A Strategy of Signal Detection for Performance Improvement in Clipping Based OFDM System

In this paper,the supervised Deep Neural Network(DNN)based signal detection is analyzed for combating with nonlinear distortions efficiently and improving error performances in clipping based Orthogonal Frequency Division Multiplexing(OFDM)ssystem.One of the main disadvantages for the OFDM is the high Peak to Average Power Ratio(PAPR).The clipping is a simple method for the PAPR reduction.However,an effect of the clipping is nonlinear distortion,and estimations for transmitting symbols are difficult despite a Maximum Likelihood(ML)detection at the receiver.The DNN based online signal detection uses the offline learning model where all weights and biases at fully-connected layers are set to overcome nonlinear distortions by using training data sets.Thus,this paper introduces the required processes for the online signal detection and offline learning,and compares error performances with the ML detection in the clipping-based OFDM systems.In simulation results,the DNN based signal detection has better error performance than the conventional ML detection in multi-path fading wireless channel.The performance improvement is large as the complexity of system is increased such as huge Multiple Input Multiple Output(MIMO)system and high clipping rate.

Performance Evaluation of Composite Electrolyte with GQD for All-Solid-State Lithium Batteries

The use a stabilized lithium structure as cathode material for batteries could be a fundamental alternative in the development of next-generation energy storage devices.However,the lithium structure severely limits battery life causes safety concerns due to the growth of lithium(Li)dendrites during rapid charge/discharge cycles.Solid electrolytes,which are used in highdensity energy storage devices and avoid the instability of liquid electrolytes,can be a promising alternative for next-generation batteries.Nevertheless,poor lithium ion conductivity and structural defects at room temperature have been pointed out as limitations.In this study,through the application of a low-dimensional graphene quantum dot(GQD)layer structure,stable operation characteristics were demonstrated based on Li^(+)ion conductivity and excellent electrochemical performance.Moreover,the device based on the modified graphene quantum dots(GQDs)in solid state exhibited retention properties of 95.3%for 100 cycles at 0.5 C and room temperature(RT).Transmission electronmicroscopy analysis was performed to elucidate the Li^(+)ion action mechanism in the modified GQD/electrolyte heterostructure.The low-dimensional structure of theGQD-based solid electrolyte has provided an important strategy for stably-scalable solid-state lithium battery applications at room temperature.It was demonstrated that lithiated graphene quantum dots(Li-GQDs)inhibit the growth of Li dendrites by regulating the modified Li^(+)ion flux during charge/discharge cycling at current densities of 2.2–5.5 mA cm,acting as a modified Li diffusion heterointerface.A full Li GQDbased device was fabricated to demonstrate the practicality of the modified Li structure using the Li–GQD hetero-interface.This study indicates that the low-dimensional carbon structure in Li–GQDs can be an effective approach for stabilization of solid-state Li matrix architecture.

Flexible Memristive Devices Based on Graphene Quantum-Dot Nanocomposites

Artificial neural networks(ANNs)are attracting attention for their high performance in various fields,because increasing the network size improves its functioning.Since large-scale neural networks are difficult to implement on custom hardware,a two-dimensional(2D)structure is applied to an ANN in the form of a crossbar.We demonstrate a synapse crossbar device from recent research by applying a memristive system to neuromorphic chips.The system is designed using two-dimensional structures,graphene quantum dots(GQDs)and graphene oxide(GO).Raman spectrum analysis results indicate a D-band of 1421 cm^(−1) that occurs in the disorder;band is expressed as an atomic characteristic of carbon in the sp2 hybridized structure.There is also a G-band of 1518 cm^(−1) that corresponds to the graphite structure.The G bands measured for RGO-GQDs present significant GQD edge-dependent shifts with position.To avoid an abruptly-formed conduction path,effect of barrier layer on graphene/ITO interface was investigated.We confirmed the variation in the nanostructure in the RGO-GQD layers by analyzing them using HR-TEM.After applying a negative bias to the electrode,a crystalline RGO-GQD region formed,which a conductive path.Especially,a synaptic array for a neuromorphic chip with GQDs applied was demonstrated using a crossbar array.