Approximate Iteration Detection and Precoding in Massive MIMO

Massive multiple-input multiple-output provides improved energy efficiency and spectral efficiency in 5 G. However it requires large-scale matrix computation with tremendous complexity, especially for data detection and precoding. Recently, many detection and precoding methods were proposed using approximate iteration methods, which meet the demand of precision with low complexity. In this paper, we compare these approximate iteration methods in precision and complexity, and then improve these methods with iteration refinement at the cost of little complexity and no extra hardware resource. By derivation, our proposal is a combination of three approximate iteration methods in essence and provides remarkable precision improvement on desired vectors. The results show that our proposal provides 27%-83% normalized mean-squared error improvement of the detection symbol vector and precoding symbol vector. Moreover, we find the bit-error rate is mainly controlled by soft-input soft-output Viterbi decoding when using approximate iteration methods. Further, only considering the effect on soft-input soft-output Viterbi decoding, the simulation results show that using a rough estimation for the filter matrix of minimum mean square error detection to calculating log-likelihood ratio could provideenough good bit-error rate performance, especially when the ratio of base station antennas number and the users number is not too large.

A Distributed Cooperative Localization Algorithm for Mobile Multi-platforms Oriented to Unpredicted Communication Topology

The cooperative localization(CL)is affected by the communication topology among the platforms.Based on the unscented Kalman filtering,the distributed CL(DCL)oriented to the unpredicted communication topology is investigated.To improve the adaptability,the character of the look-up Cholesky decomposition is exploited for the covariance matrix decomposing.Then,the distributed U transformation can be dynamically implemented according to the available communication topology.In the proposed algorithm,the global information is not required for the individual,and only the available information from the neighbor is used.Each platform’s state can be estimated independently.The error covariance of the state estimates can be updated in the single platform.The algorithm is adaptive to any serial communication topologies where the measuring to the measured platform is a starting path.The applicability of the proposed algorithm to unpredicted communication topology is improved,remaining equivalent localization performance to free connection communication.

Self-starting all-fiber PM Er:laser mode locked by a biased nonlinear amplifying loop mirror

A compact all-fiber polarization-maintaining Er:laser using a nonlinear amplifying loop mirror is reported. Fundamental single-pulse mode-locking operation can always self start, with a cavity round-trip decreased from ~ 4.7 m to~ 1.7 m. When the pulse repetition rate is 121.0328 MHz, output pulse is measured to have a center wavelength/3-d B spectral bandwidth/radio frequency signal to noise ratio(SNR)/pulse width of 1571.65 nm/18.70 nm/80 d B/477 fs, respectively. Besides, three states including the exponential growth, damping state, and steady state are investigated through the build-up process both experimentally and numerically. Excellent stability of this compact Er:laser is further evaluated,demonstrating that it can be an easy-fabrication maintenance-free ultrafast candidate for the scientific area of this kind.

Distributed cooperative localization for sparse communication network with multi-locating messages

In cooperative localization with sparse communication networks, an agent maybe only receives part of locating messages from the others. It is difficult for the receiver to utilize the part instead of global knowledge. Under the extended Kalman filtering, the utilization of the locating message is maximized by two aspects： the locating message generating and multi-locating messages fusing. For the former, the covariance upper-bound technique, by introducing amplification coefficients, is employed to remove the dependency of locating messages on the global knowledge. For the latter, an optimization model is setup; the covariance matrix determinant of the receiver＇s state estimate, expressed as a function of the amplification coefficients, is selected as the optimization criterion, under linear constraints on the amplification coefficient characteristics and the communication connectivity. Using the optimization solution, the local optimal state of the receiver agent is obtained by the weighting fusion. Simulation with seven agents is shown to evaluate the effectiveness of the proposed algorithm.

Internal energy transfer phenomenon and light-emission properties of γ-LiAlO_2 phosphor doped with Mn^(2+)

γ-LiAlO2 phosphor was synthesized using the cellulose-citric acid sol-gel method, and its light emission and energy transfer properties were investigated. Excitation and emission spectrum analysis revealed a decrease in intensity of the spectrum as the amount of Mn2＋ doping increased. Blasse＇s equation determined the maximum distance for energy transfer between Mn2＋ ions as 4.3142 nm. Dexter＇s theory verifies that the mechanism of energy transfer between Mn2＋ ions conforms to an electric dipole and electric quadrupole interaction.

Compensation Algorithm Based on Estimation State for Transmission Delay in Cooperative Localization

In order to maximize the utilization of the observation information in the cooperative localization,a compensation algorithm based on the estimation state is presented for transmission delay.Under the framework of the Kalman filter,two different processes of state estimating with and without transmission delay are investigated and contrasted.The expression of difference quantity caused by transmission delay is derived.It is used to compensate the present estimation state instead of the observed information compensation.According to the characteristics of state transition matrix,an equivalent expression of which successively impacts on the covariance factor in delay time is obtained.The simulation results show that the present estimated state is effectively corrected by transmission information and the relevance among agents is accurately updated.As a result,a higher positioning accuracy is achieved.Meanwhile,the consumption of recording and multiplication of the state transition matrix is saved.

Stability of weighted spectral distribution in a pseudo tree-like network model

The comparison of networks with different orders strongly depends on the stability analysis of graph features in evolving systems. In this paper, we rigorously investigate the stability of the weighted spectral distribution（i.e., a spectral graph feature） as the network order increases. First, we use deterministic scale-free networks generated by a pseudo treelike model to derive the precise formula of the spectral feature, and then analyze the stability of the spectral feature based on the precise formula. Except for the scale-free feature, the pseudo tree-like model exhibits the hierarchical and small-world structures of complex networks. The stability analysis is useful for the classification of networks with different orders and the similarity analysis of networks that may belong to the same evolving system.

Test Method of Laser Detection Sensitivity Based on Every Pulse Measurement and Rearrangement

It is very important to accurately measure the detection sensitivity of laser receiving equipment. Based on the traditional test method of detection probability curve, a new test method is proposed which works through measuring laser pulse one by one. Accurate measurement systems were constructed to improve the accuracy of laser energy measurement and energy regulation. A new data processing method of detection probability curve is put forward, which based on subsection statistics. The new data processing method in effect reduces the light source instability from 6.57% to 0.67%. These works improve the test accuracy of laser receiving detection sensitivity. It is a great support for the accurate evaluation of key technical indices of laser receiving equipment, which subsequently are done by models and simulation.

Scaling of weighted spectral distribution in weighted small-world networks

Many real-world systems can be modeled by weighted small-world networks with high clustering coefficients. Recent studies for rigorously analyzing the weighted spectral distribution（W SD） have focused on unweighted networks with low clustering coefficients. In this paper, we rigorously analyze the W SD in a deterministic weighted scale-free small-world network model and find that the W SD grows sublinearly with increasing network order（i.e., the number of nodes） and provides a sensitive discrimination for each input of this model. This study demonstrates that the scaling feature of the W SD exists in the weighted network model which has high and order-independent clustering coefficients and reasonable power-law exponents.

Attention-based encoder-decoder model for answer selection in question answering

One of the key challenges for question answering is to bridge the lexical gap between questions and answers because there may not be any matching word between them. Machine translation models have been shown to boost the performance of solving the lexical gap problem between question-answer pairs. In this paper, we introduce an attention-based deep learning model to address the answer selection task for question answering. The proposed model employs a bidirectional long short-term memory （LSTM） encoder-decoder, which has been demonstrated to be effective on machine translation tasks to bridge the lexical gap between questions and answers. Our model also uses a step attention mechanism which allows the question to focus on a certain part of the candidate answer. Finally, we evaluate our model using a benchmark dataset and the results show that our approach outperforms the existing approaches. Integrating our model significantly improves the performance of our question answering system in the TREC 2015 LiveQA task.

Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder

The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder （KL-SAE） is proposed in this paper, which can be applied to battle damage assessment （BDA）. This method can select automatically the hidden layer feature which contributes most to data reconstruction, and abandon the hidden layer feature which contributes least. Therefore, the structure of the network can be modified. In addition, the method can select automatically hidden layer feature without loss of the network prediction accuracy and increase the computation speed. Experiments on University ofCalifomia-Irvine （UCI） data sets and BDA for battle damage data demonstrate that the method outperforms other reference data-driven methods. The following results can be found from this paper. First, the improved KL-SAE regression network can guarantee the prediction accuracy and increase the speed of training networks and prediction. Second, the proposed network can select automatically hidden layer effective feature and modify the structure of the network by optimizing the nodes number of the hidden layer.

Towards all-fiber structure pulsed mid-infrared laser by gas-filled hollow-core fibers

We report here on a diode-pumped pulsed mid-infrared laser source based on gas-filled hollow-core fibers(HCFs)towards an all-fiber structure by the tapering method. The pump laser is coupled into an acetylene-filled HCF through a tapered single-mode fiber. By precisely tuning the wavelength of the diode to match different absorption lines of acetylene near 1.5 μm, mid-infrared emission around 3.1–3.2 μm is generated. With 2 m HCFs and3 mbar acetylene gas, a maximum average power of 130 m W is obtained with a laser slope efficiency of ~24%.This work provides a potential scheme for all-fiber mid-infrared fiber gas lasers.

Alternating Direction Method for Separable Variables Under Pair-Wise Constraints

While the convergence of alternating direction method(ADM)for two sep-arable variables has been established for years,the validity of its direct generalizationto more than two blocks has been studying now.In this paper,we propose an additionalrequirement on the constraints,i.e.,the pair-wise linear constraints and establish theconvergence of ADM for more than two blocks.Then we apply our approach to twokinds of optimization problems.We also show several numerical experiments to verifythe rationality of proposed algorithm.