A paradigm shift towards human-centric robotics is driving the development of immersive human-robot interaction, especially in manipulation tasks. This paper describes a human-robot co-manipulation strategy based on Mixed reality-enhanced Active constraint Generation and Impedance Control known as MAGIC. The proposed approach consists of an immersive mixed-reality interface to facilitate visual augmentation and impedance control based active constraints to realize haptic guidance. In our experimental study that involves the analysis of user subjects’ experiences, it can be observed that the improved task performance is over 20% and enhanced path-following accuracy is more than 30%. The usability of MAGIC is further analyzed. In terms of the human cognitive workload based on NASA-TLX, the mode of operation associated with MAGIC outperformed other conventional modes of co-manipulation. In conclusion, the results demonstrated the feasibility and usability of our proposed system paving the path for the future development of smart human-robot interactive functionalities like the auto-generation of an appropriate geometry through scene recognition and spatial understanding for context-specific active constraint.

The advance of vehicle-to-everything (V2X) networks has led to many emerging data-intensive applications at the network edge. To meet the soaring data rate requirements of these applications, numerous coding schemes has been developed. However, the high heterogeneity of edge users bring challenges to these methods, including adaptation to performance requirements, coping with unknown or varying channels, as well as inefficient multicasting. In this paper, we address those problems by developing aratelessdeep joint source-channel coding scheme featuring fine-grained control over rate and informativeness at the user. Towards this end, we first design a novel class of variational information bottleneck (VIB) by employing the multinomial-Gaussian (MG) distribution, to achieve rateless transmission over an erasure channel. We derived important results on the statistical properties of this latent distribution to facilitate efficient training of MG-VIB. Then, we apply this framework to multicasting, proposing MG-VIB-M to enhance adaptability and scalability. Simulations show that our proposed method is more flexible regarding rate-relevance tradeoffs, has greater robustness against channel imperfections, and reduces bandwidth requirements for task-oriented multicasting.

Deep learning technologies have seen widespread adoption in WiFi-based wireless sensing systems. However, they are inherently vulnerable to adversarial perturbation attacks, which has received little attention within the WiFi sensing community. To more comprehensively understand the potential threats posed by perturbation attacks, we present a novel attack method, named WiIntruder, distinguishing itself with universality, robustness, and stealthiness. This paper intends to provide a catalyst that promotes the assessment of security in existing WiFi-based sensing systems. We achieve the three aforementioned salient features in WiIntruder through the following three steps: (1) Maximizing transferability by differentiating user-state-specific feature spaces across sensing models, thereby enabling a universal perturbation attack vector applicable to a wide range of applications; (2) Mitigating the impact of perturbation signal distortion by optimizing key factors of device synchronization and wireless propagation through a heuristic particle swarm algorithm; and (3) Enhancing the diversity and stealthiness of attack patterns by randomly switching among perturbation surrogates generated by a generative adversarial network. Experimental results confirm the threat posed by WiIntruder to four common WiFi-based services, with the average accuracy decrease by 72.9% under black-box attack scenarios.

Voice authentication has been widely used on smartphones. However, it remains vulnerable to spoofing attacks, where the attacker replays recorded voice samples from authentic humans using loudspeakers to bypass the voice authentication system. In this paper, we present MagLive, a robust voice liveness detection scheme designed for smartphones to mitigate such spoofing attacks. MagLive leverages the differences in magnetic pattern changes generated by different speakers (i.e., humans or loudspeakers) when speaking for liveness detection, which are captured by the built-in magnetometer on smartphones. To extract effective and robust magnetic features, MagLive utilizes a TF-CNN-SAF model as the feature extractor, which includes a time-frequency convolutional neural network (TF-CNN) combined with a self-attention-based fusion (SAF) model. Supervised contrastive learning is then employed to achieve user-irrelevance, device-irrelevance, and content-irrelevance. MagLive imposes no additional burden on users and does not rely on active sensing or specialized hardware. We conducted comprehensive experiments with various settings to evaluate the security and robustness of MagLive. Our results demonstrate that MagLive effectively distinguishes between humans and attackers (i.e., loudspeakers), achieving an average balanced accuracy (BAC) of 99.01% and an equal error rate (EER) of 0.77%.

To bridge the digital divide, space-ground integrated networks (SGINs) are expected to deliver artificial intelligence (AI) services to every corner of the world. One key mission of SGINs is to support federated learning (FL) at a global scale. However, existing space-ground integrated FL frameworks involve ground stations or costly inter-satellite links, entailing excessive training latency and communication costs. To overcome these limitations, we propose an infrastructure-freefederated learning framework based on amodeldispersal (FedMeld) strategy, which exploits periodic movement patterns and store-carry-forward capabilities of satellites to enable parameter mixing across large-scale geographical regions. We theoretically show that FedMeld leads to global model convergence and quantify the effects of round interval and mixing ratio between adjacent areas on its learning performance. Based on the theoretical results, we formulate a joint optimization problem to design the staleness control and mixing ratio (SC-MR) for minimizing the training loss. By decomposing the problem into sequential SC and MR subproblems without compromising the optimality, we derive the round interval solution in a closed form and the mixing ratio in a semi-closed form to achieve theoptimallatency-accuracy tradeoff. Experiments using various datasets demonstrate that FedMeld achieves superior model accuracy while significantly reducing communication costs as compared with traditional FL schemes for SGINs.

Mixture-of-Experts (MoE) models improve the scalability of large language models (LLMs) by activating only a small subset of relevant experts per input. However, the sheer number of expert networks in an MoE model introduces a significant storage/memory burden for an edge device. To address this challenge, we consider a scenario where experts are dispersed across an edge network for distributed inference. Based on the popular Top-$K$expert selection strategy, we formulate a latency minimization problem by optimizing expert caching on edge servers under storage constraints. When$K=1$, the problem reduces to a monotone submodular maximization problem with knapsack constraints, for which we design a greedy-based algorithm with a$(1 - 1/e)$-approximation guarantee. For the general case where$K\geq 1$, expert co-activation within the same MoE layer introduces non-submodularity, which renders greedy methods ineffective. To tackle this issue, we propose a successive greedy decomposition method to decompose the original problem into a series of subproblems, with each being solved by a dynamic programming approach. Furthermore, we design an accelerated algorithm based on the max-convolution technique to obtain the approximate solution with a provable guarantee in polynomial time. Simulation results on various MoE models demonstrate that our method significantly reduces inference latency compared to existing baselines.

Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs). For many downstream tasks, it is necessary to fine-tune LLMs using private data. While federated learning offers a promising privacy-preserving solution to LLM fine-tuning, the substantial size of an LLM, combined with high computational and communication demands, makes it hard to apply to handle downstream tasks. More importantly, private edge servers often possess varying computing and network resources in real-world scenarios, introducing additional complexities to LLM fine-tuning. To tackle these problems, we design and implement an automated federated pipeline, named, to fine-tune LLMs on heterogeneous edge servers with minimal training cost and no additional inference latency. firstly identifies the weights to be fine-tuned based on their contributions to the LLM training. It then configures a low-rank adapter for each selected weight within the resource constraints of the edge server and aggregates these local adapters from all edge servers to fine-tune the whole LLM. Finally, it appropriately quantizes the parameters of LLM to reduce memory consumption according to the requirements of edge servers. Extensive experiments demonstrate that expedites model training and achieves higher accuracy than the state-of-the-art benchmarks.

Collaborative Perception (CP) has been shown to be a promising technique for multi-agent autonomous driving and multi-agent robotic systems, where multiple agents share their perception information to enhance the overall perception performance and expand the perception range. However, in CP, an ego agent needs to receive messages from its collaborators, which makes it vulnerable to attacks from malicious agents. To address this critical issue, we propose a unified, probability-agnostic, and adaptive framework, namely, CP-uniGuard, which is a tailored defense mechanism for CP deployed by each agent to accurately detect and eliminate malicious agents in its collaboration network. Our key idea is to enable CP to reach a consensus rather than a conflict against an ego agent's perception results. Based on this idea, we first develop a probability-agnostic sample consensus (PASAC) method to effectively sample a subset of the collaborators and verify the consensus without prior probabilities of malicious agents. Furthermore, we define collaborative consistency loss (CCLoss) for object detection task and bird's eye view (BEV) segmentation task to capture the discrepancy between an ego agent and its collaborators, which is used as a verification criterion for consensus. In addition, we propose online adaptive threshold via dual sliding windows to dynamically adjust the threshold for consensus verification and ensure the reliability of the systems in dynamic environments. Finally, we conduct extensive experiments and demonstrate the effectiveness of our framework.

Split federated learning (SFL) has emerged as a promising paradigm to democratize machine learning (ML) on edge devices by enabling layer- wise model partitioning. However, existing SFL approaches suffer significantly from the straggler effect due to the heterogeneous capabilities of edge devices. To address the fundamental challenge, we propose adaptively controlling batch sizes (BSs) and model splitting (MS) for edge devices to overcome resource heterogeneity. We first derive a tight convergence bound of SFL that quantifies the impact of varied BSs and MS on learning performance. Based on the convergence bound, we propose HASFL, a heterogeneity-aware SFL framework capable of adaptively controlling BS and MS to balance communication-computing latency and training convergence in heterogeneous edge networks. Extensive experiments with various datasets validate the effectiveness of HASFL and demonstrate its superiority over state-of-the-art benchmarks.

Recently, the increasing deployment of LEO satellite systems has enabled various space analytics (e.g., crop and climate monitoring), which heavily relies on the advancements in deep learning (DL). However, the intermittent connectivity between LEO satellites and ground station (GS) significantly hinders the timely transmission of raw data to GS for centralized learning, while the scaled-up DL models hamper distributed learning on resource-constrained LEO satellites. Thoughsplit learning(SL) can be a potential solution to these problems by partitioning a model and offloading primary training workload to GS, the labor-intensive labeling process remains an obstacle, with intermittent connectivity and data heterogeneity being other challenges. In this paper, we propose LEO-Split, asemi-supervised(SS) SL design tailored for satellite networks to combat these challenges. Leveraging SS learning to handle (labeled) data scarcity, we construct an auxiliary model to tackle the training failure of the satellite-GS non-contact time. Moreover, we propose a pseudo-labeling algorithm to rectify data imbalances across satellites. Lastly, an adaptive activation interpolation scheme is devised to prevent the overfitting of server-side sub-model training at GS. Extensive experiments with real-world LEO satellite traces (e.g., Starlink) demonstrate that our LEO-Split framework achieves superior performance compared to state-of-the-art benchmarks.

To enable training of large artificial intelligence (AI) models at the network edge, split federated learning (SFL) has emerged as a promising approach by distributing computation between edge devices and a server. However, while unstable network environments pose significant challenges to SFL, prior schemes often overlook such an effect by assuming perfect client participation, rendering them impractical for real-world scenarios. In this work, we develop an optimization framework for SFL with unstable client participation. We theoretically derive the first convergence upper bound for SFL with unstable client participation by considering activation uploading failures, gradient downloading failures, and model aggregation failures. Based on the theoretical results, we formulate a joint optimization problem for client sampling and model splitting to minimize the upper bound. We then develop an efficient solution approach to solve the problem optimally. Extensive simulations on EMNIST and CIFAR-10 demonstrate the superiority of our proposed framework compared to existing benchmarks.

Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end users with low latency, resulting in a paradigm of edge model caching. In this paper, we develop a novel model placement framework, called parameter-sharing model caching (TrimCaching). TrimCaching exploits the key observation that a wide range of AI models, such as convolutional neural networks or large language models, can share a significant proportion of parameter blocks containing reusable knowledge, thereby improving storage efficiency. To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency. We show that the formulated problem is a submodular maximization problem with submodular constraints, for which no polynomial-time approximation algorithm exists. To tackle this challenge, we study an important special case, where a small fixed number of parameter blocks are shared across models, which often holds in practice. In such a case, a polynomial-time algorithm with a $\left(1-ε\right)/2$-approximation guarantee is developed. Subsequently, we address the original problem for the general case by developing a greedy algorithm. Simulation results demonstrate that the proposed TrimCaching framework significantly improves the cache hit ratio compared with state-of-the-art content caching without exploiting shared parameters in AI models.

Given the limited computing capabilities on autonomous vehicles, onboard processing of large volumes of latency-sensitive tasks presents significant challenges. While vehicular edge computing (VEC) has emerged as a solution, offloading data-intensive tasks to roadside servers or other vehicles faces communication-computing bottleneck, such as signal blockage from other large vehicles and limited computing resources of roadside servers. To address these challenges, Reconfigurable Intelligent Surface (RIS) can be leveraged to create line-of-sight channels, mitigate interference on the ground, and extend connectivity to more edge servers by elevating RIS adaptively. To this end, we propose RAISE, an optimization framework for RIS placement in multi-server VEC systems. Specifically, RAISE optimizes RIS altitude and tilt angle together with the optimal task assignment to maximize task throughput under deadline constraints. To find a solution, a two-layer optimization approach is proposed, where the inner layer exploits the unimodularity of the task assignment problem to derive the efficient optimal strategy while the outer layer develops a near-optimal hill climbing (HC) algorithm for RIS placement with low complexity. Extensive experiments demonstrate that the proposed RAISE framework consistently outperforms existing benchmarks.

Collaborative Perception (CP) has shown a promising technique for autonomous driving, where multiple connected and autonomous vehicles (CAVs) share their perception information to enhance the overall perception performance and expand the perception range. However, in CP, ego CAV needs to receive messages from the collaborators, which makes it easy to be attacked by malicious agents. For example, a malicious agent can send harmful information to the ego CAV to mislead it. To address this critical issue, we propose a novel method, **CP-Guard**, a tailored defense mechanism for CP that can be deployed by each agent to accurately detect and eliminate malicious agents in its collaboration network. Our key idea is that CP will lead to a consensus rather than a conflict against the ego CAV's perception results. Based on this idea, we first develop a probability-agnostic sample consensus (PASAC) method that can effectively sample a subset of the collaborators and verify the consensus without prior probabilities of malicious agents. Furthermore, we design a collaborative consistency loss (CCLoss) to calculate the discrepancy between the ego CAV and the collaborators, which is used as a verification criterion for consensus. Finally, we conduct extensive experiments in collaborative bird's eye view (BEV) tasks and the results demonstrate the effectiveness of our CP-Guard.

Deep Reinforcement Learning (DRL) suffers from uncertainties and inaccuracies in the observation signal in real-world applications. Adversarial attack is an effective method for evaluating the robustness of DRL agents. However, existing attack methods targeting individual sampled actions have limited impacts on the overall policy distribution, particularly in continuous action spaces. To address these limitations, we propose the Distribution-Aware Projected Gradient Descent attack (DAPGD). DAPGD uses distribution similarity as the gradient perturbation input to attack the policy network, which leverages the entire policy distribution rather than relying on individual samples. We utilize the Bhattacharyya distance in DAPGD to measure policy similarity, enabling sensitive detection of subtle but critical differences between probability distributions. Our experiment results demonstrate that DAPGD achieves SOTA results compared to the baselines in three robot navigation tasks, achieving an average 22.03% higher reward drop compared to the best baseline.

Collaborative perception enhances sensing in multi-robot and vehicular networks by fusing information from multiple agents, improving perception accuracy and sensing range. However, mobility and non-rigid sensor mounts introduce extrinsic calibration errors, necessitating online calibration, further complicated by limited overlap in sensing regions. Moreover, maintaining fresh information is crucial for timely and accurate sensing. To address calibration errors and ensure timely and accurate perception, we propose a robust task-oriented communication strategy to optimize online self-calibration and efficient feature sharing for Real-time Adaptive Collaborative Perception (R-ACP). Specifically, we first formulate an Age of Perceived Targets (AoPT) minimization problem to capture data timeliness of multi-view streaming. Then, in the calibration phase, we introduce a channel-aware self-calibration technique based on reidentification (Re-ID), which adaptively compresses key features according to channel capacities, effectively addressing calibration issues via spatial and temporal cross-camera correlations. In the streaming phase, we tackle the trade-off between bandwidth and inference accuracy by leveraging an Information Bottleneck (IB)-based encoding method to adjust video compression rates based on task relevance, thereby reducing communication overhead and latency. Finally, we design a priority-aware network to filter corrupted features to mitigate performance degradation from packet corruption. Extensive studies demonstrate that our framework outperforms five baselines, improving multiple object detection accuracy (MODA) by 25.49% and reducing communication costs by 51.36% under severely poor channel conditions. Code will be made publicly available: github.com/fangzr/R-ACP.

Collaborative perception has recently gained significant attention in autonomous driving, improving perception quality by enabling the exchange of additional information among vehicles. However, deploying collaborative perception systems can lead to domain shifts due to diverse environmental conditions and data heterogeneity among connected and autonomous vehicles (CAVs). To address these challenges, we propose a unified domain generalization framework to be utilized during the training and inference stages of collaborative perception. In the training phase, we introduce an Amplitude Augmentation (AmpAug) method to augment low-frequency image variations, broadening the model’s ability to learn across multiple domains. We also employ a meta-consistency training scheme to simulate domain shifts, optimizing the model with a carefully designed consistency loss to acquire domain-invariant representations. In the inference phase, we introduce an intra-system domain alignment mechanism to reduce or potentially eliminate the domain discrepancy among CAVs prior to inference. Extensive experiments substantiate the effectiveness of our method in comparison with the existing state-of-the-art works.

Leveraging blockchain in Federated Learning (FL) emerges as a new paradigm for secure collaborative learning on Massive Edge Networks (MENs). As the scale of MENs increases, it becomes more difficult to implement and manage a blockchain among edge devices due to complex communication topologies, heterogeneous computation capabilities, and limited storage capacities. Moreover, the lack of a standard metric for blockchain security becomes a significant issue. To address these challenges, we propose a lightweight blockchain for verifiable and scalable FL, namely LiteChain, to provide efficient and secure services in MENs. Specifically, we develop a distributed clustering algorithm to reorganize MENs into a two-level structure to improve communication and computing efficiency under security requirements. Moreover, we introduce a Comprehensive Byzantine Fault Tolerance (CBFT) consensus mechanism and a secure update mechanism to ensure the security of model transactions through LiteChain. Our experiments based on Hyperledger Fabric demonstrate that LiteChain presents the lowest end-to-end latency and on-chain storage overheads across various network scales, outperforming the other two benchmarks. In addition, LiteChain exhibits a high level of robustness against replay and data poisoning attacks.

Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic congestion, accidents, and severe carbon emissions. In order to address this essential issue and enhance the safety and efficiency of connected and autonomous vehicles (CAVs) at multi-lane merging zones, we propose a novel collaborative decision-making framework, namedAgentsCoMerge, to leverage large language models (LLMs). Specifically, we first design a scene observation and understanding module to allow an agent to capture the traffic environment. Then we propose a hierarchical planning module to enable the agent to make decisions and plan trajectories based on the observation and the agent's own state. In addition, in order to facilitate collaboration among multiple agents, we introduce a communication module to enable the surrounding agents to exchange necessary information and coordinate their actions. Finally, we develop a reinforcement reflection guided training paradigm to further enhance the decision-making capability of the framework. Extensive experiments are conducted to evaluate the performance of our proposed method, demonstrating its superior efficiency and effectiveness for multi-agent collaborative decision-making under various ramp merging scenarios.

Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communications but also for various machine learning applications. However, a ground station (GS) may be incapable of downloading such a large volume of raw sensing data for centralized model training due to the limited contact time with LEO satellites (e.g. 5 minutes). Therefore,federated learning(FL) has emerged as the promising solution to address this problem via on-device training. Unfortunately, enabling FL on LEO satellites still face three critical challenges: i) heterogeneous computing and memory capabilities, ii) limited downlink/uplink rate, and iii) model staleness. To this end, we proposeFedSNas a general FL framework to tackle the above challenges. Specifically, we first present a novel sub-structure scheme to enable heterogeneous local model training considering different computing, memory, and communication constraints on LEO satellites. Additionally, we propose a pseudo-synchronous model aggregation strategy to dynamically schedule model aggregation for compensating model staleness. Extensive experiments with real-world satellite data demonstrate that FedSN framework achieves higher accuracy, lower computing, and communication overhead than the state-of-the-art benchmarks.

As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue,split federated learning(SFL) has emerged as an FL framework with reduced workload on edge devices via model splitting; it has received extensive attention from the research community in recent years. Nevertheless, most prior works on SFL focus only on a two-tier architecture without harnessing multi-tier cloud-edge computing resources. In this paper, we intend to analyze and optimize the learning performance of SFL under multi-tier systems. Specifically, we propose the hierarchical SFL (HSFL) framework and derive its convergence bound. Based on the theoretical results, we formulate a joint optimization problem for model splitting (MS) and model aggregation (MA). To solve this rather hard problem, we then decompose it into MS and MA sub-problems that can be solved via an iterative descending algorithm. Simulation results demonstrate that the tailored algorithm can effectively optimize MS and MA in multi-tier systems and significantly outperform existing schemes.

Collaborative perception systems leverage multiple edge devices, such as surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their advantages, challenges such as limited channel capacity and data redundancy impede their effectiveness. To address these issues, we introduce the Prioritized Information Bottleneck (PIB) framework for edge video analytics. This framework prioritizes the shared data based on the signal-to-noise ratio (SNR) and camera coverage of the region of interest (RoI), reducing spatial-temporal data redundancy to transmit only essential information. This strategy avoids the need for video reconstruction at edge servers and maintains low latency. It leverages a deterministic information bottleneck method to extract compact, relevant features, balancing informativeness and communication costs. For high-dimensional data, we apply variational approximations for practical optimization. To reduce communication costs in fluctuating connections, we propose a gate mechanism based on distributed online learning (DOL) to filter out less informative messages and efficiently select edge servers. Moreover, we establish the asymptotic optimality of DOL by proving the sublinearity of its regrets. To validate the effectiveness of the PIB framework, we conduct real-world experiments on three types of edge devices with varied computing capabilities. Compared to five coding methods for image and video compression, PIB improves mean object detection accuracy (MODA) by 17.8% while reducing communication costs by 82.65% under poor channel conditions.

The increasing complexity of deep neural networks poses significant barriers to democratizing AI to resource-limited edge devices. To address this challenge, split federated learning (SFL) has emerged as a promising solution that enables device-server co-training through model splitting. However, although system optimization substantially influences the performance of SFL, the problem remains largely uncharted. In this paper, we first provide a unified convergence analysis of SFL, which quantifies the impact of model splitting (MS) and client-side model aggregation (MA) on its learning performance, laying a theoretical foundation for this field. Based on this convergence bound, we introduce AdaptSFL, an adaptive SFL framework to accelerate SFL under resource-constrained edge computing systems. Specifically, AdaptSFL adaptively controls MS and client-side MA to balance communication-computing latency and training convergence. Extensive simulations across various datasets validate that our proposed AdaptSFL framework takes considerably less time to achieve target accuracy than existing benchmarks.

Emerging Low-Earth Orbit (LEO) mega-constellations face challenges such as limited bandwidth and highly variable user demand, which can degrade network performance and lead to inefficient satellite resource utilization. One promising solution is to enable Content Delivery Networks (CDNs) within LEO satellites by deploying cache-equipped satellites. However, many existing approaches rely on inter-satellite links, which are not widely used in practice and are typically activated only when terrestrial ground station coverage is insufficient. Furthermore, the dynamic coverage patterns of satellites and diverse regional content preferences add to the complexity of efficient CDN deployment in space. To address these challenges, we proposeSpaceCache+, a satellite-based CDN framework. We introduce a new metric,user benefit, that jointly captures user coverage and latency reduction to assess the effectiveness of cache satellite deployment. Recognizing that deployment typically occurs incrementally, we formulate theUser Benefit-centric Cache Satellite Deploymentproblem and design an efficient heuristic solution. To enhance content placement, we also propose a cache replacement policy based on zero-shot meta-learning, which adapts to both regional content popularity and satellite mobility. We evaluate the performance ofSpaceCache+using real-world constellation settings with CDN traces. Compared with benchmark strategies,SpaceCache+improves user benefit and cache hit ratio by up to 66.29% and 77.12%, respectively.

Collaborative edge sensing systems, particularly in collaborative perception systems in autonomous driving, can significantly enhance tracking accuracy and reduce blind spots with multi-view sensing capabilities. However, their limited channel capacity and the redundancy in sensory data pose significant challenges, affecting the performance of collaborative inference tasks. To tackle these issues, we introduce a Prioritized Information Bottleneck (PIB) framework for collaborative edge video analytics. We first propose a priority-based inference mechanism that jointly considers the signal-to-noise ratio (SNR) and the camera’s coverage area of the region of interest (RoI). To enable efficient inference, PIB reduces video redundancy in both spatial and temporal domains and transmits only the essential information for the downstream inference tasks. This eliminates the need to reconstruct videos on the edge server while maintaining low latency. Specifically, it derives compact, task-relevant features by employing the deterministic information bottleneck (IB) method, which strikes a balance between feature informativeness and communication costs. Given the computational challenges caused by IB-based objectives with high-dimensional data, we resort to variational approximations for feasible optimization. Compared to TOCOM-TEM, JPEG, and HEVC, PIB achieves an improvement of up to 15.1% in mean object detection accuracy (MODA) and reduces communication costs by 66.7% when edge cameras experience poor channel conditions.

Collaborative perception among multiple connected and autonomous vehicles (CAVs) can greatly enhance perceptive capabilities by allowing vehicles to exchange supplementary information. Despite significant advances, many design challenges still remain due to channel variations and data heterogeneity among collaborative vehicles. To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph to minimize the average transmission delay while mitigating the impacts caused by data heterogeneity. More specifically, we first construct the communication graph to minimize the transmission delay according to different channel information state. We then propose an adaptive data reconstruction mechanism to dynamically adjust the rate-distortion trade-off to enhance perception efficiency while reducing the temporal redundancy during data transmissions. Finally, we conceive a domain alignment scheme to align the data distribution from different vehicles to mitigate the domain gap between different vehicles and improve the performance of the target task. Comprehensive experiments demonstrate the effectiveness of our method in comparison to the existing state-of-the-art works.

Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end users with low latency, resulting in a paradigm called edge model caching. In this paper, we develop a novel model placement scheme, called parameter-sharing model caching (TrimCaching). TrimCaching exploits the key observation that a wide range of AI models, such as convolutional neural networks or large language models, can share a significant proportion of parameter blocks containing reusable knowledge, thereby improving storage efficiency. To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency. We show that the formulated problem is a submodular maximization problem with submodular constraints, for which no polynomial-time approximation algorithm exists. To overcome this challenge, we study an important special case, where a small fixed number of parameter blocks are shared across models, which often holds in practice. In such a case, a polynomial-time algorithm with (1 - E) /2-approximation guarantee is developed. Subsequently, we address the original problem for the general case by developing a greedy algorithm. Simulation results demonstrate that the proposed TrimCaching framework significantly improves the cache hit ratio compared with state-of-the-art content caching without exploiting shared parameters in AI models.

Emerging Low Earth Orbit (LEO) mega-constellations face the challenge of limited bandwidth when providing global Internet services to users. Constructing Content Delivery Networks (CDNs) in LEO mega-constellations is viewed as a feasible solution to address this issue. However, deploying cache satellites, or satellites with CDN servers, for practical and pervasive content delivery is costly and challenging. To overcome this challenge, we consider LEO mega-constellations without inter-satellite links and formulate an integer linear programming problem called User Coverage-aware Cache Satellite Deployment, which aims to maximize the minimum user coverage among all time intervals with a given number of cache satellites. To efficiently solve this problem, a heuristic algorithm called SpaceCache is also proposed. Performance evaluations are conducted based on the Starlink mega-constellation with ground station locations and real-world CDN traces. Compared with benchmark algorithms, we show that the minimum user coverage performance can be improved by up to 41.82% and the cache hit ratio by up to 24.83%.

In recent years, autonomous driving has garnered significant attention due to its potential for improving road safety through collaborative perception among connected and autonomous vehicles (CAVs). However, time-varying channel variations in vehicular transmission environments demand dynamic allocation of communication resources. Moreover, in the context of collaborative perception, it is important to recognize that not all CAVs contribute valuable data, and some CAV data even have detrimental effects on collaborative perception. In this paper, we introduce SmartCooper, an adaptive collaborative perception framework that incorporates communication optimization and a judger mechanism to facilitate CAV data fusion. Our approach begins with optimizing the connectivity of vehicles while considering communication constraints. We then train a learnable encoder to dynamically adjust the compression ratio based on the channel state information (CSI). Subsequently, we devise a judger mechanism to filter the detrimental image data reconstructed by adaptive decoders. We evaluate the effectiveness of our proposed algorithm on the OpenCOOD platform. Our results demonstrate a substantial reduction in communication costs by 23.10% compared to the non-judger scheme. Additionally, we achieve a significant improvement on the average precision of Intersection over Union (AP@IoU) by 7.15% compared with state-of-the-art schemes.

In cloud-based data marketplaces, the cardinal objective lies in facilitating interactions between data shoppers and sellers. This engagement allows shoppers to augment their internal datasets with external data, consequently leading to significant enhancements in their machine learning models. Nonetheless, given the potential diversity of data values, it becomes critical for consumers to assess the value of data before cementing any transactions. Recently, Song et al. introduced Primal (publish in ACSAC), the pioneering cloud-assisted privacy-preserving data evaluation (PPDE) strategy. This strategy relies on variants of functional encryption (FE) as the underlying framework, conferring notable performance advantages over alternative cryptographic primitives such as secure multi-party computation and homomorphic encryption. However, in this paper, we regretfully highlight that Primal is susceptible to inadvertent misuse of FE, and leaves much-desired room for performance amelioration. To combat this, we introduce a novel cryptographic primitive known as labeled function-hiding inner-product encrypted. This new primitive serves as a remedy and forms the foundation for designing the concrete framework for PPDE. Furthermore, experiments conducted on real datasets demonstrate that our framework significantly reduces the overall computation cost of the current state-of-the-art secure PPDE scheme by roughly 10× and the communication cost for the data seller by about 2×.

As a promising solution to alleviate network congestion, mobile edge caching based on unmanned aerial vehicles (UAVs) has emerged and received intensive research interests, where users could download their desired contents from UAVs with much lower latency. As for the UAV-assisted edge caching, to improve the users’ Quality of Experience while reducing the cost on content updating, how to jointly design the trajectory and caching strategy for UAVs is critical. However, considering the dynamics and uncertainty on the traffic environment, as well as the mutual effect among different UAVs, such joint design is nontrivial. In this article, we propose a collaborative joint trajectory and caching scheme for UAV-assisted networks under the dynamic and uncertain traffic environment. Unlike most existing work relying on model-based or single-agent methods, we develop a multiagent deep reinforcement learning (MADRL) approach to obtain the solution, where the specific content demand model is not needed and each UAV would learn the best decision autonomously based on its local observations. It can achieve the adaptive cooperation among different UAVs, while optimizing the overall network performance. Moreover, standing from the perspective on swarm intelligence, we further develop a dynamic clustering federated learning framework on the MADRL algorithm. By performing parameter fusion, each UAV can improve the learning efficiency.

Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the resources on devices and the central server is a highly interesting yet challenging problem. In this paper, we propose an efficient split federated learning algorithm (ESFL) to take full advantage of the powerful computing capabilities at a central server under a split federated learning framework with heterogeneous end devices (EDs). By splitting the model into different submodels between the server and EDs, our approach jointly optimizes user-side workload and server-side computing resource allocation by considering users’ heterogeneity. We formulate the whole optimization problem as a mixed-integer non-linear program, which is an NP-hard problem, and develop an iterative approach to obtain an approximate solution efficiently. Extensive simulations have been conducted to validate the significantly increased efficiency of our ESFL approach compared with standard federated learning, split learning, and splitfed learning.

Surrounding perceptions are quintessential for safe driving for connected and autonomous vehicles (CAVs), where the Bird's Eye View has been employed to accurately capture spatial relationships among vehicles. However, severe inherent limitations of BEV, like blind spots, have been identified. Collaborative perception has emerged as an effective solution to overcoming these limitations through data fusion from multiple views of surrounding vehicles. While most existing collaborative perception strategies adopt a fully connected graph predicated on fairness in transmissions, they often neglect the varying importance of individual vehicles due to channel variations and perception redundancy. To address these challenges, we propose a novelPriority-AwareCollaborativePerception (PACP) framework to employ a BEV-match mechanism to determine the priority levels based on the correlation between nearby CAVs and the ego vehicle for perception. By leveraging submodular optimization, we find near-optimal transmission rates, link connectivity, and compression metrics. Moreover, we deploy a deep learning-based adaptive autoencoder to modulate the image reconstruction quality under dynamic channel conditions. Finally, we conduct extensive studies and demonstrate that our scheme significantly outperforms the state-of-the-art schemes by 8.27% and 13.60%, respectively, in terms of utility and precision of the Intersection over Union.

The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL), allowing multiple edge devices to offload substantial training workloads to an edge server via layer-wise model split. By observing that existing PSL schemes incur excessive training latency and a large volume of data transmissions, we propose an innovative PSL framework, namely, efficient parallel split learning (EPSL), to accelerate model training. To be specific, EPSL parallelizes client-side model training andreduces the dimension of activations' gradientsfor backpropagation (BP) vialast-layer gradient aggregation, leading to a significant reduction in server-side training and communication latency. Moreover, by considering the heterogeneous channel conditions and computing capabilities at edge devices, we jointly optimize subchannel allocation, power control, and cut layer selection to minimize the per-round latency. Simulation results show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy compared with the state-of-the-art benchmarks, and the tailored resource management and layer split strategy can considerably reduce latency than the counterpart without optimization.

As a promising complement to terrestrial cellular networks, satellite networks have recently drawn increasing attention, offering seamless coverage cost-effectively. However, with the rapidly increasing users' demand for multimedia content, how to achieve efficient content transmission seamlessly becomes a critical but knotty problem. To provide an efficient solution from the routing perspective, in this paper, we propose an opportunistic content-aware routing scheme. Our scheme combines the features of in-network caching and content awareness of information-centric networking (ICN) architecture. The basic idea of the proposed scheme is to sense users' requests and find the optimal route solution with the largest potential gain. Moreover, considering the limitation of real-time signaling collection in satellite networks, we design a cached content prediction method. The method is capable of inferring the probability of content being cached based on historical popularity information, providing essential information for measuring potential gains. Extensive simulation results demonstrate that the proposed opportunistic content-aware routing scheme outperforms baseline approaches with significantly reduced delay and traffic consumption.

In unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) systems, where UAVs act as aerial relays to forward tasks from ground users (GUs) to remote edge servers (ESs) for processing, a crucial observation is that the computing power in the system depends on the computing capabilities at a single ES and the number of ESs covered by the UAV. The latter is essentially influenced by the UAV altitude, ES density, transmit power of the UAV, channel condition, etc. In this paper, we model a UAV-assisted MEC system featuring adjustable UAV altitude, random GU distribution, and random ES distribution. We adopt the signal-to-noise ratio-based coverage probability and derive a computing model to characterize communication-aware altitude-dependent computing power. Upon this, we model the sequential task-processing process, including task uploading, forwarding, and computing, as a three-stage tandem queue (M/D/1 →D/1 →D/1). Employing queueing theory, we derive analytical results for the end-to-end (e2e) service latency. Besides, we address the optimization problem of maximizing the number of completed tasks within the e2e latency constraint, referred to as task service throughput. Simulation and analytical results show that optimal UAV altitudes, yielding the maximum task computing throughput, can be obtained under given network parameters.

Multi-access edge computing (MEC) is a promising solution to supporting resource-intensive applications on mobile devices (MDs), which enables computation offloading from MDs to edge servers at their proximities. However, the quality of the communication links and the limited communication and computing resources significantly impact the performance of MEC systems. In this paper, we leverage the emerging reconfigurable intelligent surfaces (RISs) to assist the computation offloading and balance the computing workloads in a multi-server MEC system with limited communication and computing resources. Specifically, when a nearby edge server is overwhelmed by multiple computing tasks, some MDs can be redirected to potentially distant but lighter-loaded edge servers by employing passive beamforming enabled by RISs. Thus, to maximize the task completion rate, we formulate a joint optimization problem for user association, passive beamforming at RISs, receive beamforming at BSs, and computing resource allocation on edge servers. Since the problem is a mixed integer nonlinear programming (MINLP), which is challenging to solve, we first decompose it into two tractable subproblems through the block coordinate descent (BCD) technique and then solve them by the penalty dual decomposition (PDD) method and a swap matching-based algorithm, respectively. Numerical results demonstrate that the task completion rate can be significantly increased by incorporating RISs into multi-server MEC systems. Besides, the proposed algorithms outperform other benchmark schemes in terms of both the task completion rate and the design complexity.

Reconfigurable intelligent surface (RIS) provides a promising way to proactively augment propagation environments for better transmission performance in wireless communications. Existing multi-RIS works mainly focus on link-level optimization with predetermined transmission paths, which cannot be directly extended to system-level management, since they neither consider the interference caused by undesired scattering of RISs, nor the performance balancing between different transmission paths. To address this, we study an innovative multi-hop multi-RIS communication system, where a base station (BS) transmits information to a set of distributed users over multi-RIS configuration space in a multi-hop manner. The signals for each user are subsequently reflected by the selected RISs via multi-reflection line-of-sight (LoS) links. To ensure that all users have fair access to the system to avoid excessive number of RISs serving one user, we aim to find the optimal beam reflecting path for each user, while judiciously determining the path scheduling strategies with the corresponding beamforming design to ensure the fairness. Due to the presence of interference caused by undesired scattering of RISs, it is highly challenging to solve the formulated multi-RIS multi-path beamforming optimization problem. To solve it, we first derive the optimal RISs’ phase shifts and the corresponding reflecting path selection for each user based on its practical deployment location. With the optimized multi-reflection paths, we obtain a feasible user grouping pattern for effective interference mitigation by constructing the maximum independent sets (MISs). Finally, we propose a joint heuristic algorithm to iteratively update the beamforming vectors and the group scheduling policies to maximize the minimum equivalent data rate of all users. Numerical results demonstrate that the proposed transmission framework achieves superior throughput performance than benchmark schemes. Useful insights on how to leverage multi-reflection paths over RISs to boost the throughput performance are also drawn under different settings for the multi-hop multi-RIS communication systems.

In recent years, generating talking faces has become a popular research area due to their applications in various fields. However, most current models require high computational demands, which limits their practicality. To address this issue, some researchers have developed phoneme-face indexes to generate talking videos quickly and efficiently. But when the training video is too short, it is not possible to create mappings for all phonemes. To overcome this limitation, we introduced a large-scale phoneme-face dictionary to complete the feature mapping, designed a novel method for fast phoneme-face indexes search and trained a generative adversarial network (GAN) to generate video from phoneme-face sequences. Our proposed method is capable of completing the phoneme-face mapping using less than 10 seconds training video of the target person based on the large-scale dictionary and fast search algorithm and reducing the preprocessing and training time for talking videos generation.

The multiaccess edge computing/mobile-edge computing (MEC) is becoming a key technology toward “full 5G.” However, as it gets widely used, a fundamental problem is how to support as many service requests as possible under stringent Quality-of-Service (QoS) requirements and limited communications and computing resources. In this article, we study the long-term throughput maximization problem for multicell multiuser MEC systems. Different from most of the existing works that focus on energy or latency minimization problem for a single-edge system, a novel design is proposed from the service provider’s perspective to maximize the system-wide throughput under latency bounds by jointly taking user association and resource allocation for both communications and computing into account. To capture the stochastic nature of MEC environments, a Markov decision process (MDP) is employed to model the queuing states for both mobile devices and MEC servers. By combining MDP and matching theory, a joint user association and resource allocation algorithm is given, where the resource allocation policy under given user-server association is solved. Extensive numerical results demonstrate the superiority of the proposed scheme in comparison with several existing approaches.

In a post-pandemic era with personal precautions and vaccination, the emergence of COVID-19 variants with higher transmissibility and the socio-economic reopening have raised new challenges to existing human-to-human digital contact tracing systems, where privacy, efficiency, and energy-consumption issues are major concerns. In this article, we propose a novel blockchain-based human-to-infrastructure contact tracing framework for the post-pandemic era. Specifically, our approach collects and records the interaction information between persons and predeployed anchor nodes to trace the possible contacts with confirmed patients, so as to capture the indirect contacts and reduces the energy consumption of users. To address the privacy leakage and reliability issues in contact tracing, we introduce a self-sovereign identity (SSI) model-based blockchain which enables users to gain full control of their own identities and eliminate the linkage between the identity and location information in interaction records. To further preserve the privacy of confirmed patients, we introduce the private set intersection cardinality (PSI-CA) protocol to estimate the risk of infection by only counting the number of encounters between users and confirmed patients. Two self-executed smart contracts are deployed on the SSI blockchain to perform contact tracing, which guarantees the robustness of the system. The performance analysis validates the effectiveness of our approach.

Vehicular crowdsourcing is a promising paradigm that takes advantage of powerful onboard capabilities of vehicles to perform various tasks in smart cities. To fulfill this vision, a well-designed incentive mechanism is essential to stimulate the participation of vehicles. In this paper, we propose a timeliness-aware incentive mechanism for vehicular crowdsourcing by taking vehicle’s uncertain travel time into account. In view of the stochastic nature of traffic conditions, we derive a tractable expression for the probability distribution of task delay based on a discrete-time traffic model. By leveraging reverse auction framework, we model the utility of a service requester as a function in terms ofuncertaintask delay and incurred payment. To maximize the requester’s utility under a budget constraint, we cast the mechanism design as a non-monotone submodular maximization problem over a knapsack constraint. Based on this formulation, we develop atruthfulbudgetedutilitymaximizationauction (TBUMA), which is truthful, budget feasible, profitable, individually rational and computationally efficient. Through extensive trace-based simulations, we demonstrate the effectiveness of our proposed incentive mechanism.

Ubiquitous powerful personal computing facilities, such as desktop computers and parked autonomous cars, can function as micro edge computing servers by leveraging their spare resources. However, to harvest their resources for service provisioning, two significant challenges will arise: how to incentivize the server owners to contribute their computing resources, and how to guarantee the end-to-end (E2E) Quality-of-Service (QoS) for service buyers? In this paper, we address these two problems in a holistic way by advocating COMSA. Unlike the existing double auction schemes for edge computing which mostly focus on computing resource trading, COMSA addresses the joint problem of double auction mechanism design and network resource allocation by explicitly taking spectrum allocation and data routing into account, thereby providing E2E QoS guarantees for edge computing services. To handle the design complexity, COMSA employs a two-step procedure to decouple network optimization and mechanism design, which hence can be applied to general network optimization problems for edge computing. COMSA holds some critical economic properties, i.e., truthfulness, budget balance, and individual rationality. Our extensive simulation studies demonstrate the effectiveness of COMSA.

Communication limitation at the edge is widely recognized as a major bottleneck for federated learning (FL). Multi-hop wireless networking provides a cost-effective solution to enhance service coverage and spectrum efficiency at the edge, which could facilitate large-scale and efficient machine learning (ML) model aggregation. However, FL over multi-hop wireless networks has rarely been investigated. In this paper, we optimize FL over wireless mesh networks by taking into account the heterogeneity in communication and computing resources at mesh routers and clients. We present a framework that each intermediate router performsin-networkmodel aggregation before sending the data to the next hop, so as to reduce the outgoing data traffic and hence aggregate more models under limited communication resources. To accelerate model training, we formulate our optimization problem by jointly considering model aggregation, routing, and spectrum allocation. Although the problem is a non-convex mixed-integer nonlinear programming, we transform it into a mixed-integer linear programming (MILP), and develop a coarse-grained fixing procedure to solve it efficiently. Simulation results demonstrate the effectiveness of the solution approach, and the superiority of the in-network aggregation scheme over the counterpart without in-network aggregation.

The computation offloading in mobile-edge computing (MEC) systems emerges as a promising technology to enhance users' quality-of-experience over mobile devices (MDs). However, the design of computation offloading policy for MEC systems inevitably faces challenges with respect to the gap between dynamic task generation in MDs and the limited resources at an MEC server, especially for a multi-user MEC system. More specifically, whether or not offload a task to a nearby MEC server and how much communication and computing resources are allocated to the selected MDs should be carefully investigated to optimize the long-term system performance. In this paper, we handle this issue based on the Markov decision process, where collaborated resource allocations are determined according to both the queueing state of the task buffer at the MDs and the MEC server. By analyzing the average task delay of each user and the average throughput of the system, we formulate a throughput maximization problem with the constraints on delay, spectrum resource, and computing resource, and develop a throughput-optimal resource allocation policy. Simulation results show that the proposed joint communication and computing resource allocation policy is highly effective and efficient.

To support the demand of multi-Gbps sensory data exchanges for enhancing (semi)-autonomous driving, millimeter-wave bands (mmWave) vehicular-to- infrastructure (V2I) communications have attracted intensive attention. Unfortunately, the vulnerability to blockages over mmWave bands poses significant design challenges, which can be hardly addressed by manipulating end transceivers, such as beamforming techniques. In this paper, we propose to enhance mmWave V2I communications by augmenting the transmission environments through reflection, where highly-reflective cheap metallic plates are deployed as tunable reflectors without damaging the aesthetic nature of the environments. In this way, alternative indirect line-of-sight (LOS) links are established by adjusting the angle of reflectors. Our fundamental challenge is to adapt the time-consuming reflector angle tuning to the highly dynamic vehicular environment. By using deep reinforcement learning, we propose the learning-based Fast Reflection (LFR) algorithm, which autonomously learns from the observable traffic pattern to select desirable reflector angles in advance for probably blocked vehicles in near future. Simulation results demonstrate our proposal could effectively augment mmWave V2I transmission environments with significant performance gain.

Vehicle-based crowdsourcing is becoming a powerful paradigm that can outsource intensive tasks to vehicles by exploiting their on-board resources. In this paper, we focus on the problem of motivating vehicles to join the crowdsourcing system. Considering the various delay demands of tasks in smart cities, we design a delay-aware incentive mechanism to employ vehicles based on reverse auction. Specifically, by taking task delay into consideration, we model the utility of service requester as a function closely related to when its released tasks would be completed. In our mechanism, the participating vehicles bid for their preferred tasks by submitting not only the bidding prices, but also the estimated time of completion (ETC). To maximize the utility of the service requester under a budget constraint, the proposed delay-aware mechanism is cast as a nonmonotone submodular maximization problem with a knapsack constraint. Due to the NP-hardness of the formulated problem, we develop an approximate algorithm for bid selection and payment determination, which guarantees truthfulness, budget feasibility, individual rationality, profitability, and computational efficiency. Simulation results demonstrate the effectiveness of our proposed incentive mechanism.

In this paper, we propose the full-duplex and cloud radio access network (C-RAN) based multi-cell non-orthogonal multiple access schemes over 5G mobile wireless networks. To cope with the severe intra-cell and inter-cell interferences as well as perform the centralized optimization, we adopt the C-RAN architecture, where the baseband processing and resource management are conducted at a central unit (CU). With the goal of maximizing the weighted sum achievable rate, we formulate the sum rate maximization power allocation problem as a non-convex problem. Thanks to the hidden monotonicity structure of the considered problem, the optimal power allocation algorithm is developed by the monotonic optimization method. Besides, we propose another suboptimal algorithm by employing successive convex approximation method to obtain the close-to-optimal solution with a significantly reduced computational complexity. Extensive simulations are conducted to verify the effectiveness of our proposed power allocation schemes, and confirm the superiority of our proposed C-RAN architecture.

This paper investigates the power allocation problems for non-orthogonal multiple access with coordinated direct and relay transmission (CDRT-NOMA), where a base station (BS) communicates with its nearby user directly, while communicating with its far user only through a dedicated relay node (RN). The RN is assumed to operate in either half-duplex relaying (HDR) mode or full-duplex relaying (FDR) mode. Based on instantaneous channel state information (CSI), the dynamic power allocation problems under HDR and FDR schemes are formulated respectively, with the objective of maximizing the minimum user achievable rate. After demonstrating the quasi-concavity of the considered problems, we derive the optimal closed-form power allocation policies under the HDR scheme and the FDR scheme. Then, a hybrid relaying scheme dynamically switching between HDR and FDR schemes is further designed. Moreover, we also study the fixed power allocation problems for the considered CDRT-NOMA systems based on statistical CSI so as to optimize the long-term system performance. The simulations show that our proposed power allocation policies can significantly enhance the performance of CDRT-NOMA systems.

Power allocation is an important issue in order to optimize the performance of non-orthogonal multiple access (NOMA) systems. However, the power allocation problem for cooperative NOMA systems has not been well investigated. In this paper, we investigate the power allocation problems for half-duplex cooperative NOMA (HD-CNOMA) and full-duplex cooperative NOMA (FD-CNOMA) systems, respectively. From the fairness standpoint, the optimization problem for each system is formulated to maximize the minimum achievable user rate in a NOMA user pair. Even though both of the formulated problems are neither concave nor quasi-concave, the optimal closed-form solutions of both cases are still obtained with the proposed two-step method. First, we transform the initial problem into a quasi-concave problem by treating the relay transmit power, namely R, as a constant, and then solve the obtained quasiconcave problem. Second, we convert the original problem into a univariate problem of R based on the results of the first step, and eventually obtain the optimal power allocation. In addition, a hybrid half/full-duplex cooperative NOMA scheme, which dynamically switches between the HD-CNOMA and FD-CNOMA mode, is proposed. After that, a relay selection scheme is also investigated to extend the hybrid scheme into general networks with multiple users. Numerical results demonstrate that the proposed hybrid relaying scheme can achieve a significant performance improvement with respect to the conventional NOMA, HD-CNOMA, and FD-CNOMA scheme.

This paper investigates the power allocation problem of full-duplex cooperative non-orthogonal multiple access (FD-CNOMA) systems, in which the strong users relay data for the weak users via a full duplex relaying mode. For the purpose of fairness, our goal is to maximize the minimum achievable user rate in a NOMA user pair. More specifically, we consider the power optimization problem for two different relaying schemes, i.e., the fixed relaying power scheme and the adaptive relaying power scheme. For the fixed relaying scheme, we demonstrate that the power allocation problem is quasi-concave and a closed-form optimal solution is obtained. Then, based on the derived results of the fixed relaying scheme, the optimal power allocation policy for the adaptive relaying scheme is also obtained by transforming the optimization objective function as a univariate function of the relay transmit power $P_R$. Simulation results show that the proposed FD- CNOMA scheme with adaptive relaying can always achieve better or at least the same performance as the conventional NOMA scheme. In addition, there exists a switching point between FD-CNOMA and half- duplex cooperative NOMA.

Cooperative non-orthogonal multiple access (NOMA) has attracted more and more attentions recently, in which NOMA-strong users play as relays to help the data transmission of NOMA-weak users. Different from existing works, we study the problem of power allocation for cooperative NOMA systems with half-duplex relaying mode. From a fairness standpoint, our proposed scheme aims at maximizing the minimum achievable user rate in a paired user group. More specifically, we divide the cooperative NOMA systems into two categories, i.e., fixed relaying scheme and adaptive relaying scheme. Fixed relaying scheme means the transmit power at the relay node, namely , is a given fixed constant while adaptive relaying scheme implies that can adapt to channel conditions according to our strategy. It is shown that the formulated power allocation problem for fixed relaying scheme is quasi-concave while the problem for adaptive relaying scheme is not. Hence, we firstly solve the former problem using a bisection algorithm by transforming it into a sequence of convex feasibility problems. Then, relying on the derived results of fixed relaying scheme, we find that the problem for adaptive relaying scheme can be converted into a univariate function about , in which the optimum can be also obtained by a similar bisection procedure. Numerical results reveal that the proposed adaptive relaying scheme always outperforms the proposed fixed relaying scheme. In addition, we also show that the cooperative NOMA systems are especially appropriate for systems under low SNR environments or having significantly different fading coefficients between NOMA users.

Power allocation plays an important role in cooperative non-orthogonal multiple access (NOMA) systems. To guarantee the quality of service (QoS) requirements of users, we present the cross layer power allocation algorithms for half-duplex cooperative NOMA (HD-CNOMA) and full-duplex cooperative NOMA (FD-CNOMA) systems respectively. From fairness standpoint, the optimization problem for each scheme aims at maximizing the minimum user effective capacity in a NOMA user pair under different delay QoS constraints. Due to the quasi-concavity of both problems, we propose a bisection-based cross layer power allocation algorithm for both cases to obtain the optimal solution. Numerical results show that proposed schemes significantly outperforms existing fixed cooperative NOMA schemes. Moreover, we also illustrate that proposed cooperative NOMA schemes are more suitable for low SNR environments compared to the optimized conventional NOMA scheme.