The practical deployment of visible light communication (VLC)-enabled flying networks could be accelerated via the simultaneous services offered by mixed carrier communication (MCC). The MCC is a unified physical-layer waveform that uniquely integrates various communication streams for data/control and signaling to support localization and dimming. For such a composite waveform, the receiver must conduct successive decoding operations of the constituent streams; thus, optimum decoding of the individual streams is essential. Existing publications rely on a conventional way to design the MCC communication chain using a block-based standalone approach that provides suboptimal performance. However, a machine learning (ML) framework has the potential for better optimization of the overall MCC system’s performance. This article proposes a novel end-to-end learning framework toward a unified physical-layer waveform for VLC-enabled indoor flying networks. The obtained symbol error rate (SER) performance results confirm that the proposed learning framework outperforms the existing work by ensuring perfect decoding of the control stream and detecting signals for localization. A 12 to 15-dB gain in signal-to-noise ratio (SNR) is demonstrated at a target of 10−3 SER. The results also indicate that a wide range of dimming is supported without significantly altering SER performance. The topics of convergence, spectral efficiency, and complexity analysis of the proposed framework are discussed in detail.

Visible light communication (VLC) is a key enabler for high-speed indoor connectivity and the Internet of Things (IoT) devices. Unlike radio frequency (RF) communication systems, the VLC coverage is typically not uniform, which means that the user equipment’s (UEs) orientation and obstacles significantly affect the communication performance. Specifically, the performance of VLC may severely deteriorate when the Line-of-Sight (LoS) link is blocked by other users/obstructions in crowded indoor scenarios, such as shopping malls, airports, factories, and industrial facilities. Further, the received power may also fluctuate due to the random orientation of UE as Non-LoS (NLoS) power varies. This article characterizes the impact of human blockages and random UE orientation when optical intelligent reflecting surfaces (OIRSs) are integrated with VLC systems. We consider a realistic scenario by assuming the presence of multiple human blockages and random UE orientations. Specifically, this article utilizes a random process model to capture the random UE orientations in OIRS-aided indoor VLC systems. The proposed model characterizes the NLoS channel gain, calculates the received signal-to-noise ratio (SNR) statistics, and derives the probability density function (PDF) of the received SNR. Furthermore, the derived SNR statistics determine the optimal light-emitting-diode semi-angle and receiver Field-of-View (FoV) to maximize received power throughout a given indoor area. In addition, the optimal OIRS element size is computed to achieve the highest average received power within the specified indoor space. Finally, the bit error rate (BER), delay spread, and average data rate are analyzed under blockage and nonblockage scenarios. Simulation results demonstrate a 4–8 dB improvement in the required SNR to achieve a minimum BER of$10^{-3}$with the proposed OIRS-aided VLC system, attributed to its awareness of human blockages and random UE orientation.

This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track object movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency.

Advancements in UAV technology have revolutionized various industries, enabling their widespread deployment in embedded systems, autonomy, control, security, and communication. Autonomous UAVs distinguish themselves by their ability to make informed decisions by anticipating future scenarios and drawing on past experiences. Our primary focus is on analyzing a monitoring system consisting of a mission area, an autonomous UAV, a charging station, and multiple dynamic targets. The mission area is divided into distinct zones, and a time-slotted scheme facilitates UAV movement and recharging. Specifically, this paper presents a multitarget detection system based on reinforcement learning (RL)-based behavior-driven UAVs designed for indoor environments. Simulation results demonstrate that our proposed RL-based detection system significantly outperforms reference systems in terms of detection rate and convergence.

In this article, we propose a highly reliable transmission strategy based on exploiting channel diversity for Internet of Things (IoT) networks equipped with cognitive radio (CR) capabilities (referred to as CR-IoT networks). Specifically, we investigate the channel-assignment problem with the objective of minimizing the number of assigned channels for each CR transmission subject to link reliability, data rate, and success probability constraints. We use the theory of continuous-time Markov chains to derive mathematical expressions for channel availability and reliability. These expressions are employed in a binary linear program model to formulate the channel assignment optimization problem, which is suboptimally solved using a polynomial-time sequential fixing procedure. Theoretical performance analysis of the proposed channel assignment scheme is derived in terms of a lower bound on the achievable probability of successful packet transmission. Through simulation experiments, we demonstrate significant performance enhancement compared to existing reliability-unaware CR channel assignment algorithms.

Recent technological advances have reshaped many aspects of our lives, especially modern transportation systems. For instance, AI and B5G Networks have raised the level of automation as autonomous vehicles (AV) become decision-independent and self-aware. However, in-vehicle health monitoring is still an open issue. Therefore, a cooperative healthcare emergency response framework has been proposed that employs in-vehicle intelligent health monitoring and local networks for AV to minimize the time to receive emergency treatment for passengers with abnormal health conditions. The extensive simulation results show that the framework minimizes the First Emergency Treatment Time by at least 75%, and eliminates hospital waiting time, the Total Time for Emergency Treatment is minimized by at least 93%. Finally, it reduces Travel Time by nearly 50%. All results compared to the autopilot approach.

Connected and Autonomous Electric Vehicles (CAEVs) are becoming a feature of our roads in the imminent future. This disruptive technology is likely to enhance the way we get around the city in many ways by collecting accurate data in regards to the surrounding environment and events in a timely-manner. As such data that is time-sensitive where human life may be at risk requires reliable and on-time data delivery. In crowded dense environments, several issues can reduce the network performance due to the high density of objects such as skyscrapers and vehicles as well as the large number of exchanged data between connected vehicles and other objects on the road. Involving a swarm of autonomous Unmanned Aerial Vehicles (UAVs), namely drones, would enhance network connectivity, reduce CAEVs communication delay, facilitate CAEVs tasks distribution, and elevate provisioning services. In this paper, a routing scheme in an autonomous UAV-connected vehicles network is proposed that provides reduced communication delay. The proposed solution has been evaluated using simulations. The collected results show the feasibility and the advantage of the UAV-assisted connected vehicle network in meeting delay and energy consumption requirements.

Summary Spectrum sharing protocols for cognitive radio networks (CRNs) are often designed based on an exclusive channel occupancy policy. This policy does not allow concurrent cognitive radio (CR) transmissions to take place over the same channel. Unfortunately, this can significantly affect spectrum utilization and network performance. Allowing several secondary users to simultaneously share the same channel naturally improves spectrum utilization and avoids unnecessary blocking of CR transmissions. The key challenge in enabling efficient operation of multichannel CRNs is how to perform efficient power‐controlled media‐access‐control (MAC) protocols. These protocols include both power control and channel assignment. In this paper, we propose a power‐controlled MAC protocol for CRNs based on interference‐channel occupancy model with the objective of maximizing the number of served CR transmissions. It uses a novel power‐controlled channel assignment mechanism that allows several concurrent interference‐limited transmissions to simultaneously proceed over each idle channel in the same neighborhood. We present two variants of the power‐controlled channel assignment mechanism. The first proposed variant is suitable for CRNs with fixed data packet size, whereas the other is suitable for dynamic CRNs with variable packet size. The second variant of our protocol employs a users' scheduling procedure that attempts to fully utilize the available time‐frequency blocks. Simulation results indicate that employing transmission power control along with appropriate channel assignment and users' scheduling in CRNs can significantly improve the spectrum utilization and the overall network performance.

Cognitive radio (CR) is envisioned as an enabling technology for the Internet-of-Things (IoT) networking by providing spectrum opportunities to the huge number of communicating IoT devices (the so-called CR-IoT networking). Due to the nature of its wireless operating media, CR-IoT networks are vulnerable to jamming attacks. In CR-IoT networks with delay-sensitive applications, such attacks can incur longer packet transmission delay, resulting in received obsolete packets and service unavailability. Therefore, an important challenge in this domain is how to perform a secured channel assignment that can provide soft guarantees on the timeline of the packet delivery while satisfying a predefined Quality-of-Service (QoS) requirements. In this article, we propose a security-aware batch-based MAC protocol for delay-sensitive CR-IoT networks that attempts at improving the network performance by exploiting the parallel transmission capabilities of the CR-IoT devices while being jamming aware. Our protocol performs simultaneous assignment decisions for contending CR-IoT devices. Specifically, we formulate the channel assignment problem that attempts at maximizing the number of served CR-IoT users subject to user-rate demand, number of transceivers per CR-IoT device, and delay (packet invalidity) constraints as an optimization problem. This problem is shown to be a binary linear programming problem, which is, in general, NP-hard. Hence, we use a sequential fixing linear programming technique, which provides a near-optimal solution. To implement our channel assignment in a distributed manner, we adopt an access window (AW)-based access protocol that allows the users to exchange their control information and conduct the spectrum assignment. Compared to reference protocols, simulation results show that our protocol significantly improves spectrum efficiency and CR-IoT network performance.

The futuristic perception of the Internet of Things (IoT) comprises of billions of interconnected devices that can be positioned almost anywhere, starting from our own bodies to the most distant locations on the globe. Within this setup, the massive number of nodes will be competing for bandwidth. To alleviate the expected spectrum scarcity problem in IoT applications, cognitive radio (CR) technology is proposed. In CR-IoT applications, IoT nodes share the idle channels owned by the primary users (PUs). Hence, efficient channel-assignment algorithms are needed to allow CR devices access to the spectrum without interfering with the PUs. Additionally, as these devices become more intrusive in our daily lives, security concerns are escalating. Thus, addressing CR-IoT security threats is now becoming crucial. One of these security threats is jamming which may result in disrupting data transmission between CR- IoT devices. In this paper, we propose a probabilistic-based multi-channel assignment algorithm that considers primary user activity, channel conditions, jamming attack levels, and data-rate requirements to provide spectrally efficient data transmission between CR-IoT nodes subject to delay constraints. The algorithm is verified using the open large-scale Future Internet-of-Things (FIT) IoT-LAB testbed. Practical results show that our proposed algorithm is capable of enhancing network performance without requiring additional network resources or power.

Mobile Edge Computing (MEC) and Network Slicing techniques have a potential to augment 5G-IoT network services. Telecommunication operators use a diverse set of radio access technologies to provide services for users. Mobility management is one such service that needs attention for new 5G deployments. The QoS requirements in 5G networks are user specific. Network slicing along with MEC has been promoted as a key enabler for such on-demand service schemes. This paper focuses on radio resource access across heterogeneous networks for mobile roaming users. A unified service architecture is proposed enabling seamless handover between a 5G (New Generation Core) service and a 4G (Evolved Packet Core) service via the network slicing paradigm. An identifier-locator (I-L) concept that allows active source-IP sessions is used to handle the seamless hand-over. Signaling costs, service disruptions and other resource reservation requirements are considered in the evaluation to assure that profit for mobile edge operators is achieved. Simulation experiments are considered to provide performance comparisons against the state-of-the-art Distributed Mobility Management Protocol (DMM).

Integrating Cognitive Radio (CR) technology in Internet-of-Things (IoT) devices allows efficient large-scale deployment of IoT systems. Recently, research efforts are shifted toward adopting CR in IoT as a response for the spectrum scarcity problem. Unfortunately, CR Networks (CRNs) share the same security weaknesses with traditional wireless networks. CR communication is also vulnerable to jamming attacks which can significantly affect network performance, consume network resources and results in delays, that make it less suitable for IoT time-critical systems. Routing in CR-based IoT networks, in general, considered as a challenging issue. Under the jamming attack, routing becomes even more challenging. In this paper, we introduce a new jamming-aware routing and channel assignment protocol that deals with proactive jamming attacks in CR-based IoT networks without requiring extra resources. The proposed protocol attempts at improving the overall packet delivery ratio in the network while considering the primary user's activities, multi-channel fading and jamming behavior. The proposed protocol consists of three phases: route discovery, channel assignment, and path selection. The channel assignment problem along each path is formulated as an optimization problem with the objective of maximizing the end-to-end probability of success. This problem is shown to be an uni-modular problem, which can be solved in polynomial-time using linear programming techniques. Compared to reference protocols, simulation results reveal that the proposed protocol significantly improves network performance in terms of packet delivery ratio.

The expected widespread usage of Internet-of-Things (IoT) devices and the associated diversity of applications will place extra pressure on network resources including bandwidth availability. In networking within IoT, spectrum scarcity requires the consideration of the adaptive cognitive radio (CR) technology to achieve interference-free and on-demand IoT solutions for a number of applications. However, CR networks share the same security weaknesses of legacy wireless networks. Jamming is considered a common attack, where jammers can attack networks in a proactive or reactive approach. In this paper, we study the channel assignment problem under both proactive and reactive jamming attacks. Specifically, we propose a novel probabilistic-based channel assignment mechanism that aims at minimizing the invalidity ratio of CR packet transmissions subject to delay constraints. The proposed scheme exploits the statistical information of licensed primary users activities, fading conditions and jamming attacks over idle channels to provide communicating CR IoT devices with the most secured channels of lowest invalidity ratios. Simulation results show that our proposed security, availability, and quality-aware channel assignment algorithm can significantly improve network performance.

In this study, we investigate the performance of multi‐hop systems with decode‐and‐forward relaying over fading channels. Specifically, the authors derive closed‐form expressions for the probability density function, cumulative distribution function, and moment generating function of the end‐to‐end signal‐to‐noise ratio. In addition, they derive closed‐form expressions for the end‐to‐end capacity (ergodic and outage) and average symbol error rate (coherent and non‐coherent) of the system. The derived expressions can be transformed to study the performance of multi‐hop communication systems over other well‐known fading channel models by using the proper values for the and parameters. Finally, the performance of multi‐hop scenario is analysed and investigated using numerical evaluation. Results are provided to illustrate the system's performance and the impact of number of hops and fading parameters on network performance. The derived expressions, in this study, are very important for the prediction of the performance metrics and statistics such that efficient designs of multi‐hop wireless systems can be realised.

Direct-conversion radio transceivers can offer reprogrammable and low-cost hardware solutions for full-duplex (FD) cognitive radio (CR) devices. However, they are susceptible to radio frequency (RF) impairments, such as in-phase (I) and quadrature (Q) imbalance (IQI), which can significantly constrain the spectrum sensing capabilities. This paper is devoted to quantify and evaluate the effects of IQI in the context of spectrum sensing in FD CR systems, in which self-interference suppression (SIS) techniques are employed. Specifically, closed form expressions are derived for the false alarm and detection probabilities, under three different scenarios: imperfect SIS with joint transmitter (TX) and receiver (RX) IQI, imperfect SIS and ideal TX/RX RF front-end, and perfect SIS and ideal TX/RX RF front-end. The derived analytical results are validated through extensive simulations, which reveal that IQI has a detrimental impact on the spectrum sensing performance of the FD CR transceiver, which brings significant losses in the spectrum utilization.

Beamforming can significantly increase the spectral efficiency by mitigating the inter-cell interference in coordinated multi-cell multi-antenna wireless networks. A key challenge in this domain is how the serving base-stations in the network cooperate to effectively compute the transmit/receive beamforming weights in order to minimize the inter-cell interference, which maximizes the over all network performance. In this paper, we proposed a distributed iterative beamforming scheme that exploits the network duality principle to jointly compute the transmit-receive beamforming weights such that the desired received power for each user is maximized while minimizing the caused interference to un-intended receivers. Simulation results indicate that compared to previous beamforming schemes, our scheme significantly improves system performance under different transmit-receive antenna configurations and different transmit power levels.

In this paper, we present a novel guard-band (GB)-aware channel assignment mechanism that attempts at maximization the achieved network throughput by improving spectrum utilization. Unlike most of previously proposed GB-aware channel assignment algorithms that perform the assignment sequentially, the proposed mechanism performs the channel and power assignment for multiple cognitive radio (CR) transmissions at the same time (the so-called batch approach). Batching enables concurrent channel assignment for multiple CR links, which consequently allows for concurrent data transmissions. The batch approach can be realized by introducing an admission control access window (AW) for CR users to share their control information. The batch approach can effectively provide distributed decisions that offer better throughput performance while reducing the number of introduced GBs. Compared to previous GB-aware schemes, the simulation results indicate that the proposed scheme achieves a significant throughput improvement.

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.

In this paper, we present an integrated hardware design and software implementation for a cognitive-radio wireless sensor network (CR-WSN). The implemented CR-WSN is an event-driven cluster-based network, where the sensor nodes in the network are organized into a two-level hierarchy of clusters, each with its own cognitive-enabled cluster-head (CR-CH). According to CR-WSN, the sensed data by a sensor node is reported to the associated CR-CH. The CR-CH opportunistically transmits the collected information over one of the idle primary radio channels to the data collecting node (sink). Specifically, we first describe the hardware design and implementation of the CRWSN. Then, we propose a novel hybrid MAC design for the CR-WSN that ensures reliable and timely data delivery. The proposed protocol divides the MAC-layer operation into two domains: single-channel intra-cluster and cognitive inter-cluster domains. Experimental results are provided, which verify the effectiveness of the proposed MAC protocol and demonstrate a reliable, robust and scalable performance of the proposed system.

In this paper, we propose a reliable direct- sequence spread-spectrum (DSSS)-based underlay control channel (CC) design for exchanging control information in cognitive radio networks (CRNs). The proposed design allows both narrow-band data and wide-band control transmissions to simultaneously proceed while protecting the performance of primary radio (PR) users. To achieve this, we derive a closed-form expression for the minimum required transmit power for control transmissions such that predefined CRN connectivity requirements are realized. Given the derived expression, we compute the maximum permissible power for CR data transmissions such that an enforced power mask constrain over the PR channels is not violated. Simulation results indicate that our CC design enables efficient CR communications without relying on a dedicated CC.

Mitigating the effects of inter‐cell interference in a multi‐cell wireless network is essential to improve the achieved performance. In this study, the authors develop an iterative distributed beamforming scheme that addresses the effect of inter‐cell interference problem on the downlink of a multi‐cell multi‐antenna wireless system with linear beamformers. The proposed scheme attempts at improving the overall system performance by exploiting network duality principle to jointly compute the optimal transmit and receive beamforming weights such that the desired received power is maximised while minimising the caused interference to other users. Network duality allows the serving base stations and the intended users to jointly compute the beamforming weights of the transmit/receive antenna elements, which improve network performance. The proposed scheme considers variable levels of channel information feedback and different transmit–receive antenna configurations. Simulation results show that a significant performance improvement is achieved through the joint optimisation of the transmit/receive beamforming vectors. Results also indicate that compared with reference schemes, their scheme improves the achieved sum‐rate by up to 50%.

Software defined networking (SDN) provides a novel network resource management framework that overcomes several challenges related to network resources management. On the other hand, Cognitive Radio (CR) technology is a promising paradigm for addressing the spectrum scarcity problem through efficient dynamic spectrum access (DSA). CR provides unlicensed secondary users with the ability to coexist with licensed users in non-interfering mode. In this paper, we introduce a virtualization based SDN resource management framework for cognitive radio networks (CRNs). The framework uses the concept of multilayer hypervisors for efficient resources allocation. It also introduces a semi-decentralized control scheme that allows the CRN base station (BS) to delegate some of the management responsibilities to the network users. CRN resource virtualization allows dynamic, infrastructure free and efficient resources allocation to the CR users. The main objectives of the proposed framework is to reduce the CR users' reliance on the CRN BS and physical network resources while improving the network performance by reducing control overhead.

In this paper, we propose a novel spectrum-aware clustering scheme for distributed coordination in CSMA/CA-based cognitive radio networks (CRNs). The proposed scheme employs a CSMA/CA mechanism to asynchronously organize neighboring CR users with similar views to spectrum into virtual clusters, and coordinate their communications using locally available common control channels (CCCs). Our scheme differs from existing cluster-based schemes in that it exploits the Discrete Orthogonal Frequency Division Multiplexing (D-OFDM) technology to reduce neighbor-discovery time and facilitate inter-cluster communication and broadcasting. Our scheme enables efficient control channel migration, provides higher bandwidth to CR users and reduces the frequency of re-clustering. Two variants of our scheme are presented that provide different cluster size based on the considered neighborhood area. Simulation results show that the proposed scheme outperforms existing cluster-based coordination schemes without imposing synchronization.

IEEE 802.22 Wireless Regional Area Network (WRAN) is the first wireless standard based on cognitive radio (CR) technology. WRAN is designed to allow secondary users (SUs) to opportunistically utilize idle TV channels on a non-interfering manner. A major challenge in enabling efficient WRAN communications is the interference-and-coexistence problem. There are two types of co-existence; incumbent co-existence and self-coexistence. In this paper, we investigate the self-coexistence problem among multiple overlapped WRANs. Specifically, we propose an adaptive cooperative exclusive traffic-aware channel allocation scheme (TAECA) that attempts to minimize the unnecessary blocking of SU transmissions in the overlapped cells, which consequently maximizes spectrum utilization. TAECA employs a novel max-min weighted fair mechanism for adaptively allocating idle channels to the different WRANs cells depending on their prevailing traffic conditions. Simulation results indicate that compared to reference allocation mechanisms, TAECA increases the number of served SU transmissions by up to 40%, which significantly improves spectrum utilization.

Network virtualization is a promising technology that enables the deployment of multiple virtual networks over a single physical network. These virtual networks are allowed to share the set of available resources in order to provide different services to their intended users. Although many projects are studying different aspects of network virtualization, the field of wireless network virtualization is not well investigated. In this work, we propose a dynamic cognitive radio virtualization framework in which several virtual networks are built over a set of physical nodes managed and controlled by a Base Station (BS). This framework is proposed to virtualize Cognitive Radio Networks (CRNs) in order to reduce the control overhead on the BS side by delegating some of its responsibilities to the node side. The proposed framework is applied to a network with multiple overlapping cells. To cope with the self-coexistence problem, we use a resource allocation algorithm to distribute the available channels over the overlapping cells based on their traffic loads with the goal of avoiding harmful interference, enhancing blocking rates and increasing throughput.

In this work we investigate the problem of video streaming in cognitive radio networks from a cognitive radio video source to a set of cognitive radio destinations using multicasting. Our objective is to improve the overall quality of the received video by all destinations. To achieve this, we propose a cross layer approach to multicast the video packets. The video source implements the proposed approach to multicast a video packet to all destinations at the same time by selecting the best unified channel of all available channels for all destinations to send the video packet on. To select the best unified channel, the source jointly considers the required transmission time of the packet and the average spectrum availability time of the available channels. Moreover, by using the proposed approach, the video source guarantees that the required transmission time of the packet over the selected channel is less than its average spectrum availability time. We conduct simulation experiments to evaluate the performance of our proposed protocol under various network parameters. We compare our protocol with three multicast protocols. The first one selects the unified channel based on the minimum transmission time. The second one selects the unified channel based on the average spectrum availability time. The last one selects the unified channel randomly. We use packet delay, the ratio of bad frames, and control overhead as performance metrics. Simulation results demonstrate that the proposed multicasting routing protocol achieves significantly better performance compared to the other studied routing protocols in terms of all studied performance metrics. Specifically, it reduces the packet delay by at least 25%, the control overhead by at least 5%, and the ratio of bad frames 44%.

Spectrum access/sharing algorithms for dynamic spectrum access (DSA) networks are often designed without accounting for adjacent-channel interference. In practice, guard bands are needed to prevent such interference. Introducing guard bands naturally constrains the effective use of the spectrum. In this work, we investigate the problem of assigning channels/powers to opportunistic transmissions, while accounting for such a constraint. Specifically, we propose a novel guard-band-aware channel assignment scheme for DSA systems. Our scheme reduces the number of required guard channels for a given transmission by exploiting the benefit of utilizing adjacent channels and considering already reserved guard channels. We analytically formulate the channel access problem as a joint power control and channel assignment optimization problem, with the objective of minimizing the required spectrum resource for a given CR transmission. We show that the optimization problem is a binary linear program (BLP), which is, in general, NP-hard. Accordingly, we present a near-optimal solution based on sequential fixing, where the binary variables are determined iteratively by solving a sequence of linear programs. Based on the proposed channel assignment algorithm, we develop an operational MAC protocol that enables DSA users to dynamically utilize the spectrum. The proposed protocol realizes our channel assignment algorithm in a distributed manner while relying only on information provided by the two communicating users. Simulation results are provided, which verify the effectiveness of our protocol and demonstrate the significant gain achieved through guard-band-aware channel assignment.

In this paper, we propose a novel CRN MAC, called MAX-PS-MAC. MAX-PS-MAC uses a probabilistic channel assignment mechanism that attempts at maximizing the packet success probability of each transmission by exploiting statistical information regarding availability durations and link quality conditions of idle channels. While most of previously proposed CRN MAC protocols account for PR channel dynamics by requiring communicating CR users to consistently perform dynamic channel switching according to PR activities, MAX-PS-MAC avoids the significant overhead and delay of channel switching by providing communicating CR users with the idle channel that provides the highest probability of success. Simulation results indicate that compared to typical CRN MAC protocols, MAX-PS-MAC significantly reduces the forced-termination rate of CR transmissions, which consequently improves network throughput by up to 110%.

Designing an appropriate routing metric in cognitive radio networks (CRNs) is a challenging problem. In a multi-hop CRN, both spectrum handoff (quantified by channel availability time) and required transmission time can significantly impact network connectivity and routing. Specifically, spectrum handoff can cause a significant degradation in CRN performance when the availability time of an assigned channel is smaller than the required transmission time over that channel. In this case, the percentage of CR transmissions that will be terminated due to primary user (PU) activities, can significantly increase, leading to a reduction in network throughput. To reduce the percentage of terminated CR transmissions, we introduce a novel routing metric that jointly considers spectrum availability time and required CR transmission time. Based on this metric, we propose a probabilistic routing algorithm for multi-hop CRNs that attempts at computing the path with the maximum probability of success from a given source to its destination (including the channel assignment along that path). Simulation results show that our proposed algorithm leads to a significant performance improvement over two reference algorithms presented in this paper.

Spectrum sharing algorithms for cognitive radio networks (CRNs) are often designed ignoring adjacent-channel interference (i.e., interference from other transmissions operating on adjacent channels). In practice, such an assumption is unrealistic as guard bands are needed to prevent such interference. Introducing guard bands naturally constrains the effective use of the spectrum. In this work, we investigate the problem of assigning channels/powers to CR transmissions, while accounting for such a constraint. Specifically, we propose a novel guard-band-aware channel assignment scheme for CRNs. Our scheme reduces the number of required guard channels for a given transmission by exploiting the benefits of utilizing adjacent channels while considering already reserved guard channels. We analytically formulate the channel access problem as a joint power control and channel assignment optimization problem, with the objective of minimizing the required spectrum resource for a CR transmission. We show that the optimization problem is a binary linear program (BLP), which is, in general, NP-hard. Accordingly, we present a near-optimal solution based on a sequential fixing procedure. Simulation results are provided, which verify the accuracy of our algorithm and demonstrate the significant gain achieved through guard-band-aware channel assignment.

In this paper, we consider the coordinated spectrum access problem in a multiuser single-transceiver cognitive radio network (CRN). Our objective is to maximize the sum-rate achieved by all contending cognitive radio users with respect to both spectrum assignment and transmission rate. The problem is posed as a rate-maximization problem subject to hardware and interference constraints. Specifically, we show that this problem can be formulated as an integer linear programming problem (ILP) with unimodular constraint matrix, which can be optimally solved in polynomial time using linear programming. Unlike previous research, our formulation is not restricted to the information-theoretic capacity, and can be applied to any arbitrarily given rate-SINR function. We also develop a distributed CSMA/CA-based MAC protocol for CRNs to realize the optimal assignment in a distributed manner. Simulation results indicate that compared to a reference CSMA/CA CRN MAC protocol, the proposed protocol significantly improves network throughput and preserves fairness.

The cognitive radio (CR) paradigm calls for open spectrum access according to a predetermined etiquette. Under this paradigm, CR nodes access the spectrum opportunistically by continuously monitoring the operating channels. A key challenge in this domain is how the nodes in a CR network (CRN) cooperate to access the medium in order to maximize the CRN throughput. Typical multichannel MAC protocols assume that frequency channels are adjacent and that there are no constraints on the transmission power. However, a CRN may operate over a wide range of frequencies, and a power mask is often enforced on the transmission of a CR user to avoid corrupting the transmissions of spectrum-licensed primary-radio (PR) users. To avoid unnecessary blocking of CR transmissions, we propose a noveldistance-dependentMAC protocol for CRNs. Our protocol, called DDMAC, attempts to maximize the CRN throughput. It uses a novel probabilistic channel assignment mechanism that exploits the dependence between the signal's attenuation model and the transmission distance while considering the traffic profile. DDMAC allows a pair of CR users to communicate on a channel that may not be optimal from one user's perspective, but that allows more concurrent transmissions to take place, especially under moderate and high traffic loads. Simulation results indicate that, compared to typical multichannel CSMA-based protocols, DDMAC reduces the blocking rate of CR requests by up to 30%, which consequently improves the network throughput.

In this work, we investigate a statistical approach for dynamic spectrum access and radio resource management (RRM) in opportunistic cognitive radio (CR) networks. We propose a distributed MAC protocol for such networks that enables unlicensed users to dynamically utilize the available spectrum while limiting the imposed interference on primary (PR) users. Our proposed protocol is novel in three aspects. First, it does not require CR users to coordinate with PR users. Second, it does not assume any predefined CR-to-PR power mask, and thus can exploit the available spectrum more efficiently. Third, it provides the PR users with a statistical guarantee on the fraction of time that their reception may be corrupted by CR users. To avoid corrupting PR user receptions, the protocol computes the maximum power that a CR transmission can use based on current network conditions. We show how to compute this maximum power by deriving models for the PR-to-CR and PR-to-PR interference. Simulation experiments illustrate that our MAC protocol can satisfy the statistical guarantee for PR users under various user deployment models and traffic loads.

Cognitive radio (CR) is the key enabling technology for an efficient dynamic spectrum access. It aims at exploiting an underutilized licensed spectrum by enabling opportunistic communications for unlicensed users. In this work, we first develop a distributed cognitive radio MAC (COMAC) protocol that enables unlicensed users to dynamically utilize the spectrum while limiting the interference on primary (PR) users. The main novelty in COMAC lies in not assuming a predefined CR-to-PR power mask and not requiring active coordination with PR users. COMAC provides a statistical performance guarantee for PR users by limiting the fraction of the time during which the PR users' reception is negatively affected by CR transmissions. To provide such a guarantee, we develop probabilistic models for the PR-to-PR and the PR-to-CR interference under a Rayleigh fading channel model. From these models, we derive closed-form expressions for the mean and variance of interference. Empirical results show that the distribution of the interference is approximately lognormal. Based on the developed interference models, we derive a closed-form expression for the maximum allowable power for a CR transmission. We extend the min-hop routing to exploit the available channel information for improving the perceived throughput. Our simulation results indicate that COMAC satisfies its target soft guarantees under different traffic loads and arbitrary user deployment scenarios. Results also show that exploiting the available channel information for the routing decisions can improve the end-to-end throughput of the CR network (CRN).

The scarcity of unlicensed spectrum has triggered great interest in cognitive radio (CR) technology as a means to improve spectrum utilization. An important challenge in this domain is how to enable nodes in a CR network (CRN) to access the medium opportunistically. Multi-channel MAC protocols for typical ad hoc networks assume that frequency channels are adjacent and that there are no strict constraints on the transmission power. However, a CRN may occupy a wide range of frequencies. In addition, a power mask is often enforced on the transmission power of a CR user to avoid corrupting the transmissions of spectrum-licensed primary-radio (PR) users. Obviously, CR users operating in different licensed bands will be subject to different PR-to-CR interference conditions. To avoid unnecessary blocking of CR transmissions under these constraints, we propose a novel distance-dependent MAC protocol for CRNs (DDMAC) that attempts to maximize the CRN throughput. DDMAC introduces a novel suboptimal probabilistic channel assignment algorithm that exploits the dependence between the signal's attenuation model and the transmission distance while considering the traffic profile. The protocol allows a pair of CR users to communicate on a channel that may not be optimal from one user's perspective, but that allows more transmissions to take place simultaneously, especially under moderate to high traffic loads. Simulation results indicate that compared to typical multi-channel CSMA-based protocols, DDMAC decreases the connection blocking rate of CR transmission requests by up to 30%, which improves the network throughput at no additional cost in energy consumption. On the whole, our protocol is simple yet effective. It can be incorporated into existing multi-channel systems with little extra processing overhead.

We investigate the energy efficiency in a wireless sensor networks that implements a non-persistent CSMA MAC protocol with adaptive MQAM modulation at the physical layer. The system throughput is estimated based on the number of received ACK packets. The backoff probability at the MAC layer and the modulation order at the physical layer are jointly adapted according to the traffic dynamics, leading to improved system energy efficiency while satisfying a given constraint on the packet retransmission delay. Through numerical examples and simulations, we verify the significant energy-efficiency improvements achieved by this joint optimization compared to the backoff- probability-only and the modulation-order-only adaptations.