Research On Resource Allocation Strategies For Multi-user Cognitive Radio Networks

The next generation mobile system will gradually evolve to a system with diversified communication application requirements and high-capacity data exchange demand,which cause spectrum scarcity problem and a surge in communication energy consumption.Cognitive radio is regarded as a promising technology to increase frequency reuse and energy efficiency,which dynamically adjusts network parameters to adapt current wireless environment.Several works have been studied on resource allocation problems in cognitive radio network since resource allocation plays important role in spectrum efficiency and energy efficiency of cognitive radio network.The networks architectures of the next generation mobile system tend to be complicated and heterogeneous.Traditional resource allocation strategies designed for cognitive radio networks are not able to meet the demands of multi-user cognitive radio network architectures.Therefore,It is important to study resource allocation problems in multi-user cognitive radio networks.Aiming to increase the spectrum efficiency and energy efficiency,resource allocation strategies for multi-user cognitive radio networks are studied in this paper.In multi-user underlay cognitive radio network,a fair resource allocation strategy is proposed to provide a higher spectrum reuse and mitigate harmful co-channel interference(CCI)for the primary users(PUs)and secondary users(SUs).In addition,resource allocation strategies are discussed in multi-user cognitive heterogeneous networks.In interweave cognitive heterogeneous network network,non-orthogonal multiple access(NOMA)is applied to boost the number of SUs accessing to the licensed spectrum.First,a resource allocation optimization problem is discussed in a downlink interweave multi-user cognitive heterogeneous network where SUs access to licensed channels.Optimal bandwidth allocation strategy,channel resource allocation strategy and power allocation are proposed under the constraints of limited free spectrum,base station power budget and the quality of service of PUs and SUs.Besides,in an uplink underlay multi-user cognitive heterogeneous radio network,resource allocation strategy is studied to increase both spectrum and energy efficiency with the constraints of cross-tier interference and the uplink NOMA decoding demands.The main contributions of this paper can be concluded as following,(1)A utility fairness resource allocation is proposed in a multi-user cognitive radio network with CCI mitigation in this paper.In the proposed system model,the correct reception probability(CRP)model is introduced as a network utility metric.Furthermore,useful bounds on CRP are derived to analyze the performance of proposed allocation schemes.The optimal resource allocation is formulated as a worst-case user CRP maximum problem with both average CCI and average power budget constraints.However,this problem is non-convex.Therefore,this optimization problem is solved by successively performing subchannel allocation and power allocation.The concept of reference user is employed to guarantee the quality of service of the PU.A clustering algorithm inspired subchannel allocation strategy is proposed to divide SUs into multiple groups by minimizing the average mutual-signal-to-interference-ratio degree between any two SUs.In each subchannel,we formulate a max-min utility optimal power allocation problem.The nonlinear Perron-Frobenius theory is applied to solve this power allocation problem.Simulation results showed that the CRP of the worst-case SU by the proposed subchannel algorithm outperformed the upper bound of the worst-case SU by random subchannel algorithm.Besides,the proposed power is allocation scheme guaranteed the fairness among throughput of different SUs in each subchannel and has geometric convergence.(2)A resource allocation optimization problem is studied for a two-tier interweave multi-user cognitive heterogeneous network.NOMA is used to boost the number of accessible SUs sharing the limited and dynamic licensed spectrum holes.Practically,there exists a tradeoff: an SC can increase its instantaneous sum throughput by accessing more idle bandwidth,which creates higher liability due to the dynamics of licensed spectrum and contention among the multiple SCs.Aiming to maximize the sum throughput of second-tier SCs network,a mixed integer non-linear programming problem is formulated and decomposed into bandwidth resource allocation subproblem,SUs clustering subproblem,and power allocation subproblem.Based on the scale of SCs network and the activities of licensed spectrum,an optimal bandwidth configuration is introduced to maximize the average sum throughput of SCs.By analyzing the derivation of the achievable rate expression of a NOMA-enabled SU,a novel SUs clustering algorithm is developed by grouping SUs with more distinctive channel conditions.With the results of SUs clustering,power allocations are proposed within and across a NOMA cluster by using optimality conditions and difference of convex programming,respectively.The simulation results showed that the optimal bandwidth configuration was affected by the scale of second-tier SC network.Moreover,the proposed clustering algorithm improved the throughput of NOMA clusters and the proposed power allocation algorithm further enhanced the throughput of SCs.(3)An optimal resource allocation problem is proposed for an uplink two-tier cognitive heterogeneous network with NOMA.SUs in femtocell tier are allowed to access the licensed frequency band owned by macrocell tier in underlay spectrum sharing mode,which causes severe cross-tier interference.In uplink NOMA system,the received signal can be successful decoded by adopting successive interference cancellation(SIC)if the superposed signals are distinguished by different transmit power level.Interference temperature is introduced to guarantee the cross-tier interference caused by SUs in femtocell below the tolerance of PU in microcell.In order to maximum the throughput of the proposed cognitive femtocell network,an optimal power allocation problem is formulated with the constraints of the maximum cross-tier interference and the minimum power difference of superposed signal from different SUs in femtocell.A power allocation algorithm is proposed by Lagrange duality and gradient descent method.The simulation results showed that the proposed power allocation algorithm was effective on the performance of spectrum efficiency and cross-tier interference mitigation.Moreover,the sum throughput of cognitive femtocell network by proposed power allocation algorithm outperformed the sum throughput by fractional transmit power allocation algorithm.