An improved user grouping and power allocation strategy was proposed for high complexity problem of optimal user grouping and power allocation schemes for Non-Orthogonal Multiple Access (NOMA) systems. Firstly, the users were grouped, the first user of each subchannel was determined by channel gain value, and the remaining users were allocated by greedy matching method. Then, the power of user was allocated, and the power allocation problem was divided into two parts: inter-subchannel and intra-subchannel. The power was allocated by the linear water-filling algorithm for inter-subchannels, and the power was allocated by the proposed iterative power allocation algorithm for intra-subchannels. Finally, a Lagrangian function was constructed to maximize the throughput of system under the constraints of maximizing transmit power and guaranteeing the minimum data rate for each user. The simulation results show that in the case of multiple users, compared with the LWF-FTPA (Linear WaterFilling-Fractional Transmit Power Allocation) algorithm and EQ-FTPA (EQual-Fractional Transmit Power Allocation) algorithm, the proposed strategy has system throughput increased by 8% and 20% respectively, indicating that the strategy is better than traditional algorithms.

For femtocell downlink interference in the case of dense deployment of femtocells, a cluster-based resource allocation scheme was proposed. Firstly, Fractional Frequency Reuse (FFR) was used to divide all cells into difference spatial regions, which could suppress the co-tier interference among macrocells and cross-tier interference between a macrocell and femtocells. Secondly, all the femtocells were divided into disjoint clusters based on the knowledge of graph theory and convex optimization theory, then a channel allocation algorithm based on fairness of rate was adopted to assign sub-channel to the femtocell user equipment, for limiting the co-tier interference among femtocells. Finally, a distributed power control algorithm was used to adjust the power of femtocells dynamically to improve the performance of the system. The simulation results show that, compared with the conventional clustering method, the proposed algorithm can improve the Signal-to-Interference plus Noise Ratio (SINR) and throughput, and the probability of system throughput below 4Mb/s was reduced to 30%. At the same time, compared with the non-grouping algorithm, the fairness of the proposed algorithm was improved by 12%, which could make users get higher satisfaction.

In adaptive beamforming, the presence of the desired signal component in the training data, small sample size, and imprecise knowledge of the desired signal steering vector are the main causes of performance degradation. In order to solve this problem, this paper proposed a robust adaptive beamforming algorithm which performed interference-plus-noise covariance matrix reconstruction and desired signal steering vector estimation. In this algorithm, first the interference-plus-noise covariance matrix was reconstructed using Multiple Signal Classification (MUSIC) spatial spectrum in the signal-free angle section, then the constraint that prevented the convergence of the estimate of the desired signal steering vector to any of the interference steering vectors or their linear combination was derived, next this constraint was used together with the maximization of the array output power to formulate an optimization problem of estimating the desired signal steering vector, and convex optimization software was used to yield the desired signal steering vector. In the paper, the computational complexity of the proposed method was discussed and its effectiveness and superiority were validated by simulations. The simulation results demonstrate that the Signal to Interference plus Noise Ratio (SINR) of proposed adaptive beamformer is almost always close to optimal in a very large range of Signal-to-Noise Ratio (SNR) in the scenarios of random signal and interference look direction mismatch and incoherent local scattering, which is more robust than the existing beamformers.

With the trend of global low-carbon, as well as data-centric computing trends, studying the energy-saving green database systems has become a hot issue of government, business and academia. However, traditional database systems mainly focus on performance, and have little consideration on energy metrics, including energy efficiency and energy proportionality. In this paper, based on the requirement analysis on green database systems, some key issues on this topic were explored, and two critical problems were emphasized, namely the energy efficiency problem for database systems, as well as the energy proportionality problem for database clusters. Furthermore, some future directions on energy-aware green database systems were pointed out to provide some new insights for the research into this new area.

In order to meet the requirement of developing a full-customized pattern designing system for the full-electronic flat machine, after introducing the basic principles of the full-electronic flat machine and the role of the computer aided pattern designing system in the flat machine system, the software framework and main modules were analyzed. And by using the object-oriented method, the key data structure and program flows involved in the designing system were focused on, the full-electronic flat machine was realized finally. In the application with the relative flat machine, it is confirmed that relative designing jobs can be finished by utilizing this designing system.

A key problem of real-time operating systems is real-time scheduling policy. Based on the research of Dynamic Window-Constrained Scheduling (DWCS), a periodic window-constrained real-time model with mutual frames was proposed. Firstly, the mode was analyzed and some conclusions about the schedulability of this model were got. In succession, a Dynamic Dual-Window-Constrained Scheduling(DDWCS) was proposed which use the characteristic of mutual frames to improve the schedulability of this model. To evaluate the performance of the new algorithm, extensive simulation studies have been done. These simulations apply DWCS algorithm to schedule the periodic window-constrained real-time model with mutual frames and use it as a baseline to compare with the proposed algorithm. Simulation results show that the new algorithm is better than the old algorithm.