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Cooperative spectrum sharing (CSS) is a joint spectrum access technique that involves collaboration between primary user (PU) and secondary user (SU) systems. An instance of CSS occurs when the SU transmitter acts as a relay for the PU system. In forwarding the PU signal, the SU transmitter deliberately superimposes the signal intended for the SU receiver. This process unfortunately causes mutual interferences at both PU and SU receivers. Achievable rates for both systems are typically analyzed by assuming Gaussian signals and interferences. Unlike most of previous work, this paper studies the use of practical digital modulation schemes in CSS. The novelty of the proposed CSS approach lies on the smart alignment and superimposition of the SU's pulse amplitude modulated signal to the PU's phase-shift keying signal. It is shown that the SU signal does not interfere with the PU signal, it actually improves the PU signal strength. This paper then focuses on deriving the approximated theoretical transmission rates for both the PU and SU. Interestingly, the analysis shows superior transmission rates for both systems, compared to that achieved by conventional signal superimposition approaches. Simulation results then confirm the matching with the analytical results and validate the performance advantages of the proposed CSS approach. [ABSTRACT FROM AUTHOR]

As electric vehicles (EVs) proliferate and dominate the roads, public charging will be required extensively. Wide range coverage of energy information is required on fingertips for taking decisions at different levels and for all stakeholders. For this reason, a need arises for a correlated and all-inclusive framework that trades the unilateral discriminatory benefits with the combined benefit of all. At the heart of such frameworks lies communication technologies to support and manage energy transfer between vehicles and the power grid, known as the Internet of EVs. This paper adds two main contributions to the work presented in the literature on the subject matter. First, the communication requirements, standards, and protocols for applications involving this mobile prosumer management in the pre-connection stage to the electric grid are outlined. Second, and based on the aforementioned assessment, this paper proposes a decentralized hierarchical multiagent system that serves as a framework for real-time bilateral decision support for EVs and the power system. The effectiveness of the proposed framework and the credibility of using the communication middleware and means are experimentally verified on a group of connected vehicles communicating with a higher end server. Also, the seamless integration between the on-board and the higher end decision support systems is demonstrated in case studies. [ABSTRACT FROM AUTHOR]

The contemporary problem of scheduling the recharge operations of electric vehicles (EVs) has gained a lot of research attention. This is particularly true given the governmental and industrial confidence in a bright future for EVs accompanied with the widespread installation of an enormous number of charging stations across the world. As such, this paper addresses the delay-optimal scheduling of charging EVs at several charging stations (CSs) each with multiple charging outlets. At first, a centralized optimization framework is formulated using an integer linear problem (ILP) that accounts for the delayed arrival of EVs to CSs and the randomness in the requested recharge time interval. Simulation results showed the efficacy of the ILP model when compared to naive as well as sophisticated scheduling heuristics. Next, motivated by the scalability issues of the ILP model, this paper then proposes a distributed game-theoretical approach where each EV communicates with its selected CS and iterates on modifying its strategy until all EVs converge to selecting an appropriate CS that minimizes their waiting times for receiving services. The distributed game-theoretical approach recorded promising results especially when compared to the well-known shortest job first scheduling algorithm. Further, unlike the other approaches, which normally are centralized and suited for offline scheduling, the game-based method is suited for online scheduling since it played at anytime a batch of EVs requests charging services. The running time of the game is remarkably small and outperforms all other heuristics and its convergence to Nash equilibrium is guaranteed after only small number of iterations. [ABSTRACT FROM AUTHOR]

The increasing adoption of electric vehicles (EVs) due to technical advancements and environmental concerns requires wide deployment of public charging stations (CSs). In order to accelerate the EV penetration and predict the future CSs requirements and adopt proper policies for their deployment, studying the charging behavior of EV drivers is inevitable. This paper introduces a stochastic model that takes into consideration the behavioral characteristics of EV drivers in particular, in terms of their reaction to the EV battery charge level when deciding to charge or disconnect at a CS. The proposed model is applied in two case studies to describe the resultant collective behavior of EV drivers in a community using real field EV data obtained from a major North American campus network and part of London urban area. The model fits well to the datasets by tuning the model parameters. The sensitivity analysis of the model indicates that changes in the behavioral parameters affect the statistical characteristics of charging duration, vehicle connection time, and EV demand profile, which has a substantial effect on congestion status in CSs. This proposed model is then applied in several scenarios to simulate the congestion status in public parking lots and predict the future charging points needed to guarantee the appropriate level of service quality. The results show that studying and controlling the EV drivers’ behavior leads to a significant saving in CS capacity and results in consumer satisfaction, thus, profitability of the station owners. [ABSTRACT FROM AUTHOR]

This paper investigates collaborative spectrum sharing (CSS) for which a cognitive secondary transmitter acts as a decode-and-forward (DF) relay for two primary users (PUs) occupying an interfering multiple-access channel. PUs are assumed to send their signals simultaneously; hence, interference between the primary signals exists due to the multiple-access nature. The secondary user (SU) selects and forwards one of the PU's signals to a desired destination, and at the same time, the SU also transmits its own signal only if it is not harmful to PUs. The selection of a primary signal to relay is investigated, depending on SU's knowledge of two primary source–destination channels, for which closed-form outage probabilities for PUs and SU are provided accordingly. With the help of the closed-form PU's outage probability, an optimal selection strategy between the primary signals and an optimal power allocation between the primary and secondary signals are also obtained in closed form, which minimizes SU's outage probability while keeping the PU's outage performance at an SU-free level. Numerical investigation is used to verify and illustrate the analysis given in this paper. Numerical results also show that the SU's performance can vary significantly according to the amount of channel knowledge that the SU can have. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user, which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS, the strategy for combining the individual sensing results of the SUs is learned autonomously with a CNN using training sensing samples regardless of whether the individual sensing results are quantized or not. Moreover, both spectral and spatial correlation of individual sensing outcomes are taken into account such that an environment-specific CSS is enabled in DCS. Through simulations, we show that the performance of CSS can be greatly improved by the proposed DCS. [ABSTRACT FROM AUTHOR]

With the growing popularization of Electric Vehicle (EVs), Electro-mobility (in terms of where to charge EV) has become an increasingly important research problem in smart cities. One of the major concerns is the anxiety of EVs, as drivers may suffer from discomfort due to long charging time. In this paper, we leverage the battery switch technology to provide an even faster charging than plug-in charging service, by cycling switchable (fully recharged) batteries at Charging Stations (CSs). Upon that, a cost-efficient Publish/Subscribe (P/S) system is provisioned, to facilitate the design of distributed charging manner for privacy guarantee. The proposed communication framework utilizes Mobile Edge Computing (MEC)-functioned Road Side Units (RSUs) to bridge, process, and aggregate the information flow between CSs and EVs. We further design an advanced reservation-based charging system, in which the knowledge of EVs' reservations is utilized to predict how likely a CS will be congested. This benefits to a smart transportation planning on where to charge, in order to improve charging comfort. Results show the advantage of our enabling technology comparing to other benchmark solutions, in terms of minimized waiting time for the battery switch (as the benefit for EV drivers), and a higher number of batteries switched (as the benefit for CSs). [ABSTRACT FROM AUTHOR]

Cooperative spectrum sensing (CSS) is considered as a powerful approach to improve the utilization of scarce spectrum resources. However, CSS assumes that all secondary users (SU) always are honest, thus offering opportunities for attackers to launch spectrum sensing data falsification (SSDF) attack. To combat such threat, recent efforts have been made to trust mechanism. In this paper, we argue that powering CSS with trust mechanism is not enough. A vulnerability for trust mechanism is found, that is, the feedback data from initiator SUs are generally unchecked. Such feedback data corresponding to the spectrum status of primary users can be exploited by attackers to disturb trust mechanism in a collusive manner. Especially, collusive false feedback (CFF) attackers would form a clique to help each other. One of CFF attackers can disguise as an initiator SU who sends the feedback in accordance with the sensing data of their conspirators who play the role of cooperating SUs, resulting in promoting their conspirators’ sensing trust quickly. With high sensing trust, attackers can avoid the detection of trust mechanism and, hence, launch SSDF attack (fake sensing data) more easily. We conduct an in-depth investigation on CFF attack and propose a two-level defense scheme called FeedGuard from the design ideas of feedback trust and I-C frequency correlation analysis. Simulation results show that the FeedGuard scheme can enhance the accuracy of sensing trust evaluation, thus successfully reducing the power of CFF attack against CSS. [ABSTRACT FROM AUTHOR]

Increasingly, electric vehicles (EVs) are being adopted and integrated into intelligent transport systems (ITSs) as developed countries continue to the push for sustainable transport solutions that offer higher energy efficiency and reduced carbon emissions. EVs communication with roadside units (RSUs) in ITSs and smart grid is possible using onboard units for wireless transmission. This paper presents the different information models for vehicle-to-infrastructure (V2I) and vehicle-to-grid (V2G) communication based on the IEEE 1609 WAVE and IEC 61850 standards, respectively. Utilizing EVs communication with smart grid components such as charging stations (CSs) will help utilities better manage the EV load on their distribution networks. To this effect, an EV charging management scheme based on communication between EVs and RSU is presented. The performance evaluation in terms of end-to-end delay of the proposed harmonized communication over different vehicular ad-hoc network (VANET) protocols is simulated in Riverbed Modeler. The results show that protocols with lower overhead have better performance. [ABSTRACT FROM AUTHOR]

This paper proposes a new spectrum sensing technique, referred to as autonomous compressive sensing (CS)-augmented spectrum sensing, which can be developed to provide more efficient spectrum opportunity identification than geolocation database methods. First, we propose an autonomous CS-based sensing algorithm that enables the local secondary users (SUs) to automatically choose the minimum sensing time without knowledge of spectral sparsity or channel characteristics. The compressive samples are collected block-by-block in time, while the spectral is gradually reconstructed until the proposed stopping criterion is reached. Moreover, a CS-based blind cooperating user selection algorithm is proposed to select the cooperating SUs via indirectly measuring the degeneration of the signal-to-noise ratio experienced by different SUs. Numerical and real-world test results demonstrate that the proposed algorithms achieve high detection performance with reduced sensing time and number of cooperating SUs in comparison with the conventional compressive spectrum sensing algorithms. [ABSTRACT FROM AUTHOR]

Reporting channels are essential for implementing cooperative spectrum sensing (CSS) in cognitive radio technology. In this paper, a novel decentralized CSS scheme capable of operating in the absence of dedicated reporting channels is presented. Existing schemes which operate in the absence of dedicated reporting channels rely on the transmission of hard decision bits from cooperating secondary users (CSUs). The proposed scheme supports a mobile spectrum sensor termed as the leading secondary sensor node (LSSN) to assist a contending secondary transmitter in spectrum sensing. Unlike existing schemes, the proposed CSS framework offers the freedom of using multiple bits for transmitting a quantized version of the test statistics between secondary user (SU) nodes. In order to reduce energy consumption and system complexity, the CSUs do not take part in sensing but only act as relays for retransmitting the quantized test statistics from LSSN to the contending SU transmitter. The proposed network protocol also assists the SU transmitters in avoiding collisions with each other. Closed form expressions for the probability of false alarm and detection have been derived. Monte-Carlo simulations are used to validate the analytical expressions. The proposed scheme can outperform the recently proposed best-selection reporting scheme when the number of CSUs is less and the contending CSUs and the SU transmitter lie in an area of poor primary signal reception. [ABSTRACT FROM PUBLISHER]

Due to the fast-growing usage of wireless devices, cognitive radio networks have been proposed to address the spectrum scarcity problem. As the foundation of their practical applications, designing robust and secure spectrum sensing mechanisms is of great significance. In most existing works, the common control channel (CCC) is assumed to be perfect. However, this assumption may not hold in practice and imperfect CCC makes the existing methods against independent or cooperative data falsification attacks less effective. In this paper, we first analyze the impact of an imperfect CCC on the identification of malicious secondary users under independent and cooperative attacks. To better differentiate honest users and malicious users, a reputation threshold is derived for each secondary user. Based on the obtained reputation threshold, we propose a new reputation-based cooperative spectrum sensing method, which is validated to be robust against attacks under imperfect CCC. Extensive numerical simulations demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

The standard condition number (SCN) in random matrix theory (RMT) has widely been employed in cognitive radio networks (CRNs). The paper studies the SCN distributions of central and complex Wishart matrices with finite dimensions. The exact and closed-form expressions of SCN distributions are first proposed in sum of multiple polynomials. The proposed formulations provide an efficient way to determine probability density function and cumulative distribution function of the SCN in finite RMT. In particular, the specific SCN distributions of triple Wishart matrix are initially presented. The theoretical and precise sensing thresholds for cooperative spectrum sensing (CSS) systems in CRNs are calculated using the proposed SCN distributions. Numerical results indicate that the proposed theoretical SCN distributions match the empirical SCN distributions very well. Furthermore, the exact SCN-based CSS systems are able to obtain higher sensing performance than the asymptotic SCN-based schemes because precise sensing thresholds can be determined. [ABSTRACT FROM PUBLISHER]

This paper studies the exploitation of the modulation-based dimension in cognitive radio systems to perform cooperative spectrum sharing (CSS). Unlike conventional opportunistic spectrum sharing techniques where a secondary user (SU) system attempts to utilize the spectrum hole in either time, frequency, or spatial dimension created by the primary user (PU) system, this study focuses on finding accessible spectrum spaces created by the structure of modulation constellations. In this novel CSS scheme, by taking advantage of the knowledge on the PU's modulation scheme, the SU superimposes its own modulated signal on the PU's signal. We examine two CSS scenarios: 1) phase-shift-keying (PSK) is used at both the PU and SU and 2) PSK is used at the PU while pulse-amplitude modulation is used at the SU. Interestingly, the proposed superposition approach in CSS Scenario 2 enables a CSS mechanism without either impairing the performance of the primary receiver or requiring a change in its demodulation rule. At the same time, a much better symbol-error-rate (SER) performance is achievable for the SU's receiver in Scenario 2 than in Scenario 1. Theoretical SER performances of both the PU and SU are then thoroughly analyzed and confirmed by numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, a collaborative energy-harvesting cognitive radio (CR) network is considered such that the transmitter of the secondary user (SU) is allowed to harvest signal energy of the primary user (PU) when the presence of the PU is detected. The harvested energy is converted to electrical power in order to supply the sensing and transmission energy of SUs. The time frame (time slot) is divided into two phases allocated to sensing (divided into two durations: spectrum sensing and results reporting) and transmission, respectively. The time spanned by the results reporting duration depends on the number of collaborative sensing users, while the time spent on spectrum sensing duration controls the number of sensing samples. A constrained convex optimization problem of the overall probability of error is formulated incorporating constraints on time and energy resources along with PU interference protection presented as a threshold on probability of collision. We use a soft decision rule scheme while considering two energy harvesting scenarios namely, energy surplus and energy deficit. In each scenario, the convexity of the optimization problem is established analytically and the global optimal solution is obtained. Simulation results are provided to demonstrate the impact of the different parameters on the overall system performance as well as to verify the deduced analytical results. [ABSTRACT FROM PUBLISHER]

The design of an efficient charging management system for on-the-move electric vehicles (EVs) has become an emerging research problem in future connected vehicle applications, given their mobility uncertainties. Major technical challenges here involve decision-making intelligence for the selection of charging stations (CSs) and the corresponding communication infrastructure for necessary information dissemination between the power grid and mobile EVs. In this paper, we propose a holistic solution that aims to create a high impact on the improvement of end users' driving experiences (e.g., to minimize EVs' charging waiting time during their journeys) and charging efficiency at the power grid side. In particular, the CS selection decision on where to charge is made by individual EVs for privacy and scalability benefits. The communication framework is based on a mobile publish/subscribe (P/S) paradigm to efficiently disseminate CS condition information to EVs that are on the move. To circumvent the rigidity of having stationary roadside units (RSUs) for information dissemination, we promote the concept of mobility as a service (MaaS) by exploiting the mobility of public transportation vehicles (e.g., buses) to bridge the information flow to EVs, given their opportunistic encounters. We analyze various factors affecting the possibility for EVs to access CS information via opportunistic vehicle-to-vehicle (V2V) communications, and we demonstrate the advantage of introducing buses as mobile intermediaries for information dissemination, based on a common EV charging management system under the Helsinki city scenario. We further study the feasibility and benefit of enabling EVs to send their charging reservations involved for CS selection logic, via opportunistically encountered buses, as well. Results show that this advanced management system improves both performances at the CS and EV sides. [ABSTRACT FROM PUBLISHER]

Spectrum sensing and spectrum sharing are two fundamental issues in a cognitive radio network (CRN). The spectrum sensing data falsification (SSDF) attack imposes bad effects on both spectrum sensing and spectrum sharing. To deal with SSDF attacks and to incentivize secondary users (SUs) to behave well, a joint spectrum sensing and resource allocation (JSSRA) scheme in a CRN is proposed in this paper. The JSSRA problem is formulated as a weighted-proportional-fairness-based resource-allocation optimization problem under the constraint that the primary user (PU) network is sufficiently protected. The problem is decomposed into two subproblems, namely, the resource allocation subproblem and cooperative SU number decision subproblem, which are solved by the Lagrangian dual algorithm and the brute-force algorithm, respectively. Moreover, a user-selection method based on reinforcement learning is presented to select reliable SUs for cooperative spectrum sensing (CSS). In addition, the SU's trust degree is updated according to its behavior in CSS and is used in the sensed resource-allocation process. The key point of our proposed scheme is to make SUs improve the sensing reliability and prevent SUs from behaving maliciously by an incentive mechanism. Comprehensive performance evaluation is conducted by computer simulation. It is shown that the proposed JSSRA scheme deals with the SSDF attack well in cooperative sensing process to improve system robustness and achieves a significant system utility gain in resource allocation. [ABSTRACT FROM PUBLISHER]

Spectrum sensing data falsification (SSDF) attacks represent a major challenge for cooperative spectrum sensing (CSS) in cognitive radio (CR) networks. In an SSDF attack, a malicious user or many malicious users send false sensing results to the fusion center (FC) to mislead the global decision about spectrum occupancy. Thus, an SSDF attack degrades the achievable detection accuracy, throughput, and energy efficiency of CR networks (CRNs). In this paper, a novel attacker-identification algorithm is proposed that is able to skillfully detect attackers and reject their reported results. Moreover, we provide a novel attacker-punishment algorithm that aims at punishing attackers by lowering their individual energy efficiency, motivating them either to quit sending false results or leave the network. Both algorithms are based on a novel assessment strategy of the sensing performance of each user. The proposed strategy is called delivery-based assessment, which relies on the delivery of the transmitted data to evaluate the made global decision and the individual reports. Mathematical analysis and simulation results show promising performance of both algorithms compared with previous works, particularly when then the number of attackers is very large. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the performance of cooperative spectrum sensing (CSS) with multiple-antenna nodes over generalized and composite fading channels. To this end, we approximate the probability density function (pdf) of the signal-to-noise ratio (SNR) of various fading channels using the mixture Gamma (MG) distribution. Based on this, we derive an exact closed-form expression and a generic infinite series representation for the corresponding probability of energy detection, along with a finite upper bound for the involved truncation error. Both expressions have a relatively simple algebraic form that gives them convenience in handling both analytically and numerically. Furthermore, the composite effect of multipath fading and shadowing scenarios in CSS is mitigated by applying an optimal fusion rule that minimizes the total error rate (TER), where the optimal number of nodes is derived under the Bayesian criterion, assuming erroneous feedback channels. We also extend the derived average detection probability to include diversity reception techniques, namely, maximal-ratio combining, square-law combining, and square-law selection (SLS). For the SLS, we demonstrate the existence of an error rate floor as the number of antennas of the cognitive radio nodes increases in erroneous decision feedback channels. Accordingly, we derive the optimal rule for the number of antennas that minimizes the TER in the SLS framework. Monte Carlo simulations are presented to corroborate the analytical results and to provide illustrative performance comparisons and insights between different composite fading channels. [ABSTRACT FROM PUBLISHER]

By cooperative spectrum sharing schemes, a primary network (PN) leases a part of the licensed band to a secondary network (SN). A secondary transmitter (ST) acts as a relay for the primary transmitter (PT) to improve the end-to-end link quality of the PN, and as a reward, the ST gets the opportunity to transmit its own data by superposition coding. In this paper, a new cooperative spectrum sharing (CSS) scheme taking into account malicious eavesdropping (wiretapping) by a PN is proposed. In such scheme, licensed band is a decoy to an SN to wiretap the secondary transmission. Thus, secrecy in secondary transmission becomes critical to the SN. To get achievable secrecy (data) rate in secondary transmission and to fulfill the duty as a relay for primary transmission, the SN looks for a smart power-allocation (SPA) parameter that satisfies both goals. SPA parameter \alpha\mathrm{smart} provides the target secrecy data rate, and the achieved data rate at the PN is greater than or equal to the data rate obtained by the direct transmission (DT). Considering a Rayleigh block-fading channel, the SPA parameter is evaluated for each secondary transmission. Details on the evaluation of the SPA parameter are presented, and theoretical outage probabilities of the PN and the SN are derived and validated by simulations. [ABSTRACT FROM AUTHOR]

This paper proposes an effective real-time video uplink (UL) framework for mobile wireless camera networks (WCN) over an orthogonal frequency-division multiple access (OFDMA), based infrastructure. Mobile wireless camera stations (CSs) transmit their videos in real time to a base station (BS) so that these live video streams can be archived or fed to subscribers to facilitate real-time video monitoring. Based on the utility function driven by video quality, the target bit rate resulting in the highest possible utility is quickly set for each UL video. To optimize system performance, a real-time video packet scheduler and a spectral-efficient resource-allocation policy are derived. This scheduler is also capable of exploiting the inherent diversity gain due to channel variations. Using fourth-generation (4G) mobile network protocols and a realistic wireless channel model, it is demonstrated through our extensive simulations that our proposed method can significantly enhance utility, boost spectral efficiency, and stabilize video quality. [ABSTRACT FROM PUBLISHER]

Cognitive radio (CR) is proposed as an efficient way to address the issue of spectrum shortage and underutilization, in which cooperative spectrum sensing (CSS) is used to enhance sensing performance. One of the most fundamental problems of CSS is how to appropriately assign the secondary users (SUs) to sense the primary user (PU) channels. In this paper, we study the CSS problem under a more practical scenario where we take the heterogeneous characteristics of both SUs and PU channels into consideration. With the objective to maximize the achievable throughput for SUs, we propose a cluster-based CSS to obtain a proper assignment policy, in which all the SUs in the same cluster cooperate in sensing the same set of PU channels. Moreover, the cluster-based CSS problem is formulated as a Maximum-Weight one-sided Biclique (MWB) problem. We prove that the MWB problem is NP-complete, and a greedy heuristic algorithm with polynomial computational complexity is developed to find the suboptimal assignment policy. Evaluation results show that our proposed greedy algorithm can achieve a close-to-optimal solution with far less complexity. [ABSTRACT FROM PUBLISHER]

We consider, in this paper, the maximization of throughput in a dense network of collaborative cognitive radio (CR) sensors with limited energy supply. In our case, the sensors are mixed varieties (heterogeneous) and are battery powered. We propose an ant colony-based energy-efficient sensor scheduling algorithm (ACO-ESSP) to optimally schedule the activities of the sensors to provide the required sensing performance and increase the overall secondary system throughput. The proposed algorithm is an improved version of the conventional ant colony optimization (ACO) algorithm, specifically tailored to the formulated sensor scheduling problem. We also use a more realistic sensor energy consumption model and consider CR networks employing heterogeneous sensors (CRNHSs). Simulations demonstrate that our approach improves the system throughput efficiently and effectively compared with other algorithms. [ABSTRACT FROM PUBLISHER]

Spectrum sensing is an important aspect of cognitive radio networks (CRNs). Secondary users (SUs) should periodically sense the channels to ensure primary-user (PU) protection. Sensing with cooperation among several SUs is more robust and less error prone. However, cooperation also increases the energy spent for sensing. Considering the periodic nature of sensing, even a small amount of savings in each sensing period leads to considerable improvement in the long run. In this paper, we consider the problem of energy-efficient (EE) spectrum sensing scheduling with satisfactory PU protection. Our model exploits the diversity of SUs in their received signal-to-noise ratio (SNR) of the primary signal to determine the sensing duration for each user/channel pair for higher energy efficiency. We model the given problem as an optimization problem with two different objectives. The first objective is to minimize the energy consumption, and the second objective is to minimize the spectrum sensing duration to maximize the remaining time for data transmission. We solve both problems using the outer linearization method. In addition, we present two suboptimal but efficient heuristic methods. We provide an extensive performance analysis of our proposed methods under various numbers of SUs, average channel SNR, and channel sampling frequency. Our analysis reveals that all proposals with an energy minimization perspective provide significant energy savings compared with a pure transmission-time maximization (TXT) technique. [ABSTRACT FROM PUBLISHER]