IEEE Transactions on Machine Learning in Communications and Networking最新文献

{"title":"Fast Context Adaptation in Cost-Aware Continual Learning","authors":"Seyyidahmed Lahmer;Federico Mason;Federico Chiariotti;Andrea Zanella","doi":"10.1109/TMLCN.2024.3386647","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3386647","url":null,"abstract":"In the past few years, Deep Reinforcement Learning (DRL) has become a valuable solution to automatically learn efficient resource management strategies in complex networks with time-varying statistics. However, the increased complexity of 5G and Beyond networks requires correspondingly more complex learning agents and the learning process itself might end up competing with users for communication and computational resources. This creates friction: on the one hand, the learning process needs resources to quickly converge to an effective strategy; on the other hand, the learning process needs to be efficient, i.e., take as few resources as possible from the user’s data plane, so as not to throttle users’ Quality of Service (QoS). In this paper, we investigate this trade-off, which we refer to as cost of learning, and propose a dynamic strategy to balance the resources assigned to the data plane and those reserved for learning. With the proposed approach, a learning agent can quickly converge to an efficient resource allocation strategy and adapt to changes in the environment as for the Continual Learning (CL) paradigm, while minimizing the impact on the users’ QoS. Simulation results show that the proposed method outperforms static allocation methods with minimal learning overhead, almost reaching the performance of an ideal out-of-band CL solution.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"479-494"},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10495063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Reinforcement Learning-Based Robust Design for an IRS-Assisted MISO-NOMA System","authors":"Abdulhamed Waraiet;Kanapathippillai Cumanan;Zhiguo Ding;Octavia A. Dobre","doi":"10.1109/TMLCN.2024.3385748","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3385748","url":null,"abstract":"In this paper, we propose a robust design for an intelligent reflecting surface (IRS)-assisted multiple-input single output non-orthogonal multiple access (NOMA) system. By considering channel uncertainties, the original robust design problem is formulated as a sum-rate maximization problem under a set of constraints. In particular, the uncertainties associated with reflected channels through IRS elements and direct channels are taken into account in the design and they are modelled as bounded errors. However, the original robust problem is not jointly convex in terms of beamformers at the base station and phase shifts of IRS elements. Therefore, we reformulate the original robust design as a reinforcement learning problem and develop an algorithm based on the twin-delayed deep deterministic policy gradient agent (also known as TD3). In particular, the proposed algorithm solves the original problem by jointly designing the beamformers and the phase shifts, which is not possible with conventional optimization techniques. Numerical results are provided to validate the effectiveness and evaluate the performance of the proposed robust design. In particular, the results demonstrate the competitive and promising capabilities of the proposed robust algorithm, which achieves significant gains in terms of robustness and system sum-rates over the baseline deep deterministic policy gradient agent. In addition, the algorithm has the ability to deal with fixed and dynamic channels, which gives deep reinforcement learning methods an edge over hand-crafted convex optimization-based algorithms.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"424-441"},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10494408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A DDPG-Based Zero-Touch Dynamic Prioritization to Address Starvation of Services for Deploying Microservices-Based VNFs","authors":"Swarna B. Chetty;Hamed Ahmadi;Avishek Nag","doi":"10.1109/TMLCN.2024.3386152","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3386152","url":null,"abstract":"The sixth generation of mobile networks (6G) promises applications and services with faster data rates, ultra-reliability, and lower latency compared to the fifth-generation mobile networks (5G). These highly demanding 6G applications will burden the network by imposing stringent performance requirements. Network Function Virtualization (NFV) reduces costs by running network functions as Virtual Network Functions (VNFs) on commodity hardware. While NFV is a promising solution, it poses Resource Allocation (RA) challenges. To enhance RA efficiency, we addressed two critical subproblems: the requirement of dynamic service priority and a low-priority service starvation problem. We introduce ‘Dynamic Prioritization’ (DyPr), employing an ML model to emphasize macro- and microlevel priority for unseen services and address the existing starvation problem in current solutions and their limitations. We present ‘Adaptive Scheduling’ (AdSch), a three-factor approach (priority, threshold waiting time, and reliability) that surpasses traditional priority-based methods. In this context, starvation refers to extended waiting times and the eventual rejection of low-priority services due to a ‘delay. Also, to further investigate, a traffic-aware starvation and deployment problem is studied to enhance efficiency. We employed a Deep Deterministic Policy Gradient (DDPG) model for adaptive scheduling and an online Ridge Regression (RR) model for dynamic prioritization, creating a zero-touch solution. The DDPG model efficiently identified ‘Beneficial and Starving’ services, alleviating the starvation issue by deploying twice as many low-priority services. With an accuracy rate exceeding 80%, our online RR model quickly learns prioritization patterns in under 100 transitions. We categorized services as ‘High-Demand’ (HD) or ‘Not So High Demand’ (NHD) based on traffic volume, providing insight into high revenue-generating services. We achieved a nearly optimal resource allocation by balancing low-priority HD and low-priority NHD services, deploying twice as many low-priority HD services as a model without traffic awareness.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"526-545"},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10494765","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140647819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchically Federated Learning in Wireless Networks: D2D Consensus and Inter-Cell Aggregation","authors":"Jie Zhang;Li Chen;Yunfei Chen;Xiaohui Chen;Guo Wei","doi":"10.1109/TMLCN.2024.3385355","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3385355","url":null,"abstract":"Decentralized federated learning (DFL) architecture enables clients to collaboratively train a shared machine learning model without a central parameter server. However, it is difficult to apply DFL to a multi-cell scenario due to inadequate model averaging and cross-cell device-to-device (D2D) communications. In this paper, we propose a hierarchically decentralized federated learning (HDFL) framework that combines intra-cell D2D links between devices and backhaul communications between base stations. In HDFL, devices from different cells collaboratively train a global model using periodic intra-cell D2D consensus and inter-cell aggregation. The strong convergence guarantee of the proposed HDFL algorithm is established even for non-convex objectives. Based on the convergence analysis, we characterize the network topology of each cell, the communication interval of intra-cell consensus and inter-cell aggregation on the training performance. To further improve the performance of HDFL, we optimize the computation capacity selection and bandwidth allocation to minimize the training latency and energy overhead. Numerical results based on the MNIST and CIFAR-10 datasets validate the superiority of HDFL over traditional DFL methods in the multi-cell scenario.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"442-456"},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10491307","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer Learning With Reconstruction Loss","authors":"Wei Cui;Wei Yu","doi":"10.1109/TMLCN.2024.3384329","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3384329","url":null,"abstract":"In most applications of utilizing neural networks for mathematical optimization, a dedicated model is trained for each specific optimization objective. However, in many scenarios, several distinct yet correlated objectives or tasks often need to be optimized on the same set of problem inputs. Instead of independently training a different neural network for each problem separately, it would be more efficient to exploit the correlations between these objectives and to train multiple neural network models with shared model parameters and feature representations. To achieve this, this paper first establishes the concept of common information: the shared knowledge required for solving the correlated tasks, then proposes a novel approach for model training by adding into the model an additional reconstruction stage associated with a new reconstruction loss. This loss is for reconstructing the common information starting from a selected hidden layer in the model. The proposed approach encourages the learned features to be general and transferable, and therefore can be readily used for efficient transfer learning. For numerical simulations, three applications are studied: transfer learning on classifying MNIST handwritten digits, the device-to-device wireless network power allocation, and the multiple-input-single-output network downlink beamforming and localization. Simulation results suggest that the proposed approach is highly efficient in data and model complexity, is resilient to over-fitting, and has competitive performances.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"407-423"},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488445","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature-Based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy","authors":"Feng Wang;M. Cenk Gursoy;Senem Velipasalar","doi":"10.1109/TMLCN.2024.3408131","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3408131","url":null,"abstract":"In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of magnitude compared to that of existing approaches in federated learning and federated transfer learning. Specifically, in the proposed feature-based federated learning, we design the extracted features and outputs to be uploaded instead of parameter updates. For this distributed learning model, we determine the required payload and provide comparisons with the existing schemes. Subsequently, we analyze the robustness of feature-based federated transfer learning against packet loss, data insufficiency, and quantization. Finally, we address privacy considerations by defining and analyzing label privacy leakage and feature privacy leakage, and investigating mitigating approaches. For all aforementioned analyses, we evaluate the performance of the proposed learning scheme via experiments on an image classification task and a natural language processing task to demonstrate its effectiveness (\u0000<uri>https://github.com/wfwf10/Feature-based-Federated-Transfer-Learning</uri>\u0000).","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"823-840"},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10542971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141453361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Access Point Centric Clustering for Cell-Free Massive MIMO Using Gaussian Mixture Model Clustering","authors":"Pialy Biswas;Ranjan K. Mallik;Khaled B. Letaief","doi":"10.1109/TMLCN.2024.3403789","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3403789","url":null,"abstract":"This paper proposes a Gaussian mixture model (GMM) based access point (AP) clustering technique in cell-free massive MIMO (CFMM) communication systems. The APs are first clustered on the basis of large-scale fading coefficients, and the users are assigned to each cluster depending on the channel gain. As the number of clusters increases, there is a degradation in the overall data rate of the system, causing a trade-off between the cluster number and average rate per user. To address this problem, we present an optimization problem that optimizes both the upper bound on the average downlink rate per user and the number of clusters. The optimal number of clusters is intuitively determined by solving the optimization problem, and then grouping the APs and users. As a result, the computation expense is much lower than the current techniques, since the existing methods require evaluations of the network performance in multiple iterations to find the optimal number of clusters. In addition, we analyze the performance of both balanced and unbalanced clustering. Numerical results will indicate that the unbalanced clustering yields a superior rate per user while maintaining a lower level of complexity compared to the balanced one. Furthermore, we investigate the statistical analysis of the spectral efficiency (SE) per user in the clustered CFMM. The findings reveal that the SE per user can be approximated by the logistic distribution.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"675-687"},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10535986","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141187291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning for Radio Resource Allocation Under DoS Attack","authors":"Ke Wang;Wanchun Liu;Teng Joon Lim","doi":"10.1109/TMLCN.2024.3403513","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3403513","url":null,"abstract":"In this paper, we focus on the problem of remote state estimation in wireless networked cyber-physical systems (CPS). Information from multiple sensors is transmitted to a central gateway over a wireless network with fewer channels than sensors. Channel and power allocation are performed jointly, in the presence of a denial of service (DoS) attack where one or more channels are jammed by an attacker transmitting spurious signals. The attack policy is unknown and the central gateway has the objective of minimizing state estimation error with maximum energy efficiency. The problem involves a combination of discrete and continuous action spaces. In addition, the state and action spaces have high dimensionality, and the channel states are not fully known to the defender. We propose an innovative model-free deep reinforcement learning (DRL) algorithm to address the problem. In addition, we develop a deep learning-based method with a novel deep neural network (DNN) structure for detecting changes in the attack policy post-training. The proposed online policy change detector accelerates the adaptation of the defender to a new attack policy and also saves computational resources compared to continuous training. In short, a complete system featuring a DRL-based defender that is trained initially and adapts continually to changes in attack policy has been developed. Our numerical results show that the proposed intelligent system can significantly enhance the resilience of the system to DoS attacks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"703-716"},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10535299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141245171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical ML Codebook Design for Extreme MIMO Beam Management","authors":"Ryan M. Dreifuerst;Robert W. Heath","doi":"10.1109/TMLCN.2024.3402178","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3402178","url":null,"abstract":"Beam management is a strategy to unify beamforming and channel state information (CSI) acquisition with large antenna arrays in 5G. Codebooks serve multiple uses in beam management including beamforming reference signals, CSI reporting, and analog beam training. In this paper, we propose and evaluate a machine learning-refined codebook design process for extremely large multiple-input multiple-output (X-MIMO) systems. We propose a neural network and beam selection strategy to design the initial access and refinement codebooks using end-to-end learning from beamspace representations. The algorithm, called Extreme-Beam Management (\u0000<inline-formula> <tex-math>$text {X-BM}$ </tex-math></inline-formula>\u0000), can significantly improve the performance of extremely large arrays as envisioned for 6G and capture realistic wireless and physical layer aspects. Our results show an 8dB improvement in initial access and overall effective spectral efficiency improvements compared to traditional codebook methods.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"688-702"},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10533211","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141245179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DIN: A Decentralized Inexact Newton Algorithm for Consensus Optimization","authors":"Abdulmomen Ghalkha;Chaouki Ben Issaid;Anis Elgabli;Mehdi Bennis","doi":"10.1109/TMLCN.2024.3400756","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3400756","url":null,"abstract":"This paper tackles a challenging decentralized consensus optimization problem defined over a network of interconnected devices. The devices work collaboratively to solve a problem using only their local data and exchanging information with their immediate neighbors. One approach to solving such a problem is to use Newton-type methods, which are known for their fast convergence. However, these methods have a significant drawback as they require transmitting Hessian information between devices. This not only makes them communication-inefficient but also raises privacy concerns. To address these issues, we present a novel approach that transforms the Newton direction learning problem into a formulation composed of a sum of separable functions subjected to a consensus constraint and learns an inexact Newton direction alongside the global model without enforcing devices to share their computed Hessians using the proximal primal-dual (Prox-PDA) algorithm. Our algorithm, coined DIN, avoids sharing Hessian information between devices since each device shares a model-sized vector, concealing the first- and second-order information, reducing the network’s burden and improving both communication and energy efficiencies. Furthermore, we prove that DIN descent direction converges linearly to the optimal Newton direction. Numerical simulations corroborate that DIN exhibits higher communication efficiency in terms of communication rounds while consuming less communication and computation energy compared to existing second-order decentralized baselines.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"663-674"},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}