telecommunications operators

Abstract: This paper presents XingChen-UniFE, a unified large audio language model (LALM) for general speech restoration (GSR) and target speaker extraction (TSE). The proposed model employs complementary semantic and acoustic representations through a dual-stream encoder, where a self-supervised learning model provides hierarchical semantic features and a learnable acoustic branch captures fine-grained structures from complex spectra. A bidirectional cross-attention module further facilitates information exchange between the two representations. The fused representation conditions the LALM for discrete speech token generation with task and speaker information. Experiments demonstrate strong performance on both GSR and TSE and robust generalization to unseen degradation conditions.

Abstract: In a quantum augmented network, sharing optical fiber with classical traffic requires quantum wavelength selection to balance logical fidelity, block acceptance, and classical data reception. Here we develop a wavelength-selection framework for an error-corrected three-stage quantum communication protocol. Under a thermal-background model with independent errors, an ideal one-photon receiver, and ideal recovery, we derive an accepted qubit channel and a key-independent lower bound on logical entanglement fidelity. Combining this bound with block acceptance gives an entanglement-fidelity yield bound, which we maximize over evaluated wavelengths subject to fidelity and classical link-budget requirements. For the Steane [[7,1,3]] code at 5 km per traversal and -10 dBm per classical channel, the largest fidelity certificate occurs near 1537 nm, whereas the largest yield bound occurs near 1551 nm. Along the 1.25 Gb/s classical link budget with 0.2% base error, the optimized yield bound falls below 10^-6 near 10 km per traversal, while fidelity remains certified at 0.99 or higher at longer spans. These results distinguish fidelity and yield constraints and identify certified operating regions for quantum-augmented networks sharing optical fiber.

Abstract: Response-supervision timers in the 5G Non-Access Stratum (NAS) bound the time allowed for signaling exchanges before retransmission or abnormal-case handling begins. In non-terrestrial networks, that interval can include satellite propagation, terrestrial transport, and processing by multiple core-network functions. Under current 3GPP specifications, the applicable timer value for satellite access is selected according to the satellite NG-RAN radio access technology (RAT) type. However, control paths within the same low Earth orbit (LEO) network can differ in propagation delay and processing load. This article reviews the relevant 3GPP timer semantics and traces the delay components covered by different timers through 5G registration. It organizes candidate refinements along two dimensions: configuration granularity and information scope. A registration case study illustrates the trade-off between avoiding premature timer expiry and recovering promptly from message loss. The article identifies the information and coordination that future specifications would need to address to support interoperable timer configuration.

Abstract: Low Earth Orbit (LEO) satellite constellations are expected to play a key role in future 6G Non-Terrestrial Networks (NTNs), where dense deployments enable cooperative multi-satellite processing for communication and positioning. While existing works have addressed communication and localization objectives separately, the correlation and alignment between these objectives remain largely unexplored. In this paper, we investigate the correlation between Spectral Efficiency (SE) and positioning accuracy in cooperative multi-LEO satellite clusters. We consider an uplink direct-to-device scenario in which multiple satellites jointly process user signals and evaluate cluster performance using SE and Position Error Bound (PEB) metrics. We derive Time-of-Arrival (TOA)-based and Doppler-based PEBs, while communication performance is quantified through the achievable SE under joint signal processing. Using realistic LEO constellation simulations, we demonstrate that for clusters of seven satellites, SE exhibits a strong negative correlation with Doppler-based PEB (correlation coefficient of $-0.72$). In contrast, its correlation with TOA-based PEB is notably weaker, ranging between $-0.55$ and $-0.37$. These findings suggest that communication-oriented LEO clusters are largely compatible with Doppler-based positioning, but not necessarily with TOA-based positioning.

Abstract: A semantic decision engine such as Jev can return a valid answer and still miss a network deadline, select an infeasible action or leave the service unverified. We systematize 139 paper families by decision interface, execution path and check ownership. Fifty families claim that their engine fits a control loop or time budget, but only four support the claim with matched measurement. Across all 139, four report deadline attainment. The gap concentrates where the decision has no deterministic computation step. Those 72 families make 22 of the claims, none supported, and name a coverage owner in only two. Bounded tests under one event model show that each gap can reverse an admission verdict. A decision that meets a 10 s budget for every isolated request meets it for none once decisions queue ahead of replayed execution times. The same engine passes one coverage check and fails another. We derive a minimum reporting record, design rules and a research agenda for admitting decision engines to control loops.

Abstract: This article investigates whether Layer-3 radio-protocol behavior can be represented by compact sequence models suitable for deployment inside the RAN. We introduce the RRC sequence engine (RSE), a hybrid architecture in which a sequence model predicts protocol-dependent message structure while deterministic components retain control over security-sensitive or configured fields and over transport containers. Using NR protocol traces, we show that protocol-aware tokenization, cross-stack context, and explicit placeholders matter more than general-sequence model scale. A purpose-built 11M-parameter Mamba engine achieves 0.90 exact match on the evaluated next-generation Node B (gNB)-side test set with a median generation latency of 115 ms, sufficient for some timers of Layer-3, outperforming a fine-tuned 0.6B-parameter model. We discuss applications in testing, simulation, deployment specialization, and future trainable 6G control planes, as well as the validation, latency, robustness, and security challenges that remain before operational use.

Abstract: While the amount of telecommunications services grows rapidly in the whole world, humans get potentially more exposed to Non-Ionizing Radiation (NIR) from a number of different sources. This paper has a number of goals, including monitoring radiation levels in an everyday situation, an investigation of how national and international regulations organs address NIR levels and a demonstration of scientific production trends regarding this topic. The work also presents a bibliometric study about the main scientific productions and trends related to NIR measurements, with focus on the field of Telecommunications. Another contribution is a case study in Natal, Brazil, where NIR levels were monitored in four distinct locations over a period of 24 hours each and compared to the current regulations. The result is a quantitative analysis of the amount of radiation that some populations might be exposed to in distinct moments of the day. Measurements were carried out in the proximity of an antenna spot, since it could represent a source of high NIR levels (a worst case of human exposure). This campaign differs from older studies by including a more intense usage of microwaves frequencies due to operating 4G and pre-5G systems in Brazil. Finally, a statistical study involving the measurements is conducted, concluding the analysis of how some population groups might be affected by NIR.

Abstract: Next-generation connectivity depends on data traversing multiple physical media within a single end-to-end path, yet research in optical fibre, free-space optical and radio wireless, non-terrestrial networks, and underwater communications has advanced largely in isolation. This fragmentation has a wider adverse impact on communication performance, deployment and adaption as the most demanding open problems in next-generation connectivity, cross-medium channel characterisation, transport-layer protocol design across heterogeneous latency regimes, and cross-domain orchestration, sit at the boundaries between domains and can only be addressed by bringing these challenges together in cross-domain systems. Space-air-ground integrated network research has begun to treat three domains analytically, and its recent extension to the sea surface represents the most ambitious multi-domain framework proposed to date, yet neither has produced experimental results, a deployed system, or a standards engagement. The subsurface and maritime domain remains absent from both. This paper proposes the connected heterogeneous all-medium integrated network (CHAIN), a framework that treats the undersea, maritime, terrestrial, aerial and space domains as a single design space. We identify an all-photonic network backbone as the unifying physical infrastructure, Artificial intelligence (AI)-driven orchestration as the cross-domain control layer, and the joints between domains as a unification challenge. We survey the state of the art across all domains and the existing cross-domain literature, develop the CHAIN framework and its technical pillars, characterise the five core open problems the field must resolve, and set out a research roadmap from near-term measurement campaigns and testbed validation through cross-medium field trials to global-scale deployment.

Abstract: As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization tool into a distributed and interconnected structural layer. Unlike previous network generations that mostly relied on centralized cloud analytics platforms, AI-native 6G networks operate across a dynamic, multi-domain edge-cloud continuum where data originates from heterogeneous sources including user devices, radio access networks, sensing infrastructures, and vertical applications. Centralizing this massive volume of data creates severe communication overhead, unacceptable latency bottlenecks, single points of failure, and complex cross-domain governance challenges. Consequently, decentralization becomes a fundamental architectural requirement for future 6G network intelligence and zero-touch operations. Security serves as the primary enabler of this decentralized paradigm. Critical security functions, such as real-time threat detection, physical-layer attack mitigation, slice protection, and intrusion detection, require immediate access to local context and telemetry before operational data loses its value. However, moving intelligence to the edge via collaborative paradigms like federated learning (FL) and decentralized FL (DFL) introduces complex trade-offs. System security cannot be addressed in isolation; it is deeply intertwined with equally important aspects like trustworthiness, explainability, and energy sustainability. Taking these aspects into account, this paper develops a unified perspective on decentralized intelligence for 6G, arguing that decentralization, trustworthiness, explainability, and sustainability must be designed jointly rather than treated as independent requirements

Abstract: The placement of microservices in the compute continuum plays a vital role in delivering services that comply with customers' needs, such as reduced latency, storage requirements, quality of service and availability. To achieve customers' needs in the geographically dispersed architecture of the compute continuum, Service Level Objectives (SLOs) have been largely used in decision-making to place microservices. However, because low-level SLOs increase the barrier to entry for continuum users, placement decisions based on high-level business vocabulary are required if the compute continuum is to be adopted at scale. This paper proposes an architecture for microservices placement decisions in the computing continuum utilizing high-level user intents described in natural language as input. The approach utilizes Generative Artificial Intelligence to translate the intents to low-level SLOs, which are used along with the infrastructure description to decide where different microservices that compose an application must be deployed so that SLOs are met. We implement and evaluate a prototype of the architecture to demonstrate the approach's feasibility.