Recently, mobility has gathered tremendous interest as the users' desire for consecutive
connections and better quality of service has increased. An accurate prediction of user …

In this work, a multiple user deep neural network-based non-orthogonal multiple access
(NOMA) receiver is investigated considering channel estimation error. The decoding of the …

In this work, the deep learning (DL)-based fifth-generation (5G) non-orthogonal multiple
access (NOMA) detector is investigated over the independent and identically distributed (iid) …

In this paper, we investigate the non-orthogonal multiple access (NOMA) and massive
multiple-input multiple-output (M-MIMO) techniques and through simulation, and a …

In this work, the performance of the downlink non-orthogonal multiple access (NOMA)
technique is investigated for two users considering optimal power allocation factors. The …

In a defense scenario, the communicating nodes are mobile and, due to this, the fading
channel links become time selective in nature. Non-orthogonal multiple access (NOMA) is a …

In this paper, we present an efficient handoff decision strategy and a mobility state detection
(MSD) scheme for heterogeneous networks (HetNets). Due to base station (BS) …

In the current scenario, where all the services have become online, the demand for
increased capacity, low symbol error rate (SER), better data rate, and low latency with high …

Non-orthogonal multiple access (NOMA) networks play an important role in defense
communication scenarios. Deep learning (DL)-based solutions are being considered as …

In this paper, we propose a Maximum Likelihood Estimation (MLE) of user velocity based on
Rayleigh distributed Handover counts in Heterogeneous networks (HetNets). Due to the …