Enhancing Reconfigurable Intelligent Surface-Enabled Cognitive Radio Networks for Sixth Generation and Beyond: Performance Analysis and Parameter Optimization.

In this paper, we propose a novel system integrating reconfigurable intelligent surfaces (RISs) with cognitive radio (CR) technology, presenting a forward-looking solution aligned with the evolving standards of 6G and beyond networks. The proposed RIS-assisted CR networks operate with a base station (BS) transmitting signals to two users, the primary user (PU) and secondary user (SU), through direct and reflected signal paths, respectively. Our mathematical analysis focuses on deriving expressions for SU in the RIS-assisted CR system, validated through Monte Carlo simulations. The investigation covers diverse aspects, including the impact of the signal-to-noise ratio (SNR), power allocations, the number of reflected surfaces, and blocklength variations. The results provide nuanced insights into RIS-assisted CR system performance, highlighting its sensitivity to factors like the number of reflectors, fading severity, and correlation coefficient. Careful parameter selection, such as optimizing the configuration of reflectors, is shown to prevent a complete outage, showcasing the system's robustness. Additionally, the work suggests that the optimization of reflectors configuration can significantly enhance overall system performance, and RIS-assisted CR systems outperform reference schemes. This work contributes a thorough analysis of the proposed system, offering valuable insights for efficient performance evaluation and parameter optimization in RIS-assisted CR networks.

Security and Reliability Analysis of the Power Splitting-Based Relaying in Wireless Sensors Network.

This paper studies the security and reliability of the power splitting (PS)-based relaying in the Internet of Things (IoT) networks with the help of a jammer. Based on the considered system model, we derive outage probability (OP) and intercept probability (IP) under two distinguished schemes, namely, the static PS relaying (SPSR) scheme and the dynamic PS relaying (DPSR) scheme. More precisely, the PS ratio of the former is a constant number, while the latter is optimally adjusted in order to minimize the OP and counts only on the channel gain of the second hop. Numerical results are provided to not only verify the accuracy of the proposed mathematical framework but also identify the trends of both OP and IP with respect to several important parameters. Our findings unveil that the OP and IP have contradictory behavior with respect to the transmit power and number of sources. Moreover, the performance of the DPSR scheme is superior to that of the SPSR scheme.

A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications.

A reconfigurable intelligent surface (RIS) is a development of conventional relay technology that can send a signal by reflecting the signal received from a transmitter to a receiver without additional power. RISs are a promising technology for future wireless communication due to their improvement of the quality of the received signal, energy efficiency, and power allocation. In addition, machine learning (ML) is widely used in many technologies because it can create machines that mimic human mindsets with mathematical algorithms without requiring direct human assistance. Meanwhile, it is necessary to implement a subfield of ML, reinforcement learning (RL), to automatically allow a machine to make decisions based on real-time conditions. However, few studies have provided comprehensive information related to RL algorithms-especially deep RL (DRL)-for RIS technology. Therefore, in this study, we provide an overview of RISs and an explanation of the operations and implementations of RL algorithms for optimizing the parameters of RIS technology. Optimizing the parameters of RISs can offer several benefits for communication systems, such as the maximization of the sum rate, user power allocation, and energy efficiency or the minimization of the information age. Finally, we highlight several issues to consider in implementing RL algorithms for RIS technology in wireless communications in the future and provide possible solutions.

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches.

The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

A Survey on Unmanned Underwater Vehicles: Challenges, Enabling Technologies, and Future Research Directions.

Unmanned underwater vehicles (UUVs) are becoming increasingly important for a variety of applications, including ocean exploration, mine detection, and military surveillance. This paper aims to provide a comprehensive examination of the technologies that enable the operation of UUVs. We begin by introducing various types of unmanned vehicles capable of functioning in diverse environments. Subsequently, we delve into the underlying technologies necessary for unmanned vehicles operating in underwater environments. These technologies encompass communication, propulsion, dive systems, control systems, sensing, localization, energy resources, and supply. We also address general technical approaches and research contributions within this domain. Furthermore, we present a comprehensive overview of related work, survey methodologies employed, research inquiries, statistical trends, relevant keywords, and supporting articles that substantiate both broad and specific assertions. Expanding on this, we provide a detailed and coherent explanation of the operational framework of UUVs and their corresponding supporting technologies, with an emphasis on technical descriptions. We then evaluate the existing gaps in the performance of supporting technologies and explore the recent challenges associated with implementing the Thorp model for the distribution of shared resources, specifically in communication and energy domains. We also address the joint design of operations involving unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and UUVs, which necessitate collaborative research endeavors to accomplish mission objectives. This analysis highlights the need for future research efforts in these areas. Finally, we outline several critical research questions that warrant exploration in future studies.

Performance Analysis of IRS-Assisted THz Communication Systems over α-µ Fading Channels with Pointing Errors.

In this paper, we analyze the performance of an intelligent reflecting surface (IRS)-aided terahertz (THz) wireless communication system with pointing errors. Specifically, we derive closed-form analytical expressions for the upper bounded ergodic capacity and approximate expression of the outage probability. We adopt an α-µ fading channel model for our analysis that is experimentally demonstrated to be a good fit for THz small-scale fading statistics, especially in indoor communication scenarios. In the proposed analysis, the statistical distribution of the α-µ fading channel is used to derive analytical expressions for the ergodic capacity and outage probability. Our proposed analysis considers not only the IRS reflected channels, but also the direct channel between the communication nodes. The results of the derived analytical expressions are validated through Monte Carlo simulations. Through simulations, it has been noticed that pointing errors degrade the performance of the IRS-assisted THz wireless communication system which can be compensated by deploying an IRS having a large number of reflecting elements.

Security-Reliability Analysis of AF Full-Duplex Relay Networks Using Self-Energy Recycling and Deep Neural Networks.

This paper investigates the security-reliability of simultaneous wireless information and power transfer (SWIPT)-assisted amplify-and-forward (AF) full-duplex (FD) relay networks. In practice, an AF-FD relay harvests energy from the source (S) using the power-splitting (PS) protocol. We propose an analysis of the related reliability and security by deriving closed-form formulas for outage probability (OP) and intercept probability (IP). The next contribution of this research is an asymptotic analysis of OP and IP, which was generated to obtain more insight into important system parameters. We validate the analytical formulas and analyze the impact on the key system parameters using Monte Carlo simulations. Finally, we propose a deep learning network (DNN) with minimal computation complexity and great accuracy for OP and IP predictions. The effects of the system's primary parameters on OP and IP are examined and described, along with the numerical data.

Secure Cognitive Radio-Enabled Vehicular Communications under Spectrum-Sharing Constraints.

Vehicular communication has been envisioned to support a myriad of essential fifth-generation and beyond use-cases. However, the increasing proliferation of smart and intelligent vehicles has generated a lot of design and infrastructure challenges. Of particular interest are the problems of spectrum scarcity and communication security. Consequently, we considered a cognitive radio-enabled vehicular network framework for accessing additional radio spectrum and exploit physical layer security for secure communications. In particular, we investigated the secrecy performance of a cognitive radio vehicular network, where all the nodes in the network are moving vehicles and the channels between them are modeled as double-Rayleigh fading. Furthermore, adopting an underlay approach, the communication between secondary nodes can be performed by employing two interference constraint strategies at the primary receiver; (1) Strategy I: the secondary transmitter power is constrained by the interference threshold of the primary receiver, and (2) Strategy II: the secondary transmitter power is constrained by both the interference threshold of the primary receiver and the maximum transmit power of the secondary network. Under the considered strategies, we derive the exact secrecy outage probability (SOP) and ergodic secrecy capacity (ESC) expressions over double-Rayleigh fading. Moreover, by analyzing the asymptotic SOP behavior, we show that a full secrecy diversity of 1 can be achieved, when the average channel gain of the main link goes to infinity with a fixed average wiretap channel gain. From the ESC analysis, it is revealed that the ESC follows a scaling law of ΘlnΩm2Ωe2 for large Ωm and Ωe, where Ωm and Ωe are the average channel gains of the main link and wiretap link. The numerical and simulation results verify our analytical findings.

Performance Analysis of Cognitive Relay-Assisted Ambient Backscatter with MRC over Nakagami-m Fading Channels.

This study presents ambient backscatter communication (AmBC) network as a concept of "modulation in the air" that has drawn growing interest by both academia and industry recently. In particular, we investigate and analyze an AmBC system relying on cognitive radio, where the primary destination is equipped with multiple antennas and maximum ratio combining (MRC). A wireless powered relay is necessary to serve both primary and secondary destinations. Benefiting from the surrounding radio frequency (RF) source, the relay can support the backscattering signal. To facilitate the performance analysis of received nodes, this study presents exact closed-form expressions of the outage probability. For comparison, the outage and throughput performance of these nodes are considered in numerical simulation. Taking advantage of the AmBC technology, the impact of the backscatter ratio on system performance is carefully studied considering various other parameters. Simulation results demonstrate the exactness of the derived outage probabilities and show that the optimal throughput performance can be achieved at specific parameters.

Cognitive Radio-Assisted NOMA Broadcasting for 5G Cellular V2X Communications: Model of Roadside Unit Selection and SWIPT.

The outage performance is a significant problem to implement the Cognitive Radio (CR) paradigm in the Vehicle to Everything (V2X) networks. Recently, more interest has focused on Non-Orthogonal Multiple Access (NOMA) in wireless-powered communication. In the conventional CR-enabled V2X-NOMA network, spectrum sensing and limited battery capacity at the Roadside Unit (RSU) may cause serious outage performance. In this study, RSU selection scheme is adopted. This paper presents an interesting model of a system with Simultaneous Wireless Information and Power Transfer (SWIPT) and a CR-enabled V2X-NOMA network. In the downlink, the RSU harvests wireless energy from Radio Frequency (RF) signals and senses the spectrum state at the same time. A CR-enabled V2X-NOMA system performance is presented by deriving exact expressions of outage probability of distant vehicles. In the overlay CR-enabled V2X-NOMA network, the constraints are transmit power and the number of designed RSU that make significant impacts on system performance. Simulation results show that the CR-enabled V2X-NOMA get benefits from energy harvesting and RSU selection scheme.

Outage Performance Improvement by Selected User in D2D Transmission and Implementation of Cognitive Radio-Assisted NOMA.

In this paper, we investigate the outage performance in secondary network of cognitive radio (CR) employing non-orthogonal multiple access (NOMA) wireless networks over Rayleigh fading channels. The considered system model adopts device-to-device (D2D) transmission together with traditional communication to form a new system model, namely CR-D2DNOMA network. The specific user is selected from multiple D2D-Tx users (D2Ds) to communicate with far NOMA users to form qualified D2D connection with assistance of the Relay user ( R U ). The main metric in such CR-D2DNOMA network needs to be considered and we particularly introduce the closed-form expressions for outage probability in the secondary network where it is designed to serve two far NOMA users. The perfect Successive Interference Cancellation (SIC) and imperfect SIC can be further examined at the second NOMA user who detects signal based on the ability of SIC. The results show the positive impact of increasing the fading parameters on the system performance. More importantly, numerical results are provided to verify the correctness of our derivations. Additionally, the effects of asymptotic expressions on insights evaluation are also further analyzed.

Robust Transmit Antenna Design for Performance Improvement of Cell-Edge Users: Approach of NOMA and Outage/Ergodic Capacity Analysis.

In this investigation, a wireless sensor network using a non-orthogonal multiple access (NOMA) system is considered in two scenarios related to the number of serving access point/base stations, where two policies provide system performance improvement in two sensors (the near user and the far user). To improve performance efficiency, two robust transmit antenna strategies are designed related to the access point/base station (BS), namely (i) Transmit Antenna Selection (TAS) mode and (ii) two base station (TBS) approach to simultaneously serve NOMA users. First, the TAS scheme is implemented to provide suboptimal outage performance for such NOMA, in which BS equipped at least two antennas while NOMA users are equippeda single antenna. Secondly, the TBS scheme is conducted to enhance the outage performance, especially considering priority evaluation for the far user in user pairs. As an important result, such far users in two proposed schemes are studied by introducing the exact closed-form expression to examine outage behavior. Accordingly, the closed-form expressions regarding ergodic capacity can be further obtained. To corroborate the exactness of these metrics, Monte Carlo simulation is performed. In addition, the proposed schemes exhibit various performance evaluations accompanied by different related parameters such as power allocation factors, the number of transmit antenna, and transmit signal-to-noise ratio (SNR).

On Exact Outage and Throughput Performance of Cognitive Radio based Non-Orthogonal Multiple Access Networks With and Without D2D Link.

In this paper, we investigate the performance of a secondary network in a cognitive radio network employing a non-orthogonal multiple access (NOMA) scheme to form a CR-NOMA system serving many destination users. In the secondary network of our proposed system, a device-to-device (D2D) scheme is deployed to further provide the signal transmission at a close distance of NOMA users in downlink, and such performance is evaluated under the situation of interference reception from the primary network. An outage performance gap exists among these NOMA users since different power allocation factors are assigned to the different destinations. Unlike existing NOMA schemes that consider fixed power allocation factors, which are not optimal in terms of outage performance, our proposed paradigm exhibits optimal outage in the scenario of D2D transmission. In particular, the outage performances in two kinds of schemes in term of existence of D2D link are further achieved. Simulation results validate the analytical expressions, and show the advantage of each scheme in the proposed CR-NOMA system based on outage performance and throughput.

Exploiting Joint Base Station Equipped Multiple Antenna and Full-Duplex D2D Users in Power Domain Division Based Multiple Access Networks.

In this paper, we investigate power domain division-based multiple access (PDMA) to support the base stations (BS) equipped with multiple antennas to serve mobile users. Such a system deploys multiple input single output (MISO)-based wireless transmission and a full-duplex (FD) scheme. Furthermore, such MISO PDMA system consists of BS employing transmit antenna selection to reduce complexity in signal processing at the receivers. We distinguish two kinds of mobile users, device-to-device (D2D) users and traditional users. In such MISO PDMA, there exists a trade-off between outage performance of each PDMA user and power allocation factors. Since the implementation of the FD scheme at PDMA users, bandwidth efficiency will be enhanced despite the existence of self-interference related to such FD. In particular, exact expressions of outage probability are derived to exhibit system performance with respect to D2D users. Finally, valuable results from the simulated parameters together with the analytical results show that MISO PDMA can improve its performance by increasing the number of transmit antennas at the BS.

Cylindrical Dielectric Resonator Antenna-Based Sensors for Liquid Chemical Detection.

A compact, cylindrical dielectric resonator antenna (CDRA), using radio frequency signals to identify different liquids is proposed in this paper. The proposed CDRA sensor is excited by a rectangular slot through a 3-mm-wide microstrip line. The rectangular slot has been used to excite the CDRA for H E M 11 mode at 5.25 GHz. Circuit model values (capacitance, inductance, resistance and transformer ratios) of the proposed CDRA are derived to show the true behaviour of the system. The proposed CDRA acts as a sensor due to the fact that different liquids have different dielectric permittivities and, hence, will be having different resonance frequencies. Two different types of CDRA sensors are designed and experimentally validated with four different liquids (Isopropyl, ethanol, methanol and water).

Simplified Antenna Group Determination of RS Overhead Reduced Massive MIMO for Wireless Sensor Networks.

Massive multiple-input multiple-output (MIMO) systems can be applied to support numerous internet of things (IoT) devices using its excessive amount of transmitter (TX) antennas. However, one of the big obstacles for the realization of the massive MIMO system is the overhead of reference signal (RS), because the number of RS is proportional to the number of TX antennas and/or related user equipments (UEs). It has been already reported that antenna group-based RS overhead reduction can be very effective to the efficient operation of massive MIMO, but the method of deciding the number of antennas needed in each group is at question. In this paper, we propose a simplified determination scheme of the number of antennas needed in each group for RS overhead reduced massive MIMO to support many IoT devices. Supporting many distributed IoT devices is a framework to configure wireless sensor networks. Our contribution can be divided into two parts. First, we derive simple closed-form approximations of the achievable spectral efficiency (SE) by using zero-forcing (ZF) and matched filtering (MF) precoding for the RS overhead reduced massive MIMO systems with channel estimation error. The closed-form approximations include a channel error factor that can be adjusted according to the method of the channel estimation. Second, based on the closed-form approximation, we present an efficient algorithm determining the number of antennas needed in each group for the group-based RS overhead reduction scheme. The algorithm depends on the exact inverse functions of the derived closed-form approximations of SE. It is verified with theoretical analysis and simulation that the proposed algorithm works well, and thus can be used as an important tool for massive MIMO systems to support many distributed IoT devices.

PLANEX: the plant co-expression database.

BACKGROUND: The PLAnt co-EXpression database (PLANEX) is a new internet-based database for plant gene analysis. PLANEX (http://planex.plantbioinformatics.org) contains publicly available GeneChip data obtained from the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI). PLANEX is a genome-wide co-expression database, which allows for the functional identification of genes from a wide variety of experimental designs. It can be used for the characterization of genes for functional identification and analysis of a gene's dependency among other genes. Gene co-expression databases have been developed for other species, but gene co-expression information for plants is currently limited. DESCRIPTION: We constructed PLANEX as a list of co-expressed genes and functional annotations for Arabidopsis thaliana, Glycine max, Hordeum vulgare, Oryza sativa, Solanum lycopersicum, Triticum aestivum, Vitis vinifera and Zea mays. PLANEX reports Pearson's correlation coefficients (PCCs; r-values) that distribute from a gene of interest for a given microarray platform set corresponding to a particular organism. To support PCCs, PLANEX performs an enrichment test of Gene Ontology terms and Cohen's Kappa value to compare functional similarity for all genes in the co-expression database. PLANEX draws a cluster network with co-expressed genes, which is estimated using the k-mean method. To construct PLANEX, a variety of datasets were interpreted by the IBM supercomputer Advanced Interactive eXecutive (AIX) in a supercomputing center. CONCLUSION: PLANEX provides a correlation database, a cluster network and an interpretation of enrichment test results for eight plant species. A typical co-expressed gene generates lists of co-expression data that contain hundreds of genes of interest for enrichment analysis. Also, co-expressed genes can be identified and cataloged in terms of comparative genomics by using the 'Co-expression gene compare' feature. This type of analysis will help interpret experimental data and determine whether there is a common term among genes of interest.

Effects of Climate Change and Drought Tolerance on Maize Growth.

Climate change is affecting all regions of the world with different climates, and the scale of damage is increasing due to the occurrence of various natural disasters. In particular, maize production is highly affected by abnormal climate events such as heat waves and droughts. Increasing temperatures can accelerate growth and shorten the growing season, potentially reducing productivity. Additionally, enhanced temperatures during the ripening period can accelerate the process, reducing crop yields. In addition, drought stress due to water deficit can greatly affect seedling formation, early plant growth, photosynthesis, reproductive growth, and yield, so proper water management is critical to maize growth. Maize, in particular, is tall and broad-leaved, so extreme drought stress at planting can cause leaves to curl and stunt growth. It is important to understand that severe drought can have a detrimental effect on the growth and reproduction of maize. In addition, high temperatures caused by drought stress can inhibit the induction of flowering in male flowers and cause factors that interfere with pollen development. It is therefore important to increase the productivity of all food crops, including maize, while maintaining them in the face of persistent drought caused by climate change. This requires a strategy to develop genetically modified crops and drought-tolerant maize that can effectively respond to climate change. The aim of this paper is to investigate the effects of climate change and drought tolerance on maize growth. We also reviewed molecular breeding techniques to develop drought-tolerant maize varieties in response to climate change.

Expression of a homeostatic regulator, Wip1 (wild-type p53-induced phosphatase), is temporally induced by c-Jun and p53 in response to UV irradiation.

Wild-type p53-induced phosphatase (Wip1) is induced by p53 in response to stress, which results in the dephosphorylation of proteins (i.e. p38 MAPK, p53, and uracil DNA glycosylase) involved in DNA repair and cell cycle checkpoint pathways. p38 MAPK-p53 signaling is a unique way to induce Wip1 in response to stress. Here, we show that c-Jun directly binds to and activates the Wip1 promoter in response to UV irradiation. The binding of p53 to the promoter occurs earlier than that of c-Jun. In experiments, mutation of the p53 response element (p53RE) or c-Jun consensus sites reduced promoter activity in both non-stressed and stressed A549 cells. Overexpression of p53 significantly decreased Wip1 expression in HCT116 p53(+/+) cells but increased it in HCT116 p53(-/-) cells. Adenovirus-mediated p53 overexpression greatly decreased JNK activity. Up-regulation of Wip1 via the p38 MAPK-p53 and JNK-c-Jun pathways is specific, as demonstrated by our findings that p38 MAPK and JNK inhibitors affected the expression of the Wip1 protein, whereas an ERK inhibitor did not. c-Jun activation occurred much more quickly, and to a greater extent, in A549-E6 cells than in A549 cells, with delayed but fully induced Wip1 expression. These data indicate that Wip1 is activated via both the JNK-c-Jun and p38 MAPK-p53 signaling pathways and that temporal induction of Wip1 depends largely on the balance between c-Jun and p53, which compete for JNK binding. Moreover, our results suggest that JNK-c-Jun-mediated Wip1 induction could serve as a major signaling pathway in human tumors in response to frequent p53 mutation.

A gene family encoding RING finger proteins in rice: their expansion, expression diversity, and co-expressed genes.

The proteins harboring RING finger motif(s) have been shown to mediate protein-protein interactions that are relevant to a variety of cellular processes. In an effort to elucidate the evolutionary dynamics of the rice RING finger protein family, we have attempted to determine their genomic locations, expression diversity, and co-expressed genes via in silico analysis and semi-quantitative RT-PCR. A total of 425 retrieved genes appear to be distributed over all 12 of the chromosomes of rice with different distributions, and are reflective of the evolutionary dynamics of the rice genome. A genome-wide dataset harboring 155 gene expression omnibus sample plates evidenced some degree of differential evolutionary fates between members of RING-H2 and RING-HC types. Additionally, responses to abiotic stresses, such as salinity and drought, demonstrated that some degree of expression diversity existed between members of the RING finger protein genes. Interestingly, we determined that one RING-H2 finger protein gene (Os04g51400) manifested striking differences in expression patterns in response to abiotic stresses between leaf and culm-node tissues, further revealing responses highly similar to the majority of randomly selected co-expressed genes. The gene network of genes co-expressed with Os04g51400 may suggest some role in the salt response of the gene. These findings may shed further light on the evolutionary dynamics and molecular functional diversity of these proteins in complex cellular regulations.