An Efficient Stacked-LSTM Based User Clustering for 5G NOMA Systems

Non-orthogonal multiple access(NOMA)has been a key enabling technology for the fifth generation(5G)cellular networks.Based on the NOMA principle,a traditional neural network has been implemented for user clustering(UC)to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones.Consequently,the prediction of UC for the future ones is based on the current clustering information,which is never used again due to the lack of memory of the network.Therefore,to relate the input features of NOMA users and capture the dependency in the clustering information,time-series methods can assist us in gaining a helpful insight into the future.Despite its mathematical complexity,the essence of time series comes down to examining past behavior and extending that information into the future.Hence,in this paper,we propose a novel and effective stacked long short term memory(S-LSTM)to predict the UC formation of NOMA users to enhance the throughput performance of the 5G-based NOMA systems.In the proposed strategy,the S-LSTM is modelled to handle the time-series input data to improve the predicting accuracy of UC of the NOMA users by implementing multiple LSTM layers with hidden cells.The implemented LSTM layers have feedback connections that help to capture the dependency in the clustering information as it propagates between the layers.Specifically,we develop,train,validate and test the proposed model to predict the UC formation for the futures ones by capturing the dependency in the clustering information based on the time-series data.Simulation results demonstrate that the proposed scheme effectively predicts UC and thereby attaining near-optimal throughput performance of 98.94%compared to the exhaustive search method.

Joint Optimization Scheme Based on Beam Selection and Interference Cancellation in Lens Millimeter Wave NOMA Systems

Non-orthogonal multiple access(NOMA)has been integrated in millimeter-wave(mmWave)Massive MIMO systems to further enhance the spectrum efficiency, but NOMA has not been fully considered in lens mmWave systems. The fusion of these two technologies requires the joint design of beam selection and interference cancellation. In addition, when users follow the spatial cluster distribution, although the user clustering schemes based on K-means algorithm have been applied, the influence of the virtual and actual cluster center users on achievable sum rate has not been differentiated and analyzed in detail. To solve the above problems, a joint optimization scheme is proposed to maximize the achievable sum rate. The optimization problem is NP-hard, which is solved by using the divide-and-conquer approach. Concretely,based on the signal power loss analysis of directional deviation, a beam selection algorithm is designed for inter-cluster interference cancellation based on the Kmeans algorithm. Further for intra-cluster interference cancellation, a power allocation algorithm based on the bisection method is designed to guarantee the fairness of users in each cluster. The simulation results show that the proposed scheme offers a significant performance improvement in terms of both achievable sum rate and energy efficiency, and it is suitable for the large-scale user scenario due to its low complexity.

Maximum throughput design of wireless powered communication network with IRS-NOMA based on user clustering

A wireless powered communication network(WPCN)assisted by intelligent reflecting surface(IRS)is proposed in this paper,which can transfer information by non-orthogonal multiple access(NOMA)technology.In the system,in order to ensure that the hybrid access point(H-AP)can correctly decode user information via successive interference cancellation(SIC)technology,the information transmit power of user needs to satisfy a certain threshold,so as to meet the corresponding SIC constraints.Therefore,when the number of users who transfer information simultaneously increases,the system performance will be greatly restricted.To minimize the influence of SIC constraints on system performance,users are firstly clustered,and then each cluster collects energy from H-AP and finally,users transfer information based on NOMA with the assistance of IRS.Specifically,this paper aims to maximize the sum throughput of the system by jointly optimizing the beamforming of IRS and resource allocation of the system.The semi-definite relaxation(SDR)algorithm is employed to alternately optimize the beamforming of IRS in each time slot,and the joint optimization problem about user’s transmit power and time is transformed into two optimal time allocation sub-problems.The numerical results show that the proposed optimization scheme can effectively improve the sum throughput of the system.In addition,the results in the paper further reveals the positive impact of IRS on improving the sum throughput of the system.

Clustering and scheduling methods based on SLNR in downlink CoMP system

In downlink coordinated multi-point（CoMP） system, full cooperation is always not applicable in real world because of its high request in the backhaul. To deal with this problem, clustering decision is made to process transmission. In this paper clustering methods based on the metric signal-to-leakage-plus-noise（SLNR） is proposed. In addition, user scheduling schemes based on SLNR is also put up to make the scheduling set as large as possible. Simulation results show that the proposed clustering methods not only reduce the data sharing among the cooperating base stations（BSs）, but also improve the system throughput compared with the traditional clustering methods based on channel strength.