A novel dynamic step size LMS optimization scheme for interference reducing in FBMC-QAM

Filter bank multicarrier quadrature amplitude modulation(FBMC-QAM)will encounter inter-ference and noise during the process of channel transmission.In order to suppress the interference in the communication system,channel equalization is carried out at the receiver.Given that the con-ventional least mean square(LMS)equilibrium algorithm usually suffer from drawbacks such as the inability to converge quickly in large step sizes and poor stability in small step sizes when searching for optimal weights,in this paper,a design scheme for adaptive equalization with dynamic step size LMS optimization is proposed,which can further improve the convergence and error stability of the algorithm by calling the Sigmoid function and introducing three new parameters to control the range of step size values,adjust the steepness of step size,and reduce steady-state errors in small step sta-ges.Theoretical analysis and simulation results demonstrate that compared with the conventional LMS algorithm and the neural network-based residual deep neural network(Res-DNN)algorithm,the adopted dynamic step size LMS optimization scheme can not only obtain faster convergence speed,but also get smaller error values in the signal recovery process,thereby achieving better bit error rate(BER)performance.

DQL-Based Intelligent Scheduling Algorithm for Automatic Driving in Massive MIMO V2I Scenarios

Connected and autonomous vehicle(CAV)vehicle to infrastructure(V2I)scenarios have more stringent requirements on the communication rate,delay,and reliability of the Internet of vehicles(Io V).New radio vehicle to everything(NR-V2X)adopts link adaptation(LA)to improve the efficiency and reliability of road safety information transmission.In order to solve the problem that the existing LA scheduling algorithms cannot adapt to the Doppler shift and complex fast time-varying channel in V2I scenario,resulting in low reliability of information transmission,this paper proposes a deep Q-learning(DQL)-based massive multiple-input multiple-output(MIMO)LA scheduling algorithm for autonomous driving V2I scenario.The algorithm combines deep neural network(DNN)with Q-learning(QL)algorithm,which is used for joint scheduling of modulation and coding scheme(MCS)and space division multiplexing(SDM).The system simulation results show that the algorithm proposed in this paper can fully adapt to the different channel environment in the V2I scenario,and select the optimal MCS and SDM for the transmission of road safety information,thereby the accuracy of road safety information transmission is improved,collision accidents can be avoided,and bring a good autonomous driving experience.

Estimating Reduced-Reference Video Quality for Quality-Based Streaming Video

Reduced-reference （RR） video-quality estimators send a small signature to the receiver. This signature comprises the original video content as well as the video stream. RR quality estimation provides reliability and involves a small data payload. While significant in theory, RR estimators have only recently been used in practice for quality monitoring and adaptive system con- trol in streaming-video frameworks. In this paper, we classify RR algorithms according to whether they are based on a） model- ing the signal distortion, b） modeling the human visual system, or c） analyzing the video signal source. We review proposed RR techniques for monitoring and controlling quality in streaming video systems.

Catalase promotes whitefly adaptation to high temperature by eliminating reactive oxygen species

Thermal stress usually leads to excessive production of reactive oxygen species(ROS)in all aerobic organisms.Catalases(CAT)are the key antioxidant enzymes,which act as the first line of defense against ROS in the antioxidant pathway.The highly invasive and widely distributed whitefly Bemisia tabaci MED damages plants by feeding as well as by transmitting many plant viruses.Previous studies have shown that strong adaptability to high temperature helps explain the spread of MED around the world.However,the mechanism underlying high temperature adaptation of this pest is not well understood.In this study,6 CAT genes were identified from the MED genome and transcriptome dataset,among which BtCAT1,BtCAT2,and BtCAT3 were found to be highly expressed in adults.The expression of BtCAT1,BtCAT2,or BtCAT3 increased with induction temperature and induction time.The MED was exposed with mean high temperature(30℃ or 35℃)and a short-term extremely high temperature(39℃ or 41℃)after the silencing of BtCAT1,BtCAT2,or BtCAT3 to significantly increased ROS levels by at least 0.5 times and significantly decreased survival rate and fecundity of MED adults.The ROS level in the treated specimens gradually returned to a normal level after 24 h at 25℃,but the survival rate still declined significantly.Taken together,our results demonstrate that CAT could help B.tabaci adapt to long-term mean high temperatures and short-term extremely high temperatures by eliminating excessive ROS.

Design support tools to sustain climate change adaptation at the local level： A review and reflection on their suitability

For mid-latitude cities, higher summer temperatures due to climate change are a cause for concern because they aggravate the urban heat island phenomenon and reduce thermal comfort inside buildings. By acquiring the appropriate knowledge and skills, architects and urban designers can become key actors in adaptation to climate change. Two workshops bringing together architects and urban designers provided evidence of deficiencies in this area. We hypothesize that a design support tool （DST） focused on the issue of adaptation of mid- latitude cities to rising summer temperatures could help improve knowledge and skills of professionals in the field. The first section presents the results taken from a review and classification of DSTs, which highlight the toots＇ features that are LikeLy to reach this goal Toots of the ＂hybrid＂ category seem most appropriate. To verify this, seven DSTs were selected and tested by fourteen students enrolled in a graduate-level architecture design studio. The second section presents the results from this test, including an analysis of the final projects, a web- based questionnaire and two focus groups. The relevance of hybrid approaches is established, but the results bring into question the capacity of a single DST to meet the individual and multiple needs of professionals.

A less complicated adaptive equalization for UWB channels

In this paper,a new adaptive equalization scheme aiming at the intersymbol and interuser interfer-ences in the multiuser ultra-wide band(UWB)channels is presented specially for direct-sequence UWB/time-hopping UWB(DS-UWB/TH-UWB)receivers.Its computational complexity and convergence rate are compared with the conventional algorithms.The simulation results show that the computational complexity of this scheme is less than employing the RLS method alone and the convergence rate of this scheme is farther off employing LMS method alone.