In order to solve the challenging coverage problem that the long term evolution（ LTE） networks are facing, a coverage optimization scheme by adjusting the antenna tilt angle（ ATA） of evolved Node B（ e NB） is pro...In order to solve the challenging coverage problem that the long term evolution（ LTE） networks are facing, a coverage optimization scheme by adjusting the antenna tilt angle（ ATA） of evolved Node B（ e NB） is proposed based on the modified particle swarm optimization（ MPSO） algorithm.The number of mobile stations（ MSs） served by e NBs, which is obtained based on the reference signal received power（RSRP） measured from the MS, is used as the metric for coverage optimization, and the coverage problem is optimized by maximizing the number of served MSs. In the MPSO algorithm, a swarm of particles known as the set of ATAs is available; the fitness function is defined as the total number of the served MSs; and the evolution velocity corresponds to the ATAs adjustment scale for each iteration cycle. Simulation results showthat compared with the fixed ATA, the number of served MSs by e NBs is significantly increased by 7. 2%, the quality of the received signal is considerably improved by 20 d Bm, and, particularly, the system throughput is also effectively increased by 55 Mbit / s.展开更多
In this paper,we investigate the coverage optimization for LTE networks considering the network load. The network coverage is defined as the number of served users of evolved Node B(eNB)which is determined by e NBs...In this paper,we investigate the coverage optimization for LTE networks considering the network load. The network coverage is defined as the number of served users of evolved Node B(eNB)which is determined by e NBs' antenna tilt angles(ATA). The coverage is optimized by optimizing the number of served users based on the Modified Particle Swarm Optimization(MPSO)algorithm. Simulation results show that both the number of served users by each e NB and the system throughput are significantly increased. As well,the average load and the bandwidth efficiency of the network are improved.展开更多
Test coverage analysis is a structural testing technique, which helps to evaluate the sufficiency of software testing. This letter presents two test generation algorithms based on binary decision diagrams to produce t...Test coverage analysis is a structural testing technique, which helps to evaluate the sufficiency of software testing. This letter presents two test generation algorithms based on binary decision diagrams to produce tests for the Multiple-Condition Criterion(M-CC) and the Modified Condition/Decision Criterion(MC/DC), and describes the design of the C program Coverage Measurement Tool (CCMT), which can record dynamic behaviors of C programs and quantify test coverage.展开更多
Symbolic execution is an effective way of systematically exploring the search space of a program,and is often used for automatic software testing and bug finding.The program to be analyzed is usually compiled into a b...Symbolic execution is an effective way of systematically exploring the search space of a program,and is often used for automatic software testing and bug finding.The program to be analyzed is usually compiled into a binary or an intermediate representation,on which symbolic execution is carried out.During this process,compiler optimizations influence the effectiveness and efficiency of symbolic execution.However,to the best of our knowledge,there exists no work on compiler optimization recommendation for symbolic execution with respect to(w.r.t.)modified condition/decision coverage(MC/DC),which is an important testing coverage criterion widely used for mission-critical software.This study describes our use of a state-of-the-art symbolic execution tool to carry out extensive experiments to study the impact of compiler optimizations on symbolic execution w.r.t.MC/DC.The results indicate that instruction combining(IC)optimization is the important and dominant optimization for symbolic execution w.r.t.MC/DC.We designed and implemented a support vector machine based optimization recommendation method w.r.t.IC(denoted as auto).The experiments on two standard benchmarks(Coreutils and NECLA)showed that auto achieves the best MC/DC on 67.47%of Coreutils programs and 78.26%of NECLA programs.展开更多
基金The National High Technology Research and Development Program of China(863 Program)(No.2014AA01A702)the National Science and Technology Major Project(No.2013ZX03001032-004)+1 种基金the National Natural Science Foundation of China(No.6122100261201170)
文摘In order to solve the challenging coverage problem that the long term evolution（ LTE） networks are facing, a coverage optimization scheme by adjusting the antenna tilt angle（ ATA） of evolved Node B（ e NB） is proposed based on the modified particle swarm optimization（ MPSO） algorithm.The number of mobile stations（ MSs） served by e NBs, which is obtained based on the reference signal received power（RSRP） measured from the MS, is used as the metric for coverage optimization, and the coverage problem is optimized by maximizing the number of served MSs. In the MPSO algorithm, a swarm of particles known as the set of ATAs is available; the fitness function is defined as the total number of the served MSs; and the evolution velocity corresponds to the ATAs adjustment scale for each iteration cycle. Simulation results showthat compared with the fixed ATA, the number of served MSs by e NBs is significantly increased by 7. 2%, the quality of the received signal is considerably improved by 20 d Bm, and, particularly, the system throughput is also effectively increased by 55 Mbit / s.
基金supported by National 863 Program(2014AA01A702)National Major Project(2013ZX03001032-004)+1 种基金National Natural Science Foundation(61221002 and 61201170)the Fundamental Research Funds for the Central Universities(CXLX13 093)
文摘In this paper,we investigate the coverage optimization for LTE networks considering the network load. The network coverage is defined as the number of served users of evolved Node B(eNB)which is determined by e NBs' antenna tilt angles(ATA). The coverage is optimized by optimizing the number of served users based on the Modified Particle Swarm Optimization(MPSO)algorithm. Simulation results show that both the number of served users by each e NB and the system throughput are significantly increased. As well,the average load and the bandwidth efficiency of the network are improved.
文摘Test coverage analysis is a structural testing technique, which helps to evaluate the sufficiency of software testing. This letter presents two test generation algorithms based on binary decision diagrams to produce tests for the Multiple-Condition Criterion(M-CC) and the Modified Condition/Decision Criterion(MC/DC), and describes the design of the C program Coverage Measurement Tool (CCMT), which can record dynamic behaviors of C programs and quantify test coverage.
基金Project supported by the National Key R&D Program of China(No.2017YFB1001802)the National Natural Science Foundation of China(Nos.61472440,61632015,61690203,and 61532007)。
文摘Symbolic execution is an effective way of systematically exploring the search space of a program,and is often used for automatic software testing and bug finding.The program to be analyzed is usually compiled into a binary or an intermediate representation,on which symbolic execution is carried out.During this process,compiler optimizations influence the effectiveness and efficiency of symbolic execution.However,to the best of our knowledge,there exists no work on compiler optimization recommendation for symbolic execution with respect to(w.r.t.)modified condition/decision coverage(MC/DC),which is an important testing coverage criterion widely used for mission-critical software.This study describes our use of a state-of-the-art symbolic execution tool to carry out extensive experiments to study the impact of compiler optimizations on symbolic execution w.r.t.MC/DC.The results indicate that instruction combining(IC)optimization is the important and dominant optimization for symbolic execution w.r.t.MC/DC.We designed and implemented a support vector machine based optimization recommendation method w.r.t.IC(denoted as auto).The experiments on two standard benchmarks(Coreutils and NECLA)showed that auto achieves the best MC/DC on 67.47%of Coreutils programs and 78.26%of NECLA programs.
