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 scenarioswhile 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 O(NM)+O(Nlog(N)).

Conflict Resolution Strategy in Handover Management for 4G and 5G Networks

Fifth-generation(5G)cellular networks offer high transmission rates in dense urban environments.However,a massive deployment of small cells will be required to provide wide-area coverage,which leads to an increase in the number of handovers(HOs).Mobility management is an important issue that requires considerable attention in heterogeneous networks,where 5G ultra-dense small cells coexist with current fourth-generation(4G)networks.Although mobility robustness optimization(MRO)and load balancing optimization(LBO)functions have been introduced in the 3GPP standard to address HO problems,non-robust and nonoptimal algorithms for selecting appropriate HO control parameters(HCPs)still exist,and an optimal solution is subjected to compromise between LBO and MRO functions.Thus,HO decision algorithms become inefficient.This paper proposes a conflict resolution technique to address the contradiction between MRO and LBO functions.The proposed technique exploits received signal reference power(RSRP),cell load and user speed to adapt HO margin(HM)and time to trigger(TTT).Estimated HM and TTT depend on a weighting function and HO type which is represented by user status during mobility.The proposed technique is validated with other existing algorithms from the literature.Simulation results demonstrate that the proposed technique outperforms existing algorithms overall performance metrics.The proposed technique reduces the overall average HO ping-pong probability,HO failure rate and interruption time by more than 90%,46%and 58%,respectively,compared with the other schemes overall speed scenarios and simulation time.

Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

Mobile broadband(MBB)networks are expanding rapidly to deliver higher data speeds.The fifth-generation cellular network promises enhanced-MBB with high-speed data rates,low power connectivity,and ultralow latency video streaming.However,existing cellular networks are unable to perform well due to high latency and low bandwidth,which degrades the performance of various applications.As a result,monitoring and evaluation of the performance of these network-supported services is critical.Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users.This paper proposes a Bayesian model to estimate the minimum opinion score(MOS)of video streaming services for any particular cellular network.The MOS is the most commonly used metric to assess the quality of experience.The proposed Bayesian model consists of several input data,namely,round-trip time,stalling load,and bite rates.It was examined and evaluated using several test data sizes with various performance metrics.Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network.The proposed Bayesian network obtained a remarkable overall accuracy of 90.36%and outperformed the neural network.