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Found 8 papers, 2 papers with code
Learning-based Autonomous Channel Access in the Presence of Hidden Terminals
We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion.
Blockchain-Based Decentralized Energy Management Platform for Residential Distributed Energy Resources in A Virtual Power Plant
In this paper, we develop a blockchain-based VPP energy management platform to facilitate a rich set of transactive energy activities among residential users with renewables, energy storage, and flexible loads in a VPP.
When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm
Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged.
Networking and Internet Architecture
Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels
This paper aims to design a distributed deep reinforcement learning (DRL) based MAC protocol for a particular network, and the objective of this network is to achieve a global $\alpha$-fairness objective.
Networking and Internet Architecture
When Blockchain Meets AI: Optimal Mining Strategy Achieved By Machine Learning
Experimental results indicate that, without knowing the parameter values of the mining MDP model, our multi-dimensional RL mining algorithm can still achieve the optimal performance over time-varying blockchain networks.
Cryptography and Security
Carrier-Sense Multiple Access for Heterogeneous Wireless Networks Using Deep Reinforcement Learning
In particular, in a heterogeneous environment with nodes adopting different MAC protocols (e. g., CS-DLMA, TDMA, and ALOHA), a CS-DLMA node can learn to maximize the sum throughput of all nodes.
Networking and Internet Architecture
AlphaSeq: Sequence Discovery with Deep Reinforcement Learning
We demonstrate the searching capabilities of AlphaSeq in two applications: 1) AlphaSeq successfully rediscovers a set of ideal complementary codes that can zero-force all potential interferences in multi-carrier CDMA systems.
Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks
In particular, the use of neural networks in DRL (as opposed to traditional reinforcement learning) allows for fast convergence to optimal solutions and robustness against perturbation in hyper-parameter settings, two essential properties for practical deployment of DLMA in real wireless networks.
Networking and Internet Architecture
