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Cognitive radio (CR) technology is crucial for enabling dynamic spectrum management. CR allows opportunistic spectrum sharing with licensed primary radio (PR) users by allowing the so-called secondary users to dynamically access the under-utilized parts of the frequency bands that are allocated/licensed to the PR networks (PRNs). The CR system pays a utilization-dependent price to PRNs for utilizing the unused spectrum. An essential challenge in this domain is how to characterize the economic implications of spectrum sharing between the CR system and PRNs. Specifically, we consider a CR system with two competing CR service providers. This market model is known as the "duopoly model," a market structure premise that consists of two companies providing the same type of service. For such a model, we investigate the problem of maximizing the overall achieved profit in the CR system. Specifically, we mathematically formulate the profit-maximization spectrum assignment problem for the two CR providers subject to spectrum sharing, power distribution, spectrum pricing, and quality of service constraints. We demonstrate that this optimization problem is an NP-hard binary nonlinear programming problem. Therefore, we adopt a meta-heuristic optimization method based on Antlion Optimization to solve the problem suboptimally. Simulation results revealed that our proposed profit-aware optimization significantly outperforms traditional CR-based spectrum access mechanisms. [ABSTRACT FROM AUTHOR]

The integration of cognitive radio (CR) into power domain non-orthogonal multiple access (NOMA) wireless systems has been envisioned as a promising paradigm for supporting massive energy-efficient beyond fifth-generation (B5G)-based IoT connectivity. This paper presents a novel two-dimensional orthogonal multiple access (OMA)-NOMA CR-based power minimization approach for battery-constrained B5G-based IoT networks. According to the proposed approach, the time and frequency domains are integrated together with NOMA for the purpose of serving a larger number of CR users. Our approach divides the available time of each idle frequency channel into a set of time slots, each can serve a group of CR users through power-domain multiplexing. Our approach consists of two phases: user-pairing with channel assignment and power-allocation. The user-pairing associated channel-assignment problem is mathematically presented as a binary linear-programming problem, which is shown to be a uni-modular optimization with polynomial-time complexity. Given the paired users and assigned channels, the power-minimization problem under a set of quality-of-service constraints is formulated as a non-convex optimization. Then, we implement an iterative method that transfers the problem into a convex optimization. Simulation results reveal that our two-dimensional OMA-NOMA CR-based approach provides significant performance gain over the conventional CR-based OMA-NOMA power-minimization in terms of the total consumed transmission power. [ABSTRACT FROM AUTHOR]

Recent advances in unmanned aerial vehicle (UAV) technology have revolutionized various industries, finding applications in embedded systems, autonomy, control, security, and communication. Autonomous UAVs are distinguished by their ability to make informed decisions, anticipate potential scenarios, and learn from past experiences with the help of AI algorithms. This paper examines a practical monitoring system with an autonomous UAV, a charging station, and multiple targets that move randomly within a defined mission area. The mission area is divided into zones, and the UAV navigates through these zones efficiently. The primary objective is to maximize the probability of detecting targets, considering constraints such as limited battery life and charging station location. This challenge is initially framed as a search benefit maximization problem and subsequently reformulated as a Markov Decision Process (MDP) problem. To address the MDP formulation, we introduce a reinforcement learning (RL)-based approach that enables the UAV to comprehend unpredictable multi-target movements autonomously. The placement of the charging station in the proposed system is determined using the optimal median approach. The simulation results demonstrate that the proposed RL-based detection system significantly outperforms the reference systems in terms of detection rate and convergence. [ABSTRACT FROM AUTHOR]

The IEEE 802.11 MAC protocols have been implemented in many wireless ad hoc and infrastructure networks. Most of the IEEE 802.11-based networks adopt a one-hop transmission mechanism (the source and destination communicate using a direct link). While this strategy is simple, it degrades network performance as it limits the number of data transmissions that can concurrently take place. The performance of IEEE 802.11 MAC in terms of delay and overall achieved throughput can be improved when the overall transmission time for each data transmission from the access point (AP) to any node in the network is minimized. Occupying the time-frequency resources for a shorter time period results in more available resources for other potential downlink transmissions to take place. In this work, we present a modified MAC protocol for IEEE 802.11 infrastructure-based networks with device-to-device transmission strategy. The main goal of our proposed protocol is to minimize the transmission time for individual downlink transmissions while maximizing the utilization of network resources by using D2D multi-hop transmissions, thereby improving the overall achieved throughput. In our proposed mechanism, the AP selects the multi-hop path that requires the minimum transmission time to deliver the data packets to destination. This path is selected even though the direct path exists. Compared to the IEEE 802.11 standard, simulation results show significant performance improvements in terms of throughput and fairness. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensors are considered the key elements in enabling efficient Internet of Things (IoT) networking and services. One of the key objectives in designing a WSN is increasing network lifetime by reducing the overall network energy consumption. To achieve this, several techniques have been proposed to extend the network lifetime, such as clustering. Clustering divides the network into several clusters, each with its own member nodes and a cluster‐head (CH) node. In this paper, a novel clustering mechanism is developed with the objective of extending network lifetime through load balancing in wireless sensor networks (WSNs). This is achieved by electing a new node to a provides specific tasks in the WSN to decrease energy consumption and enhance network lifetime. This node is called a relay node that is responsible of delivering the received data packets from the CHs, and then forwards them to the central node (sink). Unlike previous clustering schemes, the proposed algorithm consists of three main processes: CH selection, relay node selection, and medium access control processes. These processes depend on energy level and user's distributions. The performance of the proposed algorithm is investigated through simulation experiments in terms of network lifetime under various network parameters. The results reveal that the proposed mechanism significantly enhances network lifetime compared to previously proposed algorithms. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is a new intelligent wireless technology that aims at improving spectrum utilization by allowing opportunistic access to the underutilized licensed spectrum. Wireless CR operating environment is typically characterized by its unreliable and unpredictable channel conditions and time availability due to fading and the randomness of primary radio (PR) activities. In such environment, packet fragmentation is needed to enhance the probability of success/packet delivery and reduce the needed number of packet re-transmission attempts. Specifically, the quality and availability of the PR channels along with the data packet size should be considered when designing communication protocols for CR networks (CRNs) such that the packet success probability is improved. Based on the quality and availability of the PR channels, an optimal-packet size metric is derived, which is defined as the packet size that can be transmitted over a selected channel while guarantying a predefined probability of success. In this paper, we propose three fragmentation-based channel assignment algorithms: fixed-fragment size algorithm, first-fit algorithm and near-exact fit algorithm. The first algorithm divides the packet equally over the selected channels while the other algorithms use variable fragment size. The main objectives of the algorithms are to enhance network throughput, decrease the number of dropped packets and reduce the average number of retransmission attempts. This preserves more channels for potential future CR transmissions, resulting in higher network throughput with less energy consumption. Simulation results show that the proposed algorithms significantly outperform existing CRN channel assignment algorithms with no fragmentation. [ABSTRACT FROM AUTHOR]

Recent developments in self-interference cancellation (SIC) techniques have made in-band full-duplex (IBFD) transmission systems possible. IBFD technology can enhance, or theoretically speaking double, spectrum utilization by allowing simultaneous transmission and reception using the same radio-frequency channel. Integrating the IBFD technology in cognitive radio (CR) systems can further enhance spectrum utilization and provide huge spectrum opportunities for efficient deployment of next-generation multi-hop wireless systems. Routing protocols for typical CR networks (CRNs) assume that CR users are equipped with half-duplex receivers. Limited number of protocols have been designed for FD-based CRNs. However, these protocols do not account for security threats in wireless environment, which could significantly affect system performance. In this paper, we develop a security-aware intelligent routing scheme that aims at mitigating the effects of jamming attacks on IBFD CRNs. This scheme considers the unique characteristics of the CRN environment while being IBFD-aware. Specifically, it considers the primary user’s channel-availability time, channel quality and jamming strategy. For a source-destination pair, our protocol collects a set of loop-free paths. Then, it attempts to assign channels for the hops along each path with the goal of achieving the highest Probability of Success (PoS) of each path. The per-hop channel selection for each path is derived as an optimization problem, for which sub-optimal solutions are found using a polynomial-time approximate method. Based on the computed channel assignment and PoS over each path, the path with the highest PoS between the source and destination is selected. Simulation results show that our secure-aware IBFD-based routing can significantly mitigate the jamming effects on CRN performance by improving the packet-delivery ratio, and consequently network throughput compared with existing FD-based routing protocols. [ABSTRACT FROM AUTHOR]

In this paper, we develop a joint channel assignment and adaptive Multi-Input–Multi-Output (MIMO) mode selection protocol for MIMO-based multi-channel cognitive radio networks. The proposed protocol utilizes the dynamic access capabilities of cognitive radio devices along with the multiplexing and/or diversity gains offered by MIMO systems in order to improve network throughput and communication reliability. The proposed protocol attempts at enhancing system performance by selecting the best MIMO operating modes (multiplexing, diversity, or hybrid multiplexing-diversity mode) for each idle channel according to the surrounding RF environment, and then selects the channel that provides the highest throughput. The network performance of our protocol (the overall network throughput and MIMO efficiency) is investigated under various traffic and network conditions through simulations. Compared to diversity-mode channel assignment, multiplexing-mode channel assignment, and adaptive mode selection with random channel selection protocols, our proposed protocol reveals significant performance improvement in network throughput under different traffic and network conditions. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is a promising technology that allows devices to effectively utilize the underutilized or unutilized portions of the licensed spectrum. In literature, several channel assignment and spectrum sharing algorithms for CR Networks (CRNs) have been developed to improve spectrum efficiency without considering the problem of Adjacent Channel Interference (ACI). Guard-Band (GB)-aware mechanisms have been proposed in CRNs to prevent ACI and improve spectrum efficiency. However, most of the existing GB-aware algorithms assume having channels with fixed-rate and employ a sequential channel assignment paradigm. Unfortunately, this assumption is not realistic, because channel's conditions may vary over time. In this work, we propose a GB-aware channel assignment that considers the channel-dependent achieved transmission rates (due to Rayleigh fading time-varying conditions) at different time slots while simultaneously assigning channels to several CR transmissions. This batch based proposal aims at increasing network capacity and minimizing the number of assigned channels per user subject to rate demand and interference constraints. Simulation experiments demonstrate the effectiveness of our proposed scheme, which show a significant improvement in network performance in terms of the number of served CR users. [ABSTRACT FROM AUTHOR]

The recent developments in unmanned aerial vehicles (UAVs) and indoor hybrid LiFi–WiFi networks (HLWNs) present a significant opportunity for creating low-cost, power-efficient, reliable, flexible, and ad-hoc HLWN-enabled indoor flying networks (IFNs). However, to efficiently operate and practically realize indoor HLWN, a unified physical layer (UniPHY) is indispensable for joint communication (control and data transfer) and sensing (e.g., localization). A UniPHY structure reduces costs and increases overall flexibility for HLWN-based IFNs. While conventional block-based wireless transceivers independently designed for LiFi and WiFi offer mediocre performance for a composite UniPHY waveform, a machine learning-based end-to-end learning framework for UniPHY can improve overall error performance and reduce the complexity of UAV transceiver hardware. Therefore, this paper proposes a novel generic end-to-end learning framework for a UniPHY system that can efficiently enable HLWN. The performance of the proposed learning framework based on deep neural networks (DNNs) and convolutional neural networks (CNNs) is investigated. Additionally, we assess the computational complexity of the proposed DNN and CNN learning frameworks. The results demonstrate that the performance of DNNs and CNNs varies depending on the considered channel model. Specifically, the analysis reveals that CNNs outperform traditional DNNs in WiFi (Rayleigh fading-based) channels. In contrast, traditional DNNs perform better than CNNs in LiFi (additive white Gaussian noise (AWGN)-based) channels. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of resource allocation within Beyond 5G (B5G) and the envisioned 6G wireless networks with Cognitive Radio (CR) capability. CR technology allows opportunistic and dynamic access to the unused licensed spectrum bands. Unlike the conventional orthogonal frequency division multiple access (OFDMA) schemes that impose spectrum contiguity constraints in assigning the OFDMA resource blocks (RBs), the Dis-contiguous OFDMA (D-OFDMA) approach allows for flexible time–frequency RB scheduling, in which each contending CR user can access adjacent and/or non-adjacent RBs. This can provide a more efficient and flexible spectrum utilization compared to conventional OFDMA schemes. Specifically, we formulate the spectrum assignment and access problem as an optimization problem that attempts to effectively utilize the time–frequency spectrum holes of the D-OFDMA RBs. The main goal is to improve the total network throughput by maximizing the achieved sum-rate of all contending CR users subject to achieving a set of interference and performance requirement constraints. The formulated problem is classified as a binary-linear program (BLP), which is, in general, NP-hard. Thus, we develop a sub-optimal polynomial-time solution based on a relaxing-and-rounding method. Simulation results reveal that the proposed D-OFDMA RB scheduling protocol significantly improves the network throughput while maintaining fairness compared to conventional contiguous-based OFDMA protocols. Specifically, the proposed protocol achieves up to 90% and 150% performance improvements over the contiguous frequency-OFDMA and contiguous time-OFDMA, respectively. [ABSTRACT FROM AUTHOR]

The adjacent channel interference (ACI) resulting from imperfect filtering can severely degrade the performance of any wireless communication system. Despite this fact, most of previously proposed medium access control (MAC) protocols for cognitive radio networks (CRNs) were designed while ignoring the effects of ACI (assuming ideal filtering). The effect of ACI can be reduced by introducing guard bands (GBs). However, this solution comes at the expense of degrading spectrum efficiency. In this paper, we develop an efficient GB-aware MAC protocol that attempts at maximizing network throughput while improving fairness in CRNs. Unlike most of previously proposed GB-aware MAC protocols that perform channel assignment sequentially, our MAC performs the channel assignment for multiple CR links simultaneously (the so-called batch method). Batching enables concurrent channel assignment for multiple CR links, which consequently allows for concurrent data transmissions. Batching can be realized by introducing an admission control phase for CR users to share their control information. The batch method can effectively provide distributed decisions that achieve better throughput while reducing the number of GBs. Our MAC also allows the CR users to utilize the reserved GBs of primary networks under predefined FCC power constraints. Simulation results indicate that our protocol achieves significant performance improvement compared to previous GB-aware protocols. [ABSTRACT FROM AUTHOR]

Typical MAC protocols for IEEE 802.11-based ad hoc networks employ a direct transmission strategy whenever the transmitter can directly reach the receiver (one-hop transmission). While such a design enjoys simplicity, it limits the number of admitted transmissions in a given neighborhood. Due to the performance anomaly of 802.11-based wireless networks, transmission with low data rates occupy the shared medium for longer periods of time. Occupying the transmission medium for a longer period of time results in less available transmission time for other nodes, which consequently reduces the number of transmitted packets during the same period of time, leading to a reduction in network throughput. To improve the overall network throughput, we present a cooperative multi-channel MAC protocol for single-hop wireless mobile ad hoc networks that attempts at computing the path with the minimum required transmission time for a given source–destination pair (including the channel assignment along that path). The proposed protocol attempts at improving network performance by means of cooperative communications. According to our approach, if the one-hop (direct) path between communicating nodes supports low data rate (requires longer transmission time), the source selects a multi-hop path to the destination such that the total amount of required transmission time is minimized. Through simulations, we show that our proposed protocol achieves significant throughput and fairness improvement compared to the standard IEEE 802.11-based protocol. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) networks are expected to offer a huge bandwidth to wireless users through dynamic spectrum access techniques. To realize this new network paradigm, effective distributed coordination schemes to exchange control information without assuming the availability of a prespecified common control channel (CCC) are needed. In this paper, we propose a distributed group-based spectrum-aware coordination scheme for carrier-sense multiple access with collision avoidance (CSMA/CA)-based CR networks, which is called virtual clustering distributed coordination (VCDC). By using a CSMA/CA access mechanism, VCDC allows neighboring CR users with a similar view to spectrum opportunities to asynchronously organize themselves into virtual clusters and coordinate their communications using locally available CCCs. VCDC differs from previous cluster-based schemes in that it exploits the discrete orthogonal frequency-division multiplexing (D-OFDM) technology to reduce neighbor-discovery time and facilitate intercluster communication and broadcasting. Three variants of the VCDC scheme are proposed, which are one-hop, two-hop, and constrained VCDC. These variants provide different cluster size based on the number of adopted local CCCs. We compare the performance of the VCDC scheme with existing clustering schemes. Simulation results show that VCDC constructs fewer larger size clusters with an appropriate number of common idle channels and introduces a lower percentage of clusters with no shared idle channels. Thus, VCDC performs better without imposing synchronization. [ABSTRACT FROM PUBLISHER]

The emerging IEEE 802.22-based Wireless Regional Area Network (WRAN) is the first wireless standard based on the Cognitive Radio (CR) technology. WRAN is designed to offer wireless access services in a large coverage area by allowing Secondary Users (SUs) to opportunistically exploit the under-utilized licensed portion of spectrum that is primarily allocated for TV services. To enable efficient WRAN communications, the operation of a WRAN system should address two types of coexistence problem: incumbent co-existence and self-coexistence. In this paper, we investigate the self-coexistence problem in a multi-cell WRAN system that adopts an exclusive spectrum sharing policy. Specifically, we present an adaptive channel allocation scheme based on a cooperative max–min weighted fair allocation strategy. The proposed scheme is based on a centralized sensing mechanism that identifies the available spectrum opportunities for the WRAN cells. Our scheme dynamically allocates the available spectrum (idle channels) to the WRAN cells based on their expected traffic loads such that the total number of simultaneously admitted SU transmissions in the WRAN system is maximized. The expected traffic load is accurately estimated using a sample mean estimator based on previously monitored traffic in each cell. Simulation results indicate that our scheme is quite robust to traffic estimation error. Compared to reference spectrum allocation schemes, simulation results indicate that the proposed scheme effectively exploits the available spectrum opportunities by increasing the total number of served SU transmissions, which consequently results in a significant enhancement in the overall WRAN system performance. [ABSTRACT FROM AUTHOR]

The interplay between the non-orthogonal multiple access (NOMA) and the opportunistic cognitive radio (CR)-based orthogonal frequency multiple access (OFDMA) has been recently realized as a promising paradigm to support the unprecedented massive connectivity demands of future beyond fifth-generation (B5G) wireless communication systems. In such systems, which are called multi-carrier NOMA CR-based systems, each licensed band reserved for primary users can be opportunistically utilized based on power-domain NOMA to serve a group of secondary users simultaneously. An important challenge in this domain is how to provide energy-efficient resource allocation techniques that attempt to strike a balance between the total throughput (i.e., the achieved sum-rate) and the power required to achieve that rate while satisfying network QoS demands and being aware of the unique characteristics of the CR operating environment. In this paper, we propose an energy-efficient resource allocation technique for multi-carrier NOMA CR-based systems, which aims at maximizing the overall energy efficiency (EE) of the system under a set of CR and NOMA constraints. The EE maximization problem is shown to be a fractional non-convex optimization, which is, in general, hard to optimize. To deal with the fractional and the non-convexity nature of the formulated EE maximization problem, we exploit the Dinkelbach's algorithm to transfer the EE problem to a parameterized optimization problem. Then we use an iterative optimization approach to obtain the solution for the EE maximization problem. Simulation results reveal that this EE maximization-based resource allocation technique outperforms the existing resource allocation techniques in terms of the overall EE of the system while striking a good balance between the sum-rate and the transmit power consumption. [ABSTRACT FROM AUTHOR]

The integration between orthogonal frequency division multiple access (OFDMA), non-orthogonal multiple access (NOMA), and cognitive radio (CR) technology has been recently configured as promising candidate to support the massive high-speed connectivity requirements of beyond fifth generation (B5G) systems. This combination is referred to in the literature as a hybrid OFDMA–NOMA CR network. In such a system, each primary user's (PU) idle channel, i.e., sub-band, is opportunistically utilized to serve multiple secondary users (SUs) by exploiting power-domain NOMA. This study soughs to explore the resource allocation technique for a hybrid OFDMA–NOMA CR system to further elaborate the potential capabilities of this system. Specifically, a power minimization resource allocation framework is developed, in which the objective is to serve the contending CR users, i.e., SUs, with minimum possible total transmission power, while achieving a set of relevant NOMA and CR constraints. These constraints include the dynamic nature of PU activities, rate demand, probability of success and imposed CR power mask constraints. However, the developed power minimization framework is non-convex in nature. Therefore, we employ the sequential convex approximation (SCA) along with a second-order cone approach to deal with the non-convexity issues of the developed optimization problem, and thus evaluating the corresponding optimization parameters. Compared to the conventional orthogonal resource allocation techniques, simulation results reveal that our proposed resource allocation technique for the hybrid OFDMA–NOMA CR system has a considerable performance enhancement in terms of the overall network throughput and total required transmit power. [ABSTRACT FROM AUTHOR]

Abstract: This letter proposes a multi-channel parallel transmission mechanism for channel assignment in cognitive radio (CR) networks. The proposed mechanism enables secondary users (SUs) to effectively utilize the spectrum. The main novelty in our mechanism lies in utilizing the parallel transmission capability of CRs while considering the randomness of their operating environment to provide soft guarantees on the performance of SUs. Simulation results show that utilizing CR''s parallel transmission capability while considering the time-varying nature of their operating environment allows for higher spectrum utilization and more energy saving. [Copyright &y& Elsevier]

Abstract: Cognitive radio (CR) is a revolutionary technology in wireless communications that enhances spectrum utilization by allowing opportunistic and dynamic spectrum access. One of the key challenges in this domain is how CR users cooperate to dynamically access the available spectrum opportunities in order to maximize the overall perceived throughput. In this paper, we consider the coordinated spectrum access problem in a multi-user single-transceiver CR network (CRN), where each CR user is equipped with only one half-duplex transceiver. We first formulate the dynamic spectrum access as a rate/power control and channel assignment optimization problem. Our objective is to maximize the sum-rate achieved by all contending CR users over all available spectrum opportunities under interference and hardware constraints. We first show that this problem can be formulated as a mixed integer nonlinear programming (MINLP) problem that is NP-hard, in general. By exploiting the fact that actual communication systems have a finite number of available channels, each with a given maximum transmission power, we transfer this MINLP into a binary linear programming problem (BLP). Due to its integrality nature, this BLP is expected to be NP-hard. However, we show that its constraint matrix satisfies the total unimodularity property, and hence our problem can be optimally solved in polynomial time using linear programming (LP). To execute the optimal assignment in a distributed manner, we then present a distributed CSMA/CA-based random access mechanism for CRNs. We compare the performance of our proposed mechanism with reference CSMA/CA channel access mechanisms designed for CRNs. Simulation results show that our proposed mechanism significantly improves the overall network throughput and preserves fairness. [Copyright &y& Elsevier]

The article presents a survey which examines the challenges and developments of channel access protocols proposed for multihop cognitive radio networks. It discusses key design issues in development of medium access control (MAC) protocols for such networks. It also explores the development in MAC protocol design for CRNs. It outlines several challenges including the provision of an effective distributed coordination among CR users and computation of neighborhood-dependent power mask.

Non-orthogonal multiple access (NOMA) along with cognitive radio (CR) have been recently configured as potential solutions to fulfill the extraordinary demands of the fifth generation (5G) and beyond (B5G) networks and support the Internet of Thing (IoT) applications. Multiple users can be served within the same orthogonal domains in NOMA via power-domain multiplexing, whilst CR allows secondary users (SUs) to access the licensed spectrum frequency. This work investigates the possibility of combining orthogonal frequency division multiple access (OFDMA), NOMA, and CR, referred to as hybrid OFDMA-NOMA CR network. With this hybrid technology, the licensed frequency is divided into several channels, such as a group SUs is served in each channel based on NOMA technology. In particular, a rate-maximization framework is developed, at which user pairing at each channel, power allocations for each user, and secondary users activities are jointly considered to maximize the sum-rate of the hybrid OFDMA-NOMA CR network, while maintaining a set of relevant NOMA and CR constraints. The developed sum-rate maximization framework is NP-hard problem, and cannot be solved through classical approaches. Accordingly, we propose a two-stage approach; in the first stage, we propose a novel user pairing algorithm. With this, an iterative algorithm based on the sequential convex approximation is proposed to evaluate the solution of the non-convex rate-maximization problem, in the second stage. Results show that our proposed algorithm outperforms the existing schemes, and CR network features play a major role in deciding the overall network's performance. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) technology was introduced to effectively exploit the under-utilization portions of the licensed spectrum. A primary challenge in enabling CR networking is how to coordinate the CR communications, in which CR users need to exchange their control information before data exchange can take place. Providing a reliable coordination mechanism in CR networks (CRNs) is not an easy task because of the dynamic nature of primary radio (PR) activities and the heterogeneity of both spectrum availability and CR users' distribution. In this paper, we propose a dynamic-size clustering with CSMA/CA-based algorithm to overcome the challenge of achieving reliable coordination in CR networks (CRNs). CR users in our scheme are organized into dynamic size clusters and cooperate in selecting common control channels (CCCs) and use them for cluster coordination. The proposed scheme called Dynamic-hop virtual clustering distributed coordination (DH-VCDC). This scheme provides a dynamic-hop virtual clustering based on the traffic condition of both PR and CR users while achieving a minimum number of common available data channels per cluster, in which a better trade-off between the number of common idle channels per cluster and the cluster size is achieved. DH-VCDC scheme exploits the advantages of using Multi-packet Multi-channel Discontinuous Orthogonal Division Multiplexing (DOFDM) to reduce neighbor discovery time, allow CR users to have several cluster memberships, select multiple CCCs and simplify the inter-cluster coordination. CR users in our scheme are grouped to form virtual clusters each contains at least 2 CCCs (primary and backup CCCs). Three modes of DH-VCDC are presented: one-, two- and three-hop modes. Each CR must continuously monitor both PR and CR traffic loads and accordingly chooses the appropriate DH-VCDC mode. The performance of DH-VCDC is compared with reference schemes, in which significant performance improvement is reported. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) technology has been proposed to solve the inefficient spectrum utilization resulted from the traditional fixed spectrum allocation strategy used in wireless systems. Through dynamic and opportunistic spectrum access, CR users can significantly improve spectrum utilization. Most of existing spectrum access schemes designed for CR networks (CRNs) targeted either uni-cast transmission or multi-cast transmission paradigm. Such transmission paradigms allow a CR user to transmit the same data packet over multiple channels to a receiver or a group of receivers. However, existing designs cannot support simultaneous transmissions of multiple distinct packets to different receivers. This new transmission paradigm can be realised using the Discrete Orthogonal Frequency Division Multiplexing (D-OFDM) technology, where each CR transmitter can send multiple different packets to several (multiple) receivers over different channels. In this paper, we investigate the problem of assigning channels to CR transmitters with D-OFDM capabilities, each with multiple packets to be simultaneously sent to multiple receivers. Specifically, we formulate the channel assignment problem as an optimization problem with the goal of achieving the maximum possible network sum-rate. Then, we optimally solve the optimization problem based on standard linear programming methods. To realize our channel assignment in a distributed way, we propose an admission control spectrum access mechanism that allows communicating CR users to exchange their control information. Simulation experiments are carried out to reveal the effectiveness of the proposed protocol compared to previous protocols. The results reveal that significant performance improvement can be achieved by utilizing the D-OFDM technology, in which CR users are allowed to simultaneously transmit multiple packets to multiple receivers. [ABSTRACT FROM AUTHOR]

Abstract Establishing stable routes over high mobility networks is a challenging field and consequently brought the attention of a lot of current researchers. In this article, a new reactive routing protocol has been proposed which is considered as a major extension of both Ad-hoc On-demand Distance Vector (AODV) and Mobility and Direction Aware AODV (MDA-AODV) prior routing protocols. It is named as Mobility Aware and Dual Phase Ad-hoc On-demand Distance Vector with Adaptive Hello Messages (MA-DP-AODV-AHM). It primarily concentrates on building routes considering vehicles’ speeds and direction of motion with respect to source vehicles, thereby establishing more stable routes and reducing the route breakages. Not only to this extent, but rather, we proposed an adaptive control packet announcement mechanism, which is directly connected to the periodic hello messages approach, with vehicles’ speeds, yielding a significant reduction in the amount of control overhead and congestion over the network. Moreover, the new protocol is proposed to alternate working among two phases, for the reason of guaranteeing the generation of efficient routes on a timely basis, which include MA-DP-AODV-AHM and AODV protocols. Our protocol has been evaluated in terms of control overhead, end-to-end delay, and energy consumption using QualNet simulator version 7.1 in which enormous simulations have been conducted under the base of vehicle density, packet rate, and constant bit rate connections. Simulation results summarize that MA-DP-AODV-AHM contributes efficiently in mitigating the network instability through generating effective stable routes and reducing link failures. Furthermore, it has proved its superiority over AODV and MDA-AODV. [ABSTRACT FROM AUTHOR]

Wireless communication demands rapidly increase due to the increase of Internet of Things applications, which leads researchers to build secondary networks that exploit the spectrum holes of primary networks. Cognitive radio (CR) technology is adopted in ad-hoc networks (AHNs) rather than infrastructure-based networks because AHNs have lower cost, higher coverage, and easier maintenance compared to infrastructure-based networks. Moreover, in-band-full-duplex (IBFD) in CR networks (CRNs) is gaining the interest of researchers. This mix between IBFD and CRNs brings a great enhancement in the network's performance due to efficient dynamic spectrum access. Therefore, we propose an adaptive FD-CRNs routing protocol that uses a common control channel. Adaptive FD communication is conducted in our protocol where the secondary users adapt their communication mode based on the primary users' activity on the spectrum. Communication modes used in our work are FD transmit and sense, FD transmit and receive, and sensing only. The performance of our protocol was evaluated using a java language simulator for IBFD-CRNs introduced previously in the literature. Also, we compare the performance of our protocol with three previous protocols, probabilistic and deterministic path selection in cognitive radio network, and multi-cast half-duplex routing protocol and broadcast full-duplex routing protocol. The performance metrics used are throughput and total execution time. [ABSTRACT FROM AUTHOR]

This article presents the methodology and results of the curriculum review of the Masters of Science programme in Wireless Communications offered by the Telecommunications Engineering Department at Yarmouk University in Jordan. The review is based on expert opinion collected through questionnaires and meetings about the programme and focuses on two main aspects: the first is relevance of the programme to the needs of local and regional telecommunications industry and the second is comparability to similar programmes in the EU. Through an extensive analysis of the collected data, a set of recommendations for improvement of the existing curriculum was developed. These recommendations were then used to redesign the curriculum with regard to contents, teaching methodology and assessment. [ABSTRACT FROM AUTHOR]

The pilot overhead provides fundamental limits on the performance of massive multiple-input multiple-output (MIMO) systems. This is because the performance of such systems is based on the failure of the presentation of accurate channel state information (CSI). Based on the theory of compressive sensing, this paper presents a novel channel estimation technique as the mean of minimizing the problems associated with pilot overhead. The proposed technique is based on the combination of the compressive sampling matching and sparsity adaptive matching pursuit techniques. The sources of the signals in MIMO systems are sparsely distributed in terms of spatial correlations. This distribution pattern enables then use of compressive sampling techniques to solve the channel estimation problem in MIMO systems. Simulation results demonstrate that the proposed channel estimation outperforms the conventional compressive sensing (CS)-based channel estimation algorithms in terms of the normalized mean square error (NMSE) performance at high signal-to-noise ratios (SNRs). Furthermore, it reduces the computational complexity of the channel estimation compared to conventional methods. In addition to the achieved performance gain in terms of NMSE, the presented method significantly reduces pilot overhead compared to conventional channel estimation techniques. [ABSTRACT FROM AUTHOR]

This paper introduces a new fuzzy advanced harmony search algorithm for solving single-objective buffer allocation problems (BAPs). The proposed algorithm represents the first attempt at solving BAPs using a fuzzy logic system, by tuning the advanced harmony search control parameters. The main steps of the proposed algorithm included parameter initialisation, harmony memory initialisation and evaluation, improvisation, harmony memory update, AHS parameter update, and termination criterion check. The aim of this approach is to achieve a better convergence rate and avoid the stacking of local optima. The performance of the proposed algorithm was compared with other methods used in solving BAPs. The proposed approach has shown a higher capability in finding optimal solutions compared to previous methods used for two benchmark problems. Improvement of up to 94.75% in the overall throughput is reported for the 3-stage problem, while for the 12-stage problem, a slight improvement (up to 7.58%) is also reported in the overall throughput. The results achieved indicate that the proposed algorithm is an efficient and promising tool in solving BAPs. [ABSTRACT FROM AUTHOR]

Internet-of-Things (IoT)-based cyber–physical systems are increasingly being adopted because of the recent technological advancements in sensor technology, edge computing, machine learning, and big data. Integrating machine learning into designing IoT-based cyber–physical systems is essential. However, it is considered a challenging problem. This stems from the fact that IoT devices generate extensive data that requires extensive processing to achieve adequate learning. Relying on local learning by each IoT device is not feasible in most cases due to its limited resources. On the contrary, relying on all cloud-based learning requires transmitting a large amount of data to the cloud to perform the learning process, which is inefficient in large-scale IoT deployments. Therefore, this paper proposes a novel edge-computing architecture that employs the concept of distributed multi-task learning over EC networks in large-scale IoT-based cyber–physical systems. The architecture develops multiple distributed learning algorithms, a data placement architecture, task allocation algorithms, and a network protocol. In addition, it considers the problem of learning model parameters from IoT data distributed over different edge nodes in a large geographical area without sending raw data to the cloud. The architecture supports several distributed machine models that are trained using a combination of machine learning algorithms and population-based search algorithms to optimize the learning process. Population-based search algorithms allow for maintaining a set of candidate solutions, with each solution corresponding to a unique point in the search space for an optimal solution. Having the dataset distributed over several edge nodes, with each node having its own unique set of candidate solutions, increases the chance of finding a solution that generalizes well for the overall dataset combined. Simulation experiments with real IoT datasets are conducted to evaluate the accuracy of the proposed learning models. Results show the ability to achieve high-accuracy results that are close to single-machine models but with significantly efficient edge computing resource utilization. [ABSTRACT FROM AUTHOR]

This paper presents a curriculum design approach for a Masters Programme in Telecommunications Management based on demand data obtained from surveying the needs of potential students of the proposed programme. Through online surveys disseminated at telecom companies in Jordan, it was possible to measure the demand for such a programme and to determine the required programme contents and specifications. The curriculum design is based on definition of programme outcomes and on using a house of quality approach (HOQ) to determine the list of courses required in the programme. Surveyed competencies are mapped to a long list of proposed courses in a HOQ in order to determine the importance of each of these courses. A final list of core and elective courses is then developed considering the contribution to programme outcomes and the academic standards. [ABSTRACT FROM PUBLISHER]

Software-Defined Networking (SDN) is a modern network approach that replaces the conventional network architecture with a flexible architecture by separating the control plane from the data plane. SDN simplifies network management and monitoring through logically centralized intelligence, programmability, and abstraction. SDN architectures are vulnerable to attacks, such as the Denial of Service (DoS) attack. This article reviews and classifies the research efforts on SDN and DoS. We categorized the research efforts into two groups: solutions to cope with DoS attacks on SDN and SDN-based solutions to tackle DoS attacks on networks. The first group of solutions includes six categories: Table-Entry, Scheduling, Architectural, Flow Statistics, Machine Learning, and Hybrid solutions. Furthermore, the article surveys the tools and datasets considered by the reviewed contributions. The article presents a detailed comparison among reviewed approaches in terms of network devices, network layers involved, DoS attack's types, and targets of attacks. [Display omitted] • Software-Defined Networking architectures are vulnerable to Denial-of-Service attacks. • A review on solutions to cope with Denial-of-Service attacks on Software-Defined Networking. • A review on Software-Defined Networking based solutions to tackle Denial-of-Service attacks. • About 72.9% of the solutions used control layer modules. • The article surveys the tools and datasets considered by the reviewed contributions. [ABSTRACT FROM AUTHOR]