José Carlos Marinello

Extra-large scale MIMO (XL-MIMO) is a key technology for meeting sixth-generation (6G) requirements for high-rate connectivity and uniform quality of service (QoS); however, its deployment is challenged by the prohibitive complexity of resource management based on instantaneous channel state information (CSI). To address this intractability, this work derives novel closed-form deterministic signal-to-interference-plus-noise ratio (SINR) expressions for both centralized and distributed uplink operations. Valid for Rician fading channels with minimum mean square error (MMSE) receive combining and MMSE channel estimation, these expressions depend exclusively on long-term channel statistics, providing a tractable alternative to computationally expensive instantaneous CSI-driven optimization. Building on these results, we develop statistical-CSI-based algorithms for joint subarray selection, users scheduling, and pilot assignment, leveraging the derived SINR approximations to maximize the minimum spectral efficiency (SE) among scheduled users while preserving computational tractability. The proposed framework exploits the spatial sparsity of user equipment (UE) visibility regions (VRs) to enable more aggressive pilot reuse than is possible in conventional massive MIMO. Numerical results validate the high accuracy of the derived SINR approximations and demonstrate that the proposed algorithms significantly enhance fairness and throughput in crowded scenarios.

The rapid growth of Internet of Things (IoT) applications, such as smart cities and industrial automation, necessitates efficient massive machine-type communication (mMTC) solutions for sixth-generation (6G) networks. Traditional access protocols struggle to accommodate many devices with sporadic activity and low data volumes, leading to increased latency and collisions. This paper proposes a novel grant-free random access (RA) protocol that leverages Reconfigurable Intelligent Surfaces (RIS) to enhance connectivity and channel conditions in mMTC scenarios. The protocol consists of two stages: first, the base station transmits downlink (DL) pilots while the RIS sweeps its reflection configurations, enabling devices to identify optimal transmission opportunities. In the second stage, devices utilize these opportunities to transmit data, minimizing collisions and improving throughput. By employing a predefined codebook of reflection configurations, previously optimized to thoroughly scan the covered space in a few rounds of multiple narrow beams, the protocol reduces overhead and meets a maximum latency constraint of 20 ms under certain reliability constraints, demonstrating significant performance improvements over existing methods. This approach enhances network efficiency while supporting a vast number of devices and encourages the deployment of RIS technology in future wireless communication systems.

This work proposes an Index Modulation-based Orthogonal Chirp Division Multiplexing (IM-OCDM) waveform for integrated sensing and communication (ISAC) in future 6G systems. By embedding Linear Frequency Modulated (LFM) radar pulses into the inactive subchirps of a sparse IM-OCDM grid, the design enables joint radar-communication operation without spectral overhead. To accurately identify radar and communication subchirps, we propose deep learning classifiers operating on raw time-domain I/Q sequences. Two architectures are investigated: a CNN with gated recurrent units and attention (CNN-GRU-Att), and a CNN with Transformer-based attention including explicit SNR embedding (CNN-Transformer-Att). A large-scale dataset of 44,000 samples was generated across SNR levels (0-20 dB) and radar-to-communication power ratios from 0 to 3 dB-where radar signals are increasingly dominant. Results show that the Transformer-based model consistently outperforms the GRU-based counterpart in all regimes, reaching up to 96.3% F1-score and 99.3% Area Under ROC Curve (AUROC) under high SNR. Moreover, the Transformer exhibits improved robustness under noise and signal imbalance, confirming its suitability for dynamic ISAC environments. The proposed solution enhances spectral efficiency, sensing accuracy, and subchirp classification, providing a scalable physical-layer framework for next-generation ISAC systems

Reconfigurable Intelligent Surfaces (RISs) are emerging as a key technology for future wireless systems, enabling control over the propagation environment. While most research focuses on enhancing desired signals, this paper investigates the use of RIS for destructive beamforming-intentionally creating signal nulls at specific locations. This capability is crucial for applications like physical layer security, interference mitigation, and deliberate jamming. We formulate the problem of minimizing the signal-to-noise ratio (SNR) at a target user equipment (UE) in both single-input single-output (SISO) and multiple-input single-output (MISO) scenarios by optimizing the RIS phase shifts under practical unit-modulus constraints. For the MISO case, we propose an efficient method based on manifold optimization techniques, specifically the Trust-Region algorithm, to solve the non-convex optimization problem. This approach utilizes multiple random initializations to enhance the likelihood of finding a high-quality solution. Simulation results, considering realistic transmit power and a channel model incorporating Rician fading, realistic path losses, and shadow fading, demonstrate that the proposed approach can significantly suppress the signal power at the target UE, with the suppression deepening as the number of RIS elements increases, validating the potential of RIS for precise destructive beamforming. For instance, with 256 RIS elements, a suppression of approximately 14.1 dB compared to the scenario without RIS is achieved.

Integrated sensing and communication (ISAC) systems rely on accurate localization algorithms, commonly implemented via computationally demanding methods such as the multiple signal classification (MUSIC) algorithm. Typically, finegrid searches required by MUSIC lead to high computational complexity, posing practical limitations for real-time implementations. This paper introduces bicubic interpolation applied to coarse-grid MUSIC pseudo-spectra to significantly reduce complexity without sacrificing localization accuracy. The search grid is initially defined over a uniformly spaced Cartesian coordinate system and subsequently transformed into polar coordinates to enable accurate steering vector evaluation within the MUSIC algorithm. This formulation allows for the direct application of interpolation techniques to the resulting pseudo-spectrum. The simulation results demonstrate that the proposed interpolation strategy substantially decreases the computational load while maintaining competitive precision compared to traditional finegrid MUSIC methods. Performance evaluations in terms of root mean square error (RMSE), computational time and computational complexity analysis (flops) support the effectiveness and potential application of the proposed approach in future ISAC deployments.

Manifold optimization (MO) is a powerful mathematical framework that can be applied to optimize functions over complex geometric structures, which is particularly useful in advanced wireless communication systems, such as reconfigurable intelligent surface (RIS)-aided massive MIMO (mMIMO) and extra-large scale massive MIMO (XL-MIMO) systems. MO provides a structured approach to tackling complex optimization problems. By leveraging the geometric properties of the manifold, more efficient and effective solutions can be found compared to conventional optimization methods. This paper provides a tutorial on MO technique and provides some applications of MO in the context of wireless communications systems. In particular, to corroborate the effectiveness of MO methodology, we explore five application examples in RIS-aided mMIMO system, focusing on fairness, energy efficiency (EE) maximization, intracell pilot reuse interference mitigation, and grant-free (GF) random access (RA).

The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols.

This chapter delves into the critical aspects of optimizing energy efficiency (EE) in active reconfigurable intelligent surface (RIS)-assisted massive MIMO (M-MIMO) wireless communication systems. We develop a comprehensive and unified theoretical framework to analyze the boundaries of EE within M-MIMO systems integrated with active RIS while adhering to practical constraints. Our research focuses on a formulated EE optimization problem aiming to maximize the EE for active RIS-assisted M-MIMO communication systems. Our goal is to strategically find the number of active RIS elements for outperforming the EE attainable by an entirely passive RIS. Besides, the proposed novel solution has been tailored to the innovative problem. The formulation and solution design consider analytical optimization techniques, such as lagrangian dual transform (LDT) and fractional programming (FP) optimization, facilitating the effective implementation of RIS-aided M-MIMO applications in real-world settings. In particular, our results show that the proposed algorithm can provide up to 120% higher EE than the entirely passive RIS. Besides, we found that the active RIS can operate with less than half of the reflecting elements for the entirely passive RIS. Finally, in view of active RIS achieving the complete utilization of amplification power available, it should be equipped with a reasonable number of reflecting elements above N = 49.