Mehdi Monemi

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.

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.