Censored Truncated Sequential Spectrum Sensing for Cognitive Radio Networks

Reliable spectrum sensing is a key functionality of a cognitive radio network. Cooperative spectrum sensing improves the detection reliability of a cognitive radio system but also increases the system energy consumption which is a critical factor particularly for low-power wireless technologies. A censored truncated sequential spectrum sensing technique is considered as an energy-saving approach. To design the underlying sensing parameters, the maximum energy consumption per sensor is minimized subject to a lower bounded global probability of detection and an upper bounded false alarm rate. This way both the interference to the primary user due to miss detection and the network throughput as a result of a low false alarm rate is controlled. We compare the performance of the proposed scheme with a fixed sample size censoring scheme under different scenarios. It is shown that as the sensing cost of the cognitive radios increases, the energy efficiency of the censored truncated sequential approach grows significantly.Comment: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6464630&isnumber=646450

Directional Modulation via Symbol-Level Precoding: A Way to Enhance Security

Wireless communication provides a wide coverage at the cost of exposing information to unintended users. As an information-theoretic paradigm, secrecy rate derives bounds for secure transmission when the channel to the eavesdropper is known. However, such bounds are shown to be restrictive in practice and may require exploitation of specialized coding schemes. In this paper, we employ the concept of directional modulation and follow a signal processing approach to enhance the security of multi-user MIMO communication systems when a multi-antenna eavesdropper is present. Enhancing the security is accomplished by increasing the symbol error rate at the eavesdropper. Unlike the information-theoretic secrecy rate paradigm, we assume that the legitimate transmitter is not aware of its channel to the eavesdropper, which is a more realistic assumption. We examine the applicability of MIMO receiving algorithms at the eavesdropper. Using the channel knowledge and the intended symbols for the users, we design security enhancing symbol-level precoders for different transmitter and eavesdropper antenna configurations. We transform each design problem to a linearly constrained quadratic program and propose two solutions, namely the iterative algorithm and one based on non-negative least squares, at each scenario for a computationally-efficient modulation. Simulation results verify the analysis and show that the designed precoders outperform the benchmark scheme in terms of both power efficiency and security enhancement.Comment: This manuscript is submitted to IEEE Journal of Selected Topics in Signal Processin

Optimization of wall components for encapsulation of Nigella sativa seed oil by freeze-drying

The aim of the present study was to determine the proper concentrations of whey protein isolate (WPI), Arabic gum (AG), and Zedo gum (ZG) for the encapsulation of Nigella sativa seed oil (NSO). WPI, AG, and ZG were used as wall materials for the encapsulation of NSO by mixture design and then the optimum sample with minimum Z-average, PDI, and a* and maximum zeta potential, conductivity, encapsulation efficiency, and b* was selected for examining FT-IR and SEM. The findings demonstrated that lower particle size and particle dispersion index were obtained at higher amounts of AG and WPI with the least a*. Desirability function results indicate that the mixture of 39.05% WPI, 16.758% AG, and 4.192% ZG led to the production of the optimum sample. Microencapsulation of Nigella sativa seed oil using the optimum wall composition was recognized to be useful to preserve bioactive compounds and provide flavor stability, enabling the use of microcapsules in food formulations

Deep Learning meets Blockchain for Automated and Secure Access Control

Access control is a critical component of computer security, governing access to system resources. However, designing policies and roles in traditional access control can be challenging and difficult to maintain in dynamic and complex systems, which is particularly problematic for organizations with numerous resources. Furthermore, traditional methods suffer from issues such as third-party involvement, inefficiency, and privacy gaps, making transparent and dynamic access control an ongoing research problem. Moreover detecting malicious activities and identifying users who are not behaving appropriately can present notable difficulties. To address these challenges, we propose DLACB, a Deep Learning Based Access Control Using Blockchain, as a solution to decentralized access control. DLACB uses blockchain to provide transparency, traceability, and reliability in various domains such as medicine, finance, and government while taking advantage of deep learning to not rely on predefined policies and eventually automate access control. With the integration of blockchain and deep learning for access control, DLACB can provide a general framework applicable to various domains, enabling transparent and reliable logging of all transactions. As all data is recorded on the blockchain, we have the capability to identify malicious activities. We store a list of malicious activities in the storage system and employ a verification algorithm to cross-reference it with the blockchain. We conduct measurements and comparisons of the smart contract processing time for the deployed access control system in contrast to traditional access control methods, determining the time overhead involved. The processing time of DLBAC demonstrates remarkable stability when exposed to increased request volumes.Comment: arXiv admin note: text overlap with arXiv:2303.1475

Joint Power Control in Wiretap Interference Channels

Interference in wireless networks degrades the signal quality at the terminals. However, it can potentially enhance the secrecy rate. This paper investigates the secrecy rate in a two-user interference network where one of the users, namely user 1, requires to establish a confidential connection. User 1 wants to prevent an unintended user of the network to decode its transmission. User 1 has to transmit such that its secrecy rate is maximized while the quality of service at the destination of the other user, user 2, is satisfied, and both user's power limits are taken into account. We consider two scenarios: 1) user 2 changes its power in favor of user 1, an altruistic scenario, 2) user 2 is selfish and only aims to maintain the minimum quality of service at its destination, an egoistic scenario. It is shown that there is a threshold for user 2's transmission power that only below or above which, depending on the channel qualities, user 1 can achieve a positive secrecy rate. Closed-form solutions are obtained in order to perform joint optimal power control. Further, a new metric called secrecy energy efficiency is introduced. We show that in general, the secrecy energy efficiency of user 1 in an interference channel scenario is higher than that of an interference-free channel

Anti-Cancer Drugs Effective in Retinoblastoma: Based on a Protein-Protein Interaction Network

Background: This paper investigates the effects of potential drugs on differentially expressed genes (DEGs) associated with substantial alterations in retinoblastoma malignancy.Material and Methods: The GSE125903 dataset consisting of ten samples was used in this study (seven cancer patients and three control samples). The genes were ordered according to their adjusted p value, and 2000 top differential expressed genes with adj p values less than 0.01 were chosen as statistically significant. The STRING database version 11.0 was used to display the interaction among genes. The Cytoscape3.8.2 and the Clusterviz plugin software were used to construct the modules for the PPI network, and five clusters of genes were formed. The DGIdb v4.2.0 database was used to study drug-gene interactions and identify potentially beneficial medicines for retinoblastoma malignancy. The DAVID v.6.8 database was used to study gene ontology (GO) and important biological pathways.Results: CISPLATIN, TAMOXIFEN, and CYCLOPHOSPHAMIDE are the medicines that have been shown to be successful in treating retinoblastoma in our study. Additionally, we conducted a research on three other drugs: GEMCITABINE, OLAPARIB, and MITOXANTRONE. Although it is used to treat other diseases, it seems to have no apparent effects on retinoblastoma cancer treatment.Conclusion: CISPLATIN, a drug that causes apoptosis in tumors, has been proven to be the most effective therapy for retinoblastoma and should be included in treatment regimens for this illness. Of course, we obtained this information based on bioinformatics techniques, and more clinical trials are needed for more reliable results.Keywords: Protein-Protein Interaction Network; Retinoblastoma; Anti-Cancer

Drug Repurposing for Age-Related Macular Degeneration (AMD) Based on Gene Co-Expression Network Analysis

Background: Age-related macular degeneration (AMD) is a lesser-known eye disease in the world that gradually destroys a person’s vision by creating dark spots in the center of vision. Material and Methods: Samples of AMD-related genes were extracted from the NCBI, then the gene expression network (GCN) was extracted. In addition, pathway enrichment analysis was performed to investigate the role of co-expressed genes in AMD. Finally, the drug-gene interaction network was plotted.Results: The results of this work based on bioinformatics showed that many genes are involved in AMD disease, the most important of which are the genes of TYROBP, LILRB2, LCP2, PTPRC, CFH, SPARC, HTR5A.Overexpression of these genes can be considered as basic biomarkers for this disease, we separated some of which we had from the gene co-expression network and some from the results of genes ontology (genes that have a P value ≤ 0.05). The most important drugs were isolated from the drug-gene network based on degree, which included 5 drugs including ocriplasmin, collagenase clostridium histolyticum, topiramate, primidone, butalbital.Conclusion: Among the genes we found, three genes of CFH, TYROBP, SPARC seem to be more important than the others. Among drugs, ocriplasmin, topiramate, primidone can play a more important role based on the degree in the drug-gene network, because all steps are performed with different bioinformatics methods, clinical trials must confirm or reject the results.Keywords: Age-Related Macular Degeneration; AMD; Co-Expression Network; Drug Repurposing

Hybrid Analog-Digital Transceiver Designs for Cognitive Radio Millimiter Wave Systems

Recent advances in Milimeter wave (mmWave) band mobile communications may provide solutions to the increasing traffic demand in modern wireless systems. Even though mmWave bands are scarcely occupied, the design of a prospect transceiver should guarantee the efficient coexistence with the incumbent services in these bands. To that end, in this paper, two underlay cognitive transceiver designs are proposed based on a hybrid Analog/Digital transceiver architecture that enables the mmWave spectrum access while controlling the interference to the incumbent users with low hardware complexity and power consumption. The first cognitive solution designs a codebook free cognitive hybrid pre-coder by maximizing the mutual information between its two ends subject to interference, power and hardware constraints related to the analog counterpart. The second solution is codebook based and exhibits less complexity than the first one at the cost of inferior spectral efficiency. A novel codebook free solution for the post-coder at the cognitive receiver part is further proposed, based on a hardware constrained Minimum Mean Square Error criterion. Simulations study the performance of both the proposed hybrid approaches and compare it to the one of a fully digital solution for typical wireless environments

CONSISTENT LEAST SQUARES ESTIMATOR FOR CO-ARRAY-BASED DOA ESTIMATION

Sparse linear arrays (SLAs), such as nested and co-prime arrays, have the attractive capability of providing enhanced degrees of freedom by exploiting the co-array model. Accordingly, co-array-based Direction of Arrivals (DoAs) estimation has recently gained considerable interest in array processing. The literature has suggested applying MUSIC on an augmented sample covariance matrix for co-array-based DoAs estimation. In this paper, we propose a Least Squares (LS) estimator for co-array-based DoAs estimation employing the covariance fitting method as an alternative to MUSIC. We show that the proposed LS estimator provides consistent estimates of DoAs of identifiable sources for SLAs. Additionally, an analytical expression for the large sample performance of the proposed estimator is derived. Numerical results illustrate the finite sample behavior in relation to the derived analytical expression. Moreover, the performance of the proposed LS estimator is compared to the co-array-based MUSIC

Power Allocation for Energy-Constrained Cognitive Radios in the Presence of an Eavesdropper

Reliable and agile spectrum sensing as well as secure communication are key requirements of a cognitive radio system. In this paper, secrecy throughput of a cognitive radio is maximized in order to determine the sensing threshold, the sensing time, and the transmission power. Constraints of the problem are defined as a lower-bound on the detection probability, an upper-bound on the average energy consumption per time-frame, and the maximum transmission power of the cognitive radio. We show that the problem can be solved by an on-off strategy where the cognitive radio only performs sensing and transmits data if the cognitive channel gain is greater than the average eavesdropper channel gain. The problem is then solved by a line-search over sensing time. Eventually, the secrecy throughput of the cognitive radio is evaluated employing the IEEE 802.15.4/ZigBee standard