Joint Optimization of Admission Control and Rate Adaptation for Video Sharing over Multirate Wireless Community Cloud

Emerging wireless community cloud enables usergenerated video content to be shared and consumed in a social context. However, the nature of shared wireless medium and timevarying channels seriously limits the quality of service(QoS), partially owing to the lack of mechanisms for effectively utilizing multi-rate channel resources. In this paper, the joint optimization of admission control and rate adaptation is proposed, resulting in a bandwidth-aware rate-adaptive admission control(BRAC) scheme to provide bandwidth guarantee for sharing social multimedia contents. The analytical approach leads to the following major contributions:(1) a bandwidth-aware rate selection(BRS) algorithm to optimally meet the bandwidth requirement of the data session and channel conditions at the physical layer;(2) a routing-coupled rate adaption and admission control algorithm to admit data sessions with bandwidth guarantee. Moreover, extensive numerical simulations suggest that BRAC is efficient and effective in meeting the bandwidth requirements for sharing social multimedia contents. These insights will shed light on communication system implementation for multimedia content sharing over multirate wireless community cloud.

Rate Adaptation for Decoding-and-Forward Relay Channel by Random Projections Codes

This paper investigates rate adaptation schemes for decoding-and-forward (DF) relay system based on random projections codes (RPC). We consider a classic three node relay system model, where relay node performs on half-duplex mode. Then, we give out receiving diversity relay scheme and coding diversity relay scheme, and present their jointly decoding methods. Furthermore, we discuss the performance of the two schemes with different power allocation coefficients. Simulations show that our relay schemes can achieve different gain with the help of relay node. And, we should allocate power to source node to just guarantee relay node can decode successfully, and allocate remain power to relay node as far as possible. In this way, this DF relay system not only achieves diversity gain, but also achieves higher and smooth spectrum efficiency.

Downlink call admission control with power allocation and rate scheduling for IDMA-based multi-beam satellite systems

Considering the advantage of interleave-division multiple-access(IDMA) technique and the technical bottlenecks in the existing satellite systems,IDMA is introduced into satellite communication networks.To further validate the IDMA into satellite systems,an effective call admission control(CAC) is proposed to maximize the resource utilization.After establishing the multi-beam satellite system model based on variable spreading gain(VSG) IDMA,the power allocation scheme based on SINR evolution technique and transmission rate adaptation for nonreal time interactive traffic are designed as integrated parts of the CAC,working together to improve the system performance in terms of power efficiency and throughput.Further,the analysis and simulation results show that IDMA under the proposed scheme can provide better QoS,in terms of the blocking/dropping probability,outage probability as well as delay performance.

WORST SUBCARRIER AVOIDING WATER-FILLING SUBCARRIER ALLOCATION SCHEME FOR OFDM-BASED CRN

Efficient and reliable subcarrier power joint allocation is served as a promising problem in cognitive OFDM-based Cognitive Radio Networks (CRN). This paper focuses on optimal subcarrier allocation for OFDM-based CRN. We mainly propose subcarrier allocation scheme denoted as Worst Subcarrier Avoiding Water-filling (WSAW), which is based on Rate Adaptive (RA) criterion and three constraints are considered in CRN. The algorithm divides the assignment procedure into two phases. The first phase is an initial subcarrier allocation based on the idea of avoiding selecting the worst subcarrier in order to maximize the transmission rate; while the second phase is an iterative adjustment process which is realized by swapping pairs of subcarriers between arbitrary users. The proposed scheme could assign subcarriers in accordance with channel coherence time. Hence, real time subcarrier allocation could be implemented. Simulation results show that, comparing with the similar existing algorithms, the proposed scheme could achieve larger capacity and a near-optimal BER performance.

Research on Key Technologies of Electronic Shelf Labels Based on LoRa

The demand for Electronic Shelf Labels(ESL),according to the Internet of Things(IoT)paradigm,is expected to grow considerably in the immediate future.Various wireless communication standards are currently contending to gain an edge over the competition and provide the massive connectivity that will be required by a world in which everyday objects are expected to communicate with each other.Low-Power Wide-Area Networks(LPWANs)are continuously gaining momentum among these standards,mainly thanks to their ability to provide long-range coverage to devices,exploiting license-free frequency bands.The main theme of this work is one of the most prominent LPWAN technologies,LoRa.The purpose of this research is to provide long-range,less intermediate node,less energy dissipation,and a cheaper ESL system.Much research has already been done on designing the LoRaWAN network,not capable to make a reliable network.LoRa is using different gateways to transmit the same data,collision,data jamming,and data repetition are expected.According to the transmission behavior of LoRa,50%of data is lost.In this paper,the Improved Backoff Algorithm with synchronization technique is used to decrease overlapping and data loss.Besides,the improved Adaptive Data Rate algorithm(ADR)avoids the collision in concurrently transmitted data by using different Spreading Factors(SFs).The allocation of SF has the main role in designing LoRa based network to minimize the impact of the intra-interference,cost function,and Euclidean distance.For this purpose,the K-means machine learning algorithm is used for clustering.The data rate model is using an intra-slicing technique based on Maximum Likelihood Estimation(MLE).The data rate model includes three critical communication slices,High Critical Communication(HCC),Medium Critical Communication(MCC),and Low Critical Communication(LCC),having the specified number of End devices(EDs),payload budget delay,and data rate.Finally,different combinations of gateways are used to build ESL for 200 electronic shelf labels.

DP-ASSGD: Differential Privacy Protection Based on Stochastic Gradient Descent Optimization

Recently,differential privacy algorithms based on deep learning have become increasingly mature.Previous studies provide privacy mostly by adding differential privacy noise to the gradient,but it will reduce the accuracy,and it is difficult to balance privacy and accuracy.In this paper,the DP-ASSGD algo-rithm is proposed to counterpoise privacy and accuracy.The convergence speed is improved,the number of optimized iterations is decreased,and the privacy loss is significantly reduced.On the other hand,by using the postprocessing immunity characteristics of the differential privacy model,the Laplace smoothing mecha-nism is added to make the training process more stable and the generalization ability stronger.The experiment uses the MNIST dataset,with the same privacy budget,and compared with the existing differential privacy algorithms,the accu-racy is improved by 1.8%on average.When achieving the same accuracy,the DP-ASSGD algorithm consumes less privacy budget.

Real-time tool condition monitoring method based on in situ temperature measurement and artificial neural network in turning

Tool failures in machining processes often cause severe damages of workpieces and lead to large quantities of loss,making tool condition monitoring an important,urgent issue.However,problems such as practicability still remain in actual machining.Here,a real-time tool condition monitoring method integrated in an in situ fiber optic temperature measuring apparatus is proposed.A thermal simulation is conducted to investigate how the fluctuating cutting heats affect the measuring temperatures,and an intermittent cutting experiment is carried out,verifying that the apparatus can capture the rapid but slight temperature undulations.Fourier transform is carried out.The spectrum features are then selected and input into the artificial neural network for classification,and a caution is given if the tool is worn.A learning rate adaption algorithm is introduced,greatly reducing the dependence on initial parameters,making training convenient and flexible.The accuracy stays 90%and higher in variable argument processes.Furthermore,an application program with a graphical user interface is constructed to present real-time results,confirming the practicality.

Adam revisited:a weighted past gradients perspective

Adaptive learning rate methods have been successfully applied in many fields,especially in training deep neural networks.Recent results have shown that adaptive methods with exponential increasing weights on squared past gradients(i.e.,ADAM,RMSPROP)may fail to converge to the optimal solution.Though many algorithms,such as AMSGRAD and ADAMNC,have been proposed to fix the non-convergence issues,achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these methods.In this paper,we propose a novel adaptive method weighted adaptive algorithm(WADA)to tackle the non-convergence issues.Unlike AMSGRAD and ADAMNC,we consider using a milder growing weighting strategy on squared past gradient,in which weights grow linearly.Based on this idea,we propose weighted adaptive gradient method framework(WAGMF)and implement WADA algorithm on this framework.Moreover,we prove that WADA can achieve a weighted data-dependent regret bound,which could be better than the original regret bound of ADAGRAD when the gradients decrease rapidly.This bound may partially explain the good performance of ADAM in practice.Finally,extensive experiments demonstrate the effectiveness of WADA and its variants in comparison with several variants of ADAM on training convex problems and deep neural networks.