When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective

The advent of next-generation ultra-reliable and low-latency communications (xURLLC) presents stringent and unprecedented requirements for key performance indicators (KPIs). As a disruptive technology, non-orthogonal multiple access (NOMA) harbors the potential to fulfill these stringent KPIs essential for xURLLC. However, the immaturity of research on the tail distributions of these KPIs significantly impedes the application of NOMA to xURLLC. Stochastic network calculus (SNC), as a potent methodology, is leveraged to provide dependable theoretical insights into tail distribution analysis and statistical QoS provisioning (SQP). In this article, we develop a NOMA-assisted uplink xURLLC network architecture that incorporates an SNC-based SQP theoretical framework (SNC-SQP) to support tail distribution analysis in terms of delay, age-of-information (AoI), and reliability. Based on SNC-SQP, an SQP-driven power optimization problem is proposed to minimize transmit power while guaranteeing xURLLC's KPIs on delay, AoI, reliability, and power consumption. Extensive simulations validate our proposed theoretical framework and demonstrate that the proposed power allocation scheme significantly reduces uplink transmit power and outperforms conventional schemes in terms of SQP performance.

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions

Federated learning (FL) provides a privacy-preserving solution for fine-tuning pre-trained large language models (LLMs) using distributed private datasets, enabling task-specific adaptation while preserving data privacy. However, fine-tuning the extensive parameters in LLMs is particularly challenging in resource-constrained federated scenarios due to the significant communication and computational costs. To gain a deeper understanding of how these challenges can be addressed, this article conducts a comparative analysis three advanced federated LLM (FedLLM) frameworks that integrate knowledge distillation (KD) and split learning (SL) to mitigate these issues: 1) FedLLMs, where clients upload model parameters or gradients to enable straightforward and effective fine-tuning; 2) KD-FedLLMs, which leverage KD for efficient knowledge sharing via logits; and 3) Split-FedLLMs, which split the LLMs into two parts, with one part executed on the client and the other one on the server, to balance the computational load. Each framework is evaluated based on key performance metrics, including model accuracy, communication overhead, and client-side computational load, offering insights into their effectiveness for various federated fine-tuning scenarios. Through this analysis, we identify framework-specific optimization opportunities to enhance the efficiency of FedLLMs and discuss broader research directions, highlighting open opportunities to better adapt FedLLMs for real-world applications. A use case is presented to demonstrate the performance comparison of these three frameworks under varying configurations and settings.

Goal-oriented Semantic Communications for Metaverse Construction via Generative AI and Optimal Transport

The emergence of the metaverse has boosted productivity and creativity, driving real-time updates and personalized content, which will substantially increase data traffic. However, current bit-oriented communication networks struggle to manage this high volume of dynamic information, restricting metaverse applications interactivity. To address this research gap, we propose a goal-oriented semantic communication (GSC) framework for metaverse. Building on an existing metaverse wireless construction task, our proposed GSC framework includes an hourglass network-based (HgNet) encoder to extract semantic information of objects in the metaverse; and a semantic decoder that uses this extracted information to reconstruct the metaverse content after wireless transmission, enabling efficient communication and real-time object behaviour updates to the scenery for metaverse construction task. To overcome the wireless channel noise at the receiver, we design an optimal transport (OT)-enabled semantic denoiser, which enhances the accuracy of metaverse scenery through wireless communication. Experimental results show that compared to the conventional metaverse construction, our proposed GSC framework significantly reduces wireless metaverse construction latency by 92.6\%, while improving metaverse object status accuracy and viewing experience by 45.6\% and 44.7\%, respectively.

Federated LLMs Fine-tuned with Adaptive Importance-Aware LoRA

Federated fine-tuning of pre-trained Large Language Models (LLMs) enables task-specific adaptation across diverse datasets while preserving data privacy. However, the large model size and heterogeneity in client resources pose significant computational and communication challenges. To address these issues, in this paper, we propose a novel Heterogeneous Adaptive Federated Low-Rank Adaptation (LoRA) fine-tuned LLM framework (HAFL). To accommodate client resource heterogeneity, we first introduce an importance-based parameter truncation scheme, which allows clients to have different LoRA ranks, and smoothed sensitivity scores are used as importance indicators. Despite its flexibility, the truncation process may cause performance degradation. To tackle this problem, we develop an importance-based parameter freezing scheme. In this approach, both the cloud server and clients maintain the same LoRA rank, while clients selectively update only the most important decomposed LoRA rank-1 matrices, keeping the rest frozen. To mitigate the information dilution caused by the zero-padding aggregation method, we propose an adaptive aggregation approach that operates at the decomposed rank-1 matrix level. Experiments on the 20 News Group classification task show that our method converges quickly with low communication size, and avoids performance degradation when distributing models to clients compared to truncation-based heterogeneous LoRA rank scheme. Additionally, our adaptive aggregation method achieves faster convergence compared to the zero-padding approach.

Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning

Time-triggered federated learning, in contrast to conventional event-based federated learning, organizes users into tiers based on fixed time intervals. However, this network still faces challenges due to a growing number of devices and limited wireless bandwidth, increasing issues like stragglers and communication overhead. In this paper, we apply model pruning to wireless Time-triggered systems and jointly study the problem of optimizing the pruning ratio and bandwidth allocation to minimize training loss under communication latency constraints. To solve this joint optimization problem, we perform a convergence analysis on the gradient $l_2$-norm of the asynchronous multi-tier federated learning (FL) model with adaptive model pruning. The convergence upper bound is derived and a joint optimization problem of pruning ratio and wireless bandwidth is defined to minimize the model training loss under a given communication latency constraint. The closed-form solutions for wireless bandwidth and pruning ratio by using KKT conditions are then formulated. As indicated in the simulation experiments, our proposed TT-Prune demonstrates a 40% reduction in communication cost, compared with the asynchronous multi-tier FL without model pruning, while maintaining the model convergence at the same level.

Goal-Oriented Semantic Communication for Wireless Image Transmission via Stable Diffusion

Efficient image transmission is essential for seamless communication and collaboration within the visually-driven digital landscape. To achieve low latency and high-quality image reconstruction over a bandwidth-constrained noisy wireless channel, we propose a stable diffusion (SD)-based goal-oriented semantic communication (GSC) framework. In this framework, we design a semantic autoencoder that effectively extracts semantic information from images to reduce the transmission data size while ensuring high-quality reconstruction. Recognizing the impact of wireless channel noise on semantic information transmission, we propose an SD-based denoiser for GSC (SD-GSC) conditional on instantaneous channel gain to remove the channel noise from the received noisy semantic information under known channel. For scenarios with unknown channel, we further propose a parallel SD denoiser for GSC (PSD-GSC) to jointly learn the distribution of channel gains and denoise the received semantic information. Experimental results show that SD-GSC outperforms state-of-the-art ADJSCC and Latent-Diff DNSC, with the Peak Signal-to-Noise Ratio (PSNR) improvement by 7 dB and 5 dB, and the Fr\'echet Inception Distance (FID) reduction by 16 and 20, respectively. Additionally, PSD-GSC archives PSNR improvement of 2 dB and FID reduction of 6 compared to MMSE equalizer-enhanced SD-GSC.

Enhancing xURLLC with RSMA-Assisted Massive-MIMO Networks: Performance Analysis and Optimization

Massive interconnection has sparked people's envisioning for next-generation ultra-reliable and low-latency communications (xURLLC), prompting the design of customized next-generation advanced transceivers (NGAT). Rate-splitting multiple access (RSMA) has emerged as a pivotal technology for NGAT design, given its robustness to imperfect channel state information (CSI) and resilience to quality of service (QoS). Additionally, xURLLC urgently appeals to large-scale access techniques, thus massive multiple-input multiple-output (mMIMO) is anticipated to integrate with RSMA to enhance xURLLC. In this paper, we develop an innovative RSMA-assisted massive-MIMO xURLLC (RSMA-mMIMO-xURLLC) network architecture tailored to accommodate xURLLC's critical QoS constraints in finite blocklength (FBL) regimes. Leveraging uplink pilot training under imperfect CSI at the transmitter, we estimate channel gains and customize linear precoders for efficient downlink short-packet data transmission. Subsequently, we formulate a joint rate-splitting, beamforming, and transmit antenna selection optimization problem to maximize the total effective transmission rate (ETR). Addressing this multi-variable coupled non-convex problem, we decompose it into three corresponding subproblems and propose a low-complexity joint iterative algorithm for efficient optimization. Extensive simulations substantiate that compared with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), the developed architecture improves the total ETR by 15.3% and 41.91%, respectively, as well as accommodates larger-scale access.

Task-oriented Semantics-aware Communications for Robotic Waypoint Transmission: the Value and Age of Information Approach

The ultra-reliable and low-latency communication (URLLC) service of the fifth-generation (5G) mobile communication network struggles to support safe robot operation. Nowadays, the sixth-generation (6G) mobile communication network is proposed to provide hyper-reliable and low-latency communication to enable safer control for robots. However, current 5G/ 6G research mainly focused on improving communication performance, while the robotics community mostly assumed communication to be ideal. To jointly consider communication and robotic control with a focus on the specific robotic task, we propose task-oriented and semantics-aware communication in robotic control (TSRC) to exploit the context of data and its importance in achieving the task at both transmitter and receiver. At the transmitter, we propose a deep reinforcement learning algorithm to generate optimal control and command (C&C) data and a proactive repetition scheme (DeepPro) to increase the successful transmission probability. At the receiver, we design the value of information (VoI) and age of information (AoI) based queue ordering mechanism (VA-QOM) to reorganize the queue based on the semantic information extracted from the AoI and the VoI. The simulation results validate that our proposed TSRC framework achieves a 91.5% improvement in the mean square error compared to the traditional unmanned aerial vehicle control framework.

Task-Oriented Semantics-Aware Communication for Wireless UAV Control and Command Transmission

To guarantee the safety and smooth control of Unmanned Aerial Vehicle (UAV) operation, the new control and command (C&C) data type imposes stringent quality of service (QoS) requirements on the cellular network. However, the existing bit-oriented communication framework is already approaching the Shannon capacity limit, which can hardly guarantee the ultra-reliable low latency communications (URLLC) service for C&C transmission. To solve the problem, task-oriented semantics-aware (TOSA) communication has been proposed recently by jointly exploiting the context of data and its importance to the UAV control task. However, to the best of our knowledge, an explicit and systematic TOSA communication framework for emerging C&C data type remains unknown. Therefore, in this paper, we propose a TOSA communication framework for C&C transmission and define its value of information based on both the similarity and age of information (AoI) of C&C signals. We also propose a deep reinforcement learning (DRL) algorithm to maximize the TOSA information. Last but not least, we present the simulation results to validate the effectiveness of our proposed TOSA communication framework.

Adaptive Federated Pruning in Hierarchical Wireless Networks

Federated Learning (FL) is a promising privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. Hierarchical FL (HFL), as a device-edge-cloud aggregation hierarchy, can enjoy both the cloud server's access to more datasets and the edge servers' efficient communications with devices. However, the learning latency increases with the HFL network scale due to the increasing number of edge servers and devices with limited local computation capability and communication bandwidth. To address this issue, in this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We present the convergence analysis of an upper on the l2 norm of gradients for HFL with model pruning, analyze the computation and communication latency of the proposed model pruning scheme, and formulate an optimization problem to maximize the convergence rate under a given latency threshold by jointly optimizing the pruning ratio and wireless resource allocation. By decoupling the optimization problem and using Karush Kuhn Tucker (KKT) conditions, closed-form solutions of pruning ratio and wireless resource allocation are derived. Simulation results show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50 percent communication cost.