Collaborative mobile edge computing (MEC) has emerged as a promising paradigm to enable low-capability edge nodes to cooperatively execute computation-intensive tasks. However, straggling edge nodes (stragglers) significantly degrade the performance of MEC systems by prolonging computation latency. While coded distributed computing (CDC) as an effective technique is widely adopted to mitigate straggler effects, existing CDC schemes exhibit two critical limitations: (i) They cannot successfully decode the final result unless the number of received results reaches a fixed recovery threshold, which seriously restricts their flexibility; (ii) They suffer from inherent poles in their encoding/decoding functions, leading to decoding inaccuracies and numerical instability in the computational results. To address these limitations, this paper proposes an approximated CDC scheme based on barycentric rational interpolation. The proposed CDC scheme offers several outstanding advantages. Firstly, it can decode the final result leveraging any returned results from workers. Secondly, it supports computations over both finite and real fields while ensuring numerical stability. Thirdly, its encoding/decoding functions are free of poles, which not only enhances approximation accuracy but also achieves flexible accuracy tuning. Fourthly, it integrates a novel BRI-based gradient coding algorithm accelerating the training process while providing robustness against stragglers. Finally, experimental results reveal that the proposed scheme is superior to existing CDC schemes in both waiting time and approximate accuracy.

Integrated Sensing and Communication (ISAC) is regarded as a promising approach to improve the original performance for unmanned aerial vehicle (UAV) networks. Nevertheless, it is still limited by the UAV's energy constraints and dynamic links with user equipment (UE). To address these issues, we attempt to deploy the Reconfigurable Intelligent Surface (RIS) for signal reflection, improving sensing and communication performance. Additionally, employing downlink-uplink decoupling (DUDe) can enable each UE to associate with diverse UAVs for downlink (DL) and uplink (UL), further enhancing transmission quality. Therefore, we study the RIS deployment, decoupled UE- UAV association and trajectory design for a RIS-aided UAV network. A joint optimization problem is formulated for maximizing the sum rate in UL and DL. Specifically, we transform the joint problem as a Markov Decision Process, and employ a distributed multi-agent deep reinforcement learning (MADRL) approach to select policies. Moreover, we develop a robust Proximal Policy Optimization (PPO) algorithm to train the AC networks, wherein the Random Environment Distribution is utilized for adapting to varying scenarios and we design an intrinsic reward to expand UAV s' exploration range. Simulation results validate the feasibility and superiority of the RED-PPO approach through comparative analysis.

In fully decoupled networks with separate uplink/downlink (UL/DL) base station (BS) deployments and high user mobility, ensuring efficient UL/DL user association remains a critical challenge. The dynamic environment and complex channel conditions necessitate state perception for optimal association strategies, while the densification of nodes demands scalable solutions to handle increased combinatorial complexity in UL and DL transmissions. This paper introduces a novel framework leveraging unmanned aerial vehicle (UAV) sensing-assisted to predict user mobility and channel dynamics, combined with multiple association mechanism to address dense node interactions. Accordingly, a joint optimization problem is formulated, where Kriging-based prediction is adopted to assist the user association for both UL and DL. To solve it, a hierarchical matching game is developed to decompose the joint problem into decoupled UL and DL games. Particularly, a low-complexity Kriging prediction-based hierarchical matching algorithm is designed to obtain the solution. Simulation results in dynamic network scenarios demonstrate that the effectiveness of proposed approach and the superiority is validated by comparisons.

This paper presents a combinatorial online learning framework for the reliable deployment of containerized Digital Twin (DT) systems in mobile edge computing, addressing challenges such as unpredictable edge server failures and variable writable-layer sharing permissions. By jointly optimizing read-only layer placement, writable layer creation, and cross-server layer loading while adhering to long-term latency and energy constraints, the framework enhances DT service reliability. We first decouple the original problem into a series of deterministic subproblems via Lyapunov optimization, and then propose a contextual bandit mechanism to explore the unknown layer sharing permission information. Theoretical analysis establishes regret bounds, while simulation experiments validate the effectiveness of the proposed framework.

Traditional indoor location data sharing methods using centralized servers face issues like safe and reliable transmission, personal privacy leaks, location information tampering, and computing and storage loads, hampering the growth of personalized indoor services. In this paper, a novel mobile blockchain-enabled federated learning (MBFL) data sharing framework for indoor positioning is presented. Then, we derive training latency and reward of the individual user, and formulate latency-limited resource allocation as a non-cooperative game. We propose an efficient alternating iterative algorithm to achieve the Nash equilibrium of this game. Numerical results demon-strate that the proposed alternating iterative algorithm achieves rapid convergence. Furthermore, when confronted with model poisoning attacks, the MBFL method exhibits superior security performance compared to the traditional FL method.

The fusion of multiple probability densities has important applications in many fields, including, for example, multi-sensor signal processing, robotics, and smart environments. In this paper, we demonstrate that deep learning based methods can be used to fuse multi-object densities. Given a scenario with several sensors with possibly different field-of-views, tracking is performed locally in each sensor by a tracker, which produces random finite set multi-object densities. To fuse outputs from different trackers, we adapt a recently proposed transformer-based multi-object tracker, where the fusion result is a global multi-object density, describing the set of all alive objects at the current time. We compare the performance of the transformer-based fusion method with a well-performing model-based Bayesian fusion method in several simulated scenarios with different parameter settings using synthetic data. The simulation results show that the transformer-based fusion method outperforms the model-based Bayesian method in our experimental scenarios. The code is available at https://github.com/Lechili/DeepFusion.

In multi-UAV networks, the downlink (DL) and uplink (UL) associations between a UAV and a user equipment (UE) is typically coupled, which restricts each UE to associate to the same UAV for both DL and UL. However, this mode may not be efficient since UAV networks can be heterogeneous (e.g., multi-tier UAV networks) and can experience high link uncertainty due to the mobility of UAVs. The introduction of full-duplex communication in a multi-UAV network further complicates the UE-UAV association. For this reason, the idea of DL-UL decoupling (DUDe) is introduced in this work, with which each UE is allowed to associate with separate UAVs for UL and DL transmissions. Besides, the UE-UAV association depends on the flight trajectory of the UAVs, which makes the DUDe design challenging. In this article, we study the joint decoupled UL-DL association and trajectory design problem for full-duplex multi-UAV networks. A joint optimization problem is formulated with the objective of maximizing the UEs’ sum-rate in both UL and DL. Since the problem is non-convex with sophisticated states and an individual UAV may not know the reward functions of other UAVs, a robust partially observable Markov decision process (POMDP) model is proposed to characterize the model uncertainty. A multi-agent deep reinforcement learning (MADRL) approach is proposed which enables each UAV to select its policy in a distributed manner. To train the actor-critic neural networks in the MADRL approach, an improved clip and count-based proximal policy optimization (PPO) algorithm is developed. In particular, a modified clip distribution is designed to deal with the hard restrictions between current and old policies, and an intrinsic reward is introduced to enhance the exploration capability. Simulation results illustrate the superiority of our proposed schemes when compared to the benchmarks. The codes are made publicly available in GitHub (https://github.com/isdai/MADRL-PPO).

We study the joint decoupled uplink (UL)-downlink (DL) association and trajectory design problem for full-duplex multi-UAV networks. A joint optimization problem is formulated aiming to maximize the sum-rate of user equipments (UEs) in both UL and DL. Since the formulated problem is non-convex and with sophisticated states, a multi-agent deep reinforcement learning (MADRL) approach is employed for enabling each agent (i.e., UAV) to select policy in a distributed manner. Moreover, in order to obtain the optimal policy, a clip-and-count based proximal policy optimization (PPO) algorithm is proposed to train actor-critic neural networks. In particular, a modified clip distribution is designed to deal with the hard restrictions between current and old policies, and an intrinsic reward is introduced to enhance the exploration capability. Simulation results demonstrate the significant performance improvement of our proposed schemes when compared to the benchmarks.

User association is a crucial aspect which greatly affects the performance of wireless networks. In this work, we investigate the user association problem in full-duplex cellular networks, wherein base stations (BSs) are densely deployed with highly variable transmit powers and topologies (e.g., heterogeneous networks). To enhance the system performance, decoupled UL-DL (DUDe) association is considered, which enables each user equipment (UE) to associate with different BSs in uplink (UL) and downlink (DL), respectively. Considering the challenges raised by asymmetric information (e.g., channel gains and intercell interferences) between UEs and BSs, we propose a contract-theory based distributed user association approach. Specifically, the association process is modeled as a labor market, where the BSs act as employers and offer two-dimensional contracts to employees (i.e., UEs) for maximizing the utility of the BS. Theoretical proof for contract feasibility is presented by providing sufficient and necessary conditions. To reach the optimality, a contract-theoretic decoupled user association algorithm is developed, in which a BS broadcasts the drafted contracts, and each UE self-selects the optimal contract by considering her own demands. Numerical results are presented to demonstrate the performance of the proposed approach in terms of node utilities and social surplus. Impacts of system settings on the network performance are also investigated.

We study the joint user association and resource allocation problem in both uplink (UL) and downlink (DL) for full-duplex heterogeneous cellular networks (HCNs), wherein base stations (BSs) are densely deployed with reusable subchannels and highly variable transmit powers. To reap the benefits of BS densification, decoupled multiple-association (DMA) is considered, which enables each user equipment (UE) to associate with multiple BSs for UL and DL in a decoupled manner. Furthermore, in order to provide the best service, appropriate holistic subchannel and power allocation are jointly studied and an optimization problem is formulated. However, it is challenging to solve the joint problem due to its combinatorial nature. To this end, we formulate a novel distributed four-sided matching game in which the UEs, BSs, subchannels, and power levels are ranked based on designed preference metrics for optimal matching. To obtain the solution, a low-complexity algorithm is developed. The convergence of the algorithm to a stable matching is proved and the worst-case complexity is analyzed. Numerical results are presented to demonstrate the performance of the proposed scheme in terms of the UEs’ sum-rate in UL and DL, respectively. The superiority of DMA is also investigated by comparisons.

User association is indispensable for the operation of wireless network and has critical impacts on system performance. For most existing work, user associations are typically coupled, which require a user equipment (UE) to associate with the same base station (BS) in uplink (UL) and downlink (DL). However, wireless networks are becoming heterogeneous and densifying, which generates intrinsic distinctions (transmission power, data traffic and backhaul capacity etc.) between UL and DL. Accordingly, coupled association may no longer be optimal. In this work, we explore decoupled user association in full-duplex ultra-dense networks (UDNs), which allows a UE to associate with different BSs in UL and DL respectively. Furthermore, to fully exploit the benefits of UDNs, multiple association, referring to associating a UE with multiple BSs, is jointly adopted in UL and DL. Considering the dynamic and complicated association process, an evolutionary game (EG) is formulated, where UEs are players, and their strategies are association selections in UL/DL. Particularly, evolutionary equilibrium is viewed as the stable solution to the formulated problem. Moreover, an EG-based algorithm with low complexity is proposed for decoupled multiple association. Numerical results validate the convergence of the proposed algorithm for strategy adoption. Besides, the impacts of information exchange delay and learning rate are investigated for providing a better association decision.

User association is a crucial factor that affects the performance of wireless networks. In current cellular networks, user association is typically coupled, which means an user equipment (UE) must associate with the same base station (BS) in uplink (UL) and downlink (DL). For single-tier wireless networks, such mechanism is simple and effective. However, in heterogeneous ultra-dense networks (UDNs), there are distinct differences in transmission power, data traffic and channel quality etc., for which coupled association could restrict the performance of system. To cope with it, the concept of decoupled UL-DL (DUDe) association has been introduced recently, which enables a UE to associate with different BSs in UL and DL. In this paper, we investigate decoupled UL-DL user association in UDNs. Considering the existence of asymmetric information (i.e., channel gains and intercell interferences), which can be seen as the private information for UE, we propose a contract-theoretic user association approach. Particularly, we model the decoupled association process as a monopoly labor market, where BSs act as employers and offer contracts to employees (i.e., UEs). The contract items cover the available associated bandwidths, transmitted powers and corresponding prices. Then BS broadcasts these drafted contract information, and UE selects to sign the optimal contract by considering her own demands. Numerical results show significant superiorities of DUDe than coupled UL-DL association in perspective of nodes utilities and social surplus, and compared with the existing user association methods, contract-theoretic approach shows a certain improvement in performance.

Caching popular contents at base stations (BSs) has been regarded as an effective approach to alleviate the backhaul load and to improve the quality of service. To meet the explosive data traffic demand and to save energy consumption, energy efficiency (EE) has become an extremely important performance index for the 5th generation (5G) cellular networks. In general, there are two ways for improving the EE for caching, that is, improving the cache‐hit rate and optimizing the cache size. In this work, we investigate the energy efficient caching problem in backhaul‐aware cellular networks jointly considering these two approaches. Note that most existing works are based on the assumption that the content catalog and popularity are static. However, in practice, content popularity is dynamic. To timely estimate the dynamic content popularity, we propose a method based on shot noise model (SNM). Then we propose a distributed caching policy to improve the cache‐hit rate in such a dynamic environment. Furthermore, we analyze the tradeoff between energy efficiency and cache capacity for which an optimization is formulated. We prove its convexity and derive a closed‐form optimal cache capacity for maximizing the EE. Simulation results validate the proposed scheme and show that EE can be improved with appropriate choice of cache capacity.

No abstract available.