Non‐orthogonal multiple access (NOMA) is considered a key candidate technology for next‐generation wireless communication systems due to its high spectral efficiency and massive connectivity. Incorporating the concepts of multiple‐input‐multiple‐output (MIMO) into NOMA can further improve the system efficiency, but the hardware complexity increases. This study develops an energy‐efficient (EE) subchannel assignment framework for MIMO‐NOMA systems under the quality‐of‐service and interference constraints. This framework handles an energy‐efficient co‐training‐based semi‐supervised learning (EE‐CSL) algorithm, which utilizes a small portion of existing labeled data generated by numerical iterative algorithms for training. To improve the learning performance of the proposed EE‐CSL, initial assignment is performed by a many‐to‐one matching (MOM) algorithm. The MOM algorithm helps achieve a low complex solution. Simulation results illustrate that a lower computational complexity of the EE‐CSL algorithm helps significantly minimize the energy consumption in a network. Furthermore, the sum rate of NOMA outperforms conventional orthogonal multiple access.
Wireless sensor network (WSN) is deployed to monitor certain physical quantities in a region. This monitoring problem could be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. A moving window procedure is proposed to detect the systematic error, which occurs at an unknown time. It can detect the deviation in the mean of sensor measurements keeping variance as constant. The performance measures, such as the average run length (ARL) to detection delay and false alarms are computed for various window sizes. The performance comparison is done against traditional cumulative sum (CUSUM) method. The detection of change in mean using CUSUM is done with smaller delay compared to the proposed moving window detection procedure. In order to calculate CUSUM statistics, the number of measurements to keep in sensor memory increases with time. However, in the proposed moving window detection procedure, the number of stored measurements is limited by the size of the window. Therefore, it is advantageous to use the moving window procedure for change detection in sensor nodes that have very limited memory. A high probability of detection is achieved at the cost of larger window size and higher detection delay. However, we are able to achieve the maximum probability of detection even at a window size of 11.
A distributed non-binary fault tolerant event detection technique is proposed for a wireless sensor network (WSN) consisting of a large number of sensors. The sensor nodes may be faulty due to harsh environment and manufacturing reasons. In the existing works on event detection, the detection of event is decided by only one threshold level. The objective of this paper is to extend the fault recognition and correction algorithm for non-binary event detection. The analysis presented here takes into account both the symmetric and non-symmetric error in a straightforward manner. In addition, simulation is done for symmetric error and 75 percentage of the errors can be corrected. The theoretical analysis shows that more than 95 percentage of symmetric errors can be corrected and almost 92 percentage of non-symmetric errors can be corrected (for k=2, i.e. half of the neighbors give correct decision), even when as many as 10 percentage of the sensor nodes are faulty.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.