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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>
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>
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>
On PHY and MAC Performance in Body Sensor Networks
Ullah, Sana,Higgins, Henry,Islam, S. M. Riazul,Khan, Pervez,Kwak, Kyung Sup Hindawi Publishing Corporation 2009 Eurasip Journal on Wireless Communications and Net Vol.2009 No.-
<P>This paper presents an empirical investigation on the performance of body implant communication using radio frequency (RF) technology. In body implant communication, the electrical properties of the body influence the signal propagation in several ways. We use a Perspex body model (30 cm diameter, 80 cm height and 0.5 cm thickness) filled with a liquid that mimics the electrical properties of the basic body tissues. This model is used to observe the effects of body tissue on the RF communication. We observe best performance at 3cm depth inside the liquid. We further present a simulation study of several low-power MAC protocols for an on-body sensor network and discuss the derived results. Also, the traditional preamble-based TMDA protocol is extended towards a beacon-based TDMA protocol in order to avoid preamble collision and to ensure low-power communication.</P>