IET Communications最新文献

This paper discusses a new mapping scheme known as face coded modulation (FCM) system. In FCM, peak energy symbols are mapped onto an innermost ring according to the eight sockets in the human face, that is, brain, mouth, nostrils, eyes and ears. For example, FCM is formed when the constellation diagram from M-ary quadrature amplitude modulation (MQAM) system is modified to reduce peak-to-average power ratio (PAPR) by relocating the four corner symbols of the MQAM, with peak energy, to the innermost ring in a way that forms the figure of a cross. Unlike APSK, FCM mapping introduces non-uniform sequence of symbols on the ring, face width factor and multiple modulator circuits that can be used to lower power requirements for high power amplifiers (HPA) as used in MQAM transmission systems. Symbol error rate (SER) for FCM is calculated and the results compared with MQAM and MPSK. It is shown that at equal energy efficiency, FCM scheme has a better response to errors than both MPSK and MQAM and a better energy efficiency due to lowered PAPR than MQAM. Moreover, the simulation results exhibit a tight match for the proposed analytical framework when assessed under Additive White Gaussian Noise (AWGN) channel.

PageRank talent mining in power system is an effective means for enterprises to recruit talents, which can correctly recommend talents in practical applications. At present, the mining evaluation index system is not perfect, and the consistency coefficient between the evaluation results and the actual situation is low in practical applications. Therefore, PageRank talent mining algorithm in power system based on cognitive load and dilated convolutional neural network (DPCNN) is proposed. The cognitive load and DPCNN are used to establish a talent capability evaluation system, calculate the index weight value, construct the PageRank talent capability evaluation model of the power system according to the corresponding weight of the index, determine the membership range of the index, calculate the comprehensive score of the appraiser's ability, and determine the ability level of the appraiser, thus realizing the PageRank talent mining algorithm of the power system. The experimental results show that the algorithm has high accuracy and objectivity, good encryption effect, cannot crack the attack node, the prediction error and the prediction relative error are closest to the standard value, the maximum error is 0.51, the maximum relative error is 0.82, and can achieve the accurate prediction of talent demand.

This paper addresses the resource allocation (RA) for ultra-dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non-convex and NP-hard. Therefore, this paper considers and studies a semi-distributed resource block (RB) allocation scheme, in order to achieve a well-balanced trade-off between performance and complexity. In the context of semi-distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster-based RB allocation. We first improve the K-means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K-means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster-based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade-off between the performance and the implementation complexity.

Delta compression, as a complementary technique for data deduplication, has gained widespread attention in network storage systems. It can eliminate redundant data between non-duplicate but similar chunks that cannot be identified by data deduplication. The network transmission overhead between servers and clients can be greatly reduced by using data deduplication and delta compression techniques. Resemblance detection is a technique that identifies similar chunks for post-deduplication delta compression in network storage systems. The existing resemblance detection approaches fail to detect similar chunks with arbitrary similarity by setting a similarity threshold, which can be suboptimal. In this paper, the authors propose Chunk2vec, a resemblance detection scheme for delta compression that utilizes deep learning techniques and Approximate Nearest Neighbour Search technique to detect similar chunks with any given similarity range. Chunk2vec uses a deep neural network, Sentence-BERT, to extract an approximate feature vector for each chunk while preserving its similarity with other chunks. The experimental results on five real-world datasets indicate that Chunk2vec improves the accuracy of resemblance detection for delta compression and achieves higher compression ratio than the state-of-the-art resemblance detection technique.

Cache-enabled device-to-device (D2D) network has been deemed as an effective technique to offload the data traffic. However, the gain of the caching schemes is closely related to the homogeneity among users' preference distribution. To tackle this issue, recommendation is a promising proactive approach. It increases the request probability of recommended contents, reshaping users' contents demand patterns, and improving caching performance. Moreover, considering the heterogeneous network settings, i.e. content retrieval costs vary, the routing design becomes a non-negligible factor on caching performance optimization. On these grounds, the average system cost of D2D-enabled wireless caching networks with multiple BSs is first described. Then the routing strategies are designed together with caching and recommendation policies by minimizing the average cost of these networks. The optimization problem is proven as NP-hard. To facilitate the analysis, the original problem is decoupled into two sub-problems and solve them respectively. Afterwards, all the variables are optimized in an alternating manner until the convergence is achieved. The proposed algorithm's convergence performance and benefits over benchmark strategies in terms of total cost and cache hit ratio are supported by Monte-Carlo simulation results.

Gamma spectrum denoising can reduce the adverse effects of statistical fluctuations of radioactivity, gamma ray scattering, and electronic noise on the measured gamma spectrum. Traditional denoising methods are intricate and require analytical expertise in gamma spectrum analysis. This paper proposes a segmentation-enhanced Convolutional Neural Network-Stacked Denoising Autoencoder (CNN-SDAE) method based on convolutional feature extraction network and stacked denoising autoencoder to achieve gamma spectrum denoising, which adopts the idea of data segmentation to enhance the learning ability of the neural network. By dividing the complete gamma spectrum into multiple segments and then using the segmentation-enhanced CNN-SDAE method for denoising, the method can achieve adaptive denoising without manually setting the threshold. The experimental results show that our method can effectively achieve gamma spectrum denoising while retaining the characteristics of the gamma spectrum. Compared with traditional methods, the denoising speed and effectiveness have been significantly improved, and the proposed method demonstrates an approximately 1.72-fold enhancement in smoothing performance than the empirical mode decomposition method. Furthermore, in terms of retaining gamma spectrum characteristics, it also achieves a performance improvement of approximately three orders of magnitude than the wavelet method.

This paper proposes a whale optimization algorithm (WOA)-based partial transmit sequence (PTS) scheme called WOA-PTS to reduce the high peak-to-average power ratio (PAPR) for universal filtered multi-carrier (UFMC) systems. High PAPR is a prevalent challenge encountered in multi-carrier systems. In the conventional PTS technique, the optimization of phase rotation factors is achieved through multiplication with the sub-blocks. In this undertaking, WOA optimization is integrated as a phase optimizer in the PTS-based PAPR reduction scheme. The experimental results show that, when compared with PTS-based UFMC signal, the UFMC signal with WOA-PTS can achieve 4.15 dB PAPR reduction at the complementary cumulative distribution function value of 10⁻³, additionally power spectral density performance and bit error rate also improved.