Tradeoff In Cognitive Radio Networks Research Articles

Energy–Spectral Efficiency Tradeoff in Cognitive Radio Networks

In this paper, we propose a general framework to evaluate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in cognitive radio networks (CRNs). The proposed framework is discussed in three typical CRN paradigms: underlay CRNs (UCRNs), overlay CRNs (OCRNs), and interweave CRNs (ICRNs). The EE–SE relation for three CRNs is expressed in the closed-form formulation, in which the optimal (suboptimal) EE solution as the function of SE is deduced with the corresponding limits. The theoretical analysis and numerical results indicate that the EE–SE relation in CRNs is not contrary, i.e., an optimal EE–SE tradeoff can be achieved. The proposed framework provides a useful guidance in the design of practical green CRNs.

On the Spectrum- and Energy-Efficiency Tradeoff in Cognitive Radio Networks

Increasing spectrum-efficiency (SE) as well as energy-efficiency (EE) has attracted much attention recently due to the fact that the future wireless networks need to address the issues of high throughput and low power consumption. However, the objective for optimizing SE sometimes conflicts with the one for optimizing EE, and the methods for improving EE may result in a decrease in SE. In this paper, we consider the SE-EE tradeoff for cognitive radio (CR) networks with co-operative spectrum sensing (CSS). First, we formulate the general problem, and analyze two special cases: the SE maximization problem and the EE maximization problem. The SE and EE are optimized separately via joint optimization of sensing duration and final decision threshold in CSS. Based on the solutions of the two special cases, the general problem for SE-EE tradeoff is solved. Then, we consider the tradeoff of SE and EE from two perspectives: (1) maximizing EE while satisfying SE requirement; and (2) maximizing SE while satisfying EE requirement. Efficient algorithms for sensing strategy design are proposed for each scenario. Finally, we demonstrate the effectiveness of the proposed sensing strategies and illustrate the tradeoff between SE and EE via simulations.

Sensing‐energy efficiency tradeoff for cognitive radio networks

In this study, the authors focus on the tradeoff between spectrum sensing and energy efficiency of cognitive radio networks (CRN). Considering two interference-avoidance schemes, that is, channel handoff and stop-and-wait, the authors, respectively, model the energy efficiency (EE) of CRN as the mean of the throughput-to-power ratio. Research shows that stop-and-wait scheme is a special case of channel handoff from an EE perspective. Based on the proposed EE model, the authors build up an EE optimisation problem under the constraint of the sensing quality, and formulate the sensing-energy efficiency tradeoff (SET) for CRN. The similarity and difference between the SET and the sensing-throughput tradeoff are also investigated. Simulation analysis confirms that there is indeed an optimal sensing time to make the EE maximum, and it is larger than that for maximum throughput. Another interesting result is that the EE and the throughput of CRN can be enhanced together by optimising MAC frame structure in combination with sensing bandwidth adjustment and power control. Our work provides some insights for green CRN in view of MAC frame optimisation.

Energy-Infeasibility Tradeoff in Cognitive Radio Networks: Price-Driven Spectrum Access Algorithms

We study the feasibility of the total power minimization problem subject to power budget and Signal-to-Interference-plus-Noise Ratio (SINR) constraints in cognitive radio networks. As both the primary and the secondary users are allowed to transmit simultaneously on a shared spectrum, uncontrolled access of secondary users degrades the performance of primary users and can even lead to system infeasibility. To find the largest feasible set of secondary users (i.e., the system capacity) that can be supported in the network, we formulate a vector-cardinality optimization problem. This nonconvex problem is however hard to solve, and we propose a convex relaxation heuristic based on the sum-of-infeasibilities in optimization theory. Our methodology leads to the notion of admission price for spectrum access that can characterize the tradeoff between the total energy consumption and the system capacity. Price-driven algorithms for joint power and admission control are then proposed that quantify the benefits of energy-infeasibility balance. Numerical results are presented to show that our algorithms are theoretically sound and practically implementable.

The Optimal Spectrum Sensing Time for Maximizing Throughput of 802.11-Based MAC Protocol for Cognitive Radio Networks Under Unsaturated Traffic Conditions

Cognitive radio has attracted considerable attention as an enabling technology for addressing the problem of radio frequency shortages. In cognitive radio networks (CRNs), secondary users (SUs) are allowed to opportunistically utilize the licensed spectrum bands of primary users (PUs) when these bands are temporarily unused. Thus, SUs should monitor the licensed spectrum bands to detect any PU signal. According to the sensing outcomes, SUs should vacate the spectrum bands or may use them. Generally, the spectrum sensing accuracy depends on the sensing time which influences the overall throughput of SUs. That is, there is a fundamental tradeoff between the spectrum sensing time and the achievable throughput of SUs. To determine the optimal sensing time and improve the throughput of SUs, considerable efforts have been expended under the saturated traffic and ideal channel assumptions. However, these assumptions are hardly valid in practical CRNs. In this paper, we provide the framework of an 802.11-based medium access control for CRNs, and we analyze this framework to find the optimal spectrum sensing time under the saturated and unsaturated traffic condition. Through simulation, the proposed analytic model is verified and the fundamental problem of the sensing-throughput tradeoff for CRNs is investigated.

Security-reliability trade-off for cognitive radio networks in the presence of eavesdropping attack

In this paper, we consider a cognitive radio network that consists of one cognitive base station (CBS) and multiple cognitive users (CUs) in the presence of an eavesdropper. In the cognitive radio network, CBS first detects whether there is spectrum hole through spectrum sensing and then communicates with CUs over the detected spectrum hole. Due to the broadcast nature of wireless transmission, the eavesdropper can overhear the cognitive transmissions between CBS and CUs and attempts to decode its overheard signals for interception purpose. In order to effectively defend against the eavesdropping attack, we propose a multiuser scheduling scheme for cognitive transmissions, where a CU with the highest instantaneous capacity to CBS is selected and scheduled to communicate with CBS. We analyze the security-reliability trade-off performance of proposed multiuser scheduling scheme for cognitive transmissions with the imperfect spectrum sensing over Rayleigh fading channels, where the security and reliability are evaluated in terms of the intercept probability and the outage probability, respectively. Numerical results illustrate that as the intercept probability requirement loosens, the outage probability of proposed multiuser scheduling scheme decreases accordingly, showing the trade-off between security and reliability. In addition, as the number of CUs increases, numerical intercept probability and outage probability of the multiuser scheduling scheme significantly improve, implying the security and reliability benefits through multiuser scheduling.

Sensing-Energy Tradeoff in Cognitive Radio Networks With Relays

Earlier works studied the tradeoff between sensing performance and throughput of secondary users (SUs) for cooperative spectrum sensing. However, The tradeoff between sensing performance and energy consumption has not been well investigated in cognitive radio (CR) networks with relays. In this paper, the issues on cooperative spectrum sensing in a CR network with amplify-and-forward relay are studied. In particular, an optimization problem is formulated to analyze the tradeoff between sensing performance and energy consumption. The objective is to minimize energy consumption for spectrum sensing under given detection probability and false alarm probability constraints. We do so via finding an optimal pair of the parameters, i.e., the number of samples (proportional to sensing time) and the amplification gain. First, the case of fixed amplification gain is considered, in which the minimum number of samples is identified. Then, the case of varying amplification gain is further discussed, where an appropriate amplification gain is found such that the energy consumption is minimized while the sensing performance is kept above a threshold and the required throughput of the SU can be satisfied. The analytical and simulation results show that there exists an optimal pair of the number of samples and the amplification gain to strike the best tradeoff between sensing performance and energy consumption.