Pico Cell Densification Study in LTE Heterogeneous Networks

Heterogeneous Network (HetNet) deployment has been considered as the main approach to boost capacity and coverage in Long Term Evolu-tion (LTE) networks in order to fulfill the huge future demand on mo-bile broadband usage. In order to study the improvement on network performance, i.e. capacity, coverage and user throughput, from pico cell densification in LTE HetNets, a network densification algorithm which determines the placement locations of the pico sites based on pathloss has been designed and applied to build several network models with different pico cell densities. The study has been taken based on a real radio network in a limited urban area using an advanced Matlab-based radio network simulator. The simulation results show that the network performance generally is enhanced by introducing more pico cells to the network

Pico Cell Densification Study in LTE Heterogeneous Networks

Heterogeneous Network (HetNet) deployment has been considered as the main approach to boost capacity and coverage in Long Term Evolu-tion (LTE) networks in order to fulfill the huge future demand on mo-bile broadband usage. In order to study the improvement on network performance, i.e. capacity, coverage and user throughput, from pico cell densification in LTE HetNets, a network densification algorithm which determines the placement locations of the pico sites based on pathloss has been designed and applied to build several network models with different pico cell densities. The study has been taken based on a real radio network in a limited urban area using an advanced Matlab-based radio network simulator. The simulation results show that the network performance generally is enhanced by introducing more pico cells to the network

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

Abstract With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications

Probiotic Escherichia coli Nissle 1917 protect chicks from damage caused by Salmonella enterica serovar Enteritidis colonization

As a foodborne pathogen of global importance, Salmonella enterica serovar Enteritidis (S. Enteritidis) is a threat to public health that is mainly spread by poultry products. Intestinal Enterobacteriaceae can inhibit the colonization of S. Enteritidis and are regarded as a potential antibiotic substitute. We investigated, in chicks, the anti-S. Enteritidis effects of Escherichia coli (E. coli) Nissle 1917, the most well-known probiotic member of Enterobacteriaceae. Eighty 1-d-old healthy female AA broilers were randomly divided into 4 groups, with 20 in each group, namely the negative control (group P), the E. coli Nissle 1917-treated group (group N), the S. Enteritidis-infected group (group S) and the E. coli Nissle 1917-treated and S. Enteritidis-infected group (group NS). From d 5 to 7, chicks in groups N and NS were orally gavaged once a day with E. coli Nissle 1917 and in groups P and S were administered the same volume of sterile PBS. At d 8, the chicks in groups S and NS were orally gavaged with S. Enteritidis and in groups P and N were administered the same volume of sterile PBS. Sampling was conducted 24 h after challenge. Results showed that gavage of E. coli Nissle 1917 reduced the spleen index, Salmonella loads, and inflammation (P < 0.05). It improved intestinal morphology and intestinal barrier function (P < 0.05). S. Enteritidis infection significantly reduced mRNA expression of angiotensin-converting enzyme 2 (ACE2) and solute carrier family 6-member 19 (SLC6A19) in the cecum and the content of Gly, Ser, Gln, and Trp in the serum (P < 0.05). Pretreatment with E. coli Nissle 1917 yielded mRNA expression of ACE2 and SLC6A19 in the cecum and levels of Gly, Ser, Gln, and Trp in the serum similar to that of uninfected chicks (P < 0.05). Additionally, E. coli Nissle 1917 altered cecum microbiota composition and enriched the abundance of E. coli, Lactobacillales, and Lachnospiraceae. These findings reveal that the probiotic E. coli Nissle 1917 reduced S. Enteritidis infection and shows enormous potential as an alternative to antibiotics

Infection Heterogeneity and Microbiota Differences in Chicks Infected by Salmonella enteritidis

This study was conducted to compare the infection heterogeneity and cecal microbiota in chicks infected by S. enteritidis. Forty-eight 8-d-old female Arbor Acres chicks were challenged with S. enteritidis and euthanized 24 h later. The eight chicks with the highest Salmonella tissue loads were assigned to group S (S. enteritidis-susceptible), and the eight chicks with the lowest Salmonella tissue loads were assigned to group R (S. enteritidis-resistant). Chicks in group S showed a higher liver index (p &lt; 0.05), obvious liver lesions, and an decreasing trend for the villus height-to-crypt depth ratio (p &lt; 0.10), compared with those in group R. Gene expression of occludin, MUC2, and IL10 was higher, whereas that of iNOS and IL6 was lower (p &lt; 0.05), in chicks of group R relative to those in group S. Separation of the cecal microbial community structure has been found between the two groups. The S. enteritidis-susceptible chicks showed higher abundance of pathogenic bacteria (Fusobacterium and Helicobacter) in their cecal, while Desulfovibrio_piger was enriched in the cecal of S. enteritidis-resistant chicks. In summary, chicks showed heterogeneous responses to S. enteritidis infection. Enhanced intestinal barrier function and cecal microbiota structure, especially a higher abundance of Desulfovibrio_piger, may help chicks resist S. enteritidis invasion