Mehdi Rasti

Hosein Zarini, Narges Gholipoor, Mohamad Robat Mili et al.

Passive beamforming in reconfigurable intelligent surfaces (RISs) enables a feasible and efficient way of communication when the RIS reflection coefficients are precisely adjusted. In this paper, we present a framework to track the RIS reflection coefficients with the aid of deep learning from a time-series prediction perspective in a terahertz (THz) communication system. The proposed framework achieves a two-step enhancement over the similar learning-driven counterparts. Specifically, in the first step, we train a liquid state machine (LSM) to track the historical RIS reflection coefficients at prior time steps (known as a time-series sequence) and predict their upcoming time steps. We also fine-tune the trained LSM through Xavier initialization technique to decrease the prediction variance, thus resulting in a higher prediction accuracy. In the second step, we use ensemble learning technique which leverages on the prediction power of multiple LSMs to minimize the prediction variance and improve the precision of the first step. It is numerically demonstrated that, in the first step, employing the Xavier initialization technique to fine-tune the LSM results in at most 26% lower LSM prediction variance and as much as 46% achievable spectral efficiency (SE) improvement over the existing counterparts, when an RIS of size 11x11 is deployed. In the second step, under the same computational complexity of training a single LSM, the ensemble learning with multiple LSMs degrades the prediction variance of a single LSM up to 66% and improves the system achievable SE at most 54%.

Farzad Azizi, Benyamin Teymuri, Rojin Aslani et al.

This paper focuses on improving the resource allocation algorithm in terms of packet delivery ratio (PDR), i.e., the number of successfully received packets sent by end devices (EDs) in a long-range wide-area network (LoRaWAN). Setting the transmission parameters significantly affects the PDR. Employing reinforcement learning (RL), we propose a resource allocation algorithm that enables the EDs to configure their transmission parameters in a distributed manner. We model the resource allocation problem as a multi-armed bandit (MAB) and then address it by proposing a two-phase algorithm named MIX-MAB, which consists of the exponential weights for exploration and exploitation (EXP3) and successive elimination (SE) algorithms. We evaluate the MIX-MAB performance through simulation results and compare it with other existing approaches. Numerical results show that the proposed solution performs better than the existing schemes in terms of convergence time and PDR.

Atefeh Termehchi, Mehdi Rasti

In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joint design problem of an event-triggered control and an energy-efficient resource allocation in a fifth generation (5G) wireless network. We formally state the problem as a multi-objective optimization one, aiming to minimize the number of updates on the actuators' input and the power consumption in the downlink transmission. To address the problem, we propose a model-free hierarchical reinforcement learning approach \textcolor{blue}{with uniformly ultimate boundedness stability guarantee} that learns four policies simultaneously. These policies contain an update time policy on the actuators' input, a control policy, and energy-efficient sub-carrier and power allocation policies. Our simulation results show that the proposed approach can properly control a simulated ICPS and significantly decrease the number of updates on the actuators' input as well as the downlink power consumption.

Ashkan Goharfar, Jaber Babaki, Mehdi Rasti et al.

With the emerge of the Internet of Things (IoT), localization within indoor environments has become inevitable and has attracted a great deal of attention in recent years. Several efforts have been made to cope with the challenges of accurate positioning systems in the presence of signal interference. In this paper, we propose a novel deep learning approach through Gradient Boosting Enhanced with Step-Wise Feature Augmentation using Artificial Neural Network (AugBoost-ANN) for indoor localization applications as it trains over labeled data. For this purpose, we propose an IoT architecture using a star network topology to collect the Received Signal Strength Indicator (RSSI) of Bluetooth Low Energy (BLE) modules by means of a Raspberry Pi as an Access Point (AP) in an indoor environment. The dataset for the experiments is gathered in the real world in different periods to match the real environments. Next, we address the challenges of the AugBoost-ANN training which augments features in each iteration of making a decision tree using a deep neural network and the transfer learning technique. Experimental results show more than 8\% improvement in terms of accuracy in comparison with the existing gradient boosting and deep learning methods recently proposed in the literature, and our proposed model acquires a mean location accuracy of 0.77 m.

Mehdi Monemi, Mohammad Amir Fallah, Mehdi Rasti et al.

Beam squint poses a fundamental challenge in wideband hybrid beamforming, particularly for mmWave and THz systems that demand both ultra-wide bandwidth and high directional beams. While conventional phase shifter-based beamformers may offer partial mitigation, True Time Delay (TTD) units provide a fundamentally more effective solution by enabling frequency-independent beam steering. However, the high cost of TTD units has recently driven much interest in Sparse-TTD architectures, which combine a limited number of TTDs with a higher number of conventional PSs to balance performance and cost. This paper provides a critical examination of beam squint mitigation strategies in wideband hybrid beamformers, comparing Full-TTD, Sparse-TTD, and Non-TTD architectures. We analyze recent Non-TTD approaches, specifically the scheme leveraging the wideband beam gain (WBBG) concept, evaluating their performance and cost characteristics against TTD-based solutions. A key focus is placed on the practical limitations of Sparse-TTD architectures, particularly the often-overlooked requirement for wideband PSs operating alongside TTDs, which can significantly impact performance and implementation cost in real-world scenarios, especially for ultra-wideband applications. Finally, we conduct a cost-performance analysis to examine the trade-offs inherent in each architecture and provide guidance on selecting the most suitable hybrid beamforming structure for various fractional bandwidth regimes.

Fahimeh Aghaei, Mehdi Monemi, Mehdi Rasti et al.

Motivated by increasing wireless capacity demands and 6G advancements, the newly defined Frequency Range 3 (FR3, 7.125-24.25 GHz), also known as the upper mid-band, has emerged as a promising spectrum candidate. It offers a balance between the large bandwidth potential of millimeter-wave bands and the favorable propagation characteristics of sub-6 GHz bands. As a result, the upper mid-band presents a strong opportunity to enhance both coverage and capacity, particularly for 6G systems and Cellular Vehicle-to-Base Station (C-V2B) communications. Harnessing this potential, however, requires addressing key technical challenges through accurate and realistic channel modeling across diverse urban environments, including Suburban, Urban, and HighRise Urban scenarios. To this end, we employ a ray-tracing tool to characterize downlink propagation and enable detailed channel modeling for reliable C-V2B links. We evaluate data rate performance across FR1 (sub-6 GHz), FR3, and FR2 (mmWave) bands using antenna array configurations designed for different urban environments. The results show that, under equal aperture sizes, FR3 achieves higher data rates than FR2 for cell-edge User Equipment (UEs) in both interference-free and full-interference scenarios, indicating that the additional array gain at mmWave is insufficient to fully compensate for the severe experienced path loss. Integrating one-hand-grip pedestrian UEs model into ray tracer shows that transitioning from vehicular to pedestrian UEs results in negligible differences in coverage probability (about 1\%--3\%) across all frequencies, with the minimum differences observed in FR3, particularly at 8.2 GHz.

Seonghoon Yoo, Seulhyun Kwon, Kawon Han et al.

Distributed integrated sensing and communication (D-ISAC) enables multiple spatially distributed nodes to cooperatively perform sensing and communication. However, achieving coherent cooperation across distributed nodes is challenging due to practical impairments. In particular, residual phase synchronization errors result in imperfect channel state information (CSI), while angle-of-arrival (AoA) uncertainties induce radar cross-section (RCS) variations. These impairments jointly degrade target detection performance in D-ISAC systems. To address these challenges jointly, this paper proposes a robust beamforming design for coherent D-ISAC systems. Multiple distributed nodes coordinated by a central unit (CU) jointly perform joint transmission coordinated multipoint (JT-CoMP) communication and multi-input multi-output (MIMO) radar sensing to detect a target while serving multiple user equipments (UEs). We formulate a robust beamforming problem that maximizes the expected Kullback-Leibler divergence (KLD) under statistical RCS variations while satisfying system power and per-user minimum signal-to-interference-plus-noise ratio (SINR) constraints under imperfect CSI to ensure the communication quality of service (QoS). The problem is solved using semidefinite relaxation (SDR) and successive convex approximation (SCA), and numerical results show that the proposed method achieves up to 3 dB signal-to-clutter-plus-noise ratio (SCNR) gain over the conventional beamforming schemes for target detection while maintaining the required communication QoS.

Elaheh Ataeebojd, Nhan Thanh Nguyen, Seonghoon Yoo et al.

Stacked intelligent metasurfaces (SIMs) provide wave-domain degrees of freedom that can empower integrated sensing and communication (ISAC) through flexible beampattern synthesis and interference management, while reducing hardware cost. In this paper, we investigate energy-efficient resource allocation for a downlink SIM-aided multi-user ISAC system that supports the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) via puncturing, while simultaneously illuminating sensing targets. We formulate an energy efficiency (EE) maximization problem that jointly optimizes resource block (RB) allocation, transmit power control, and SIM phase shifts. The formulated problem is highly challenging due to the large number of variables optimized on different time scales. To overcome this, we leverage the intrinsic two-timescale structure induced by the puncturing approach to decompose the original problem into two tractable subproblems: EE maximization for eMBB users in each time slot and EE maximization for URLLC users and sensing targets in each mini-slot. To address each subproblem, we develop an iterative algorithm that transforms the original non-convex formulation into a sequence of tractable subproblems, yielding convex updates for RB allocation and power control, along with low-complexity updates for SIM phase shifts. Simulation results show that the proposed design achieves up to 230% improvement in EE over a No-SIM baseline. In addition, it requires significantly fewer transmit antennas than conventional BS architectures, while preserving the EE achieved and satisfying the communication and sensing quality of service (QoS) requirements. Moreover, the results reveal fundamental trade-offs between EE and heterogeneous QoS requirements across communication and sensing functionalities.

Mohammad Amir Fallah, Mehdi Monemi, Mehdi Rasti et al.

Three-dimensional (3D) spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within a very small volume in both radial and angular domains in the near-field zone. Recently the implementation of channel-state-information (CSI)-independent machine learning (ML)-based approaches have been developed for effective SBF using extremely large-scale programmable metasurface (ELPMs). These methods involve dividing the ELPMs into subarrays and independently training them with Deep Reinforcement Learning to jointly focus the beam at the desired focal point (DFP). This paper explores near-field SBF using ELPMs, addressing challenges associated with lengthy training times resulting from independent training of subarrays. To achieve a faster CSI-independent solution, inspired by the correlation between the beamfocusing matrices of the subarrays, we leverage transfer learning techniques. First, we introduce a novel similarity criterion based on the phase distribution image (PDI) of subarray apertures. Then we devise a subarray policy propagation scheme that transfers the knowledge from trained to untrained subarrays. We further enhance learning by introducing quasi-liquid layers as a revised version of the adaptive policy reuse technique. We show through simulations that the proposed scheme improves the training speed about 5 times. Furthermore, for dynamic DFP management, we devised a DFP policy blending process, which augments the convergence rate up to 8-fold.

Fahimeh Aghaei, Mehdi Monemi, Mehdi Rasti et al.

Driven by the escalating demand for wireless capacity and advancements in 6G research, the new Frequency Range 3 (FR3) referred to upper mid-band (7.125-24.25 GHz) has emerged as a highly compelling spectrum candidate. This range offers a trade-off exploiting the high bandwidth capabilities of millimeter wave frequencies and the superior propagation characteristics of sub-6 GHz bands. As such, the upper mid-band presents an opportunity to enhance both coverage and capacity particularly in the context of 6G and Cellular Vehicle-to-Base Station (C-V2B). Crucially, realizing this potential requires overcoming technical challenges through accurate and realistic channel modeling, especially in dense, high-rise urban environments. To address this, we employ a ray-tracing tool to analyze downlink propagation characteristics, enabling detailed channel modeling for reliable C-V2B communication. Our analysis evaluates the signal-to-noise ratio (SNR) and signal-to-interference-plus-noise ratio (SINR) across sub-6 GHz, FR3, and mmWave bands using antenna array configurations designed for high-rise urban areas. Results show that, under equal aperture sizes across frequencies, FR3 achieves superior SNR compared to mmWave in interference-free conditions. Moreover, under the full-interference case, FR3 yields higher SINR for cell-edge User Equipment (UEs). This indicates that the increased array gain at mmWave cannot fully compensate for the severe path loss experienced by cell-edge UEs.

Mehdi Rasti, Elaheh Ataeebojd, Shiva Kazemi Taskooh et al.

Current spectrum-sharing frameworks struggle with adaptability, often being either static or insufficiently dynamic. They primarily emphasize temporal sharing while overlooking spatial and spectral dimensions. We propose an adaptive, AI-driven spectrum-sharing framework within the O-RAN architecture, integrating discriminative and generative AI (GenAI) to forecast spectrum needs across multiple timescales and spatial granularities. A marketplace model, managed by an authorized spectrum broker, enables operators to trade spectrum dynamically, balancing static assignments with real-time trading. GenAI enhances traffic prediction, spectrum estimation, and allocation, optimizing utilization while reducing costs. This modular, flexible approach fosters operator collaboration, maximizing efficiency and revenue. A key research challenge is refining allocation granularity and spatio-temporal dynamics beyond existing models.

Hosein Zarini, Ata Khalili, Hina Tabassum et al.

In this paper, we develop a resource allocation framework to optimize the downlink transmission of a backhaul-aware multi-cell cognitive radio network (CRN) which is enabled with multi-carrier non-orthogonal multiple access (MC-NOMA). The considered CRN is composed of a single macro base station (MBS) and multiple small BSs (SBSs) that are referred to as the primary and secondary tiers, respectively. For the primary tier, we consider orthogonal frequency division multiple access (OFDMA) scheme and also Quality of Service (QoS) to evaluate the user satisfaction. On the other hand in secondary tier, MC-NOMA is employed and the user satisfaction for web, video and audio as popular multimedia services is evaluated by Quality-of-Experience (QoE). Furthermore, each user in secondary tier can be served simultaneously by multiple SBSs over a subcarrier via Joint Transmission (JT). In particular, we formulate a joint optimization problem of power control and scheduling (i.e., user association and subcarrier allocation) in secondary tier to maximize total achievable QoE for the secondary users. An efficient resource allocation mechanism has been developed to handle the non-linear form interference and to overcome the non-convexity of QoE serving functions. The scheduling and power control policy leverage on Augmented Lagrangian Method (ALM). Simulation results reveal that proposed solution approach can control the interference and JT-NOMA improves total perceived QoE compared to the existing schemes.