Jinfeng Du

Fabrizio Carpi, Sivarama Venkatesan, Jinfeng Du et al.

Downlink massive multiple-input multiple-output (MIMO) precoding algorithms in frequency division duplexing (FDD) systems rely on accurate channel state information (CSI) feedback from users. In this paper, we analyze the tradeoff between the CSI feedback overhead and the performance achieved by the users in systems in terms of achievable rate. The final goal of the proposed system is to determine the beamforming information (i.e., precoding) from channel realizations. We employ a deep learning-based approach to design the end-to-end precoding-oriented feedback architecture, that includes learned pilots, users' compressors, and base station processing. We propose a loss function that maximizes the sum of achievable rates with minimal feedback overhead. Simulation results show that our approach outperforms previous precoding-oriented methods, and provides more efficient solutions with respect to conventional methods that separate the CSI compression blocks from the precoding processing.

Tze-Yang Tung, Homa Esfahanizadeh, Jinfeng Du et al.

Semantic communication, when examined through the lens of joint source-channel coding (JSCC), maps source messages directly into channel input symbols, where the measure of success is defined by end-to-end distortion rather than traditional metrics such as block error rate. Previous studies have shown significant improvements achieved through deep learning (DL)-driven JSCC compared to traditional separate source and channel coding. However, JSCC is impractical in existing communication networks, where application and network providers are typically different entities connected over general-purpose TCP/IP links. In this paper, we propose designing the source and channel mappings separately and sequentially via a novel multi-level reliability interface. This conceptual interface enables semi-JSCC at both the learned source and channel mappers and achieves many of the gains observed in existing DL-based JSCC work (which would require a fully joint design between the application and the network), such as lower end-to-end distortion and graceful degradation of distortion with channel quality. We believe this work represents an important step towards realizing semantic communications in wireless networks.

Merim Dzaferagic, Marco Ruffini, Nina Slamnik-Krijestorac et al.

Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical domain. This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.

ohaned Chraiti, Dmitry Chizhik, Jinfeng Du et al.

To establish reliable and long-range millimeter-wave (mmWave) communication, beamforming is deemed to be a promising solution. Although beamforming can be done in the digital and analog domains, both approaches are hindered by several constraints when it comes to mmWave communications. For example, performing fully digital beamforming in mmWave systems involves using many radio frequency (RF) chains, which are expensive and consume high power. This necessitates finding more efficient ways for using fewer RF chains while taking advantage of the large antenna arrays. One way to overcome this challenge is to employ (partially or fully) analog beamforming through proper configuration of phase-shifters. Existing works on mmWave analog beam design either rely on the knowledge of the channel state information (CSI) per antenna within the array, require a large search time (e.g., exhaustive search) or do not guarantee a minimum beamforming gain (e.g., codebook based beamforming). In this paper, we propose a beam design technique that reduces the search time and does not require CSI while guaranteeing a minimum beamforming gain. The key idea derives from observations drawn from real-life measurements. It was observed that for a given propagation environment (e.g., coverage area of a mmWave BS) the azimuthal angles of dominant signals could be more probable from certain angles than others. Thus, pre-collected measurements could used to build a beamforming codebook that regroups the most probable beam designs. We invoke Bayesian learning for measurements clustering. We evaluate the efficacy of the proposed scheme in terms of building the codebook and assessing its performance through real-life measurements. We demonstrate that the training time required by the proposed scheme is only 5% of that of exhaustive search. This crucial gain is obtained while achieving a minimum targeted beamforming gain.