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Concurrent transmission region. The PMS is located at (40 m, ) and the CRx is located at (75 m, ). The CTx can be located anywhere in the fi gure, but its concurrent transmission with the PMS is only allowed if the CTx is located within the highlighted concurrent transmission region .
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Location awareness allows to define a concurrent transmission region where a primary and a secondary networks can coexist. We investigate the impact of joint rate and power control on the performance of a cognitive radio ad hoc network overlaying a primary system. The proposed strategies adequately adjust the secondary user transmit power to increa...
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... Radio (CR) is a promising technology, which can deal with the growing demand and scarcity of the wireless spectrum [1], [2]. CR allows secondary users (SUs) to access licensed spectrum bands. Since primary users (PUs) have the spectrum priority, SUs must avoid interfering with the PUs. Based on spectrum sensing information or location awareness, the SUs may adapt their transmit powers and adopt strategies to protect the primary link. In [3] it was described how CR networks may be equipped with location awareness features, and CR positioning systems were presented in [4], [5]. In [6], the effects of location awareness on concurrent transmissions for cognitive radio ad hoc networks (CRAHN) were analyzed. Many related works are based on similar concurrent spectrum access models. In [7], an optimal power control algorithm for the CR transmitter was proposed to maximize the concurrent transmission region. In [8], a dynamic spectrum sharing method was presented which aims to maximize the number of concurrent transmissions; the SUs start their transmission with the minimum power allowing channel reuse by other SUs. In [9], the use of channel rate control as well as its impact on the performance of a CRAHN overlaying an infrastructure-based system was investigated. The proposed algorithm adequately adjusts the secondary user rate to be as large as possible while respecting a given constraint on the achievable primary rate. The results obtained in [9] outper- formed the method proposed in [6] and indirectly improved the performance of other schemes based on similar assumptions, as [7], [8]. However, power control was not considered in [9] which can be an ef fi cient mechanism to increase the data rate or to minimize the energy consumption. Many power control schemes were proposed to maximize the secondary capacity or to reduce the energy consumption [10]–[16]. In [10], the SUs adjust their power to maximize their data rate. Optimal power control [11] and adaptive rate and power control [12] were proposed to maximize the SU capacity subject to average interference power and peak transmit power constraints. General models with multiple PUs and SUs were studied in [13] and [14], where power allocation for sum-rate maximization under mutual interference between the PUs and the SUs was addressed. The optimal power allocation in OFDM CR networks was studied in [15], where a fast and low com- plexity algorithm to achieve the optimal solution was proposed. The fi rst axiomatic approach to power control in CR was presented in [16], where the necessity of interference-aware power control for CR users was discussed. In this letter we consider a similar approach as in [6], [9], but including power control and investigating the achievable data rate, the concurrent transmission probability and the energy ef- fi ciency. Our goal is to highlight the importance of transmission power control to expand the concurrent transmission region where PUs and SUs can coexist without interfering each other. We present two strategies: mean data rate maximization on the CRAHN and mean energy ef fi ciency maximization of SUs. Thus, we investigate how a joint power and rate control scheme can be used in a CRAHN to increase its throughput or to extend the autonomy of its devices. II. S YSTEM M ODEL AND P ROBLEM F ORMULATION As in [6], [9], we assume that the nodes are close enough as to consider an interference-limited spectrum sharing scenario in which a CRAHN operates inside the primary coverage area. The system model is composed by a CR transmitter (CTx), a CR receiver (CRx), a primary transmitter or primary mobile station (PMS), and a primary receiver or base station (BS). The anal- ysis focuses on the primary uplink. The BS is at the origin of coordinates and the mobile devices locations are represented by their polar coordinates as: ; , 2, 3, representing the PMS, the CRx and the CTx, respectively, within the coverage area of the BS, which is ( ), as illustrated in Fig. 1. We consider a log-distance path loss model, so that the received power is given by , where is the transmit power, is the distance between transmitter and receiver, is the path-loss exponent, and accounts for other factors as the car- rier frequency and antenna gains. In order to characterize the quality of the links, we consider the primary network capacity, , and the secondary capacity , where unitary band- width is assumed, and and are the signal-to-in- terference ratios (SIR) of the primary and secondary links, respectively. The coexistence probability [6], [9] for concurrent transmission of the primary and secondary links is , where and are the minimum capacities required by the primary and secondary links, respectively. The constraint results from the CTx oper- ating outside the “forbidden region” (circular region of radius centered at the BS), thus avoiding interference to the PUs. If the CTx operates inside the “effective cognitive region” (circular region of radius centered at CRx) then and the quality of the secondary link is guaranteed. When both conditions are satis fi ed, a “concurrent transmission region” exists and the concurrent transmission probability is . This region is highlighted in Fig. 1. In [6], [9] equal primary and secondary transmit powers were assumed. Here we consider that the CTx can reduce its transmit power when it is near the boundary of the forbidden region to avoid reducing the concurrent transmission region and, there- fore, the coexistence probability. The proposed power control mechanism takes into account the ...
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... Radio (CR) is a promising technology, which can deal with the growing demand and scarcity of the wireless spectrum [1], [2]. CR allows secondary users (SUs) to access licensed spectrum bands. Since primary users (PUs) have the spectrum priority, SUs must avoid interfering with the PUs. Based on spectrum sensing information or location awareness, the SUs may adapt their transmit powers and adopt strategies to protect the primary link. In [3] it was described how CR networks may be equipped with location awareness features, and CR positioning systems were presented in [4], [5]. In [6], the effects of location awareness on concurrent transmissions for cognitive radio ad hoc networks (CRAHN) were analyzed. Many related works are based on similar concurrent spectrum access models. In [7], an optimal power control algorithm for the CR transmitter was proposed to maximize the concurrent transmission region. In [8], a dynamic spectrum sharing method was presented which aims to maximize the number of concurrent transmissions; the SUs start their transmission with the minimum power allowing channel reuse by other SUs. In [9], the use of channel rate control as well as its impact on the performance of a CRAHN overlaying an infrastructure-based system was investigated. The proposed algorithm adequately adjusts the secondary user rate to be as large as possible while respecting a given constraint on the achievable primary rate. The results obtained in [9] outper- formed the method proposed in [6] and indirectly improved the performance of other schemes based on similar assumptions, as [7], [8]. However, power control was not considered in [9] which can be an ef fi cient mechanism to increase the data rate or to minimize the energy consumption. Many power control schemes were proposed to maximize the secondary capacity or to reduce the energy consumption [10]–[16]. In [10], the SUs adjust their power to maximize their data rate. Optimal power control [11] and adaptive rate and power control [12] were proposed to maximize the SU capacity subject to average interference power and peak transmit power constraints. General models with multiple PUs and SUs were studied in [13] and [14], where power allocation for sum-rate maximization under mutual interference between the PUs and the SUs was addressed. The optimal power allocation in OFDM CR networks was studied in [15], where a fast and low com- plexity algorithm to achieve the optimal solution was proposed. The fi rst axiomatic approach to power control in CR was presented in [16], where the necessity of interference-aware power control for CR users was discussed. In this letter we consider a similar approach as in [6], [9], but including power control and investigating the achievable data rate, the concurrent transmission probability and the energy ef- fi ciency. Our goal is to highlight the importance of transmission power control to expand the concurrent transmission region where PUs and SUs can coexist without interfering each other. We present two strategies: mean data rate maximization on the CRAHN and mean energy ef fi ciency maximization of SUs. Thus, we investigate how a joint power and rate control scheme can be used in a CRAHN to increase its throughput or to extend the autonomy of its devices. II. S YSTEM M ODEL AND P ROBLEM F ORMULATION As in [6], [9], we assume that the nodes are close enough as to consider an interference-limited spectrum sharing scenario in which a CRAHN operates inside the primary coverage area. The system model is composed by a CR transmitter (CTx), a CR receiver (CRx), a primary transmitter or primary mobile station (PMS), and a primary receiver or base station (BS). The anal- ysis focuses on the primary uplink. The BS is at the origin of coordinates and the mobile devices locations are represented by their polar coordinates as: ; , 2, 3, representing the PMS, the CRx and the CTx, respectively, within the coverage area of the BS, which is ( ), as illustrated in Fig. 1. We consider a log-distance path loss model, so that the received power is given by , where is the transmit power, is the distance between transmitter and receiver, is the path-loss exponent, and accounts for other factors as the car- rier frequency and antenna gains. In order to characterize the quality of the links, we consider the primary network capacity, , and the secondary capacity , where unitary band- width is assumed, and and are the signal-to-in- terference ratios (SIR) of the primary and secondary links, respectively. The coexistence probability [6], [9] for concurrent transmission of the primary and secondary links is , where and are the minimum capacities required by the primary and secondary links, respectively. The constraint results from the CTx oper- ating outside the “forbidden region” (circular region of radius centered at the BS), thus avoiding interference to the PUs. If the CTx operates inside the “effective cognitive region” (circular region of radius centered at CRx) then and the quality of the secondary link is guaranteed. When both conditions are satis fi ed, a “concurrent transmission region” exists and the concurrent transmission probability is . This region is highlighted in Fig. 1. In [6], [9] equal primary and secondary transmit powers were assumed. Here we consider that the CTx can reduce its transmit power when it is near the boundary of the forbidden region to avoid reducing the concurrent transmission region and, there- fore, the coexistence probability. The proposed power control mechanism takes into account the ...
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... this case the goal is to maximize the secondary capacity. The optimization problem can be formulated as follows The RE-PC strategy utilizes the maximum allowed transmit power that guarantees the quality of the primary link, corresponding to the solution of the right side of (7). The objective is to maximize the energy ef fi ciency of the secondary link. The optimization problem in this case is formulated as The EE-PC strategy utilizes the minimum possible transmit power while still ensuring the quality of the secondary link, thus corresponding to the solution of the left side of (7). IV. N UMERICAL R ESULTS In this section we discuss numerical results for the topology in Fig. 1. We consider , path loss exponent , , , , , and . When power control is not used then . In addition, for simulation purposes, were uniformly distributed in the coverage area of the BS, representing different possible CTx locations. For the sake of better illustrating the impact of the proposed secondary joint power and rate control schemes, the numerical results also include the scheme proposed in [9] which is referred here as Fixed-Power Rate Control (FP-RC). Fig. 2 shows histograms with the transmit power distribution of the CTx for both RE-PC and EE-PC power control strategies, considering nine topologies. Each topology represents a different relative position for the CRx and the PMS. The numerical results show that, when the PMS is too close to the BS ( , scenarios i, ii and iii) the mean transmit power used by RE-PC algorithm tends to the maximum value when the CRx moves away from the BS, while for the EE-PC algorithm the mean power is close to twice of that used by the primary trans- mitter. This is because increasing the transmission power of the secondary transmitter does not signi fi cantly affect the primary link for these scenarios. When the PMS is at half the coverage radius ( , scenarios iv, v and vi), the mean transmit power utilized by the RE-PC strategy tends to , however the mean power utilized by the EE-PC algorithm is close to half of . When the PMS is too far from the BS ( , scenarios vii, viii and ix) the mean transmit powers utilized by both algorithms are very low, being close to the minimum value for EE-PC. This is because reducing below is the only way to guarantee the quality of the primary link. From the above it is clear that the two strategies yield different secondary power allocations. In the sequel we investigate the impact of these different power allocations in the concurrent transmission probability, mean data rate and energy ef fi ciency. Fig. 3(a) shows the concurrent transmission probability as a function of , where it is clear that power control (RE-PC and EE-PC) has a positive impact. Additionally, it should be noted that increases when decreases and that , when , for without power control and for (which means that the CTx is outside the coverage area of the BS) when power control is used. Fig. 3(b) and (c) show, respectively, the mean data rate and the mean energy ef fi ciency for the three strategies as a function of . For , the transmission rate for RE-PC is 2.66 bps higher than that of FP-RC but its energy ef fi ciency is 25.89 bits/J less; for the throughput and energy ef fi ciency are similar for both strategies. On the other hand, the energy ef fi ciency for EE-PC is 44 bits/J or more higher than that of FP-RC for all values of but at the cost of a smaller mean achievable rate. Fig. 4(a) shows how for all values of the concurrent transmission probability is higher when power control is employed. Moreover, it must be noted that increases when increases and that , when , for and without and with power control, respectively. Fig. 4(b) and (c) show the mean data rate and the energy ef- fi ciency in terms of . Note how the throughput of the three strategies increases with , having RE-PC, as expected, the highest performance and EE-PC the poorest performance regarding the transmission rate. However, increasing also increases the mean energy ef fi ciency, being EE-PC the most ef fi cient algorithm regarding energy consumption and RE-PC the most expensive one. Note that the goal of each proposed strategy is successfully achieved, offering important system design and operation alternatives. V. C ONCLUSIONS This letter proposes the use of joint power and rate control on the secondary link to expand the concurrent transmission region that guarantees the coexistence of primary and secondary links in a CRAHN. Two power control strategies are introduced, RE-PC maximizes the throughput of the secondary network and EE-PC minimizes the energy consumption of the secondary transmitters. As a consequence, either the data rate or the energy ef fi ciency is increased without affecting the performance of the primary network, considerably improving the performance of the secondary network. R ...
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Citations
... For inter-cluster and intra-cluster communication k-hop clustering scheme is proposed in [8] also the nodes related parameters are deeply studied. The joint rate and power control impact CR adhoc network performance is investigated in [41]. Few papers are referred in this review research where the authors have applied different methods algorithms so that the network efficiency improved in a performance, accuracy and lifetime manner. ...
Since the two decade the Internet of Things (IoT) plays an important role in the field of communication technology. Out of maximum of IoT devices are battery operated. So there should be energy efficient devices that can operate more functions with less power consumption. The main power constraints in the IoT devices are in the communication like spectrum selection, data transmission, etc. To perform communication between the two nodes the spectrum must be available. But as spectrums are limited and a lot of data is to be transmitted there may the issue of spectrum scarcity. The dynamic spectrum access is done using Cognitive Radio (CR) technology that can overcome spectrum scarcity issue. This paper gives the research work on energy efficient, clustering algorithm and spectrum sensing for CR based IoT networks in terms of the methods, merits, demerits and implementation. For efficiency in the spectrum sensing and energy consumption in 5G wireless communication network and data transfer between the IoT devices this study is essential. The biblometric analysis is shown by using VOSviewer to visualize the bibliometric information and the result as an analysis of ROC curve for Rayleigh and Rician channel is plotted using Matlab.
... In the absence of competition, the TPC problem is able to express as an optimization problem, where the objective is to maximize or minimize a single utility function for all SUs subject to the constraints mentioned above. The relationship between minimization of energy consumption and maximization of performance for a secondary link is investigated in [16]. In [17] the authors propose a TPC algorithm which demonstrate how the performance of the network can be increased by combining the power control with directional antennas. ...
Cognitive radio is born as a method that proposes a solution to the problem, dynamically managing the resource. One of the stages that integrate this technology is the spectrum decision, in which the frequency bands are selected and assigned based on the quality of service requirements of the cognitive users. This paper analyses the fundamental elements that make up the cognitive radio and the allocation of spectral bands. Among the main problems in the insertion of new applications and wireless technologies, there is the lack of radio spectrum for its allocation, due to the inefficient way of distribution of the available radio spectrum, which is currently assigned statically. In addition, a synthesis of the development platforms most used in the research on this technology and of the activities developed in order to find guidelines for the adaptable use of the spectrum.
... Sanchez et al. [15], investigated the impact of joint rate and power control over the performance of CRAHN using overlay mode. The power control of SU is controlled to adjust multiple SU's over the given channel based on two control strategies i.e Rate-Efficient Power Control (RE-PC) for increasing SU capacity and Energy-Efficient Power Control (EE-PC) for increasing energy efficiency. ...
With the increase in user demand for internet access on move, spectrum resource seems to deplete and leads to spectrum crunch. Recent researches reports that this spectrum crunch is not due to spectrum scarcity but due to spectrum underutilization because of legacy static spectrum allocation of spectrum bands. This spectrum utilization and efficiency can be improved by using Dynamic Spectrum Access (DSA) techniques, which correlate with cognitive radio technology in one way or the other. There are three basic approach of communication for cognitive radio technology: Inter-weaved approach, Underlay approach and Overlay approach. Extensive researches has been proposed so far based on the inter-weaved approach and little or negligible using underlay or overlay approach. Using these modes the cognitive users can coexist with the primary users at same geographic time and location. In this paper simple and unique Adaptive Power Control (APC) technique for underlay approach for cognitive radio mobile network is proposed. This techniques introduces a Power Adaptive Transmission (PAT) metric which overcomes three major issues. Firstly, this proposed techniques work efficiently over highly active licensed networks with marginal increased throughput of 0.2 Mbps. Secondly, APC this technique adapts to the requirement of cognitive user and Lastly, primary user power is monitored, to prevent interference and maintain the Quality of Service (QoS) of primary user. Under simulation testing the proposed APC technique outperforms various other underlay as well hybrid techniques for power control under cognitive radio environment with 11% increase in throughput and 32% decrease in delay using APC.
... To deal with the paradox, licensed spectrum sharing has been regarded as an effective way to supplement spectrums for mobile cellular systems under the current static assignment policy [6], [7]. The under-utilized non-cellular spectrums are allowed to be accessed by certain granted mobile network operators (MNOs) as the secondary users (SUs), as long as the incumbent users, i.e., primary users (PUs), are inactive. ...
... The network parameters are set the same as in Fig. 5. Assume that there are |L| = 5 session requests in the network. The source and destination of each session l, (s(l), d(l)), is (5, 1), (8,6), (4,7), (10,11), (2, 3), respectively. The rate requirements of them are 32 Mbps, 18 Mbps, 14 Mbps, 26 Mbps, 20 Mbps, respectively. ...
The fast proliferation of Internet of Things (IoT) exacerbates the current spectrum shortage. As a promising solution, licensed spectrum sharing has received great attentions recently. However, due to the unpredictable activities of incumbent users, the availability of the shared spectrum is uncertain, making the quality of service (QoS) provisioning based on it a difficult task, especially under multi-hop scenarios. In this paper, considering the energy-efficiency issue, we propose an end-to-end (E2E) QoS guaranteed green traffic off-loading scheme over the uncertain shared spectrums in multi-hop networks. Specifically, we formulate it into a two-step problem, an off-loaded traffic maximization (OTM) problem and an energy-efficiency improvement (EEI) problem. The former one is to determine the admitted sessions. To achieve the E2E QoS guarantee under the uncertain spectrum supply, we develop a concept of equivalent link capacity, and construct a risk-averse joint chance constraint. Since the formulated problem is intractable, we first develop a data-driven conservative transformation to make the joint chance constraint a set of deterministic individual ones. Then, we design a convex-hull-based and a substitution-based linearization approach to make all the constraints tractable. As for the second step, the EEI problem, under the same constraints as in OTM problem, is designed to refine the resource scheduling for the admitted sessions to optimize the energy-efficiency. To deal with the fractional-form energy-efficiency objective, we further develop a Dinkelbach method based iterative algorithm for obtaining the solution. Numerical results have shown the effectiveness of the proposed scheme.
... Since the CRAHN is an energy constrained system, it is very important to charge the system expediently and efficiently, Kalamkar et al. [13] applied the wireless powered technology into the CRAHN, in the meantime, they used the Lagrangian multiplier method to derive the optimal power and spectrum leasing time. Based on the energy efficient, a joint rate and power control algorithm for SUs is proposed in [14]. ...
Although the Cognitive Radio Ad-Hoc Network (CRAHN) is an effective technology to fully utilize the spectrum resource, the appearance of selfish nodes seriously reduces the communication efficiency of CRAHN and generates unfair resource competition. In this paper, a new incentive strategy is proposed to tackle selfish nodes in CRAHN. In our CRAHN model, the Secondary-User (SU) cooperates with the Primary-User (PU) in a spectrum leasing mode. Since PU can select multiple SUs as relays but only leases a common authorized spectrum usage time to SUs, the SU has the selfish tendency to reduce its power in relay task, which seriously damage the partnership between PU and SUs. We propose an evaluation coefficient to evaluate the behavior of each SU, where the evaluation coefficient establishes the reward and punishment mechanism to suppress the selfish behavior of SU in relay task. Meanwhile, in order to solve resource allocation problem, a Stackelberg game between PU and SUs is formulated and the optimal solutions are determined in a distributed manner. Simulation results validate that the incentive strategy can effectively suppress the selfish behavior of SUs, in the meantime, the total communication throughput is increased.
... The energy consumption problem reduced by saving the power via controlling the control messages that are asserted for synchronization purpose and the neighborhood relationship criteria in order to identify the path between source and destination. Meanwhile the again the transmission of packets that required energy for designing good protocols [7,8] using few packet collisions and reduce power consumption. Single-hop and the multi-hop network are the networks that attained by MANET [9,10]. ...
Energy is the important criterion for a decentralized network. By the cooperative physical layer network coding scheme, the requirement of energy transmission can be reduced. This could be implemented using two routing algorithms namely CCSPR and COSPNCR. An algorithm is constructed for reducing power consumption and rate control. In contradiction of the conventional power aware algorithms, an efficient power aware routing (EPAR) method is proposed which distinguishes the capability of nodes by its residual battery power, and through the expected energy that spent in reliably data packets forwarding on a specific link. To evaluate this, the data routing is performed with high mobility in dynamic environment. By using this algorithm, less cost path can be chosen and data rate can also be controlled. Thus energy consumption can be decreased and hence lifetime of a node can be improved. This approach first identifies the path having high lifetime and through that path, the communication will take place. This work consumes a lesser amount of energy for the routing, thus makes power efficient routing. Hence the throughput is increased and the power consumption is reduced. The conventional shortest path routing method on regular line and the grid line networks attains the gain of energy savings up to 45–75% correspondingly. By using EPAR algorithm, the energy consumption rate is minimized, so the network life time and the performance are improved. The energy consumption rate can be decreased to 80% by using EPAR approach in Mobile Ad hoc Networks.
... Additionally, it is possible to define the SEE as the ratio between the effective ST and the secondary transmit power. Using (21), the SEE can be expressed as [34] ...
The next generations of wireless communications are expected to have great demand for security and spectrum efficiency, and the current secrecy solutions may not be enough. In this paper we propose an optimization framework to address the physical layer security in cogni-tive radio networks when the secondary users employ improper Gaussian signaling. We resort to genetic algorithms to find optimal values of the secondary transmit power and the degree of impropriety, simultaneously. Then, two different problems regarding the system performance are solved: minimizing the secrecy outage probability and maximizing the secondary achievable rate. In both problems we evaluate, besides the secrecy outage probability, the effective secure throughput and the secure energy efficiency of the system as well. The results show that the secondary network using improper signaling outperforms conventional proper signaling in terms of secrecy outage probability and the effective secure throughput, while in terms of the secure energy efficiency, adopting proper signals attain better performance than improper ones.
... Considering a CRN that consists essentially of base stations (BSs), cognitive radio nodes, and basic networks, the lifespan of the network depends on the energy consumption of BSs and the use of energy by the cognitive radio nodes. Consequently, the energy consumed for the transmission, reception, and the other objectives related to cognitive is the main element which affects the duration of network existence [15,16]. ...
In recent years, there has been a rapid evolution of wireless technologies that has led to the challenge of high demand for spectral resources. To overcome this challenge, good spectrum management is required that calls for more efficient use of the spectrum. In this paper, we present a general system, which makes a tradeoff between the spectral efficiency (SE) and energy efficiency (EE) in the cellular cognitive radio networks (CCRN) with their respective limits. We have analyzed the system taking into account the different types of power used in the CCRN, namely the spectrum detection power (Zs) and the relay power (Zr). Optimal policy for emission power allocation formulated in the function of sub-channel activity index (SAI) as an optimization problem in order to maximize spectrum utilization and minimize the energy consumption in the base station of the secondary system energy consumption, is subject to different constraints of the main user system. We also evaluate the collaborative activity index of the sub-channel describing the activity of the primary users in the CCRN. The theoretical analyses and simulation results sufficiently demonstrate that the SE and EE relationship in the CCRN is not contrary and thus the achievement of optimal tradeoff between SE and EE. By making a rapprochement with a cognitive cellular network where SBSs adopts an equal power allocation strategy for sub-channels, the results of our proposed scheme indicate a significant improvement. Therefore, the model proposed in this paper offers a better tradeoff between SE and EE.
... The use of different techniques to increase the spectral and energy efficiency in CR has become a popular research topic in recent years [12]- [14], in order to increase its throughput [12], [13] or to extend the battery lifetime [12], [14]. In this work we propose two transmission strategies for SUs equipped with two radios each. ...
... The use of different techniques to increase the spectral and energy efficiency in CR has become a popular research topic in recent years [12]- [14], in order to increase its throughput [12], [13] or to extend the battery lifetime [12], [14]. In this work we propose two transmission strategies for SUs equipped with two radios each. ...
... The use of different techniques to increase the spectral and energy efficiency in CR has become a popular research topic in recent years [12]- [14], in order to increase its throughput [12], [13] or to extend the battery lifetime [12], [14]. In this work we propose two transmission strategies for SUs equipped with two radios each. ...
Cognitive radio (CR) has emerged as a key technology to deal with the frequency spectrum scarcity. The efficient and opportunistic use of the spectrum requires rapid coordination between the secondary users (SUs). Besides, future wireless networks demand even faster transmission rates, smaller end-toend delay, and greater energy efficiency. In this work we propose two transmission strategies for SUs equipped with two radios, one of them uses both radios for data transfer while the other uses one radio for continuous detection of available channels. We evaluate the performance of the proposed strategies as a function of multiple system parameters. Our results indicate that both proposed strategies increase energy efficiency and decrease the delay between SUs. Besides, numerical results show that in scenarios with low primary activity the best choice is the strategy based on parallel transmission, while in scenarios with high primary activity the best choice is the strategy based on spectrum sensing.
... There are three basic operational models that might be used to implement the cognitive radio networks (CRNs)-(i) overlay, (ii) underlay and (iii) cooperative. In overlay model: the secondary users (SUs) senses the free spectrum to use these spectrum when 10 PUs are absent at their allocated spectrum, in underlay model: the SUs coexist with the PUs and simultaneously use the bands when the PU is active at their band at that situation, the secondary users use the band under the interference constraint of the primary user to preserve the quality of service (QoS) requirement of the primary user and finally, in the cooperation model: the secondary 15 user cooperates with the primary user to improve the QoS of primary user by using cooperative diversity and simultaneously transmits it's own data signal. ...
... The authors in [1], [2], [3], [4] study channel allocation. 20 Whereas the authors in [5], [6] study channel and transmit power allocation and the authors in [7], [8], [9], [10], [11], [12], [13], [14], [15] study transmit power and rate allocation. As our proposed work is on transmit power and rate allocation, we provide a little detailed overview on that only. ...
... The authors also 40 provide sufficient condition under which it is beneficial for the SU to use the PU channel even the sensing result indicates that the PU is busy. The authors in [10] study a location awareness based cognitive radio ad hoc network (CRAHN) ...
In this paper, we propose an optimization framework to determine the distribution of power and bits/channel use to secondary users in a competitive cognitive radio networks. The objectives of the optimization framework are to minimize total transmission power, maximize total bits/channel use and also to maintain quality of service. An upper bound on probability of bit error and lower bound on bits/channel use requirement of secondary users are considered as quality of service. The optimization problem is also constrained by total power budget across channels for a user. Simulating the framework in a centralized manner shows that more transmit power is allocated in a channel with higher noise power and bits/channel use is directly proportional to signal to interference plus noise power ratio. The proposed framework is more capable of supporting high bits/channel use requirement than existing resource allocation framework. We also develop the game theoretic user based distributed approach of the proposed framework. We see that user based distributed solution also follows centralized solution.
