A Large-Scale Equilibrium Model of Energy Emergency Production : Embedding Social Choice Rules into Nash Q-Learning Automatically Achieving Consensus of Urgent Recovery Behaviours

<p>Spectrum today is regulated based on fixed licensees. In the past radio operators have been allocated a frequency band for exclusive use. This has become problem for new users and the modern explosion in wireless services that, having arrived late find there is a scarcity in the remaining available spectrum. Cognitive radio (CR) presents a solution. CRs combine intelligence, spectrum sensing and software reconfigurable radio capabilities. This allows them to opportunistically transmit among several licensed bands for seamless communications, switching to another channel when a licensee is sensed in the original band without causing interference. Enabling this is an intelligent dynamic channel selection strategy capable of finding the best quality channel to transmit on that suffers from the least licensee interruption. This thesis evaluates a Q-learning channel selection scheme using an experimental approach. A cognitive radio deploying the scheme is implemented on GNU Radio and its performance is measured among channels with different utilizations in terms of its packet transmission success rate, goodput and interference caused. We derive similar analytical expressions in the general case of large-scale networks. Our results show that using the Q-learning scheme for channel selection significantly improves the goodput and packet transmission success rate of the system.</p>

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Q-Learning with Fisher Score for Feature Selection of Large-Scale Data Sets

Knowledge Science, Engineering and Management - Lecture Notes in Computer Science ◽

10.1007/978-3-030-82147-0_25 ◽

2021 ◽

pp. 306-318

Author(s):

Min Gan ◽

Li Zhang

Keyword(s):

Feature Selection ◽

Large Scale ◽

Data Sets ◽

Fisher Score ◽

Q Learning ◽

Large Scale Data ◽

Scale Data ◽

Large Scale Data Sets ◽

Selection Of

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Three Social Choice Rules

Applied Behavioral Economics Research and Trends - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-5225-1826-6.ch004 ◽

2017 ◽

pp. 54-71

Author(s):

Andrei Marius Vlăducu

Keyword(s):

Social Choice ◽

Status Quo ◽

Choice Rule ◽

Borda Count ◽

Social Choice Rule ◽

Status Quo Bias ◽

Approval Voting ◽

The People ◽

Viable Solution ◽

Social Choice Rules

The authors analyze three social choice rules (plurality voting, approval voting and Borda count) from a behavioral economics perspective aiming three objectives: 1) if it is a viable solution to use these procedures during mass elections; 2) why individuals prefer a specific social choice rule and not another; 3) how status quo bias and framing effect influence the preference of individuals for a certain social choice rule. The research is conducted with 87 participants to a lab experiment and data suggest that for using approval voting and Borda count during mass elections is necessary to increase the people level of information about their benefits. When making a decision in a political or economic context seem that people tend to prefer simple plurality rule do to its availability and maybe because of its strong reliance with status quo bias.

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Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System

Electronics ◽

10.3390/electronics9111844 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1844

Author(s):

Minhoe Kim ◽

Woongsup Lee ◽

Dong-Ho Cho

Keyword(s):

Communication Systems ◽

Large Scale ◽

Multiple Input Multiple Output ◽

Base Station ◽

Q Learning ◽

Multiple Input ◽

Beam Selection ◽

Input Multiple Output ◽

Optimal Beam ◽

Resource Allocation Scheme

In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

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