The Heterogeneous Network (HetNet) has emerged as one of the most promising developments toward achieving the target of the Long Term Evolution-Advanced (LTE-A) systems. However, Co-Channel Interference (CCI) is one of the critical challenges of HetNet, that degrades the overall performance of a system. Therefore, an appropriate Radio Resource Management (RRM) mechanism is required to deploy and expand the HetNets properly. In this paper, a new RRM strategy called Fractional Frequency Reuse with Three Sectors and Three Layers (FFR-3SL) technique is proposed. The FFR-3SL efficiently utilizes the radio resources and alleviates the effects of CCI in LTE-A HetNets and thereby improving the system performance. In order to implement the proposed strategy, the entire macrocell coverage area is segmented into three sectors and three layers, while the total bandwidth is divided into seven subbands. Subsequently, the subbands are accurately distributed among femtocells and macrocells by employing the proposed algorithm. As a result, the co-tier and cross-tier interferences are managed on a prior basis. The Monte Carlo simulation is performed to evaluate and compare the performance of the proposed method with the existing methods. The simulation results show that the proposed method achieves higher throughput and better capacity in LTE-A HetNet system. Furthermore, the efficiency of the system is improved with regard to user satisfaction in terms of signal to interference and noise ratio (SINR) values.

The heterogeneous network (HetNets) emerge as one of the most auspicious improvements toward realizing the objective specifications of Long Term Evolution-Advanced (LTE-A) networks. Though, similar all other wireless networks, HetNet also yield from the interference problems, i.e., the co-tier and cross-tier interferences. Regarding proper functionality and efficiency of the HetNets, the avoidance and management of the said interferences are very crucial. In this study, a power control algorithm is suggested to decrease the interferences disadvantages in the HetNets. To calculate the signal power, the SINR (Signal to Interference and Noise Ratio) and RSRP (Reference Signal Received Power) are used for downlink and uplink respectively. The selection decision is taken by signal strength information. The UE (User Equipment) is switched accordingly. A simulation is used to investigate and find out the nearest cell to UE in a HetNet.

The emergence of optical wireless networks (OWNs) is a potential solution to the quest for the increasing bandwidth demand. Existing bandwidth assignment strategies are not suitable for OWNs, considering factors such as differences between the physical properties of radio networks and OWNs. In order to eliminate collision, minimize delay and enhance system utilization and fairness, we propose the non-contention bandwidth assignment protocol called adaptive polling medium access control (APMAC) protocol for OWNs. The APMAC protocol involves association, data transmission and dissociation phases. Moreover, the APMAC protocol exploits features of the IEEE 802.15.7 visible light communication (VLC) standard. While assigning bandwidth to the visible light nodes (VLNs), the visible light access point (VLAP) establishes a polling table that contains the identity, buffer size and round-trip time of each VLN that issued bandwidth request. The contents of the polling table enable the computation of the maximum transmission unit and time-slot for each VLN that requests bandwidth assignment. In order to achieve convincing results, we simulate the protocol under varying network sizes ranging from 1 to 10 VLNs per access point, then we compare the results against the medium transparent medium access control (MT–MAC) protocol that is a non-contention MAC protocol. We demonstrate numerical results of our study considering average waiting time, packet collision, system utilization and fairness. Numerical results reveal that the APMAC protocol outperforms the MT–MAC protocol.

For Long Term Evolution Advanced (LTE-A) network, although there exist many studies that focus on improving the performance with relays, security issues are often neglected. Due to the broadcast nature of wireless channels, relay nodes in LTE-A network may act maliciously, affect communication, reduce quality, and cause delays. Recently, physical (PHY) layer security has attracted researchers to provide secure communication and data privacy. In this study, we propose using unsupervised learning approach at the destination node to detect malicious relay attacks in cooperative LTE-A network based on received source signal in the PHY layer. Outlier detection algorithms such as one class support vector machine (OCSVM), local outlier factor (LOF) and isolation forest (iForest) are applied to detect various malicious relay behaviors such as garbling, regenerative, and false data injection type attacks. As input to these algorithms, feature vectors are constructed by using amplitude, phase, and relative phase information of modulated baseband symbols. The performance of the outlier detectors are evaluated with respect to precision, accuracy, and under the area curve (AUC) measures under changing signal-to-noise ratio (SNR) levels, different modulation types, allocated number of resource blocks (RBs), and varying data size. The results demonstrate the effectiveness of our proposed outlier detection approach for detecting malicious relays in the LTE-A network. Accuracy and precision of the algorithms are observed to be above 90% for 10 dB and larger SNR levels for the relay attack scenarios considered here. AUC values for all algorithms for all SNR levels is also above 0.9 for the attack detection cases, and the performance of the LOF algorithm with 0.95 and above AUC values is superior to other algorithms. The results verify the contribution of this study, which is the demonstration of the effectiveness of one class outlier detection approaches for detecting malicious relays in the LTE-A network.

Adjusting the insulin dose in patients with diabetes is crucial for maintaining optimal blood glucose levels. Currently, the insulin dose adjustment of patients registered at the Diabetes Clinic of Kocaeli University Hospital is followed manually using clinical forms, which is time-consuming and often fails to adequately account for individual variations. In this study, a rule-based insulin dose adjustment algorithm was developed based on clinical guidelines. The algorithm analyzes blood glucose levels measured at specific times over the past three days to determine the necessary insulin dose adjustments. The algorithm is implemented as part of the measurement module in a mobile application, the so-called AIDCARE application, which provides an artificial intelligence-enabled self-management tool for diabetic patients.