energy and Spectral Efficiency in IoT-Cellular Networks via Active SIM-Equipped LEO Satellites

This paper investigates a low Earth orbit (LEO) satellite communication system enhanced by an active stacked intelligent metasurface (ASIM), mounted on the backplate of the satellite solar panels to efficiently utilize limited onboard space and reduce the main satellite power amplifier requirements. The system serves multiple ground users via rate-splitting multiple access (RSMA) and IoT devices through a symbiotic radio network. Multi-layer sequential processing in the ASIM improves effective channel gains and suppresses inter-user interference, outperforming active RIS and beyond-diagonal RIS designs. Three optimization approaches are evaluated: block coordinate descent with successive convex approximation (BCD-SCA), model-assisted multi-agent constraint soft actor-critic (MA-CSAC), and multi-constraint proximal policy optimization (MCPPO). Simulation results show that BCD-SCA converges fast and stably in convex scenarios without learning, MCPPO achieves rapid initial convergence with moderate stability, and MA-CSAC attains the highest long-term spectral and energy efficiency in large-scale networks. Energy-spectral efficiency trade-offs are analyzed for different ASIM elements, satellite antennas, and transmit power. Overall, the study demonstrates that integrating multi-layer ASIM with suitable optimization algorithms offers a scalable, energy-efficient, and high-performance solution for next-generation LEO satellite communications.

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arXiv2026

A Novel 3D Antenna Architecture with Spatial Resource Allocation for Massive MIMO HAPS

Spatial correlation poses a significant challenge in massive multiple-input multiple-output (MIMO) high-altitude platform station (HAPS) systems. The inherent spatial correlation among antenna elements on the HAPS induces high correlation and interference among users' channel gains. To mitigate this issue, we propose an integrated approach that combines spatial resource allocation and user clustering. In our proposed solution, we assign the same resource blocks to users with orthogonal channel gains, while users with non-orthogonal channel gains receive different resource blocks. Additionally, we propose a sectorized antenna architecture for the massive MIMO HAPS base station, specifically designed to directly transmit three-dimensional beams to users and reduce spatial correlation among antenna elements. This work addresses the joint optimization problem of power allocation and resource allocation to maximize the overall data rate of the massive MIMO HAPS system. Simulation results revealed the role of spatial resource allocation in managing spatial correlation and interference among users.

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arXiv2025

HAPS-ISAC for 6G: Architecture, Design Trade-offs, and a Practical Roadmap

To meet the ambitious goals of next-generation 6G networks, including ultra-high data rates and ubiquitous coverage, we propose a novel high-altitude platform station (HAPS)-based integrated sensing and communication (ISAC) architecture. Operating in the stratosphere, the HAPS functions as both a powerful communication hub and an advanced environmental sensor. Combined with a fleet of cooperative uncrewed aerial vehicles (UAVs), this dual-purpose system forms a scalable and intelligent 3D network. Simulation results indicate that this approach significantly boosts network performance, improves sensing accuracy, and ensures a fairer service distribution across users, outperforming conventional UAV-only baselines. We conclude by outlining the prospective applications and a deployment roadmap for this technology for smart cities and other large-scale environments.

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arXiv2025

Max-Min Fairness-Oriented Beamforming Design in HAPS-Enabled ISAC for 6G Networks

This paper presents a high-altitude platform station (HAPS)-enabled integrated sensing and communication (ISAC) system designed for sixth-generation (6G) networks. Positioned in the stratosphere, HAPS serves as a super-macro base station, leveraging advanced beamforming techniques to enable communication and sensing simultaneously. This research addresses the need for equitable service distribution in 6G networks by focusing on fairness within the HAPS-ISAC system. It tackles a non-convex optimization problem that balances sensing beampattern gain and signal-to-interference-plus-noise ratio (SINR) requirements among communication users (CUs) using a max-min fairness approach while adhering to power constraints. The proposed HAPS-ISAC framework ensures efficient resource allocation, reliable coverage, and improved sensing accuracy. Simulation results validate the potential of HAPS-ISAC as a pivotal enabler for 6G networks and integrated communication-sensing systems.

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arXiv2025

Optimizing Network Performance and Resource Allocation in HAPS-UAV Integrated Sensing and Communication Systems for 6G

This paper proposes an innovative approach by leveraging uncrewed aerial vehicles (UAVs) as base stations (BSs) and a high-altitude platform station (HAPS) as the central processing unit (CPU) in an integrated sensing and communication (ISAC) system for 6G networks. We explore the challenges, applications, and advantages of ISAC systems in next-generation networks, highlighting the significance of optimizing position and power control. Our approach integrates HAPS and UAVs to enhance wireless coverage, particularly in remote areas. UAVs function as dual-purpose access points (APs), using their maneuverability and line-of-sight (LoS) aerial-to-ground (A2G) links to transmit combined communication and sensing signals. The scheme operates in two time slots: in the first slot, UAVs transmit dedicated signals to communication users (CUs) and potential targets. UAVs detect targets in specific ground locations and, after signal transmission, receive reflected signals from targets. In the second slot, UAVs relay these signals to HAPS, which performs beamforming to align signals for each CU from various UAVs. UAVs decode information from HAPS and adjust transmissions to maximize the beam pattern efficiency toward the desired targets. We formulate a multi-objective optimization problem to maximize both the minimum signal-to-interference-plus-noise ratio (SINR) for CUs and the echo signal power from the targets. This is achieved by finding the optimal power allocation for CUs in each UAV, subject to constraints on the maximum total power in each UAV and the transmitted beam pattern gain. Simulation results demonstrate the effectiveness of this approach in enhancing network performance, resource allocation, fairness, and system optimization. Using HAPS as the CPU, computational tasks are offloaded from UAVs, which conserves energy and improves network performance.

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arXiv2024

MIMO-NOMA Enabled Sectorized Cylindrical Massive Antenna Array for HAPS with Spatially Correlated Channels

The high altitude platform station (HAPS) technology is garnering significant interest as a viable technology for serving as base stations in communication networks. However, HAPS faces the challenge of high spatial correlation among adjacent users' channel gains which is due to the dominant line-of-sight (LoS) path between HAPS and terrestrial users. Furthermore, there is a spatial correlation among antenna elements of HAPS that depends on the propagation environment and the distance between elements of the antenna array. This paper presents an antenna architecture for HAPS and considers the mentioned issues by characterizing the channel gain and the spatial correlation matrix of the HAPS. We propose a cylindrical antenna for HAPS that utilizes vertical uniform linear array (ULA) sectors. Moreover, to address the issue of high spatial correlation among users, the non-orthogonal multiple access (NOMA) clustering method is proposed. An algorithm is also developed to allocate power among users to maximize both spectral efficiency and energy efficiency while meeting quality of service (QoS) and successive interference cancellation (SIC) conditions. Finally, simulation results indicate that the spatial correlation has a significant impact on spectral efficiency and energy efficiency in multiple antenna HAPS systems.

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arXiv2024

Multi-BD Symbiotic Radio-Aided 6G IoT Network: Energy Consumption Optimization with QoS Constraint Approach

The commensal symbiotic radio (CSR) system is proposed as a novel solution for connecting systems through green communication networks. This system enables us to establish secure, ubiquitous, and unlimited connectivity, which is a goal of 6G. The base station uses MIMO antennas to transmit its signal. Passive IoT devices, called symbiotic backscatter devices (SBDs), receive the signal and use it to charge their power supply. When the SBDs have data to transmit, they modulate the information onto the received ambient RF signal and send it to the symbiotic user equipment, which is a typical active device. The main purpose is to enhance energy efficiency in this network by minimizing energy consumption (EC) while ensuring the minimum required throughput for SBDs. To achieve this, we propose a new scheduling scheme called Timing-SR that optimally allocates resources to SBDs. The main optimization problem involves non-convex objective functions and constraints. To solve this, we use mathematical techniques and introduce a new approach called sequential quadratic and conic quadratic representation to relax and discipline the problem, leading to reducing its complexity and convergence time. The simulation results demonstrate that the proposed approach outperforms other outlined schemes in reducing EC.

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arXiv2023

Low-PAPR Joint Channel Estimation and Data Detection in ZP-OTFS System

Orthogonal Time Frequency Space (OTFS) systems face significant challenges in channel estimation due to high pilot overhead and peak-to-average power ratio (PAPR). To address these issues, we propose a two-step channel estimation method for Zero-Pad OTFS (ZP-OTFS), a modified OTFS system characterized by multiple zero rows along the delay axis. This method strategically inserts pilot sequences into the zero bins of the ZP-OTFS system, effectively mitigating overhead and PAPR. Comprehensive simulation results validate the effectiveness of our proposed method, demonstrating its superior performance over traditional embedded pilot estimation in high Signal-to-Noise Ratio (SNR) scenarios. Specifically, our method achieves a lower normalized mean square error (NMSE) and better bit error rates (BER) at high SNRs.

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arXiv2022

Superimposed Channel Estimation in OTFS Modulation Using Compressive Sensing

Orthogonal time frequency space (OTFS) technique is a two-dimensional modulation method that multiplexes information symbols in the delay-Doppler (DD) domain. OTFS combats high Doppler shift existing in high speed wireless communication. However, conventional channel estimation in OTFS suffers from high pilot overhead because guard symbols occupy a significant part of the DD domain grids. In this paper, a superimposed channel estimation is proposed which can completely estimate channel parameters without considering pilot overhead and performance degradation. As the channel state information (CSI) in the DD domain is sparse, a sparse recovery algorithm orthogonal matching pursuit (OMP) is used. Besides, our proposed method does not suffer from high peak to average power ratio (PAPR). To detect information symbols, a message passing (MP) detector, which exploits the sparsity of DD channel representation, is employed.

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arXiv2022

Power Allocation for a HAPS-Enabled MIMO-NOMA System with Spatially Correlated Channels

High-altitude platform station (HAPS) systems are considered to have great promise in the multi-tier architecture of the sixth generation (6G) and beyond wireless networks. A HAPS system can be used as a super macro base station (SMBS) to communicate with users directly since there is a significant line-of-sight (LoS) link between a HAPS and terrestrial users. One of the problems that HAPS SMBS systems face, however, is the high spatial correlation between the channel gain of adjacent users, which is due to the LoS link between the HAPS and terrestrial users. In this paper, in addition to utilizing the spatial correlation of channel gain between multiple users to improve user services, we consider correlated channel gain for each user. In the proposed method, terrestrial users with a high spatial correlation between their LoS channel gain are grouped into NOMA clusters. Next, an algorithm is proposed to allocate power among terrestrial users to maximize the total rate while satisfying the quality-of-service (QoS) and successive interference cancellation (SIC) conditions. Simulation results show that a HAPS SMBS has superior data rate and energy efficiency in comparison to a terrestrial BS.

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arXiv2021

Low-Complexity Resource Allocation for Dense Cellular Vehicle-to-Everything (C-V2X) Communications

Vehicular communications are the key enabler of traffic reduction and road safety improvement referred to as cellular vehicle-to-everything (C-V2X) communications. Considering the numerous transmitting entities in next generation cellular networks, most existing resource allocation algorithms are impractical or non-effective to ensure reliable C-V2X communications which lead to safe intelligent transportation systems. We study a centralized framework to develop a low-complexity, scalable, and practical resource allocation scheme for dense C-V2X communications. The NP-hard sum-rate maximization resource allocation problem is formulated as a mixed-integer non-linear non-convex optimization problem considering both cellular vehicular links (CVLs) and non-cellular VLs (NCVLs) quality-of-service (QoS) constraints. By assuming that multiple NCVLs can simultaneously reuse a single cellular link (CL), we propose two low-complexity sub-optimal matching-based algorithms in four steps. The first two steps provide a channel gain-based CVL priority and CL assignment followed by an innovative scalable min-max channel-gain-based CVL-NCVL matching. We propose an analytically proven closed-form fast feasibility check theorem as the third step. The objective function is transformed into a difference of convex (DC) form and the power allocation problem is solved optimally using majorization-minimization (MaMi) method and interior point methods as the last step. Numerical results verify that our schemes are scalable and effective for dense C-V2X communications. The low-complexity and practicality of the proposed schemes for dense cellular networks is also shown. Furthermore, it is shown that the proposed schemes outperform other methods up to %6 in terms of overall sum-rate in dense scenarios and have a near optimal performance.

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arXiv2021

Distributed pricing-based resource allocation for dense device-to-device communications in beyond 5G networks

Considering the dramatic increase of data rate demand in beyond fifth generation (5G) networks due to the numerous transmitting nodes, dense device-to-device (D2D) communications where multiple D2D pairs can simultaneously reuse a cellular link can be considered as a communication paradigm for future wireless networks. Since distributed methods can be more practical compared to complex centralized schemes, we propose a low-complexity distributed pricing-based resource allocation algorithm to allocate power to cellular user equipments (CUEs) and D2D pairs constrained to the minimum quality-of-service (QoS) requirements of CUEs and D2D pairs in two phases. The price of each link is set by the base station (BS). CUEs and D2D pairs maximize their rate-and-interference-dependent utility function by adjusting their power in the first phase, while the price of each link is updated at the BS based on the minimum QoS requirements of CUEs and D2D pairs in the second phase. The proposed utility function controls D2D admission, hence it is suitable for dense scenarios. The proposed method is fast due to the closed-form solution for the first phase power allocation. Numerical results verify the effectiveness of the proposed method for practical dense beyond 5G scenarios by allocating resources to multiple D2D pairs and taking advantage of the spatial reuse gain of D2D pairs. Furthermore, the utility function definition is discussed to illustrate its effectiveness for SE maximization. Finally, the SE of the proposed method is compared to other centralized and distributed algorithms to demonstrate the higher sum-rate performance of the proposed algorithm.

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arXiv2021

Centralized QoS-Aware Resource Allocation for D2D Communications with Multiple D2D Pairs in One Resource Block

Device-to-device (D2D) communications can result in spectral efficiency (SE) improvement of cellular networks when interference is addressed properly. For further improvement of overall network sum-rate, it can be assumed that multiple D2D pairs reuse the same resource block (RB) of a cellular user equipment (CUE). In this paper, we study a centralized joint power and spectrum resource allocation problem to maximize the overall sum-rate of the network while guaranteeing the quality-of-service (QoS) requirements of CUEs and admitted D2D pairs. The proposed method uses a distance based approach to select a CUE at each step with a distance and power based approach to increase the number of admitted D2D pairs. A power minimization approach when the number of admitted D2D pairs are increasing and a power maximization approach at the end of resource allocation for each RB is proposed. Numerical results show that the proposed algorithm significantly improves SE compared to the situation where at most one D2D pair can reuse the RB of a CUE. It also shows that the proposed algorithm has a slight lower performance compared to the optimal scheme but dramatically decreases the matching complexity and signaling overhead.

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arXiv2018

Energy Efficient Precoder in Multi-User MIMO Systems with Imperfect Channel State Information

This article is on the energy efficient precoder design in multi-user multiple-input-multiple-output (MU-MIMO) systems which is also robust with respect to the imperfect channel state information (CSI) at the transmitters. In other words, we design the precoder matrix associated with each transmitter to maximize the general energy efficiency of the network. We investigate the problem in two conventional cases. The first case considers the statistical characterization for the channel estimation error that leads to a quadratically constrained quadratic program (QCQP) with a semi-closed-form solution. Then, we turn our attention to the case which considers only the uncertainty region for the channel estimation error; this case eventually results in a semi-definite program (SDP).

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