In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed to solve the problem of premature convergence and redundant particles of the original PSO used in visible light positioning (VLP) sys...
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In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed to solve the problem of premature convergence and redundant particles of the original PSO used in visible light positioning (VLP) systems. In the proposed IPSO algorithm, an adaptive particle initialization method based on min-max algorithm is used to adjust the number of particles and ensure that there are always particles near the target node (TN). Moreover, a nonlinear decreasing strategy of inertia weight is designed to ensure the stability of particle velocity during the iterative process. Simulation results show that, compared with the original PSO algorithm, the averaged positioning accuracy of the proposed IPSO-min-max algorithm is enhanced significantly at the expense of limited time consumption. What's more, we also find that for the proposed IPSO-min-max algorithm the increase of particle generation spacing will reduce the positioning delay but with the penalty in positioning accuracy. Therefore, it is necessary to select an appropriate particle spacing value according to specific requirements.
min-max algorithm was widely used as a simple received signal strength (RSS-) based algorithm for indoor localization due to its easy implementation. However, the original min-max algorithm only achieves coarse estima...
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min-max algorithm was widely used as a simple received signal strength (RSS-) based algorithm for indoor localization due to its easy implementation. However, the original min-max algorithm only achieves coarse estimation in which the target node (TN) is regarded as the geometric centroid of the area of interest determined by measured RSS values. Although extended min-max (E-min-max) methods using weighted centroid instead of geometric centroid were recently proposed to cope with this problem, the improvement in the localization accuracy is still limited. In this paper, an improved min-max algorithm with area partition strategy (min-max-APS) is proposed to achieve better localization performance. In the proposed algorithm, the area of interest is first partitioned into four subareas, each of which contains a vertex of the original area of interest. Moreover, a minimum range difference criterion is designed to determine the target affiliated subarea whose vertex is "closest" to the target node. Then the target node's location is estimated as the weighted centroid of the target affiliated subarea. Since the target affiliated subarea is smaller than the original area of interest, the weighted centroid of the target affiliated subarea will be more accurate than that of the original area of interest. Simulation results show that the localization error (LE) of the proposed min-max-APS algorithm can drop below 0.16 meters, which is less than one-half of that of the E-min-max algorithm, and is also less than one-seventh of that of the original min-max algorithm. Moreover, for the proposed min-max-APS, 90% of the LE are smaller than 0.38 meters, while the same percentage of the LE are as high as 0.49 meters for the E-min-max and 1.12 meters for the original min-max, respectively.
With the explosive growth of mobile communication technology, the number of access terminals has increased dramatically, which will make the pilot contamination caused by pilot reuse extremely worse due to limited pil...
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With the explosive growth of mobile communication technology, the number of access terminals has increased dramatically, which will make the pilot contamination caused by pilot reuse extremely worse due to limited pilot resources. It is difficult to apply a traditional pilot assignment algorithm to serve the numerous access terminals simultaneously in real time with low complexity, so there is necessity to design a scalable pilot assignment scheme in the case of massive access. In this paper, we propose a scalable deep learning-based pilot assignment algorithm to maximize the sum spectral efficiency (SE) of cell-free large-scale distributed multiple-input multiple-output (MIMO) systems with massive access. The mapping between user locations and pilot assignment schemes is learned by a deep neural network (DNN). The training samples of the DNN are generated by a min-max algorithm, which minimizes the maximum interference to alleviate pilot contamination. The output of the pretrained DNN is used as the initial value of the min-max algorithm to achieve better pilot assignment schemes and reduce the algorithm complexity. The simulation results show that the proposed algorithm has better convergence with massive access and achieves a higher sum SE in near real time.
Waveform diversity, which can be used to resolve range ambiguity and suppress range folded clutter in pulse-Doppler radar system, has attracted an increasing amount of attention in recent years. Especially for high pu...
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ISBN:
(纸本)9781728189420
Waveform diversity, which can be used to resolve range ambiguity and suppress range folded clutter in pulse-Doppler radar system, has attracted an increasing amount of attention in recent years. Especially for high pulse repetition frequency (PRF), the range folded clutter severely impacts the detection of small targets with long range and low speed. Using diverse waveforms, pulses of the transmitted sequence of radar are agile. It is possible to construct corresponding matched filters (MFs) to suppress the range folded clutter. However, the performance greatly depends on the cross-correlation among pulses of the transmitted sequence. In this paper, diverse nonlinear frequency modulation (NLFM) waveforms, the spectra of which are based on different window functions, are designed to construct a signal library, and positive and negative frequency modulation rate are both considered. Then, a min-max algorithm is proposed for sequentially selecting NLFM waveforms from the signal library to generate an optimal transmitted sequence. Pulse-to-pulse random initial phases are also added to each pulse of the sequence to further improve the irrelevance among pulses. Moreover, to eliminate the range sidelobe modulation (RSM) effect in Doppler processing, a cyclic algorithm for designing joint mismatched filters (JMMFs) with finite impulse response (FIR) is provided. Simulations show that the optimized pulse-agile sequence based on NLFM waveforms yields satisfactory performance on the ambiguity function, and range folded clutter can be effectively suppressed.
In this paper, we investigate data transmission time minimization problem in low earth orbits (LEO) satellite-terrestrial integrated networks (STINs) with a central earth station (CES) for relay, which non-orthogonal ...
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ISBN:
(纸本)9781728172361
In this paper, we investigate data transmission time minimization problem in low earth orbits (LEO) satellite-terrestrial integrated networks (STINs) with a central earth station (CES) for relay, which non-orthogonal multiple access (NOMA) scheme is used for data transmission from the Internet of Things (IoT) devices to CES, and orthogonal multiple access (OMA) scheme is used for data transmission from CES to satellite. Firstly, to tackle this problem, we decouple this problem into two sub-problems: subcarriers assignment in terrestrial network and data allocation in satellite subcarriers. Secondly, we formulate the subcarriers assignment as a many-to-one matching, and proposed the matching algorithm for subcarriers assignment. To keep fairness in satellite channels, we make that the rate in each satellite subcarriers is the same, and propose a min-max algorithm to minimize the max completion time. Finally, the simulations have shown that our proposed scheme has better performance than the existing approach, and the NOMA scheme always outperforms the OMA scheme.
The range-based localization method is widely used in wireless sensor localization systems. Many existing localization algorithms are unbiased estimators. However, the estimation performance presents biased features i...
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The range-based localization method is widely used in wireless sensor localization systems. Many existing localization algorithms are unbiased estimators. However, the estimation performance presents biased features in the real localization systems. On the other hand, many biased location estimators show some essential advantages over unbiased estimators, e.g., robust to the noise, more accurate estimation, and low complexity. In this paper, we deeply investigate the performance of biased estimator, min-max, to achieve a new accuracy limit, and propose a hybrid Kalman filtering algorithm, which recursively locates the target based on biased feature. The first contribution is that we formulate the biased Cramer-Rao lower bound of the min-max algorithm to indicate that the biased localization algorithm can outperform the unbiased algorithms, e.g., maximum likelihood, as if the estimation bias were attained. The second contribution is that we propose a hybrid Kalman filtering algorithm while employing the min-max to construct a constrain region and using the dynamic Gaussian model for calculation in non-Gaussian environments. Our algorithm is robust to complicated environments with high accuracy. And, we implement it in an IoT target tracking platform. Both theoretical analysis and experimental evaluation indicate that the proposed algorithm outperform the unbiased optimal estimation methods. And our algorithm can control the estimation error in only 1 m.
In this study, a GA (Genetic algorithm) basesented to reduce the chess game tree space. GA is exploited in some studies and by chess engines in order to: 1) tune the weights of the chess evaluation function or 2) to s...
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In this study, a GA (Genetic algorithm) basesented to reduce the chess game tree space. GA is exploited in some studies and by chess engines in order to: 1) tune the weights of the chess evaluation function or 2) to solve particular problems in chess like finding mate in number of moves. Applying GA for reducing the search space of the chess game tree is a new idea being proposed in this study. A GA-based chess engine is designed and implemented where only the branches of the game tree produced by GA are traversed. Improvements in the basic GA to reduce the problem of GA tactic are evident here. To evaluate the efficiency of this new proposed chess engine, it is matched against an engine where the Alpha-Beta pruning and min-max algorithm are applied.
Many exist localization algorithms are unbiased estimators. However, the estimation performance presents biased feature in the real location systems. On the other hand, many biased location estimators show advantages ...
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ISBN:
(纸本)9781538627235
Many exist localization algorithms are unbiased estimators. However, the estimation performance presents biased feature in the real location systems. On the other hand, many biased location estimators show advantages that unbiased estimators can not achieve, e.g., robust to the noise, more accurate estimation and low complexity. In this paper, we propose a biased localization estimator and a hybrid Kalman filtering algorithm. The proposed algorithm is robust to the complicated environment with high accuracy. Both theoretical analysis and experimental evaluation indicate that the proposed algorithm outperform the unbiased optimal estimation methods.
Non-binary low-density parity-check (NB-LDPC) codes over GF(q) (q > 2) have better error-correcting performance than their binary counterparts when the codeword length is moderate. In this paper, a modified trellis...
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ISBN:
(纸本)9781479969593
Non-binary low-density parity-check (NB-LDPC) codes over GF(q) (q > 2) have better error-correcting performance than their binary counterparts when the codeword length is moderate. In this paper, a modified trellis-based min-max decoder is proposed for NB-LDPC codes. By relaxing the constraints on which messages can be included, the trellis syndrome computation is simplified without sacrificing the error-correcting performance. In addition, the iterative comparisons needed in computing the check-to-variable messages are replaced by one-step message selection. The decoding complexity of NB-LDPC codes grows substantially with q, and small q is preferred to achieve low complexity and high speed for data storage systems. Making use of the properties of GF(4), the hardware units are further simplified and efficient decoder architectures are developed for codes over GF(4). Compared to prior efforts, the proposed design requires smaller area, consumes much less power, achieves higher throughput, and also has slightly better error-correcting performance.
Space-time adaptive processing (STAP) is an effective strategy for clutter suppression in airborne radar systems. Limited training data, high computational load and the heterogeneity of training data constitute the ma...
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Space-time adaptive processing (STAP) is an effective strategy for clutter suppression in airborne radar systems. Limited training data, high computational load and the heterogeneity of training data constitute the main challenges in STAP. In this letter, we propose a new detection strategy based on selecting an optimum subset of antenna-pulse pairs associated with maximum separation between the target and the clutter trajectory. The proposed strategy reduces redundancy while addressing the above three inter-linked challenges for detecting slow-moving targets especially in heterogeneous cases. An iterative min-max algorithm is proposed to solve the antenna-pulse selection problem, which is NP-hard combinatorial optimization. Extensive simulation results confirm the effectiveness of the proposed strategy.
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