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.
The paper discusses scalable learning for wireless data-driven systems.
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…
Paper develops deep neural networks for wireless tasks with reduced complexity.
Deep RL improves power control and scheduling for wireless multicast systems.
Generative model learns wireless channel distributions efficiently.
Designs adaptive controller for networked control systems with wireless data transmission.
Study compares different levels of supervision for training graph embeddings in wireless networks.
Wi-GATr learns to simulate wireless signals with high accuracy and speed.
A novel method for efficient CDRL over 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…
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…
This paper reduces labeling costs for meta-learning in wireless systems.
New techniques improve channel prediction in noisy wireless systems.
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…
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…
Improved DL models robust against adversarial attacks for wireless signal classification.
We consider a multicast scheme recently proposed for a wireless downlink in [1]. It was shown earlier that power control can significantly improve its performance. However for this system, obtaining optimal power control is intractable because of a very large state space. Therefore in this paper we use deep reinforceme…
New system preserves message meaning in wireless networks, improving data rate.
This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constr…
Millimeter Wave (MmWave) communication is one of the key technology of the fifth generation (5G) wireless systems to achieve the expected 1000x data rate. With large bandwidth at mmWave band, the link capacity between users and base stations (BS) can be much higher compared to sub-6GHz wireless systems. Meanwhile, due …
FLORAS uses orthogonal sequences for SISO FL, offering both DP and convergence guarantees.
Learning optimal resource allocation policies in wireless systems can be effectively achieved by formulating finite dimensional constrained programs which depend on system configuration, as well as the adopted learning parameterization. The interest here is in cases where system models are unavailable, prompting method…
Deep actor-critic learning optimizes power control in mobile networks.
Wireless communication systems operate in complex time-varying environments. Therefore, selecting the optimal configuration parameters in these systems is a challenging problem. For wireless links, \emph{rate selection} is used to select the optimal data transmission rate that maximizes the link throughput subject to a…
Resource allocation improved using machine learning from terminal positions.
Facial recognition is a key enabling component for emerging Internet of Things (IoT) services such as smart homes or responsive offices. Through the use of deep neural networks, facial recognition has achieved excellent performance. However, this is only possibly when trained with hundreds of images of each user in dif…
Paper proposes a graph model for optimal AP deployment in indoor optical wireless networks.
Adversarial attacks are ineffective when the surrogate models are trained with different channel effects.
The growing role that artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing research directions. This paper motivates the research in the novel direction of \textit{vision-aided wireless communications}, which aims at …
Generative diffusion models improve channel sampling from limited data.
This paper addresses privacy in federated learning with wireless clients and base stations.
This article improves communication efficiency in distributed ML over wireless networks.
We consider recommendation systems that need to operate under wireless bandwidth constraints, measured as number of broadcast transmissions, and demonstrate a (tight for some instances) tradeoff between regret and bandwidth for two scenarios: the case of multi-armed bandit with context, and the case where there is a la…
The paper studies calibration in ML models for wireless networks, showing key theoretical and practical insights.
Optimizes wireless power control using graph neural networks and counterfactual optimization.
Adversaries can fool deep learning modulator classifiers over wireless channels.
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 …
Wireless systems perform rate adaptation to transmit at highest possible instantaneous rates. Rate adaptation has been increasingly granular over generations of wireless systems. The base-station uses SINR and packet decode feedback called acknowledgement/no acknowledgement (ACK/NACK) to perform rate adaptation. SINR i…
Edge devices learn a global model collaboratively over wireless channels.
CHOOSE enhances shallow Transformers for wireless symbol detection.
State-augmented algorithm optimizes wireless network resource management.
Paper proposes MAMRL for efficient energy dispatch in self-powered edge computing systems.
Paper proposes DRL for unsupervised IoT localization.
The paper develops a learning algorithm for distributed training and inference in wireless networks.
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…
Paper presents a WiFi-based indoor sensor localization technique.
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…
Proposes efficient calibration for indoor localization models.