A Construction Method of Arbitrary-Length Polar Codes

To remove the restriction on code length of polar codes,this paper proposes a construction scheme,called stepwise polar codes,which can gen-erate arbitrary-length polar codes.The stepwise polar codes are generated by sub-polar codes with different code lengths.To improve coding performance,sub-polar codes are united by polarization effect priority algorithm,which can reduce the number of in-completely polarized channels.Then,the construction method of the generator matrix of the stepwise po-lar code is presented.Furthermore,we prove that the proposed scheme has lower decoding complexity than punctured,multi-kernel polar codes.Simulation results show that the proposed method can achieve similar decoding performance compared with the conventional punctured polar codes,rate-compatible punctured polar code,PC-short and asymmetric polar codes(APC)when code length N=48 and 72,respectively.

Joint Resource Allocation and Admission Control Mechanism in Software Defined Mobile Networks

This paper presented a joint resource allocation(RA) and admission control(AC) mechanism in software defined mobile networks(SDMNs). In this mechanism, the joint RA and AC problem can be formulated as an optimization problem with the aim of maximizing the number of admitted users while simultaneously minimizing the number of allocated channels. Since the primal problem is modeled to be a mixed integer nonlinear problem(MINLP), we attain the suboptimal solutions to the primal MINLP by convex relaxation. Additionally, with the global information collected by the SDMNs controller, a centralized joint RA and AC(CJRA)algorithm is proposed by the Lagrange dual decomposition technique to obtain the global optimum. Meanwhile, we propose an OpenFlow rules placement strategy to realize CJRA in an efficient way. Moreover, a distributed algorithm is also developed to find the local optimum, showing a performance benchmark for the centralized one. Finally, simulation results show that the proposed centralized algorithm admits more users compared with the distributed.

Remaining Time Prediction for Business Processes with Concurrency Based on Log Representation

Remaining time prediction of business processes plays an important role in resource scheduling and plan making.The structural features of single process instance and the concurrent running of multiple process instances are the main factors that affect the accuracy of the remaining time prediction.Existing prediction methods does not take full advantage of these two aspects into consideration.To address this issue,a new prediction method based on trace representation is proposed.More specifically,we first associate the prefix set generated by the event log to different states of the transition system,and encode the structural features of the prefixes in the state.Then,an annotation containing the feature representation for the prefix and the corresponding remaining time are added to each state to obtain an extended transition system.Next,states in the extended transition system are partitioned by the different lengths of the states,which considers concurrency among multiple process instances.Finally,the long short-term memory(LSTM)deep recurrent neural networks are applied to each partition for predicting the remaining time of new running instances.By extensive experimental evaluation using synthetic event logs and reallife event logs,we show that the proposed method outperforms existing baseline methods.