Performance Analysis Of VSF-OFCDM Femtocells

In this thesis, we analyze the performance of a variable spreading factor (VSF) OFCDM employed in femtocells, with OFDM used in macrocells in a hybrid heterogenous network. Orthogonal subcarriers are assigned to macro users and for femtocell users, non-contiguous subcarrier grouping is employed. We derive the analytic expression of the BER for uplink VSF-OFCDM femto and OFDM macro users for the case of maximal ratio combining receiver. We evaluate the performance of femto/macro users in VSF-OFCDM system through numerical and Monte Carlo simulation studies. Improvement in BER of the femtocell users is also noted. The relationship between the femto spreading factor and femto/macro BER is analyzed. We present, the relationship between the channel load and optimum spreading factor employed by femtocell users for the energy efficient performance of macro users. Femto wall penetration loss, that is the important parameter to evaluate the femto performance, is also taken into account. Also, effect of femto wall penetration on macro BER is evaluated for various spreading factors. Following our study, we find that interference-limited system favors increased time spreading especially when number of subcarriers is limited and noise-limited system favors increased frequency domain spreading. When large number of subcarriers are available, optimum spreading (from macro perspective) favors increased frequency domain spreading regardless of the femto-macro loads, or whether operating environment is noise or interference limited. Once the optimum spreading factor is determined, increase or decrease in the femto Eb/No does not matter. Also, femto wall penetration factor not only effects the femto BER directly, but also reduce the potential interference faced by macro user equipment (UEs). As a result macro BER is improved, but the choice of optimal spreading factor for macro UEs remain unaffected with the variation in femto wall penetration loss.

Performance Analysis Of VSF-OFCDM Femtocells

In this thesis, we analyze the performance of a variable spreading factor (VSF) OFCDM employed in femtocells, with OFDM used in macrocells in a hybrid heterogenous network. Orthogonal subcarriers are assigned to macro users and for femtocell users, non-contiguous subcarrier grouping is employed. We derive the analytic expression of the BER for uplink VSF-OFCDM femto and OFDM macro users for the case of maximal ratio combining receiver. We evaluate the performance of femto/macro users in VSF-OFCDM system through numerical and Monte Carlo simulation studies. Improvement in BER of the femtocell users is also noted. The relationship between the femto spreading factor and femto/macro BER is analyzed. We present, the relationship between the channel load and optimum spreading factor employed by femtocell users for the energy efficient performance of macro users. Femto wall penetration loss, that is the important parameter to evaluate the femto performance, is also taken into account. Also, effect of femto wall penetration on macro BER is evaluated for various spreading factors. Following our study, we find that interference-limited system favors increased time spreading especially when number of subcarriers is limited and noise-limited system favors increased frequency domain spreading. When large number of subcarriers are available, optimum spreading (from macro perspective) favors increased frequency domain spreading regardless of the femto-macro loads, or whether operating environment is noise or interference limited. Once the optimum spreading factor is determined, increase or decrease in the femto Eb/No does not matter. Also, femto wall penetration factor not only effects the femto BER directly, but also reduce the potential interference faced by macro user equipment (UEs). As a result macro BER is improved, but the choice of optimal spreading factor for macro UEs remain unaffected with the variation in femto wall penetration loss.

Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network

Background Although targeted drugs have contributed to impressive advances in the treatment of cancer patients, their clinical benefits on tumor therapies are greatly limited due to intrinsic and acquired resistance of cancer cells against such drugs. Drug combinations synergistically interfere with protein networks to inhibit the activity level of carcinogenic genes more effectively, and therefore play an increasingly important role in the treatment of complex disease. Results In this paper, we combined the drug similarity network, protein similarity network and known drug-protein associations into a drug-protein heterogenous network. Next, we ran random walk with restart (RWR) on the heterogenous network using the combinatorial drug targets as the initial probability, and obtained the converged probability distribution as the feature vector of each drug combination. Taking these feature vectors as input, we trained a gradient tree boosting (GTB) classifier to predict new drug combinations. We conducted performance evaluation on the widely used drug combination data set derived from the DCDB database. The experimental results show that our method outperforms seven typical classifiers and traditional boosting algorithms. Conclusions The heterogeneous network-derived features introduced in our method are more informative and enriching compared to the primary ontology features, which results in better performance. In addition, from the perspective of network pharmacology, our method effectively exploits the topological attributes and interactions of drug targets in the overall biological network, which proves to be a systematic and reliable approach for drug discovery.