Yaya Etiabi, Wessam Ajib, El Mehdi Amhoud, Halima Elbiaze
The evolution from 5G to 6G networks marks a transition toward the "intelligence of everything." While 5G enables enhanced connectivity, 6G aspires to embed intelligence directly into communication infrastructures, creating systems that are adaptive, context aware, and capable of autonomous decision making. Realizing such systems depends on harmonizing three fundamental functions that compete for shared network resources and often pursue conflicting goals: (i) sensing for environmental and contextual information acquisition, (ii) communication for reliable and timely information exchange, and (iii) computation for information processing and decision making. This harmonization necessitates shifting from independent optimization to their joint design as a unified system, a paradigm known as Integrated Sensing, Communication, and Computation (ISCC). In parallel, AI has emerged both as an enabling tool for ISCC optimization and as a workload whose requirements influence ISCC system design. This survey comprehensively examines ISCC from this dual perspective by distinguishing between AI-aided ISCC, in which learning-based methods support the joint design and optimization of sensing, communication, and computation, and AI-oriented ISCC, in which these functions are jointly configured to support learning, inference, and distributed intelligence. The survey further examines the co-design of AI and ISCC, where AI requirements and ISCC constraints jointly shape system and model decisions. It further provides a comparative synthesis of the resulting design approaches, system architectures, performance metrics, and evaluation practices, relates these developments to application-specific requirements across different domains, and identifies open research problems toward scalable, adaptive, and trustworthy AI-ISCC systems.
Future 6G wireless networks are expected to evolve toward AI-native systems that support intelligent, environment-aware services, including positioning and mapping. Current research has already integrated AI into several aspects of communication, particularly wireless channel estimation. However, most learning-based channel estimators focus on communication-oriented objectives rather than explicitly estimating multipath parameters such as delay, angle of arrival (AoA), and angle of departure (AoD). These parameters provide essential geometric information for downstream tasks including positioning and radio simultaneous localization and mapping (SLAM). In this work, we propose a Spectral-Convolutional Factorized Transformer (SCFT) for joint multipath component (MPC) estimation from noisy complex channel estimates and physical beam-pair descriptors. SCFT combines local spectral convolution, permutation-equivariant beam-set attention, and a query-based Transformer decoder to detect and classify line-of-sight (LoS) and non-line-of-sight (NLoS) paths and estimate their observed delays, AoAs, and AoDs. Experiments on realistic ray-tracing data show that SCFT reduces the generalized optimal sub-pattern assignment (GOSPA) error by 13.4% compared with ResNet, the strongest competing baseline. We further demonstrate this advantage in independent snapshot positioning and radio SLAM, where SCFT reduces position RMSE by 18.5% and 65.0%, respectively, relative to ResNet. These findings highlight the importance of explicitly recovering multipath geometry, as improvements in MPC estimation can translate into substantial gains in downstream tasks.
Federated learning traditionally relies on a fixed aggregation rule despite shifting client state and wireless dynamics. We present FedMaJ, a framework that dynamically selects aggregation rules via Majority Judgment voting. During voting rounds, clients evaluate candidate models across performance, resource utilization, channel condition, and fairness metrics. The server adopts the majority-preferred rule and retains it until the next vote, scheduled dynamically based on accuracy trends. Evaluated in over-the-air federated learning with explicit energy and resource cost modeling, synthetic and Fashion-MNIST experiments show that FedMaJ achieves competitive learning performance and superior aggregation-influence fairness. Under extreme-evaluation perturbations, Majority Judgment yields significantly more stable rule selections than mean-based scoring, establishing ordinal voting as a robust control layer for adaptive aggregation.