arXiv subjects

Rahim Tafazolli

Publications and source records attributed to Rahim Tafazolli.

At least 19 recordsLinked to original sources

arXiv2026

Towards a Connected Heterogeneous All-Medium Integrated Network (CHAIN) for Converged Connectivity Across Land, Sea, Air, and Space

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.

cs.NI↗

arXiv2026

From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity

The ambitious requirements of sixth-generation (6G) networks are driving communication systems from reliable bit delivery toward meaning-aware and task-oriented connectivity. Large models (LMs), with strong multimodal understanding and generation capabilities, have accelerated this shift and made semantic communication (SemCom) increasingly practical. Yet current LM-driven SemCom remains fragmented: semantic representations are typically tied to specific modalities, models, or tasks. While the bit provides a universal unit for digital transport, there is still no analogous unit for representing and processing semantics, which limits interoperability, theoretical unification, and scalable system design. We argue that tokens provide a natural candidate for this missing abstraction. Two trends support this: unified multimodal LMs now encode text, images, audio, video, and robot actions in one token space, while distributed LM inference already generates substantial token-level traffic through expert routing, cache transfer, and speculative decoding. Token communication (TokenCom) emerges by unifying these trends, using the LM's native processing unit as a communication abstraction above the bit level and enabling importance assignment, error handling, and resource allocation directly at token granularity. This survey traces the evolution from LM-driven SemCom to TokenCom. We review three major directions of LM-driven SemCom: source-centric semantic coding, channel semantics for physical-layer tasks, and collaborative edge-device intelligence. We then examine the token abstraction, the transmission techniques it requires, and two emerging paradigms, namely TokenCom for LM services and for embodied and agentic intelligence. Finally, we identify open challenges toward unified, scalable, and AI-native 6G communication systems.

eess.SP↗

arXiv2026

Coexistence of 5G NR and Wi Fi 6E/7 at 6 GHz: Experimental Interference Measurements

This paper presents the first conducted-interference measurements of a commercial Very Low Power (VLP) Wi-Fi 6E/7 device into both the gNB uplink and UE downlink receiver chains of a live 5G New Radio (NR) system, using a complete O-RAN/SDR stack with 5G core in band~n102 (6\,GHz). We use a Software-Defined Radio (SDR) testbed built on OpenAirInterface with band~n102 support (40 MHz, 30 kHz Subcarrier Spacing). We sweep the injected Wi-Fi power and record throughput, block error rate, and signal-to-noise ratio on both the gNB uplink and UE downlink paths. Neither receiver shows measurable degradation below 75 dBm. Above this threshold, performance degrades progressively. The UE is more resilient at lower data rates and unaffected by beacon-only transmissions. A complementary link-budget analysis maps these measured thresholds to equivalent VLP-to-victim distances. These distances fall well inside the 545--685 Listen Before Talk (LBT) exclusion zone, confirming that a compliant VLP device would vacate the channel before its emissions could harm either receiver.

cs.NI↗

arXiv2026

Wireless TokenCom: RL-Based Tokenizer Agreement for Multi-User Wireless Token Communications

Token Communications (TokenCom) has recently emerged as an effective new paradigm, where tokens are the unified units of multimodal communications and computations, enabling efficient digital semantic- and goal-oriented communications in future wireless networks. To establish a shared semantic latent space, the transmitters/receivers in TokenCom need to agree on an identical tokenizer model and codebook. To this end, an initial Tokenizer Agreement (TA) process is carried out in each communication episode, where the transmitter/receiver cooperate to choose from a set of pre-trained tokenizer models/ codebooks available to them both for efficient TokenCom. In this correspondence, we investigate TA in a multi-user downlink wireless TokenCom scenario, where the base station equipped with multiple antennas transmits video token streams to multiple users. We formulate the corresponding mixed-integer non-convex problem, and propose a hybrid reinforcement learning (RL) framework that integrates a deep Q-network (DQN) for joint tokenizer agreement and sub-channel assignment, with a deep deterministic policy gradient (DDPG) for beamforming. Simulation results show that the proposed framework outperforms baseline methods in terms of semantic quality and resource efficiency, while reducing the freezing events in video transmission by 68% compared to the conventional H.265-based scheme.

cs.LG↗

arXiv2026

LiTCom: A Lightweight Transmitter and Inference-Capable Receiver Framework for 6G Uplink

This paper introduces LiTCom, a lightweight transmitter and inference-capable receiver framework, designed to enable robust 6G uplink communication under low signal-to-noise (SNR) conditions. It embraces the resource asymmetry between edge devices and the network infrastructure. LiTCom simplifies transmitter design by applying basic low-pass filtering for source coding and minimal channel coding, significantly reducing the processing complexity. The receiver employs large-scale generative artificial intelligence (GenAI) models to infer high semantic-fidelity content from highly distorted and degraded signals beyond traditional decoding capabilities. Furthermore, efficient power allocation strategies are developed by exploiting data importance to improve system performance, which is measured by the introduced quality of experience (QoE) metric. Simulation results validate the effectiveness of the proposed LiTCom framework and the lightweight coding design. Compared with the 5G NR-like baseline (using JPEG source coding and LDPC channel coding) and the Deep-JSCC baseline, LiTCom achieves SNR gains up to 8 dB and 2.5 dB, respectively, while reducing over 95% transmitter-side computations.

eess.SP↗

arXiv2026

Blockchain-Linked Auditable Decision Management for Telecom/IoT Fraud-Control Requests

Telecom fraud-control studies often stop at detector-level classification, but deployment use requires request-level policy resolution, lifecycle traceability, and auditability. This paper reframes fraud control as blockchain-linked auditable decision management for synthetic telecom/IoT fraud-control requests, and its main result is that the QLoRA-tuned LLM branch becomes much more usable than zero-shot prompting but mainly approaches, rather than outperforms, a lower-cost centralized ensemble. The framework maps each synthetic deployment record to a managed request, blocks explicit out-of-boundary cases through a deterministic hard-fraud gate, scores non-hard requests using centralized ML (M1), federated meta-learning (M2), or LLM-family risk sources (M3), and resolves actions through a shared five-state policy, two-zone refinement mechanism, and local Ethereum-compatible audit layer. Evaluation uses separate synthetic training data and a 100,000-record deployment replay corpus, so the study should be read as controlled drift-replay evidence rather than field validation or proof of live deployability. On validation, M1 gives the strongest balance, with legitimate-request FPR 0.0890 under the 0.10 operating cap and soft-fraud recall 0.8341. On labeled deployment replay, however, the legitimate-FPR gap becomes large: M1 rises to 0.1646 and M3-QLoRA to 0.1801, while M3-QLoRA reduces the M3-Base legitimate FPR from 0.3915 and reaches 0.8240 soft-fraud recall. Blockchain telemetry shows that lifecycle gas, cost, latency, and throughput differences are driven by submitted off-chain decision profiles rather than changes in fraud logic.

cs.CR↗

arXiv2026

Token Communications: A Large Model-Driven Framework for Cross-modal Context-aware Semantic Communications

In this paper, we introduce token communications (TokCom), a large model-driven framework to leverage cross-modal context information in generative semantic communications (GenSC). TokCom is a new paradigm, motivated by the recent success of generative foundation models and multimodal large language models (GFM/MLLMs), where the communication units are tokens, enabling efficient transformer-based token processing at the transmitter and receiver. In this paper, we introduce the potential opportunities and challenges of leveraging context in GenSC, explore how to integrate GFM/MLLMs-based token processing into semantic communication systems to leverage cross-modal context effectively at affordable complexity, present the key principles for efficient TokCom at various layers in future wireless networks. In a typical image semantic communication setup, we demonstrate a significant improvement of the bandwidth efficiency, achieved by TokCom by leveraging the context information among tokens. Finally, the potential research directions are identified to facilitate adoption of TokCom in future wireless networks.

cs.MM↗

arXiv2026

Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence

Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder to operate multi-tenant, multi-objective RANs in a safe and auditable manner. In parallel, agentic AI systems with explicit planning, tool use, memory, and self-management offer a natural way to structure long-lived control loops. This article surveys how such agentic controllers can be brought into O-RAN: we review the O-RAN architecture, contrast agentic controllers with conventional ML/RL xApps, and organise the task landscape around three clusters: network slice life-cycle, radio resource management (RRM) closed loops, and cross-cutting security, privacy, and compliance. We then introduce a small set of agentic primitives (Plan-Act-Observe-Reflect, skills as tool use, memory and evidence, and self-management gates) and show, in a multi-cell O-RAN simulation, how they improve slice life-cycle and RRM performance compared to conventional baselines and ablations that remove individual primitives. Security, privacy, and compliance are discussed as architectural constraints and open challenges for standards-aligned deployments. This framework achieves an average 8.83\% reduction in resource usage across three classic network slices.

cs.LG↗

arXiv2026

Fundamental Limits of Random Downlink Integrated Sensing and Communication over Rician Channels

This paper studies the stochastic performance of a downlink multiple-input multiple-output integrated sensing and communication (ISAC) system over Rician fading channels. Rician fading is important in line-of-sight (LoS)-dominated deployments, where a deterministic propagation component can strongly affect sensing and communication reliability. The base station (BS) simultaneously serves a user and senses a target. The BS-user channel contains LoS and non-line-of-sight components. The user LoS angle may be fixed or random, and the target angle may follow an arbitrary distribution potentially correlated with the user angle. Compared with Rayleigh fading, the deterministic LoS component introduces angle-dependent terms and leads to generally independent but non-identically distributed random vectors, requiring new analysis. We analyze two beamforming strategies: subspace joint beamforming (SJB), optimal for the shared waveform structure, and linear beamforming (LB), a practical alternative using separate sensing and communication beamformers. For both schemes, we derive communication outage probability (OP) and sensing OP based on the Cramer--Rao bound (CRB). We also identify special cases with simpler expressions. For LB, we derive upper and lower bounds on sensing OP and a tractable approximation. We characterize large-system and high-power scaling laws. LB without dirty paper coding (DPC) is interference-limited at high power due to radar self-interference. Results show the Rician K-factor affects communication more strongly than sensing, with non-monotonic behavior across regimes. LB with DPC achieves the best overall performance in strong LoS environments and is the only scheme achieving ultra-high communication reliability in Rayleigh fading, while SJB provides a robust lower-complexity alternative across operating conditions.

cs.IT↗

arXiv2026

Demo: Real-time Generative Multicasting with On-Device Intent-aware Semantic Decomposition

We present a demonstration for generative multicasting with on-device, intent-aware semantic decomposition. At the transmitter, DNN-based segmentation extracts a semantic map from the source video, decomposing it into multiple sub-signal classes based on multi-user receiver intents. The transmitter broadcasts the semantic map to all users over shared wireless/network resources, thereby utilizing orthogonal resources only to transmit the sub-signal classes intended for each user. Users partially reconstruct and partially synthesize the signal by combining the received intended classes with non-intended classes locally synthesized by a generative model from the semantic map. We derive the rate-distortion/perception curves for reconstruction/synthesis with the generative model, to adaptively set compression rates for the semantic map and intended classes. Generative multicasting significantly reduces the wireless/network resources required for existing/emerging multimedia multicasting applications. The system is real-time on a Google Coral Edge TPU with 4 TOPS (int8). This is the first demonstration of generative multicasting representing a substantial advancement in on-device generative SemCom.

cs.NI↗

arXiv2026

Effective Depth in Joint Source-Channel Coding: An Implicit Equilibrium Analysis

A fundamental design question in deep joint source-channel coding (Deep JSCC) remains insufficiently explored: given a channel signal-to-noise ratio (SNR), what effective computation depth is required for semantic reconstruction? Existing Deep JSCC systems typically employ fixed-depth neural architectures selected through empirical hyperparameter tuning, which may lead to unnecessary computation under favorable channel conditions and insufficient refinement under severe channel noise. This paper proposes \emph{Implicit-JSCC}, an implicit equilibrium framework in which semantic encoding and decoding are formulated as fixed-point equilibrium processes. The effective encoder and decoder depths are determined by residual-based solver convergence rather than manually predefined layer numbers, while parameter sharing across equilibrium iterations enables depth-independent parameter complexity. To analyze the resulting effective-depth behavior, we develop a Gaussian-process-inspired kernel evolution framework that models equilibrium iterations as an effective-depth propagation process. Since channel noise is injected between the encoder and decoder, the analysis tracks channel-induced representation perturbations across receiver-side equilibrium iterations and derives a theory-guided depth--SNR relationship. After offline calibration of the system-specific parameters, the resulting model characterizes the required receiver-side refinement depth under different SNRs. Extensive experiments show that Implicit-JSCC achieves competitive reconstruction performance while enabling residual-based adaptive inference and controllable computation--quality tradeoffs. The depth--SNR model further provides a characterization of the SNR-dependent refinement depth required to reach a prescribed perturbation tolerance.

eess.SP↗

arXiv2026

SubEdge: A Subscriber-Centric Edge Computing Subsystem in 6G Networks for AI

Beyond traditional connectivity, 6G is envisioned to transform mobile networks into a distributed fabric that provides native integrated communication, computing, and intelligence services. AI-native terminals (e.g., robots, autonomous vehicles, and smart glasses) require real-time inference from individualised, manufacturer-specific models that cannot be executed on-board nor shared across subscribers, making per-subscriber edge compute the necessary complement to per-subscriber connectivity. Existing Network for AI (Net4AI) architectures provision compute for application providers through shared deployments and do not address per-subscriber provisioning. This paper proposes SubEdge, a Net4AI subsystem that provisions integrated communication and compute resources on a per-subscriber basis, ensuring the coupled migration of both dimensions to maintain service continuity during mobility. SubEdge contributes the computing context--a per-subscriber data structure binding a Subscription Permanent Identifier (SUPI) to its inference container, edge node, and service entitlement--and a mobility-event-driven mechanism that simultaneously migrates the subscriber's compute instance and its traffic-routing policy when the serving cell changes. SubEdge operates as an Application Function over existing Network Exposure Function (NEF) APIs with zero 3GPP core modifications. Experimental evaluation on a real-world testbed shows that SubEdge's mobility-driven joint communication-and-compute migration reduces 95th-percentile latency from 22.9 ms to 12.2 ms with zero packet loss across six mobility events, sustains 99.92% frame delivery for an end-to-end 30 fps inference workload, and completes 1,560 migration operations across batches of up to 50 simultaneously migrating subscribers with 100% success.

cs.NI↗

arXiv2026

Inference-Driven Uplink for 6G: Architecture, Principles, and Challenges

Next-generation wireless networks (6G) face a critical uplink challenge arising from stringent device-side resource constraints and the growing demand for intelligent services. This article introduces InferCom, an inference-driven uplink architecture designed to enable robust communication under low signal-to-noise (SNR) conditions. It adopts a compute-asymmetric design with a lightweight transmitter and an inference-capable receiver empowered by generative artificial intelligence models. Grounded in the information bottleneck principle, InferCom redefines communications through task-agnostic compression, inference-driven reconstruction, error distribution channel code, and quality of experience-aware retransmission. A case study demonstrates that InferCom outperforms conventional 5G NR and Deep-JSCC in terms of transmitter-side computational complexity, uplink coverage and retransmission efficiency. Finally, we outline key challenges and research directions for inference driven uplink design in future intelligent 6G networks.

eess.SP↗

arXiv2026

Hybrid Bit and Semantic Communications for UAV-Enabled Wireless Power Transfer Networks: A Decision-Assisted Deep Reinforcement Learning Approach

Semantic communications which can significantly reduce spectrum consumption in wireless networks, have recently become a popular research area. When combined with wireless power transfer (WPT), semantic communications can help achieve high spectral efficiency for energy-limited devices in wireless communications. In energy-constrained and link budget-limited scenarios such as UAV networks, the integration of semantic communications and WPT enables highly energyefficient transmission mechanisms. In this paper, we investigate semantic communications in UAV-enabled WPT networks. To achieve adaptability to varying signal-to-noise ratio (SNR) and task requirements, we introduce a multi-layer hybrid bit and semantic communication framework. We adopt a semantic communication efficiency metric and aim to maximize it by jointly optimizing UAV trajectory, energy harvesting base station (EHBS) selection, user association, semantic mode selection, and energy harvesting time allocation. To address this complex longterm optimization problem, we introduce the distributional soft actor-critic (DSAC) algorithm and introduce a decision assistant to further enhance the convergence performance of DSAC. Simulation results validate the effectiveness of the proposed method and framework and demonstrate that our algorithm can achieve superior long-term optimization performance in dynamic network environments.

cs.IT↗

arXiv2026

Secure Semantic Communication over Wiretap Channels: Rate-Distortion-Equivocation Tradeoff

This paper investigates an information-theoretic model of secure semantic-aware communication. For this purpose, we consider the lossy joint source-channel coding (JSCC) of a memoryless semantic source transmitted over a memoryless wiretap channel. The source consists of two correlated parts that represent semantic and observed aspects of the information. Our model assumes separate fidelity and secrecy constraints on each source component and, in addition, encompasses two cases for the source output, in order to evaluate the performance gains if the encoder has an extended access to the source. Specifically, in Case 1, the encoder has direct access only to the samples from a single (observed) source component, while in Case 2 it has additional direct access to the samples of the underlying semantic information. We derive single-letter converse and achievability bounds on the rate-distortion-equivocation region. The converse bound explicitly contains rate-distortion functions, making it easy to evaluate, especially for some common distributions. The proposed achievability coding scheme involves novel stochastic superposition coding with two private parts to enable analysis of the equivocation for each source component, separately. Our results generalise some of the previously established source and source-channel coding problems. The general results are further specialised to Gaussian and Bernoulli sources transmitted over Gaussian and binary wiretap channels, respectively. The numerical evaluations illustrate the derived bounds for these distributions.

cs.IT↗

arXiv2026

Secure Joint Source-Channel Coding of Multimodal Semantic Sources

We study the problem of secure joint source-channel coding for multimodal semantic sources transmitted over noisy wiretap channels. The source model consists of $m$ modalities (e.g., image, audio, and sensor data), all represented as random variables. The encoder observes independent and identically distributed samples of an arbitrary non-empty subset of modalities. The samples are encoded and transmitted over a discrete memoryless wiretap channel. The legitimate receiver reconstructs all modalities. We extend the rate-distortion-perception problem formulation to multimodal sources. We establish converse and achievability bounds on the fundamental limits of transmission rate, fidelity, and secrecy, under per-modality distortion and perception constraints, and per-subset equivocation constraints. We show that the fundamental limit for secrecy consists of three operationally distinct components: the level of compression, the secret key rate, and the statistics of the wiretap channel.

cs.IT↗

arXiv2026

Optimization-as-a-Service via Multi-Agent Large Language Model for Radio Access Networks

The physical resource block (PRB) allocation in Radio Access Networks (RANs) traditionally relies on case-by-case manual problem construction or, more recently, learning-based artificial intelligence (AI) methods. However, the sixth-generation (6G) RAN environments confront unprecedented service diversity and exponential dynamics, featuring volatile fluctuations in active base stations (BSs), user scale, and stringent Quality-of-Service (QoS) requirements. Faced with such conditions, both manual models and standard AI algorithms remain fundamentally rigid, lacking the flexibility to adapt and self-evolve. To provide a one-size-fits-all solution, we propose treating the PRB allocation problem as an Optimization-as-a-Service (OaaS) provided by a large language model multi-agent (LLM-MA) system. This fundamentally reshapes RAN resource allocation by utilizing agents to dynamically construct optimization problems and automatically determine objectives tailored to real-time scenarios. Our closed-loop architecture, integrating scene understanding, objective generation, solver, and reflection agents, enables context-aware, self-correcting formulation. To eliminate the computational latency of iterative reflection, we introduce a one-shot reflection distillation mechanism, training a lightweight student model to directly predict refined objective parameters. We theoretically bound the performance gap of this one-shot policy. Experimental results demonstrate our framework achieves near-optimal resource allocation with ultra-low inference latency.

cs.NI↗

arXiv2026

Joint Beamforming and Position Optimization for FIRES-NOMA Assisted Wireless Communication Systems

To address the limitations of conventional reconfigurable intelligent surfaces (RIS) in spatial control capability, this paper proposes a fluid integrated reflecting and emitting surface (FIRES) assisted non-orthogonal multiple access (NOMA) multi-user communication system. In this system, each FIRES element can continuously and flexibly adjust its position in response to environmental variations, enabling simultaneous service to users in both transmission and reflection zones. This significantly enhances the system's spatial degrees of freedom (DoF) and service adaptability. To maximize the system's total sum rate, we formulate a non-convex optimization problem that jointly optimizes the base station beamforming, the transmission/reflection coefficients of the FIRES, and the element positions. An alternating optimization (AO) algorithm is developed, incorporating successive convex approximation (SCA), semi-definite relaxation (SDR), and majorization-minimization (MM) techniques. In particular, to address the complex channel coupling introduced by the coexistence of direct and FIRES paths, the MM framework is employed in the element position optimization subproblem, enabling an efficient iterative solution strategy. Simulation results validate that the proposed system achieves up to a 27% increase in total sum rate compared to conventional STAR-RIS systems and requires approximately 50% fewer RIS elements to attain the same performance, highlighting its effectiveness for cost-efficient large-scale deployment.

eess.SP↗