Analysis of Soft Decision Trees for Passive-Expert Reinforcement Learning

This paper explores the use of soft decision trees [1] in basic reinforcement applications to examine the efficacy of using passive-expert like networks for optimal Q-Value learning on Artificial Neural Networks (ANN). The soft decision tree networks were built using the PyTorch machine learning and the OpenAi’s Gym environment frameworks. The conducted research study aimed at assessing the performance of soft decision tree networks on Cartpole as provided in the OpenAi Gym software package. The baseline performance metric that the soft decision tree networks were compared against was a simple Deep Neural Network using several linear layers with ReLU and Softmax activation functions for the input and output layers, respectively. All networks were trained using the Backpropagation algorithm provided generically by PyTorch’sAutograd module.

Power and rate adaption in wireless communication systems with energy harvesting-based on soft decision decoding

In this paper,the online power control and rate adaptation for a wireless communication system with energy harvesting(EH)are investigated,in which soft decision decoding is adopted by the receiver.To efficiently utilize the harvested energy and maximize the actual achievable transmission rate under the constraints of the available channel codes and modulation schemes,the transmit power,code rate and modulation order are jointly optimized.The Lyapunov framework is used to transform the long-term optimization problem into a per time slot optimization problem.Since there is no theoretical formula for the error rate of soft decision decoding,the optimization problem cannot be solved analytically.A table to find the optimal modulation order and code rate under the different values of signal-to-noise ratio(SNR)is built first,and then a numerical algorithm to find the solution to the optimization problem is given.The feasibility and performance of the proposed algorithm are demonstrated by simulation.The simulation results show that compared with the algorithms to maximize the theoretical channel capacity,the proposed algorithm can achieve a higher actual transmission rate.

Soft Decision Tree for Regression

Decision tree(DT)plays an important role in pattern recognition and machine learning,which is widely used for regression tasks because of its natural interpretability.Nevertheless,the traditional decision tree is constructed by recursive Boolean division.The discrete decision-making process in DT makes it non-differentiable,and causes the problem of hard decision boundary.To solve this problem,a probability distribution model—Staired-Sigmoid is proposed in this paper.The Staired-Sigmoid model is used to differentiate the decision-making process,by which the samples can be assigned to two sub-trees more finely.Based on Staired-Sigmoid,we further propose the soft decision tree(SDT)for regression tasks,where the samples are assigned to different sub-nodes according to a continuous probability distribution.This process is differentiable,and all parameters in SDT can be optimized by gradient descent algorithms.Owing to its constructing rules,SDT is more stable than decision tree,and it is easier to overcome the problem of overfitting.We validate SDT on several datasets obtained from UCI.Experiments demonstrate that SDT achieves better performance than decision tree,and it significantly alleviates the overfitting.

Design of Scheduling Decision Mechanism for Agile Manufacturing System

Design of scheduling decision mechanism is a key issue of scheduling decision method and strategy for agile manufacturing system. Effective scheduling decision mechanism helps to improve the operational agility of manufacturing system. Several scheduling decision mechanisms are discussed, including scheduling forecasting mechanism, cooperation mechanism and cell scheduling mechanism. Also soft decision mechanism is put forward as a promising prospect for agile manufacturing system, and some key techniques in soft decision mechanism are introduced.

A physical layer secret key consistency enhancement scheme using constellation decision information

Secret key generation based on a wireless channel(WC-SKG)is a promising solution to address the security issues in wireless communication.However,the consistency of channel estimation between two legal communication nodes in WC-SKG is often poor due to the receiver noise,signal power,etc.,leading to a low secret key generation rate(SKGR).Although there are several denoising algorithms such as orthogonal transformation to address this issue,existing schemes overlook the fact that data symbols are also affected by the channel.This results in existing schemes only using the pilot symbols for channel estimation and not fully utilizing the received signal power of the WC-SKG.To address this issue,we propose a consistency enhancement algorithm based on constellation decision information(CEA-CDI),which utilizes both pilot symbols and soft decision information of data symbols to improve SKGR.Monte Carlo simulation and numerical results demonstrate that our proposed scheme can improve performance by approximately 16 dB compared to initial channel estimation.

Improved speech absence probability estimation based on environmental noise classification

An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement.A relevant noise estimation approach,known as the speech presence uncertainty tracking method,requires seeking the "a priori" probability of speech absence that is derived by applying microphone input signal and the noise signal based on the estimated value of the "a posteriori" signal-to-noise ratio(SNR).To overcome this problem,first,the optimal values in terms of the perceived speech quality of a variety of noise types are derived.Second,the estimated optimal values are assigned according to the determined noise type which is classified by a real-time noise classification algorithm based on the Gaussian mixture model(GMM).The proposed algorithm estimates the speech absence probability using a noise classification algorithm which is based on GMM to apply the optimal parameter of each noise type,unlike the conventional approach which uses a fixed threshold and smoothing parameter.The performance of the proposed method was evaluated by objective tests,such as the perceptual evaluation of speech quality(PESQ) and composite measure.Performance was then evaluated by a subjective test,namely,mean opinion scores(MOS) under various noise environments.The proposed method show better results than existing methods.

LDPC Code’s Decoding Algorithms for Wireless Sensor Network:a Brief Review

As an effective error correction technology,the Low Density Parity Check Code(LDPC)has been researched and applied by many scholars.Meanwhile,LDPC codes have some prominent performances,which involves close to the Shannon limit,achieving a higher bit rate and a fast decoding.However,whether these excellent characteristics are suitable for the resource-constrained Wireless Sensor Network(WSN),it seems to be seldom concerned.In this article,we review the LDPC code’s structure brief.ly,and them classify and summarize the LDPC codes’construction and decoding algorithms,finally,analyze the applications of LDPC code for WSN.We believe that our contributions will be able to facilitate the application of LDPC code in WSN.

Sensor node-assisted asynchronous cooperative spectrum sensing for cognitive radio network

In order to take advantage of the asynchronous sensing information, alleviate the sensing overhead of secondary users （SUs） and improve the detection performance, a sensor node-assisted asynchronous cooperative spectrum sensing （SN-ACSS） scheme for cognitive radio （CR） network （CRN） was proposed. In SN-ACSS, each SU is surrounded by sensor nodes （SNs）, which asynchronously make hard decisions and soft decisions based on the Bayesian fusion rule instead of the SU. The SU combines these soft decisions and makes the local soft decision. Finally, the fusion center （FC） fuses the local soft decisions transmitted from SUs with different weight coefficients to attain the final soft decision. Besides, the impact of the statistics of licensed band occupancy on detection performance and the fact that different SNs have different sensing contributions are also considered in SN-ACSS scheme. Numerical results show that compared with the conventional synchronous cooperative spectrum sensing （SCSS） and the existing ACSS schemes, SN-ACSS algorithm achieves a better detection performance and lower cost with the same number of SNs.

Iterative multi-user detection and decoding for space-time block coding systems

To restrain the interference of co-channel users using space-time block coding （STBC）, the proposed Gaussian-forcing soft decision multi-user detection （GFSDMUD） algorithm is applied in fiat-fading channels by using the relation among the users＇ signals, which can enhance the capacity by introducing co-channel users. During iterations, extrinsic information is calculated and exchanged between a soft multi-user detector and a bank of turbo decoders to achieve refined estimates of the users＇ signals. The simulations show that the proposed iterative receiver techniques provide significant performance improvement around 2 dB over conventional noniterative methods. Furthermore, iterative multi-user space-time processing techniques offer substantial performance gains around 8 dB by adding the number of receiver antennas from 4 to 6, and the system performance can be enhanced by using this strategy in multi-user STBC systems, which is very important for enlarging the system capacity.