Resource Allocation for Green Cognitive Radios: Energy Efficiency Maximization

Abstract

Green cognitive radios are promising in future wireless communications due to high energy efficiency. Energy efficiency maximization problems are formulated in delay-insensitive green cognitive radio and delay-sensitive green cognitive radio. The optimal resource allocation strategies for delay-insensitive green cognitive radio and delay-sensitive green cognitive radio are designed to maximize the energy efficiency of the secondary user. The peak interference power and the average/peak transmit power constraints are considered. Two algorithms based on the proposed resource allocation strategies are proposed to solve the formulated problems. Simulation results show that the maximum energy efficiency of the secondary user achieved under the average transmit power constraint is higher than that achieved under the peak transmit power constraint. It is shown that the design of green cognitive radio should take the tradeoff between its complexity and its achievable maximum energy efficiency into consideration. Related Works and Motivation.Since the operation of a CR should protect the quality of service (QoS) of the PU, a metric that evaluates the performance of the protection of the PU should be imposed. Basically, there are three metrics for protecting the PU from intolerance interference caused by the SU, namely, a peak interference power constraint (PIP), an average interference power (AIP) constraint, and an outage probability (OP) constraint [9][10][11][12][13]. A PIP constraint requires that the instantaneous interference power caused by the SU is below a prescribed interference power threshold, whereas the AIP constraint regulates the long-term interference power over all fading states to be not more than the maximum

Optimization of Resource Allocation Model With Energy-Efficient Cooperative Sensing in Green Cognitive Radio Networks

“…while (Convergence=false) and (k ≤ I 1 ) do Compute p * using (37). Update µ and ν by (40) and 41 this simulation are N = [10,15,20], α arit = 10, and P (H 0 ) = 0.8. However, transmission rate is adopted to be C 0 = log 2 (1 + γ s ) = 6.658 bits/sec/Hz where γ s considered as SNR and is 20 dB, and C 1 = log 2 1 + γs 1+γp = 6.613 bits/sec/Hz by assuming γ p = −15 dB.…”

“…Besides, Zhou et al [14] provided a hybrid RA for D2D communications deployment as LTE-A networks based on C-RAN architectures, which include common centralized interference mitigation that was performed in the centralized BBU pool and a distributed joint channel selection and power allocation algorithm. In [15], the optimal RA strategies for delay-insensitive andsensitive are considered to optimize the available EE, using the two proposed algorithms upon optimal RA with low complexity average metric constraint. Besides, the authors of [16] proposed optimal linear weights and optimal power allocation for SUs, to maximize the CR systems probability of detection.…”

Green IoT for Eco-Friendly and Sustainable Smart Cities: Future Directions and Opportunities

“…Regarding harvesting energy, many studies focus on greening the communication network based on harvested energy, such as [124][125][126]. In [124], the authors focused on resource allocation techniques used for maximizing the energy efficiency of the green cognitive radio network.…”

“…Regarding harvesting energy, many studies focus on greening the communication network based on harvested energy, such as [124][125][126]. In [124], the authors focused on resource allocation techniques used for maximizing the energy efficiency of the green cognitive radio network. Furthermore, Ge et al [125] discussed the cognitive radio network secured based on multiple-input single-output using to minim transmit the information signal's power.…”

Efficient power allocation algorithm in downlink cognitive radio networks

“…CR technology is suitable for use in fifth-generation (5G) mobile networks for providing high spectrum usage with good service quality. However, the problem of resource allocation (RA) in CR networks (CRNs) has been the focus of extensive research attention [3][4][5][6][7][8][9].…”