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Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues
Islam, S. M. Riazul,Zeng, Ming,Dobre, Octavia A.,Kwak, Kyung-Sup IEEE 2018 IEEE wireless communications Vol.25 No.2
<P>This article presents advances in resource allocation for downlink non-orthogonal multiple access (NOMA) systems, focusing on user pairing and power allocation algorithms. The former pairs the users to obtain high capacity gain by exploiting the channel gain difference between the users, while the latter allocates power to users in each cluster to balance system throughput and user fairness. Additionally, the article introduces the concept of cluster fairness and proposes the divide-and-next-largest-difference-based user pairing algorithm to distribute the capacity gain among the NOMA clusters in a controlled manner. Furthermore, performance comparison between multiple-input multiple-output NOMA (MIMO-NOMA) and MIMO orthogonal multiple access (MIMO-OMA) is conducted when users have pre-defined quality of service. Simulation results are presented, which validate the advantages of NOMA over OMA. Finally, the article provides avenues for further research on resource allocation for downlink NOMA.</P>
Statistical Characterization of a 3-D Propagation Model for V2V Channels in Rectangular Tunnels
Avazov, Nurilla,Islam, S. M. Riazul,Park, Daeyoung,Kwak, Kyung Sup IEEE 2017 IEEE antennas and wireless propagation letters Vol.16 No.-
<P>In this letter, we investigate the statistical characterization of a 3-D propagation model for multiple-input–multiple-output vehicle-to-vehicle (V2V) communications inside a rectangular tunnel under nonisotropic scattering conditions. The proposed model captures the spatial, temporal, and the frequency statistical distributions of the received multipath signals. A generalized analytical expression is derived for the space–time–frequency correlation function and thoroughly investigated. We analyze the impact of various model parameters, including antenna element spacing and tunnel width, on the V2V channel statistics.</P>
Type-2 fuzzy ontology–aided recommendation systems for IoT–based healthcare
Ali, Farman,Islam, S.M. Riazul,Kwak, Daehan,Khan, Pervez,Ullah, Niamat,Yoo, Sang-jo,Kwak, K.S. Elsevier 2018 Journal of Computer Communications Vol.119 No.-
<P><B>Abstract</B></P> <P>The number of people with a chronic disease is rapidly increasing, giving the healthcare industry more challenging problems. To date, there exist several ontology and IoT-based healthcare systems to intelligently supervise the chronic patients for long-term care. The central purposes of these systems are to reduce the volume of manual work in recommendation systems. However, due to the increase of risk and uncertain factors of the diabetes patients, these healthcare systems cannot be utilized to extract precise physiological information about patient. Further, the existing ontology-based approaches cannot extract optimal membership value of risk factors; thus, it provides poor results. In this regards, this paper presents a type-2 fuzzy ontology–aided recommendation systems for IoT-based healthcare to efficiently monitor the patient's body while recommending diets with specific foods and drugs. The proposed system extracts the values of patient risk factors, determines the patient's health condition via wearable sensors, and then recommends diabetes-specific prescriptions for a smart medicine box and food for a smart refrigerator. The combination of type-2 Fuzzy Logic (T2FL) and the fuzzy ontology significantly increases the prediction accuracy of a patient's condition and the precision rate for drug and food recommendations. Information about the patient's disease history, foods consumed, and drugs prescribed is designed in the ontology to deliver decision-making knowledge using Protégé Web Ontology Language (OWL)-2 tools. Semantic Web Rule Language (SWRL) rules and fuzzy logic are employed to automate the recommendation process. Moreover, Description Logic (DL) and Simple Protocol and RDF Query Language (SPARQL) queries are used to evaluate the ontology. The experimental results show that the proposed system is efficient for patient risk factors extraction and diabetes prescriptions.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The available healthcare systems are imperfect to extract precise physiological information of patients. </LI> <LI> The classical ontologies are unable to recommend diets without knowing the current condition of a patient. </LI> <LI> Wearable sensors with type-2 fuzzy logic efficiently monitor the patient's body. </LI> <LI> Fuzzy ontology-based knowledge precisely suggests diabetes-specific prescriptions. </LI> <LI> Type-2 fuzzy ontology significantly increases the prediction accuracy of a patient's condition. </LI> </UL> </P>
D-MoSK Modulation in Molecular Communications
Kabir, Md Humaun,Riazul Islam, S. M.,Kyung Sup Kwak IEEE 2015 IEEE transactions on nanobioscience Vol.14 No.6
<P>Molecular communication in nanonetworks is an emerging communication paradigm that uses molecules as information carriers. In molecule shift keying (MoSK), where different types of molecules are used for encoding, transmitter and receiver complexities increase as the modulation order increases. We propose a modulation technique called depleted MoSK (D-MoSK) in which, molecules are released if the information bit is 1 and no molecule is released for 0. The proposed scheme enjoys reduced number of the types of molecules for encoding. Numerical results show that the achievable rate is considerably higher and symbol error rate (SER) performance is better in the proposed technique.</P>