A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Federated learning optimizes task and resource allocation in balloon networks.
Weather balloons deploy sensors to collect stratospheric data.
Develops a computationally tractable differentially private mean estimator called the balloon mean.
Balloons are two-dimensional spheres. Hoops are one dimensional loops. Knotted Balloons and Hoops (KBH) in 4-space behave much like the first and second homotopy groups of a topological space - hoops can be composed as in π_1, balloons as in π_2, and hoops "act" on balloons as π_1 acts on π_2. We observe that ordinary …
Given a linearly ordered set I, every surjective map p: A --> I endows the set A with a structure of set of preferences by "replacing" the elements of I with their inverse images via p considered as "balloons" (sets endowed with an equivalence relation), lifting the linear order on A, and "agglutinating" this structure…
Optimizes wireless power control using graph neural networks and counterfactual optimization.
Paper proposes a graph model for optimal AP deployment in indoor optical wireless networks.
State-augmented algorithm optimizes wireless network resource management.
Survey on ML for wireless network optimization across PHY, MAC, and network layers.
The paper develops a learning algorithm for distributed training and inference in wireless networks.
A novel method for efficient CDRL over wireless networks.
Study compares different levels of supervision for training graph embeddings in wireless networks.
In the last decade, there has been a great technological advance in the infrastructure of mobile technologies. The increase in the use of wireless local area networks and the use of satellite services are also noticed. The high utilization rate of mobile devices for various purposes makes clear the need to track wirele…
Paper presents a WiFi-based indoor sensor localization technique.
In-network learning outperforms Federated and Split learning in wireless networks.
Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techn…
This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the alternating-direction method of multipliers (ADMM) so as to gain the desired degree of paralleliza…
Paper develops deep neural networks for wireless tasks with reduced complexity.
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance…
Optimizes convergence time of federated learning over wireless networks.
Inter-Cell Interference Coordination (ICIC) is a promising way to improve energy efficiency in wireless networks, especially where small base stations are densely deployed. However, traditional optimization based ICIC schemes suffer from severe performance degradation with complex interference pattern. To address this …
Optimizes wireless network resource management with state-augmented policies.
We generalize the theorems in {\it Mirror Principle I} and {\it II} to the case of general projective manifolds without the convexity assumption. We also apply the results to balloon manifolds, and generalize to higher genus.
In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resourc…
A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security. Such a fingerprint should be able to distinguish between devices sending the same message, and should be robust against standard spoofing techniques. Since the information in wirele…
The paper discusses scalable learning for wireless data-driven systems.
There is a growing interest in the wireless communications community to complement the traditional model-based design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always…
Deep actor-critic learning optimizes power control in mobile networks.
In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station …
Improved DL models robust against adversarial attacks for wireless signal classification.
This letter tackles channel assignment in uplink wireless communication systems.
Two clustering algorithms optimize edge controller placement in wireless networks.
Deep RL improves power control and scheduling for wireless multicast systems.
Wi-GATr learns to simulate wireless signals with high accuracy and speed.
Optimizes energy efficiency in wireless sensor networks with limited information.
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
Study on communication delays in decentralized learning networks.
New algorithm reduces age of information in wireless networks with unknown channel reliability.
DSGD with OAC-MAC scheme achieves better convergence in noisy wireless networks.
Graph neural networks optimize radio resource management policies for wireless networks.
For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenge…
We show that compact fully connected (FC) deep learning networks trained to classify wireless protocols using a hierarchy of multiple denoising autoencoders (AEs) outperform reference FC networks trained in a typical way, i.e., with a stochastic gradient based optimization of a given FC architecture. Not only is the co…
A deep RL framework optimizes resource allocation in wireless networks.
The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This p…
The predominant use of wireless access networks is for media streaming applications, which are only gaining popularity as ever more devices become available for this purpose. However, current access networks treat all packets identically, and lack the agility to determine which clients are most in need of service at a …
Designs adaptive controller for networked control systems with wireless data transmission.
This article improves communication efficiency in distributed ML over wireless networks.
Deep neural networks (DNNs) have been employed for designing wireless networks in many aspects, such as transceiver optimization, resource allocation, and information prediction. Existing works either use fully-connected DNN or the DNNs with specific structures that are designed in other domains. In this paper, we show…