Resource Allocation In Cognitive Radio Networks Research Articles

Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

The computational complexity of resource allocation processes, in cognitive radio networks (CRNs), is a major issue to be managed. Furthermore, the complicated solution of the optimal algorithm for handling resource allocation in CRNs makes it unsuitable to adopt in real-world applications where both cognitive users, CRs, and primary users, PUs, exist in the identical geographical area. Hence, this work offers a primarily price-based power algorithm to reduce computational complexity in uplink scenarios while limiting interference to PUs to allowable threshold. Hence, this paper, compared to other frameworks proposed in the literature, proposes a two-step approach to reduce the complexity of the proposed mathematical model. In the first step, the subcarriers are assigned to the users of the CRN, while the cost function includes a pricing scheme to provide better power control algorithm with improved reliability proposed in the second stage. The main contribution of this paper is to lessen the complexity of the proposed algorithm and to offer flexibility in controlling the interference produced to the users of the primary networks, which has been achieved by including a pricing function in the proposed cost function. Finally, the performance of the proposed power and subcarrier algorithm is confirmed for orthogonal frequency-division multiplexing (OFDM). Simulation results prove that the performance of the proposed algorithm is better than other algorithms, albeit with a lesser complexity of <i>O</i>(<i>NM</i>) + <i>O</i>(<i>N</i>log(<i>N</i>)).

Efficient power allocation algorithm in downlink cognitive radio networks

In cognitive radio networks (CRNs), the computational complexity of resource allocation algorithms is a significant problem that must be addressed. However, the high computational complexity of the optimal solution for tackling resource allocation in CRNs makes it inappropriate for use in practical applications. Therefore, this study proposes a power-based pricing algorithm (PPA) primarily to reduce the computational complexity in downlink CRN scenarios while restricting the interference to primary users to permissible levels. A two-stage approach reduces the computational complexity of the proposed mathematical model. Stage 1 assigns subcarriers to the CRN's users, while the utility function in Stage 2 incorporates a pricing method to provide a power algorithm with enhanced reliability. The PPA's performance is simulated and tested for orthogonal frequency-division multiplexing-based CRNs. The results confirm that the proposed algorithm's performance is close to that of the optimal algorithm, albeit with lower computational complexity of O M log M .

Energy-Efficient Resource Allocation in Cognitive Radio Networks Under Cooperative Multi-Agent Model-Free Reinforcement Learning Schemes

The most prominent challenge to the wireless community is to meet the demand for radio resources. Cognitive Radio (CR) is envisioned as a potential solution that utilizes its cognition ability intended to enhance the proper utilization of available radio resources and improves energy efficiency. However, due to the co-existence of Primary Base Stations (PU-BSs) and Cognitive Base Stations (CR-BSs) in CR networks, the problem of aggregated interference occurs which poses a critical challenge for resource allocation in CR networks. Moreover, in practical scenarios, it is difficult to form the correct network model due to complex network dynamics beforehand. Therefore, this work presents Multi-Agent Model-Free Reinforcement Learning schemes namely Q-Learning (Q-L) and State-Action-Reward- (next) State- (next) Action (SARSA) for resource allocation which mitigates interference and eliminate the need of network model. The proposed schemes are implemented in a decentralized cooperative manner with CRs act as multi-agent, forms a stochastic dynamic team to obtain optimal energy-efficient resource allocation strategy. Numerical results reveal that: 1) proposed cooperative scheme 1 (Cooperative Q-L scheme) expedites the convergence; 2) proposed cooperative scheme 2 (Cooperative SARSA scheme) achieves significant improvement in network capacity. Both the proposed cooperative schemes demonstrate its effectiveness by providing significant improvement in energy efficiency and maintain users’ QoS.

Approach of Robust Resource Allocation in Cognitive Radio Network With Spectrum Leasing

In this paper, the spectrum leasing mode is adopted as a promising approach to fully use spectrum resource in Cognitive Radio Networks (CRNs). In CRNs, Primary Transmitter (PT) can fulfill agreement with Secondary Users (SUs) that PT leases part of licensed spectrum to SUs in order to exchange additional transmission from SUs. We focus on the optimal resource allocation in CRNs satisfying the requirements both of PT and SUs. Correspondingly, we propose a resource optimization model with consideration of uncertainty of channel gain, which attempts to reduce the energy consumption of PT on the premise of guaranteeing the Quality of Service (QoS) and improve the adaptiveness to the uncertain communication environment. In the meantime, the information transmission of the SUs can be ensured. When the communication outage probability caused by the channel gain uncertainty is considered, a probability constrained robust optimization resource allocation algorithm is proposed to obtain the optimal solutions. Numerical results show that the proposed algorithm is effective in saving the energy of PT and satisfying the robustness requirements. The results also show the system performance achieved by the proposed algorithm is better than that of some other existing algorithms.

Energy Consummation in cognitive radio network using Different Methods

The cognitive radio and resource allocation techniques have been proposed for efficiently utilizing the radio resources. Cognitive radio is an emerging technology intended to enhance the utilization of the radio frequency spectrum and allocate the available resources correctly. The cooperative communication system, with the same total power and bandwidth of legacy wireless communication systems, can increase the data rate of the future wireless communication system. A combination of cognitive radio with resource allocation can further improve the future wireless network performance and reduces the energy consummation. Efficient resource allocation in cognitive radio network (CRN) is essential in order to meet the challenges of future wireless networks. In this Paper, we are going to discuss the different Localization Techniques, objectives and protocols used in the literature for resource allocation in CRN. This paper also highlights the use of power control, cooperation types, network configurations, and Energy consummation used in CRN. Finally, directions for future research are outlined of proposed algorithms for energy minimization.

Optimal resource allocation solutions for heterogeneous cognitive radio networks

Cognitive radio networks (CRN) are currently gaining immense recognition as the most-likely next-generation wireless communication paradigm, because of their enticing promise of mitigating the spectrum scarcity and/or underutilisation challenge. Indisputably, for this promise to ever materialise, CRN must of necessity devise appropriate mechanisms to judiciously allocate their rather scarce or limited resources (spectrum and others) among their numerous users. ‘Resource allocation (RA) in CRN', which essentially describes mechanisms that can effectively and optimally carry out such allocation, so as to achieve the utmost for the network, has therefore recently become an important research focus. However, in most research works on RA in CRN, a highly significant factor that describes a more realistic and practical consideration of CRN has been ignored (or only partially explored), i.e., the aspect of the heterogeneity of CRN. To address this important aspect, in this paper, RA models that incorporate the most essential concepts of heterogeneity, as applicable to CRN, are developed and the imports of such inclusion in the overall networking are investigated. Furthermore, to fully explore the relevance and implications of the various heterogeneous classifications to the RA formulations, weights are attached to the different classes and their effects on the network performance are studied. In solving the developed complex RA problems for heterogeneous CRN, a solution approach that examines and exploits the structure of the problem in achieving a less-complex reformulation, is extensively employed. This approach, as the results presented show, makes it possible to obtain optimal solutions to the rather difficult RA problems of heterogeneous CRN.

Dynamic Resource Allocation for Heterogeneous Services in Cognitive Radio Networks With Imperfect Channel Sensing

Resources in cognitive radio networks (CRNs) should dynamically be allocated according to the sensed radio environment. Although some work has been done for dynamic resource allocation in CRNs, many works assume that the radio environment can perfectly be sensed. However, in practice, it is difficult for the secondary network to have the perfect knowledge of a dynamic radio environment in CRNs. In this paper, we study the dynamic resource allocation problem for heterogeneous services in CRNs with imperfect channel sensing. We formulate the power and channel allocation problem as a mixed-integer programming problem under constraints. The computational complexity is enormous to solve the problem. To reduce the computational complexity, we tackle this problem in two steps. First, we solve the optimal power allocation problem using the Lagrangian dual method under the assumption of known channel allocation. Next, we solve the joint power and channel allocation problem using the discrete stochastic optimization method, which has low computational complexity and fast convergence to approximate to the optimal solution. Another advantage of this method is that it can track the changing radio environment to dynamically allocate the resources. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.