Study on Energy Consumption and Coverage of Hierarchical Cooperation of Small Cell Base Stations in Heterogeneous Networks

The demand for communication services in the era of intelligent terminals is unprecedented and huge. To meet such development, modern wireless communications must provide higher quality services with higher energy efficiency in terms of system capacity and quality of service (QoS), which could be achieved by the high-speed data rate, the wider coverage and the higher band utilization. In this paper, we propose a way to offload users from a macro base station(MBS) with a hierarchical distribution of small cell base stations(SBS). The connection probability is the key indicator of the implementation of the unload operation. Furthermore, we measure the service performance of the system by finding the conditional probability-coverage probability with the certain SNR threshold as the condition, that is, the probability of obtaining the minimum communication quality when the different base stations are connected to the user. Then, user-centered total energy consumption of the system is respectively obtained when the macro base station(MBS) and the small cell base stations(SBS) serve each of the users. The simulation results show that the hierarchical SBS cooperation in heterogeneous networks can provide a higher system total coverage probability for the system with a lower overall system energy consumption than MBS.Comment: 6 pages, 7 figures, accepted by ICACT201

Adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity

Uzimajući u obzir nezadovoljavajuće djelovanje grupiranja srodnog širenja algoritma grupiranja, kada se radi o nizovima podataka složenih struktura, u ovom se radu predlaže prilagodljivi nadzirani algoritam grupiranja srodnog širenja utemeljen na strukturnoj sličnosti (SAAP-SS). Najprije se predlaže nova strukturna sličnost rješavanjem nelinearnog problema zastupljenosti niskoga ranga. Zatim slijedi srodno širenje na temelju podešavanja matrice sličnosti primjenom poznatih udvojenih ograničenja. Na kraju se u postupak algoritma uvodi ideja eksplozija kod vatrometa. Prilagodljivo pretražujući preferencijalni prostor u dva smjera, uravnotežuju se globalne i lokalne pretraživačke sposobnosti algoritma u cilju pronalaženja optimalne strukture grupiranja. Rezultati eksperimenata i sa sintetičkim i s realnim nizovima podataka pokazuju poboljšanja u radu predloženog algoritma u usporedbi s AP, FEO-SAP i K-means metodama.In view of the unsatisfying clustering effect of affinity propagation (AP) clustering algorithm when dealing with data sets of complex structures, an adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity (SAAP-SS) is proposed in this paper. First, a novel structural similarity is proposed by solving a non-linear, low-rank representation problem. Then we perform affinity propagation on the basis of adjusting the similarity matrix by utilizing the known pairwise constraints. Finally, the idea of fireworks explosion is introduced into the process of the algorithm. By adaptively searching the preference space bi-directionally, the algorithm’s global and local searching abilities are balanced in order to find the optimal clustering structure. The results of the experiments with both synthetic and real data sets show performance improvements of the proposed algorithm compared with AP, FEO-SAP and K-means methods

An Improved K-means Algorithm and Its Application for Assessment of Culture Industry Listed Companies

Owing to K-means algorithm has the shortcoming that it always neglects the influence of cluster size when the Euclidean distances between samples and cluster center is calculated. In order to overcome the lack, the influence of cluster size is introduced into K-means algorithm in this paper. Therefore an improved K-means algorithm based on gravity is proposed, namely GK-means algorithm. The experimental simulation results show that GK-means algorithm has better performance compared with K-means algorithm. So the GK-means algorithm is adopted for assessing the performance of culture industry listed companies in this paper. Furthermore some satisfactory results are also obtained

End to End Performance Analysis of Relay Cooperative Communication Based on Parked Cars

Parking lots (PLs) are usually full with cars. If these cars are formed into a self-organizing vehicular network, they can be new kind of road side units (RSUs) in urban area to provide communication data forwarding between mobile terminals nearby and a base station. However cars in PLs can leave at any time, which is neglected in the existing studies. In this paper, we investigate relay cooperative communication based on parked cars in PLs. Taking the impact of the car's leaving behavior into consideration, we derive the expressions of outage probability in a two-hop cooperative communication and its link capacity. Finally, the numerical results show that the impact of a car's arriving time is greater than the impact of the duration the car has parked on outage probability.Comment: 7 pages, 7 figures, accepted by ICACT201

Federated Learning with Manifold Regularization and Normalized Update Reaggregation

Federated Learning (FL) is an emerging collaborative machine learning framework where multiple clients train the global model without sharing their own datasets. In FL, the model inconsistency caused by the local data heterogeneity across clients results in the near-orthogonality of client updates, which leads to the global update norm reduction and slows down the convergence. Most previous works focus on eliminating the difference of parameters (or gradients) between the local and global models, which may fail to reflect the model inconsistency due to the complex structure of the machine learning model and the Euclidean space's limitation in meaningful geometric representations. In this paper, we propose FedMRUR by adopting the manifold model fusion scheme and a new global optimizer to alleviate the negative impacts. Concretely, FedMRUR adopts a hyperbolic graph manifold regularizer enforcing the representations of the data in the local and global models are close to each other in a low-dimensional subspace. Because the machine learning model has the graph structure, the distance in hyperbolic space can reflect the model bias better than the Euclidean distance. In this way, FedMRUR exploits the manifold structures of the representations to significantly reduce the model inconsistency. FedMRUR also aggregates the client updates norms as the global update norm, which can appropriately enlarge each client's contribution to the global update, thereby mitigating the norm reduction introduced by the near-orthogonality of client updates. Furthermore, we theoretically prove that our algorithm can achieve a linear speedup property for non-convex setting under partial client participation.Experiments demonstrate that FedMRUR can achieve a new state-of-the-art (SOTA) accuracy with less communication

Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient

Over-the-air computation is a communication-efficient solution for federated learning (FL). In such a system, iterative procedure is performed: Local gradient of private loss function is updated, amplified and then transmitted by every mobile device; the server receives the aggregated gradient all-at-once, generates and then broadcasts updated model parameters to every mobile device. In terms of amplification factor selection, most related works suppose the local gradient's maximal norm always happens although it actually fluctuates over iterations, which may degrade convergence performance. To circumvent this problem, we propose to turn local gradient to be normalized one before amplifying it. Under our proposed method, when the loss function is smooth, we prove our proposed method can converge to stationary point at sub-linear rate. In case of smooth and strongly convex loss function, we prove our proposed method can achieve minimal training loss at linear rate with any small positive tolerance. Moreover, a tradeoff between convergence rate and the tolerance is discovered. To speedup convergence, problems optimizing system parameters are also formulated for above two cases. Although being non-convex, optimal solution with polynomial complexity of the formulated problems are derived. Experimental results show our proposed method can outperform benchmark methods on convergence performance

Swarm Intelligence Optimization Algorithms and Their Application

Swarm intelligence optimization algorithm is an emerging technology tosimulate the evolution of the law of nature and acts of biological communities, it has simple and robust characteristics. The algorithm has been successfully applied in many fields. This paper summarizes the research status of swarm intelligence optimization algorithm and application progress. Elaborate the basic principle of ant colony algorithm and particle swarm algorithm. Carry out a detailed analysis of drosophila algorithm and firefly algorithm developed in recent years, and put forward deficiencies of each algorithm and direction for improvement