Token Communications: A Unified Framework for Cross-modal Context-aware Semantic Communications

In this paper, we introduce token communications (TokCom), a unified 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, present the key principles for efficient TokCom at various layers in future wireless networks. We demonstrate the corresponding TokCom benefits in a GenSC setup for image, leveraging cross-modal context information, which increases the bandwidth efficiency by 70.8% with negligible loss of semantic/perceptual quality. Finally, the potential research directions are identified to facilitate adoption of TokCom in future wireless networks.

Direct Uplink Connectivity in Space MIMO Systems with THz and FSO Inter-Satellite Links

This paper investigates uplink transmission from a single-antenna mobile phone to a cluster of satellites, emphasizing the role of inter-satellite links (ISLs) in facilitating cooperative signal detection. The study focuses on non-ideal ISLs, examining both terahertz (THz) and free-space optical (FSO) ISLs concerning their ergodic capacity. We present a practical scenario derived from the recent 3GPP standard, specifying the frequency band, bandwidth, user and satellite antenna gains, power levels, and channel characteristics in alignment with the latest 3GPP for non-terrestrial networks (NTN). Additionally, we propose a satellite selection method to identify the optimal satellite as the master node (MN), responsible for signal processing. This method takes into account both the user-satellite link and ISL channels. For the THz ISL analysis, we derive a closed-form approximation for ergodic capacity under two scenarios: one with instantaneous channel state information (CSI) and another with only statistical CSI shared between satellites. For the FSO ISL analysis, we present a closed-form approximate upper bound for ergodic capacity, accounting for the impact of pointing error loss. Furthermore, we evaluate the effects of different ISL frequencies and pointing errors on spectral efficiency. Simulation results demonstrate that multi-satellite multiple-input multiple-output (MIMO) satellite communication (SatCom) significantly outperforms single-satellite SatCom in terms of spectral efficiency. Additionally, our approximated upper bound for ergodic capacity closely aligns with results obtained from Monte Carlo simulations.

Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDS

This paper introduces a secure communication architecture for Unmanned Aerial Vehicles (UAVs) and ground stations in 5G networks, addressing critical challenges in network security. The proposed solution integrates the Advanced Encryption Standard (AES) with Elliptic Curve Cryptography (ECC) and CRYSTALS-Kyber for key encapsulation, offering a hybrid cryptographic approach. By incorporating CRYSTALS-Kyber, the framework mitigates vulnerabilities in ECC against quantum attacks, positioning it as a quantum-resistant alternative. The architecture is based on a server-client model, with UAVs functioning as clients and the ground station acting as the server. The system was rigorously evaluated in both VPN and 5G environments. Experimental results confirm that CRYSTALS-Kyber delivers strong protection against quantum threats with minimal performance overhead, making it highly suitable for UAVs with resource constraints. Moreover, the proposed architecture integrates an Artificial Intelligence (AI)-based Intrusion Detection System (IDS) to further enhance security. In performance evaluations, the IDS demonstrated strong results across multiple models with XGBoost, particularly in more demanding scenarios, outperforming other models with an accuracy of 97.33% and an AUC of 0.94. These findings underscore the potential of combining quantum-resistant encryption mechanisms with AI-driven IDS to create a robust, scalable, and secure communication framework for UAV networks, particularly within the high-performance requirements of 5G environments.

Deep Learning-Based Traffic-Aware Base Station Sleep Mode and Cell Zooming Strategy in RIS-Aided Multi-Cell Networks

Advances in wireless technology have significantly increased the number of wireless connections, leading to higher energy consumption in networks. Among these, base stations (BSs) in radio access networks (RANs) account for over half of the total energy usage. To address this, we propose a multi-cell sleep strategy combined with adaptive cell zooming, user association, and reconfigurable intelligent surface (RIS) to minimize BS energy consumption. This approach allows BSs to enter sleep during low traffic, while adaptive cell zooming and user association dynamically adjust coverage to balance traffic load and enhance data rates through RIS, minimizing the number of active BSs. However, it is important to note that the proposed method may achieve energy-savings at the cost of increased delay, requiring a trade-off between these two factors. Moreover, minimizing BS energy consumption under the delay constraint is a complicated non-convex problem. To address this issue, we model the RIS-aided multi-cell network as a Markov decision process (MDP) and use the proximal policy optimization (PPO) algorithm to optimize sleep mode (SM), cell zooming, and user association. Besides, we utilize a double cascade correlation network (DCCN) algorithm to optimize the RIS reflection coefficients. Simulation results demonstrate that PPO balances energy-savings and delay, while DCCN-optimized RIS enhances BS energy-savings. Compared to systems optimised by the benchmark DQN algorithm, energy consumption is reduced by 49.61%

GSM: A GNN-based Space-MIMO Framework for Direct-to-Cell Communications

This paper proposes a graph neural network (GNN)-based space multiple-input multiple-output (MIMO) framework, named GSM, for direct-to-cell communications, aiming to achieve distributed coordinated beamforming for low Earth orbit (LEO) satellites. Firstly, a system model for LEO multi-satellite communications is established, where multiple LEO satellites collaborate to perform distributed beamforming and communicate with terrestrial user terminals coherently. Based on the system model, a weighted sum rate maximization problem is formulated. Secondly, a GNN-based method is developed to address the optimization problem. Particularly, the adopted neural network is composed of multiple identical GNNs, which are trained together and then deployed individually on each LEO satellite. Finally, the trained GNN is quantized and deployed on a field-programmable gate array (FPGA) to accelerate the inference by customizing the microarchitecture. Simulation results demonstrate that the proposed GNN scheme outperforms the benchmark ones including maximum ratio transmission, zero forcing and minimum mean square error. Furthermore, experimental results show that the FPGA-based accelerator achieves remarkably low inference latency, ranging from 3.863 to 5.883 ms under a 10-ns target clock period with 8-bit fixed-point data representation.

EMF-Efficient MU-MIMO Networks: Harnessing Aerial RIS Technology

The rollout of the fifth-generation (5G) networks has raised some concerns about potential health effects from increased exposure to electromagnetic fields (EMF). To address these concerns, we design a novel EMF-aware architecture for uplink communications. Specifically, we propose an aerial reconfigurable intelligent surface (ARIS) assisted multi-user multiple-input multiple-output (MIMO) system, where the ARIS features a reconfigurable intelligent surface (RIS) panel mounted on an unmanned aerial vehicle (UAV), offering a flexible and adaptive solution for reducing uplink EMF exposure. We formulate and solve a new problem to minimize the EMF exposure by optimizing the system parameters, such as transmit beamforming, resource allocation, transmit power, ARIS phase shifts, and ARIS trajectory. Our numerical results demonstrate the effectiveness of EMF-aware transmission scheme over the benchmark methods, achieving EMF reductions of over 30% and 90% compared to the fixed ARIS and non-ARIS schemes, respectively.

Advanced Nonlinear SCMA Codebook Design Based on Lattice Constellations

The design of efficient sparse codebooks in sparse code multiple access (SCMA) system have attracted tremendous research attention in the past few years. This paper proposes a novel nonlinear SCMA (NL-SCMA) that can subsume the conventional SCMA system which is referred to as linear SCMA, as special cases for downlink channels. This innovative approach allows a direct mapping of users' messages to a superimposed codeword for transmission, eliminating the need of a codebook for each user. This mapping is referred to as nonlinear mapping (codebook) in this paper. Hence, the primary objective is to design the nonlinear mapping, rather than the linear codebook for each user. We leverage the Lattice constellation to design the superimposed constellation due to its advantages such as the minimum Euclidean distance (MED), constellation volume, design flexibility and shape gain. Then, by analyzing the error patterns of the Lattice-designed superimposed codewords with the aid of the pair-wise error probability, it is found that the MED of the proposed nonlinear codebook is lower bounded by the ``single error pattern''. To this end, an error pattern-inspired codebook design is proposed, which can achieve large MEDs of the nonlinear codebooks. Numerical results show that the proposed codebooks can achieve lower error rate performance over both Gaussian and Rayleigh fading channels than the-state-of-the-art linear codebooks.

Generative Semantic Communications with Foundation Models: Perception-Error Analysis and Semantic-Aware Power Allocation

Generative foundation models can revolutionize the design of semantic communication (SemCom) systems allowing high fidelity exchange of semantic information at ultra low rates. In this work, a generative SemCom framework with pretrained foundation models is proposed, where both uncoded forward-with-error and coded discard-with-error schemes are developed for the semantic decoder. To characterize the impact of transmission reliability on the perceptual quality of the regenerated signal, their mathematical relationship is analyzed from a rate-distortion-perception perspective, which is proved to be non-decreasing. The semantic values are defined to measure the semantic information of multimodal semantic features accordingly. We also investigate semantic-aware power allocation problems aiming at power consumption minimization for ultra low rate and high fidelity SemComs. To solve these problems, two semantic-aware power allocation methods are proposed by leveraging the non-decreasing property of the perception-error relationship. Numerically, perception-error functions and semantic values of semantic data streams under both schemes for image tasks are obtained based on the Kodak dataset. Simulation results show that our proposed semanticaware method significantly outperforms conventional approaches, particularly in the channel-coded case (up to 90% power saving).

Diffusion-based Generative Multicasting with Intent-aware Semantic Decomposition

Generative diffusion models (GDMs) have recently shown great success in synthesizing multimedia signals with high perceptual quality enabling highly efficient semantic communications in future wireless networks. In this paper, we develop an intent-aware generative semantic multicasting framework utilizing pre-trained diffusion models. In the proposed framework, the transmitter decomposes the source signal to multiple semantic classes based on the multi-user intent, i.e. each user is assumed to be interested in details of only a subset of the semantic classes. The transmitter then sends to each user only its intended classes, and multicasts a highly compressed semantic map to all users over shared wireless resources that allows them to locally synthesize the other classes, i.e. non-intended classes, utilizing pre-trained diffusion models. The signal retrieved at each user is thereby partially reconstructed and partially synthesized utilizing the received semantic map. This improves utilization of the wireless resources, with better preserving privacy of the non-intended classes. We design a communication/computation-aware scheme for per-class adaptation of the communication parameters, such as the transmission power and compression rate to minimize the total latency of retrieving signals at multiple receivers, tailored to the prevailing channel conditions as well as the users reconstruction/synthesis distortion/perception requirements. The simulation results demonstrate significantly reduced per-user latency compared with non-generative and intent-unaware multicasting benchmarks while maintaining high perceptual quality of the signals retrieved at the users.

A Physical Layer Security Framework for Integrated Sensing and Semantic Communication Systems

In this paper, we address a physical layer security (PLS) framework for the integrated sensing and semantic communication (ISASC) system, where a multi-antenna dual-function semantic base station serves multiple single-antenna semantic communication users (SCUs) and monitors a malicious sensing target (MST), in the presence of a single-antenna eavesdropper (EVE), with both the MST and EVE aiming to wiretap information from the SCUs' signals. To enhance PLS, we employ joint artificial noise (AN) and dedicated sensing signal (DSS) in addition to wiretap coding. To evaluate the sensing accuracy, we derive the Cramer-Rao bound (CRB) as a function of the communication, sensing, and AN beamforming (BF) vectors. Subsequently, to assess the PLS level of the ISASC system, we determine a closed-form expression for the semantic secrecy rate (SSR). To achieve an optimal trade-off region between these two competing objectives, we formulate a multi-objective optimization problem for the joint design of the BF vectors. We apply semi-definite programming, Gaussian randomization method, and golden-section search techniques to address this problem. Simulation results demonstrate that the proposed scheme outperforms baseline schemes, achieving a superior trade-off between SSR and CRB.