Throughput Analysis of IEEE 802.11 Multirate WLANs with Collision Aware Rate Adaptation Algorithm

This paper presents a mathematical model that analyzes the throughput of the IEEE 802.11b distributed coordination function （DCF） with the collision aware rate adaptation （CARA） algorithm. IEEE 802.11 WLANs provide multiple transmission rates to improve system throughput by adapting the transmission rate to the current channel conditions. The system throughput is determined by some stations using low transmission rates due to bad channel conditions. CARA algorithm does not disturb the existing IEEE 802.11b formats and it can be easily incorporated into the commercial wireless local area networks （WLAN） devices. Finally, we verify our findings with simulation.

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.

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.

UEs Power Reduction Evolution with Adaptive Mechanism over LTE Wireless Networks

At present, the major drawback for mobile phones is the issue of power consumption. As one of the alternatives to decrease the power consumption of standard, power-hungry location-based services usually require the knowledge of how individual phone features consume power. A typical phone feature is that the applications related to multimedia streaming utilize more power while receiving, processing, and displaying the multimedia contents, thus contributing to the increased power consumption. There is a growing concern that current battery modules have limited capability in fulfilling the long-term energy need for the progress on the mobile phone because of increasing power consumption during multimedia streaming processes. Considering this, in this paper, we provide an offline meaning sleep-mode method to compute the minimum power consumption comparing with the power-on solution to save power by implementing energy rate adaptation(RA) mechanism based on mobile excess energy level purpose to save battery power use. Our simulation results show that our RA method preserves efficient power while achieving better throughput compared with the mechanism without rate adaptation(WRA).

Markov Based Rate Adaption Approach for Live Streaming over HTTP/2

Dynamic adaptive streaming over HTTP （DASH） has been widely deployed. However, large latency in HTTP/1.1 cannot meet the requirements of live streaming. Data- pushing in HTFP/2 is emerging as a promising technology. For video live over HTTP/2, new challenges arise due to both low-delay and small buffer constraints. In this paper, we study the rate adaption problem over HTFP/2 with the aim to improve the quality of experience （QoE） of live streaming. To track the dynamic characteristics of the streaming system, a Markov-theoretical approach is employed. System variables are taken into account to describe the system state, by which the system transi- tion probability is derived. Moreover, we design a dynamic reward function considering both the quality of user experience and dynamic system variables. Therefore, the rate adaption problem is formulated into a Markov decision based optimization problem and the best streaming policy is obtained. At last, the effectiveness of our proposed rate adaption scheme is demonstrated by numerous experiment results.

Adaptive coherence estimator based on the Krylov subspace technique for airborne radar

A novel adaptive detector for airborne radar space-time adaptive detection （STAD） in partially homogeneous environments is proposed. The novel detector combines the numerically stable Krylov subspace technique and diagonal loading technique, and it uses the framework of the adaptive coherence estimator （ACE）. It can effectively detect a target with low sample support. Compared with its natural competitors, the novel detector has higher proba- bility of detection （PD）, especially when the number of the training data is low. Moreover, it is shown to be practically constant false alarm rate （CFAR）.

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.

Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery

In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.

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.

Assessment of Adaptive Rate Response Provided by Accelerometer, Minute Ventilation and Dual Sensor Compared with Normal Sinus Rhythm During Exercise： A Self-controlled Study in Chronotropically Competent Subjects

Background：Dual sensor （DS） for rate adaption was supposed to be more physiological.To evaluate its superiority,the DS （accelerometer [ACC] and minute ventilation [MV]） and normal sinus rate response were compared in a self-controlled way during exercise treadmill testing.Methods：This self-controlled study was performed in atrioventricular block patients with normal sinus function who met the indications of pacemaker implant.Twenty-one patients came to the 1-month follow-up visit.Patients performed a treadmill test 1-month post implant while programmed in DDDR and sensor passive mode.For these patients,sensor response factors were left at default settings （ACC =8,MV =3） and sensor indicated rates （SIRs） for DS,ACC and MV sensor were retrieved from the pacemaker memories,along with measured sinus node （SN） rates from the beginning to 1-minute after the end of the treadmill test,and compared among study groups.Repeated measures analysis of variance and profile analysis,as well as variance analysis of randomized block designs,were used for statistical analysis.Results：Fifteen patients （15/2 l） were determined to be chronotropically competent.The mean differences between DS SIRs and intrinsic sinus rates during treadmill testing were smaller than those for ACC and MV sensor （mean difference between SIR and SN rate：ACC vs.SN,MV vs.SN,DS vs.SN,respectively,34.84,17.60,16.15 beats/min）,though no sensors could mimic sinus rates under the default settings for sensor response factor （ACC vs.SN P-adjusted 〈 0.001; MV vs.SN P-adjusted =0.002; DS vs.SN P-adjusted =0.005）.However,both in the range of 1st minute and first 3 minutes of exercise,only the DS SIR profile did not differ from sinus rates （P-adjusted =0.09,0.90,respectively）.Conclusions：The DS under default settings provides more physiological rate response during physical activity than the corresponding single sensors （ACC or MV sensor）.Further study is needed to determine if individual optimization would further improve adaptive performance of the DS.

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.

Delay-aware downlink beamforming with discrete rate adaptation for green cloud radio access network

With the popularity of variety delay-sensitive services, how to guarantee the delay requirements for mobile users （MUs） is a great challenge for downlink beamformer design in green cloud radio access networks （C-RANs）. In this paper, we consider the problem of the delay-aware downlink beamforming with discrete rate adaptation to minimize the power consumption of C-RANs. We address the problem via a mixed integer nonlinear program （MINLP）, and then reformulate the MINLP problem as a mixed integer second-order cone program （MI-SOCP）, which is a convex program when the integer variables are relaxed as continuous ones. Based on this formulation, a deflation algorithm, whose computational complexity is polynomial, is proposed to derive the suboptimal solution. The simulation results are presented to validate the effectiveness of our proposed algorithm.

Fair and Bandwidth-Efficient Broadcast Link Rate Adaption in Wireless LANs

Broadcast delivery of bulk multimedia files is an important wireless LAN application for densely populated scenarios such as inside high-speed train carriages. However, it is a challenge to adapt the broadcast link rate due to heterogeneity and varying channel conditions of the broadcast receivers to achieve both high bandwidth efficiency and fairness. In this paper, the broadcast link rate adaption problem is formulated as a quadratic programming problem with a broadcast link rate adaption algorithm named FBB （Fair and Bandwidth-efficient Broadcast）. Simulation results show that the algorithm significantly outper- forms fixed rate broadcast with only a small loss compared to its theoretical performance. The algorithm has been successfully applied in a practical wireless LAN access point.

Tuning the Learning Rate for Stochastic Variational Inference

Stochastic variational inference （SVI） can learn topic models with very big corpora. It optimizes the variational objective by using the stochastic natural gradient algorithm with a decreasing learning rate. This rate is crucial for SVI; however, it is often tuned by hand in real applications. To address this, we develop a novel algorithm, which tunes the learning rate of each iteration adaptively. The proposed algorithm uses the Kullback-Leibler （KL） divergence to measure the similarity between the variational distribution with noisy update and that with batch update, and then optimizes the learning rates by minimizing the KL divergence. We apply our algorithm to two representative topic models： latent Dirichlet allocation and hierarchical Dirichlet process. Experimental results indicate that our algorithm performs better and converges faster than commonly used learning rates.

Rate Adaptation Schemes for Different Service Requirements in the Code Division Multiple Access Environment

the of the important requirements of any candidate architecture for the future personal communication System is the flexibility to support several different service requirements. These services may require different bearer rates or different Ed/N0 requirements. This paper will propose two rate adaptation schemes which could be applied to the Code Division Multiple Access (CDMA) architecture for accepting different service requirements.'.

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.

Speed up Training of the Recurrent Neural Network Based on Constrained optimization Techniques

In this paper, the constrained optimization technique for a substantial prob-lem is explored, that is accelerating training the globally recurrent neural net-work. Unlike most of the previous methods in feedforward neuxal networks, the authors adopt the constrained optimization technique to improve the gradiellt-based algorithm of the globally recuxrent neural network for the adaptive learn-ing rate during training. Using the recurrent network with the improved algo-rithm, some experiments in two real-world problems, namely filtering additive noises in acoustic data and classification of temporal signals for speaker identifi-cation, have been performed. The experimental results show that the recurrent neural network with the improved learning algorithm yields significantly faster training and achieves the satisfactory performance.

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.