Early Acceptance Matching Game for User-Centric Clustering in Scalable Cell-free MIMO Networks

The canonical setup is the primary approach adopted in cell-free multiple-input multiple-output (MIMO) networks, in which all access points (APs) jointly serve every user equipment (UE). This approach is not scalable in terms of computational complexity and fronthaul signaling becoming impractical in large networks. This work adopts a user-centric approach, a scalable alternative in which only a set of preferred APs jointly serve a UE. Forming the optimal cluster of APs for each UE is a challenging task, especially, when it needs to be dynamically adjusted to meet the quality of service (QoS) requirements of the UE. This complexity is even exacerbated when considering the constrained fronthaul capacity of the UE and the AP. We solve this problem with a novel many-to-many matching game. More specifically, we devise an early acceptance matching algorithm, which immediately admits or rejects UEs based on their requests and available radio resources. The proposed solution significantly reduces the fronthaul signaling while satisfying the maximum of UEs in terms of requested QoS compared to state-of-the-art approaches.

Soft Partitioning of Latent Space for Semantic Channel Equalization

Semantic channel equalization has emerged as a solution to address language mismatch in multi-user semantic communications. This approach aims to align the latent spaces of an encoder and a decoder which were not jointly trained and it relies on a partition of the semantic (latent) space into atoms based on the the semantic meaning. In this work we explore the role of the semantic space partition in scenarios where the task structure involves a one-to-many mapping between the semantic space and the action space. In such scenarios, partitioning based on hard inference results results in loss of information which degrades the equalization performance. We propose a soft criterion to derive the atoms of the partition which leverages the soft decoder's output and offers a more comprehensive understanding of the semantic space's structure. Through empirical validation, we demonstrate that soft partitioning yields a more descriptive and regular partition of the space, consequently enhancing the performance of the equalization algorithm.

Latent Space Alignment for Semantic Channel Equalization

We relax the constraint of a shared language between agents in a semantic and goal-oriented communication system to explore the effect of language mismatch in distributed task solving. We propose a mathematical framework, which provides a modelling and a measure of the semantic distortion introduced in the communication when agents use distinct languages. We then propose a new approach to semantic channel equalization with proven effectiveness through numerical evaluations.

Cooperative Sensing of Side Lobes Interference for mmWave Blockages Localization and Mapping

Radio localization and sensing are anticipated to play a crucial role in enhancing radio resource management in future networks. In this work, we focus on millimeter-wave communications, which are highly vulnerable to blockages, leading to severe attenuation and performance degradation. In a previous work, we proposed a novel mechanism that senses the radio environment to estimate the angular position of a moving blocker with respect to the sensing node. Building upon this foundation, this paper investigates the benefits of cooperation between different entities in the network by sharing sensed data to jointly locate the moving blocker while mapping the interference profile to probe the radio environment. Numerical evaluations demonstrate that cooperative sensing can achieve a more precise location estimation of the blocker as it further allows accurate estimation of its distance rather than its relative angular position only, leading to effective assessment of the blocker direction, trajectory and possibly, its speed, and size.

Distributed Intelligent Integrated Sensing and Communications: The 6G-DISAC Approach

This paper introduces the concept of Distributed Intelligent integrated Sensing and Communications (DISAC), which expands the capabilities of Integrated Sensing and Communications (ISAC) towards distributed architectures. Additionally, the DISAC framework integrates novel waveform design with new semantic and goal-oriented communication paradigms, enabling ISAC technologies to transition from traditional data fusion to the semantic composition of diverse sensed and shared information. This progress facilitates large-scale, energy-efficient support for high-precision spatial-temporal processing, optimizing ISAC resource utilization, and enabling effective multi-modal sensing performance. Addressing key challenges such as efficient data management and connect-compute resource utilization, 6G- DISAC stands to revolutionize applications in diverse sectors including transportation, healthcare, and industrial automation. Our study encapsulates the project vision, methodologies, and potential impact, marking a significant stride towards a more connected and intelligent world.

Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach

Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requirements on future 6G mobile network architectures. To meet these demands, it is essential to transcend classical boundaries and integrate communication, computation, control, and intelligence. This paper presents the 6G-GOALS approach to goal-oriented and semantic communications for AI-Native 6G Networks. The proposed approach incorporates semantic, pragmatic, and goal-oriented communication into AI-native technologies, aiming to facilitate information exchange between intelligent agents in a more relevant, effective, and timely manner, thereby optimizing bandwidth, latency, energy, and electromagnetic field (EMF) radiation. The focus is on distilling data to its most relevant form and terse representation, aligning with the source's intent or the destination's objectives and context, or serving a specific goal. 6G-GOALS builds on three fundamental pillars: i) AI-enhanced semantic data representation, sensing, compression, and communication, ii) foundational AI reasoning and causal semantic data representation, contextual relevance, and value for goal-oriented effectiveness, and iii) sustainability enabled by more efficient wireless services. Finally, we illustrate two proof-of-concepts implementing semantic, goal-oriented, and pragmatic communication principles in near-future use cases. Our study covers the project's vision, methodologies, and potential impact.

Semantic Channel Equalizer: Modelling Language Mismatch in Multi-User Semantic Communications

We consider a multi-user semantic communications system in which agents (transmitters and receivers) interact through the exchange of semantic messages to convey meanings. In this context, languages are instrumental in structuring the construction and consolidation of knowledge, influencing conceptual representation and semantic extraction and interpretation. Yet, the crucial role of languages in semantic communications is often overlooked. When this is not the case, agent languages are assumed compatible and unambiguously interoperable, ignoring practical limitations that may arise due to language mismatching. This is the focus of this work. When agents use distinct languages, message interpretation is prone to semantic noise resulting from critical distortion introduced by semantic channels. To address this problem, this paper proposes a new semantic channel equalizer to counteract and limit the critical ambiguity in message interpretation. Our proposed solution models the mismatch of languages with measurable transformations over semantic representation spaces. We achieve this using optimal transport theory, where we model such transformations as transportation maps. Then, to recover at the receiver the meaning intended by the teacher we operate semantic equalization to compensate for the transformation introduced by the semantic channel, either before transmission and/or after the reception of semantic messages. We implement the proposed approach as an operation over a codebook of transformations specifically designed for successful communication. Numerical results show that the proposed semantic channel equalizer outperforms traditional approaches in terms of operational complexity and transmission accuracy.

Sensing of Side Lobes Interference for Blockage Prediction in Dense mmWave Networks

The integration of sensing capability in the design of wireless communication systems is foreseen as a key enabler for efficient radio resource management in next-generation networks. This paper focuses on millimeter-wave communications, which are subject to severe attenuation due to blockages, ultimately detrimental to system performance. In this context, the sensing functionality can allow measuring or even imaging the wireless environment allowing anticipation of possible link failures, thus enabling proactive resource reallocation such as handover. This work proposes a novel mechanism for opportunistic environment sensing, which leverages existing network infrastructure with low complexity. More specifically, our approach exploits the fluctuations of interference, perceived in antenna side lobes, to detect local activity due to a moving blocker around the reference communication link. Numerical evaluations show that the proposed method is promising as it allows effective assessment of the blocker direction, trajectory and possibly, its location, speed, and size.

Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks

This paper addresses the efficient management of Mobile Access Points (MAPs), which are Unmanned Aerial Vehicles (UAV), in 5G networks. We propose a two-level hierarchical architecture, which dynamically reconfigures the network while considering Integrated Access-Backhaul (IAB) constraints. The high-layer decision process determines the number of MAPs through consensus, and we develop a joint optimization process to account for co-dependence in network self-management. In the low-layer, MAPs manage their placement using a double-attention based Deep Reinforcement Learning (DRL) model that encourages cooperation without retraining. To improve generalization and reduce complexity, we propose a federated mechanism for training and sharing one placement model for every MAP in the low-layer. Additionally, we jointly optimize the placement and backhaul connectivity of MAPs using a multi-objective reward function, considering the impact of varying MAP placement on wireless backhaul connectivity.

Dual-Attention Deep Reinforcement Learning for Multi-MAP 3D Trajectory Optimization in Dynamic 5G Networks

5G and beyond networks need to provide dynamic and efficient infrastructure management to better adapt to time-varying user behaviors (e.g., user mobility, interference, user traffic and evolution of the network topology). In this paper, we propose to manage the trajectory of Mobile Access Points (MAPs) under all these dynamic constraints with reduced complexity. We first formulate the placement problem to manage MAPs over time. Our solution addresses time-varying user traffic and user mobility through a Multi-Agent Deep Reinforcement Learning (MADRL). To achieve real-time behavior, the proposed solution learns to perform distributed assignment of MAP-user positions and schedules the MAP path among all users without centralized user's clustering feedback. Our solution exploits a dual-attention MADRL model via proximal policy optimization to dynamically move MAPs in 3D. The dual-attention takes into account information from both users and MAPs. The cooperation mechanism of our solution allows to manage different scenarios, without a priory information and without re-training, which significantly reduces complexity.