AlexNet Classifier and Support Vector Regressor for Scheduling and Power Control in Multimedia Heterogeneous Networks - Zarini, Hosein; Khalili, Ata; Tabassum, Hina; Rasti, Mehdi; Saad, Walid (IEEE Transactions on Mobile Computing, 2023)
In this paper, the downlink transmission of a two-tier heterogeneous network (HetNet) is considered in which a macro base station (MBS) serves the macro users using orthogonal frequency division multiple access (OFDMA) and small base stations (SBSs) serve the small-cell users through multi-carrier non-orthogonal multiple access (MC-NOMA) and joint transmission (JT). In particular, assuming the subcarriers are already allocated to macro users, the problem of scheduling (i.e., joint user association and subcarrier allocation) and power control is studied with the goal of maximizing the total users' perceived quality-of -experience (QoE) for small-cell users, while a minimum data rate for macro users is guaranteed. To solve the joint optimization problem, a near-optimal and computationally efficient two-phase solution approach is proposed based on the tools from optimization and machine learning (ML). In the first phase, the optimization problem is solved to obtain the scheduling decisions and transmit power variables. In the second phase, the optimized scheduling decisions and transmit power variables serve as training samples for an AlexNet classifier and support vector regressor (SVR), respectively. Simulation results reveal that the integration of JT into MC-NOMA, outperforms the conventional MC-NOMA scheme by up to 24%, 19%, and 21% for the web, video and audio multimedia services, respectively. Compared to a conventional convolutional neural network, our results demonstrate that for the web, video, and audio-services, AlexNet increases the scheduling prediction accuracy up to 14%, 11%, and 17%, while SVR increases the power prediction accuracy up to 8%, 7%, and 12%, respectively.
CRIS-Team Scopus Importer:2023-04-28
Keywords: AlexNet; Heterogeneous network (HetNet); joint transmission (JT); multi-carrier non-orthogonal multiple access (MC-NOMA); quality of experience (QoE); support vector regressor (SVR)

Efficient Codebook Design and User Scheduling for Large STAR-RIS-Aided Downlink URLLC Transmission - Darabi, Mostafa; Khalili, Ata; Lampe, Lutz; Schober, Robert (IEEE Communications Letters, 2025)
To ensure ultra-reliable, low-latency communications (URLLC) despite blockages, we propose using simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RISs). These surfaces can transmit and reflect signals, expanding coverage on both sides of the RIS. However, phase shifter adjustments in STAR-RIS are limited by coupling constraints. We introduce a codebook design for phase-shift adjustment that accounts for these constraints. Leveraging this design, we jointly optimize base station beamforming and STAR-RIS phase-shifting to maximize the number of admitted URLLC users in a downlink multi-user communication system. Numerical results show that the proposed STAR-RIS codebook surpasses existing designs and effectively admits more URLLC users.
CRIS-Team Scopus Importer:2025-02-17
Keywords: 6G cellular networks; reconfigurable intelligent surfaces; simultaneously transmitting and reflecting; URLLC

Efficient UAV Hovering, Resource Allocation, and Trajectory Design for ISAC with Limited Backhaul Capacity - Khalili, Ata; Rezaei, Atefeh; Xu, Dongfang; Dressler, Falko; Schober, Robert (IEEE Transactions on Wireless Communications, 2024)
In this paper, we investigate the joint resource allocation and trajectory design for a multi-user, multi-target unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) system, where the link capacity between a ground base station (BS) and the UAV is limited. The UAV conducts target sensing and information transmission in orthogonal time slots to prevent interference. As is common in practical systems, sensing is performed while the UAV hovers, allowing the UAV to acquire high-quality sensing data. Subsequently, the acquired sensing data is offloaded to the ground BS for further processing. We jointly optimize the UAV trajectory, UAV velocity, beamforming for the communication users, power allocated to the sensing beam, and time of hovering for sensing to minimize the power consumption of the UAV while ensuring the communication quality of service (QoS) and successful sensing. Due to the prohibitively high complexity of the resulting nonconvex mixed integer non-linear program (MINLP), we employ a series of transformations and optimization techniques, including semidefinite relaxation, big-M method, penalty approach, and successive convex approximation, to obtain a low-complexity suboptimal solution. Our simulation results reveal that 1) the proposed design achieves significant power savings compared to two baseline schemes; 2) stricter sensing requirements lead to longer sensing times, highlighting the challenge of efficiently managing both sensing accuracy and sensing time; 3) the optimized trajectory design ensures precise hovering directly above the targets during sensing, enhancing sensing quality and enabling the application of energy-focused beams; and 4) the proposed trajectory design balances the capacity of the backhaul link and the downlink rate of the communication users.
CRIS-Team Scopus Importer:2024-09-27
Keywords: backhaul link; hovering; ISAC; MINLP; radar pulse sensing; Resource allocation; trajectory design; UAV

Energy-Aware Resource Allocation and Trajectory Design for UAV-Enabled ISAC - Khalili, Ata; Rezaei, Atefeh; Xu, Dongfang; Schober, Robert (GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023)
In this paper, we investigate joint resource allocation and trajectory design for multi-user multi-target unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC). To be compatible with practical UAV-based sensing systems, sensing is carried out while the UAV hovers. In particular, we jointly optimize the two-dimensional trajectory, the velocity, and the downlink information and sensing beamformers of a fixed-altitude UAV for minimization of the average power consumption, while ensuring the quality of service of the communication users and the sensing tasks. To tackle the resulting non-convex mixed integer non-linear program (MINLP), we exploit semidefinite relaxation, the big-M method, and successive convex approximation to develop an alternating optimization-based algorithm. Our simulation results demonstrate the significant power savings enabled by the proposed scheme compared to two baseline schemes employing heuristic trajectories.
CRIS-Team Scopus Importer:2024-12-16
Keywords: Simulation; Quality of service; Autonomous aerial vehicles; Sensors; Trajectory; Resource management; Task analysis

Energy-Efficient Resource Allocation and Antenna Selection for IRS-assisted Multi-Cell Downlink Networks - Rezaei, Atefeh; Khalilimahmoudabadi, Ataollah; Jalali, Jalal; Shafiei, Hossein; Wu, Qingqing (IEEE Wireless Communications Letters, 2022)
This letter considers a network-assisted intelligent reflecting surface (IRS) technology. We aim to adopt an energy-efficient strategy via an antenna selection (AS) framework that determines which base station (BS) antennas transmit the data to the user equipment. In particular, we select the best set of antennas to increase energy efficiency (EE) while reducing power consumption. Also, the network takes advantage of the IRS system to increase the coverage and overall throughput of the network. We first propose an efficient algorithm for the considered scenario based on the successive convex approximation (SCA). Then we employ the Dinkelbach method that jointly selects the best set of antennas and optimizes their beamforming. Second, by introducing the slack variable and SCA method, we propose a tight approximation to solve the passive beamforming at the IRS. Simulation results unveil the performance of the proposed method and its influence on the power consumption at each antenna’s RF chain.
CRIS-Team Scopus Importer:2022-04-08
Keywords: antenna selection (AS); Antennas; Array signal processing; energy efficiency (EE).; Intelligent reflecting surface (IRS); Interference; MISO communication; Optimization; Power demand; Transmitting antennas

Energy-Efficient THz NOMA for SWIPT-aided Miniature UAV Networks - Jalali, Jalal; Khalili, Ata; Tabassum, Hina; Berkvens, Rafael; Famaey, Jeroen; Saad, Walid (IEEE Communications Letters, 2024)
This paper focuses on maximizing the energy efficiency (EE) of a cooperative network involving miniature unmanned aerial vehicles (UAV) operating at terahertz (THz) frequencies, leveraging simultaneous wireless information and power transfer (SWIPT). In this context, the considered system encompasses a source node that adopts SWIPT, thereby allowing the miniature UAV to receive information and power simultaneously. The harvested energy is then employed to relay data to designated destination nodes. The EE of the system is maximized by optimizing non-orthogonal multiple access (NOMA) power allocation coefficients, SWIPT power splitting (PS) ratios, and UAV’s trajectory. This optimization problem is solved in two stages. First, the PS ratios and trajectory are optimized using a successive convex approximation. Next, NOMA power coefficients are optimized using a quadratic transform approach. Simulation results validate the effectiveness of the proposed algorithm compared to baselines.
CRIS-Team Scopus Importer:2024-03-15
Keywords: Autonomous aerial vehicles; EE; NOMA; NOMA; Optimization; Resource management; Simultaneous wireless information and power transfer; SWIPT; Terahertz communications; THz; Trajectory; UAV

Integrated Sensing and Communication in Distributed Antenna Networks - Xu, Dongfang; Khalili, Ata; Yu, Xianghao; Kwan Ng, Derrick Wing; Schober, Robert (2023 IEEE International Conference on Communications Workshops: Sustainable Communications for Renaissance, ICC Workshops 2023, 2023)
In this paper, we investigate the resource allocation design for integrated sensing and communication (ISAC) in distributed antenna networks (DANs). In particular, coordinated by a central processor (CP), a set of remote radio heads (RRHs) provide communication services to multiple users and sense several target locations within an ISAC frame. To avoid severe interference between the information transmission and the radar echo, we propose to divide the ISAC frame into a communication phase and a sensing phase. During the communication phase, the data signal is generated at the CP and then conveyed to the RRHs via fronthaul links. As for the sensing phase, based on pre-determined RRH-target pairings, each RRH senses a dedicated target location with a synthesized highly-directional beam and then transfers the samples of the received echo to the CP via its fronthaul link for further processing of the sensing information. Taking into account the limited fronthaul capacity and the quality-of-service requirements of both communication and sensing, we jointly optimize the durations of the two phases, the information beamforming, and the covariance matrix of the sensing signal for minimization of the total energy consumption over a given finite time horizon. To solve the formulated non-convex design problem, we develop a low-complexity alternating optimization algorithm which converges to a suboptimal solution. Simulation results show that the proposed scheme achieves significant energy savings compared to the two baseline schemes. Moreover, our results reveal that for efficient ISAC in wireless networks, energy-focused short-duration pulses are favorable for sensing while low-power long-duration signals are preferable for communication.
CRIS-Team Scopus Importer:2023-12-08

IRS-Based Energy Efficiency and Admission Control Maximization for IoT Users with Short Packet Lengths - Jalali, Jalal; Khalili, Ata; Rezaei, Atefeh; Berkvens, Rafael; Weyn, Maarten; Famaey, Jeroen (IEEE Transactions on Vehicular Technology, 2023)
In this paper, we study a multiuser multiple-input single-output (MISO) machine type communication (MTC)-enabled intelligent reflecting surface (IRS) system, where a multi-antenna access point (AP) transmits information symbols to a set of Internet of Things (IoT) users with short packet transmission. In particular, the total energy efficiency (EE) and the number of IoT users that could be served fairly are maximized by jointly optimizing active and passive beamformers. An efficient algorithm based on alternating optimization (AO) is proposed to solve the main optimization problem iteratively. To this end, we adopt the difference of convex functions (DC) and successive convex approximation (SCA) to make a concave-convex function. Then, we employ the fractional programming based on the quadratic form to obtain a sub-optimal solution for the active beamformers at the AP and the number of admitted users. In the passive beamforming case, a penalty-based approach is utilized together with the SCA technique to handle the unit-modulus constraints at the IRS. Simulation results unveil an interesting tradeoff between EE and user admissibility performance. Besides, the results show the effectiveness of the IRS deployment in improving EE and successfully admitted users.
CRIS-Team Scopus Importer:2023-10-27
Keywords: Admission control; alternating optimization (AO); energy efficiency (EE); intelligent reflecting surface (IRS); machine type communication (MTC); short packet transmission

Joint Offloading Policy and Resource Allocation in IRS-aided MEC for IoT Users with Short Packet Transmission - Jalali, Jalal; Khalili, Ata; Berkvens, Rafael; Famaey, Jeroen (2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), 2023)
This paper focuses on leveraging a mobile edge computing (MEC) server at an access point (AP) to address the delay and reliability sensitivity requirement of multi-user machine-type communication (MTC). By offloading tasks to the MEC server, latency for low-power MTC devices can be minimized. Meanwhile, intelligent reflecting surfaces (IRSs) are supported to facilitate robust offloading, enhance spectrum efficiency, and improve coverage by influencing incident radio-frequency wave propagation via modifying the phase shifts with passive reflecting components. Therefore, we investigate joint radio resource allocation and edge offloading decision optimization in a multi-user IRS-assisted MEC network, wherein a multi-antenna AP receives information symbols from a set of Internet of Things (IoT) users with short packet transmission. In particular, we minimize the system's power utilization subject to offloading MTC-enabled IoT users' quality of service (QoS) requirements, transmit power feasibility, capacity limitation, and IRS phase shift. The non-convex nature of the formulated problem poses a challenge to solving it effectively. To address this, we propose an efficient iterative algorithm based on successive convex approximation (SCA) and a penalty-based approach for handling unit-modulus constraints in the presence of passive reflecting elements at the IRS. Simulation results demonstrate the superior performance of our algorithm compared to other baseline schemes.
CRIS-Team Scopus Importer:2024-12-16
Keywords: Intelligent reflecting surface (IRS); machine-type communication (MTC); mobile edge computing (MEC); short packet transmission

Resilient Disaster Relief in Industrial IoT: UAV Trajectory Design and Resource Allocation in 6G Non-Terrestrial Networks - Mohammadisarab, Amir; Nouruzi, Ali; Khalili, Ata; Mokari, Nader; Arand, Bijan Abbasi; Jorswieck, Eduard A. (IEEE Open Journal of the Communications Society, 2024)
This explores strategies to augment the connectivity among users within Hierarchical Non-Terrestrial Networks (HNTNs) dedicated to Disaster Relief Services (DRS). The primary goal is to optimize radio resources, computing capacities, and the trajectories of Unmanned Aerial Vehicles (UAVs) at each time slot, aiming to maximize the number of satisfactory connections (NSCs). UAVs function as aerial base stations (ABSs), establishing links for reduced capability (RedCap) user equipment (UE) using power domain non-orthogonal multiple access (PD-NOMA). Given the potential inoperability of terrestrial networks during disasters, swift data transmission is critical for mission-critical (MC) UEs. Therefore, end-to-end (E2E) delay is a crucial quality of service (QoS) constraint. The proposed problem is solved using a multi-agent recurrent deterministic policy gradient (MARDPG) algorithm, where the ABSs collaborate to maximize the NSCs and determine their optimal policy by interacting with the environment. Additionally, a sharing experience module (SEM) is incorporated, enabling agents to encode actions and observations using long short-term memory (LSTM), allowing each agent to utilize the historical actions and observations of other agents. To demonstrate the superiority of MARDPG, three algorithmic benchmarks and four different system models are implemented. The numerical results demonstrate the impact of various parameters, such as the number of subcarriers, users, and the maximum tolerable E2E delay on the NSCs. Furthermore, different scenarios indicate that MARDPG outperforms the benchmarks, achieving approximately a 6 percent optimality gap and a 91 percent fairness for achievable rate among users.
CRIS-Team Scopus Importer:2024-05-31
Keywords: DRS; HNTNs; MARDPG; PD-NOMA; UAVs

Resource Allocation for UAV-Assisted Industrial IoT User with Finite Blocklength - Rezaei, Atefeh; Khalili, Ata; Dressler, Falko (IEEE Vehicular Technology Conference, 2023)
We consider a relay system empowered by an unmanned aerial vehicle (UAV) that facilitates downlink information delivery while adhering to finite blocklength requirements. The setup involves a remote controller transmitting information to both a UAV and an industrial Internet of Things (IIoT) or remote device, employing the non-orthogonal multiple access (NOMA) technique in the first phase. Subsequently, the UAV decodes and forwards this information to the remote device in the second phase. Our primary objective is to minimize the decoding error probability (DEP) at the remote device, which is influenced by the DEP at the UAV. To achieve this goal, we optimize the blocklength, transmission power, and location of the UAV. However, the underlying problem is highly non-convex and generally intractable to be solved directly. To overcome this challenge, we adopt an alternative optimization (AO) approach and decompose the original problem into three sub-problems. This approach leads to a sub-optimal solution, which effectively mitigates the non-convexity issue. In our simulations, we compare the performance of our proposed algorithm with baseline schemes. The results reveal that the proposed framework outperforms the baseline schemes, demonstrating its superiority in achieving lower DEP at the remote device. Furthermore, the simulation results illustrate the rapid convergence of our proposed algorithm, indicating its efficiency and effectiveness in solving the optimization problem.
CRIS-Team Scopus Importer:2024-12-16
Keywords: AO; DEP; Industrial IoT; NOMA; UAV relay

Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G Networks - Nouruzi, Ali; Rezaei, Atefeh; Khalili, Ata; Mokari, Nader; Javan, Mohammad Reza; Jorswieck, Eduard A.; Yanikomeroglu, Halim (IEEE International Conference on Communications, 2023)
In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e, throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.
CRIS-Team Scopus Importer:2023-12-15