Joint power allocation and beamforming with users selection for cognitive radio networks via discrete stochastic optimization

Cognitive radio is a promising technique to dynamic utilize the spectrum resource and improve spectrum efficiency. In this paper, we study the problem of mutual interference cancellation among secondary users (SUs) and interference control to primary users (PUs) in spectrum sharing underlay cognitive radio networks. Multiple antennas are used at the secondary base station to form multiple beams towards individual SUs, and a set of SUs are selected to adapt to the beams. For the interference control to PUs, we study power allocation among SUs to guarantee the interference to PUs below a tolerable level while maximizing SUs?? QoS. Based on these conditions, the problem of joint power allocation and beamforming with SUs selection is studied. Specifically, we emphasize on the condition of imperfect channel sensing due to hardware limitation, short sensing time and network connectivity issues, which means that only the noisy estimate of channel information for SUs can be obtained. We formulate the optimization problem to maximize the sum rate as a discrete stochastic optimization problem, then an efficient algorithm based on a discrete stochastic optimization method is proposed to solve the joint power allocation and beamforming with SUs selection problem. We verify that the proposed algorithm has fast convergence rate, low computation complexity and good tracking capability in time-varying radio environment. Finally, extensive simulation results are presented to demonstrate the performance of the proposed scheme.     

混合频谱共享方式下多用户动态频谱分配算法

为解决混合overlay/underlay频谱共享方式下多用户动态频谱分配问题,构建了混合频谱共享方式下动态频谱分配模型,提出了基于Q学习的多用户动态频谱分配算法. 该算法在不对主用户产生有害干扰的前提下,以最大化次用户总吞吐量为目标,构建了与次用户相对应的虚拟次用户作为智能体. 通过与环境交互学习,进行信道和共享方式初选;频谱分配系统根据冲突情况和各智能体的学习结果调整信道分配策略直至次用户间无冲突. 仿真结果表明,该算法在无信道检测和信道先验知识的条件下,能根据前一时隙信道状态和次用户传输速率需求,实现动态信道分配和频谱共享方式确定,避免次用户间冲突,减少主次用户间冲突,有效提升次用户总吞吐量.     

认知无线电中基于MIMO波束成形的协同频谱接入设计

协同认知无线电技术由于其高效的频谱利用效率已经吸引越来越多的关注。在协同认知网络中,当第一用户（Primary User,PU）之间的信道状况恶劣时,特定的第二用户（Secondary User, SU）被选为中继协同PU完成信息传递,作为回报,PU分配一定的信道资源给SU,使其用于传输自己的信息。当SU系统中一个节点拥有多天线时,假设其可以很好的获悉其与PU之间的信道状态信息,通过波束成形设计可以使其在中继PU信息的同时完成自身信息的发送,而且使两者之间的信号互不干扰。这种频谱共享式的接入方式可以节约信道资源,提高频谱利用率。本文对采用放大转发（Amplify and Forward, AF）中继协议的SU发射端分别采用最小化加权均方误差和准则（Minimizing Sum of Weighted Mean Square Errors, MSWMSE）和迫零准则（Zero Forcing, ZF）对波束成形参数进行设计。仿真结果表明,两种设计方式都可以满足消除用户间干扰的要求;另外,由于基于MSWMSE准则的波束成形参数能够更好的平衡噪声和用户间干扰项对信号的损耗,因而获得更优的性能表现,而且通过调整加权系数可以满足PU用户不同的性能要求。      

Reinforcement Learning Enhanced Iterative Power Allocation in Stochastic Cognitive Wireless Mesh Networks

As the scarce spectrum resource is becoming over-crowded, cognitive wireless mesh networks have great flexibility to improve the spectrum utilization by opportunistically accessing the licensed frequency bands. One of the critical challenges for realizing such network is how to adaptively allocate transmit powers and frequency resources among secondary users (SUs) of the licensed frequency bands while maintaining the quality-of-service (QoS) requirement of the primary users (PUs). In this paper, we consider the power control problem in the context of cognitive wireless mesh networks formed by a number of clusters under the total transmit power constraint by each SU as well as the mean-squared error (MSE) constraint by PUs. The problem is modeled as a non-cooperative game. A distributed iterative power allocation algorithm is designed to reach the Nash equilibrium (NE) between the coexisting interfered links. It offers an opportunity for SUs to negotiate the best use of power and frequency with each other. Furthermore, how to adaptively negotiate the transmission power level and spectrum usage among the SUs according to the changing networking environment is discussed. We present an intelligent policy based on reinforcement learning to acquire the stochastic behavior of PUs. Based on the learning approach, the SUs can adapt to the dynamics of the interference environment state and reach new NEs quickly through partially cooperative information sharing via a common control channel. Theoretical analysis and numerical results both show effectiveness of the intelligent policy.     

基于中断概率最小化的认知无线网络衰落信道条件下的功率控制

Cognitive radio allows Secondary Users (SUs) to dynamically use the spectrum resource li-censed to Primary Users (PUs ), and significantly improves the efficiency of spectrum utilization and is viewed as a promising technology. In cognitive radio networks, the problem of power control is an important issue. In this paper, we mainly focus on the problem of power control for fading channels in cognitive radio networks. The spectrum sharing un-derlay scenario is considered, where SUs are al-lowed to coexist with PUs on the condition that the outage probability of PUs is below the maximum outage probability threshold limitation due to the interference caused by SUs. Moreover, besides the outage probability threshold which is defined to protect the performance of PUs, we also consider the maximum transmit power constraints for each SU. With such a setup, we emphasize the problem of power control to minimize the outage probability of each SU in fading channels. Then, based on the statistical information of the fading channel, the closed expression for outage probability is given in fading channels. The Dual-Iteration Power Control (DIPC) algorithm is also proposed to minimize the outage probability based on Perron-Frobenius theo-ry and gradient descent method under the constraint condition. Finally, simulation results are illustrated to demonstrate the performance of the proposed scheme.     

Joint routing and channel assignment using online learning in cognitive radio networks

Cognitive radio networks (CRNs) are the solution for the problem of underutilizing the licensed spectrum for which there are more requests in the last couple of decades. In CRNs, Secondary users (SUs) are permitted to access opportunistically the licensed spectrum owned by primary users (PUs). In this paper, we address the problem of joint routing and channel assignment for several flows generated by source SUs to a given destination. We consider a more realistic model based on Markov modulated Poisson process for modeling the PUs traffic at each channel and the SUs try to exploit short lived spectrum holes between the PUs packets at the selected channel. The SUs want to cooperatively minimize the end-to-end delay of source SUs flows meanwhile the quality of service requirements of the PUs would be met. To consider partial observation of SUs about PUs activity at all channels and quick adaptation of SUs decisions to environment changes and cooperative interaction of SUs, we use decentralized partially observable markov decision process for modeling the problem. Then, an online learning based scheme is proposed for solving the problem. Simulation results show that the performance of the proposed method and the optimal method is close to each other. Also, simulation results show that the proposed method greatly outperforms related works at control of interference to the PUs while maintains the end-to-end delay of SU flows in a low level.

     

Cooperative bargaining solution for efficient and fair spectrum management in cognitive wireless networks

This paper studies the fairness among the primary users (PUs) and the secondary users (SUs) for resource allocation in cognitive radio systems. We propose a novel co‐opetition strategy based on the Kalai–Smorodinsky bargaining solution to balance the system efficiency and the fairness among users. The strategy formulates the spectrum sharing problem as a nonlinear and integral sum utility maximization subject to a set of constraints describing the co‐opetition among the PUs and the SUs. Then, we solve the maximization problem by proposing a heuristical method that consists of four steps: multi‐PU competition, PU's subcarrier contribution, multi‐SU competition, and SU's subcarrier contribution. Extensive simulation results are presented by comparing the co‐opetition strategy with several conventional ones, including the Kalai–Smorodinsky bargaining solution, sum rate maximization as well as the Max–Min. Results indicate that the co‐opetition strategy can jointly balance the system efficiency and fairness in multiuser resource allocation, as it is able to support more satisfied users and in the meanwhile improve the utility of those unsatisfied. Moreover, the co‐opetition can help enable the coexistence of the PUs and the SUs in cognitive radio systems. Copyright © 2013 John Wiley & Sons, Ltd.     

Cooperative spectrum sharing of multiple primary users and multiple secondary users

This paper proposes a multiple-input multiple-output (MIMO) based cooperative dynamic spectrum access (DSA) framework that enables multiple primary users (PUs) and multiple secondary users (SUs) to cooperate in spectrum sharing. By exploiting MIMO in cooperative DSA, SUs can relay the primary traffic and send their own data at the same time, which greatly improves the performance of both PUs and SUs when compared to the non-MIMO time-division spectrum sharing schemes. Especially, we focus on the relay selection optimization problem among multiple PUs and multiple SUs. The network-wide cooperation and competition are formulated as a bargaining game, and an algorithm is developed to derive the optimal PU-SU relay assignment and resource allocation. Evaluation results show that both primary and secondary users achieve significant utility gains with the proposed framework, which gives all of them incentive for cooperation.     

Quality of service‐aware coordinated dynamic spectrum access: prioritized Markov model and call admission control

In this paper, we propose a heterogeneous‐prioritized spectrum sharing policy for coordinated dynamic spectrum access networks, where a centralized spectrum manager coordinates the access of primary users (PUs) and secondary users (SUs) to the spectrum. Through modeling the access of PUs and multiple classes of SUs as continuous‐time Markov chains, we analyze the overall system performance with consideration of a grade‐of‐service guarantee for both the PUs and the SUs. In addition, two new call admission control (CAC) strategies are devised in our models to enhance the maximum admitted traffic of SUs for the system. Numerical results show that the proposed heterogeneous‐prioritized policy achieves higher maximum admitted traffic for SUs. The trade‐off between the system's serving capability and the fairness among multiple classes of SUs is also studied. Moreover, the proposed CAC strategies can achieve better performance under max‐sum, proportional, and max‐min fairness criteria than the conventional CAC strategies. Copyright © 2011 John Wiley & Sons, Ltd.     

Resource allocation based on dynamic hybrid overlay/underlay for heterogeneous services of cognitive radio networks

In cognitive radio networks (CRNs), hybrid overlay and underlay sharing transmission mode is an effective technique to improve the efficiency of radio spectrum. Unlike existing works in literatures where only one secondary user (SU) uses both overlay and underlay mode, the different transmission modes should dynamically be allocated to different SUs according to their different quality of services (QoS) to achieve the maximal efficiency of radio spectrum. However, dynamic sharing mode allocation for heterogeneous services is still a great challenge in CNRs. In this paper, we propose a new resource allocation method based on dynamic allocation hybrid sharing transmission mode of overlay and underlay (Dy-HySOU) to obtain extra spectrum resource for SUs without interfering with the primary users. We formulate the Dy-HySOU resource allocation problem as a mixed-integer programming to optimize the total system throughput with simultaneous heterogeneous QoS guarantee. To decrease the algorithm complexity, we divide the problem into two sub-problems: subchannel allocation and power allocation. Cutset is used to achieve the optimal subchannel allocation, and the optimal power allocation is obtained by Lagrangian dual function decomposition and subgradient algorithm. Simulation results show that the proposed algorithm further improves spectrum utilization with simultaneous fairness guarantee, and the achieved Dy-HySOU diversity gain is satisfying.     

Optimal Resource Allocation for Spectrum Sensing Based Cognitive Radio Networks with Statistical QoS Guarantees

Resource allocation under spectrum sensing based dynamic spectrum sharing strategy is a critically important issue for cognitive radio networks (CRNs), because they need to not only satisfy the interference constraint caused to the primary users (PUs), but also meet the quality-of-service (QoS) requirements for the secondary users (SUs). In this paper, we develop the optimal spectrum sensing based resource allocation scheme for the delay QoS constrained CRNs. Specifically, we aim at maximizing the maximum constant arrival rate of the SU that can be supported by the time-varying service process subject to the given statistical delay QoS constraint. In our derived power allocation scheme, not only the average transmit and interference power constraints are considered, but also the impact of the PUs?? transmission to the CRNs and the PUs?? spectrum-occupancy probability are taken into consideration. Moreover, the spectrum sensing errors are also taken into consideration. Simulation results show that, (1) the effective capacity of the secondary link decreases when the statistical delay QoS constraint becomes stringent; (2) given the QoS constraint, the effective capacity of the secondary link varies with the interference power constraint and the SNR of the primary link.     

认知中继协同无线通信系统功率分配和混合频谱接入方案

文章首先对Underlay 认知-中继协作（cognitive radio relay cooperation, CR-RC）系统信源和中继的功率分配问题进行了研究,获得了Underlay CR-RC系统信源、中继独立功率分配(independent power allocation, IPA）和联合功率分配（joint power allocation, JPA）方案,并给出了基于IPA和JPA的CR-RC系统的中断概率和各态历经容量的封闭解析解;其次通过对Underlay CR-RC系统中断性能的比较分析,提出了高频谱效率、高能量效率的混合Interweave-Underlay CR-RC方案。在该方案中,定义了主、从用户中断概率约束,充分考虑了主、从系统的服务质量(quality of service, QoS),当即使从用户的发射功率为零,主系统的QoS仍不能满足时,从用户以最大功率发送信号,无需考虑其对主用户的影响;当由于很强的主用户干扰,使得从用户的QoS不能得到满足时,从用户不再发射信号,发信功率为零;当主、从系统的QoS能够同时满足时,以Underlay模式工作。      

Mutual interference considered power allocation in OFDM-based cognitive networks: the multiple SUs case

Power allocation for secondary users (SUs) in cognitive networks is an important issue to ensure the SUs’ quality of service. When the mutual interference between the primary users (PUs) and the SUs is taken into consideration, it is wanted to achieve the conflict-free power allocation while synchronously maximizing the capacity of the secondary network. In this paper, the optimal power allocation problem is considered in orthogonal frequency division multiplexing cognitive networks. The single SU case is primarily formulated as a constrained optimization problem. On this basis, the multiple SUs case is then studied and simulated in detail. During the analysis, the mutual interference among the PUs and the SUs is comprehensively formulated as the restrictions on the SU’s transmission power and the optimization problems are finally resolved by iterative water-filling algorithms. Consequently, the proposed power allocation scheme restrains the interference to the primary network, as well as maximizing the capacity of the secondary network. Specifying the multiple-SUs case, simulation results are exhibited in a simplified scenario to confirm the efficiency of the proposed water-filling algorithm, and the influence of the mutual interference on the power allocation and the system capacity is further illustrated.     

Spectrum sharing in multi-channel cooperative cognitive radio networks: a coalitional game approach

In this paper, we study a coalitional game approach to resource allocation in a multi-channel cooperative cognitive radio network with multiple primary users (PUs) and secondary users (SUs). We propose to form the grand coalition by grouping all PUs and SUs in a set, where each PU can lease its spectrum to all SUs in a time-division manner while the SUs in return assist PUs’ data transmission as relays. We use the solution concept of the core to analyze the stability of the grand coalition, and the solution concept of the Shapley value to fairly divide the payoffs among the users. Due to the convexity of the proposed game, the Shapley value is shown to be in the core. We derive the optimal strategy for the SU, i.e., transmitting its own data or serving as a relay, that maximizes the sum rate of all PUs and SUs. The payoff allocations according to the core and the Shapley value are illustrated by an example, which demonstrates the benefits of forming the grand coalition as compared with non-coalition and other coalition schemes.     

Interference‐aware spectrum handover for cognitive radio networks

Cognitive radio (CR) is a promising technique for future wireless networks, which significantly improves spectrum utilization. In CR networks, when the primary users (PUs) appear, the secondary users (SUs) have to switch to other available channels to avoid the interference to PUs. However, in the multi‐SU scenario, it is still a challenging problem to make an optimal decision on spectrum handover because of the the accumulated interference constraint of PUs and SUs. In this paper, we propose an interference‐aware spectrum handover scheme that aims to maximize the CR network capacity and minimize the spectrum handover overhead by coordinating SUs’ handover decision optimally in the PU–SU coexisted CR networks. On the basis of the interference temperature model, the spectrum handover problem is formulated as a constrained optimization problem, which is in general a non‐deterministic polynomial‐time hard problem. To address the problem in a feasible way, we design a heuristic algorithm by using the technique of Branch and Bound. Finally, we combine our spectrum handover scheme with power control and give a convenient solution in a single‐SU scenario. Experimental results show that our algorithm can improve the network performance efficiently.Copyright © 2012 John Wiley & Sons, Ltd.     

基于稳定匹配的认知无线网络协作物理层安全机制

在Underlay认知无线网络中,次用户被允许在主用户进行数据发送时接入主用户的频谱.此时,主用户将对次用户和窃听者造成干扰.利用协作干扰技术,主用户产生的干扰可以被用来改善次用户的物理层安全.基于此,本文针对包含多个主次用户的Underlay认知无线网络,提出了一种新的协作物理层安全机制.为了在保证主用户通信质量的前提下,最大化网络中次用户的总的安全容量,该机制将对次用户进行合理的频谱接入选择和功率控制.另外,考虑到个体理性和自私性对于频谱接入稳定性的影响,该机制利用稳定匹配理论将频谱接入选择问题建模为一对一的双边匹配问题,通过构建主次用户之间的稳定匹配来保证频谱接入的稳定性.仿真结果表明,使用本文所提安全机制,可以在保证主用户通信质量的前提下,稳定而又有效地改善网络中次用户获得的总的安全容量.     

MaxDiv: an optimal randomized spectrum access with maximum diversity scheme for cognitive radio networks

Cognitive radio network (CRN) is an emerging technology that can increase the utilization of spectrum underutilized by primary users (PUs). In the literature, most exiting investigations on CRNs have focused on how secondary users (SUs) can coexist harmlessly with the PUs. Despite the importance of such a coexistence issue, it is also crucial to investigate the coexistence of SUs because (i) the PUs usually rarely use the licensed spectrum and (ii) the advantages of CRN will significantly increase the number of SUs in the future. To address this challenging issue, we propose, in this paper, an optimal randomized spectrum access scheme, whose main ideas include the following: (i) an SU shares its sensing results with neighboring SUs and (ii) with the regional sensing results, an SU will access available channels with a non‐uniform probability distribution. We first formulate a multichannel optimal randomized multiple access (MC‐ORMA) problem that aims to maximize the throughput of the CRN; we then develop efficient distributed algorithms to solve the MC‐ORMA problem; we derive the closed‐form value of collision probability for each SU; and finally, we conduct extensive numerical experiments and compare our theoretical analysis with simulation results to demonstrate the advantages of our scheme. Copyright © 2011 John Wiley & Sons, Ltd.     

针对NOMA和CR网络的功率分配方法

在基于非正交多址（NOMA）和认知无线电（CR）网络的混合系统中进行功率分配,需要同时考虑主用户（PUs）和从用户（SUs）的服务质量。文章考虑在一个小区中主从用户同时存在,从用户采用NOMA的方式接入系统,并且从用户对主用户的造成的干扰不能影响到主用户的正常通信。本文提出的功率分配方法,可以实现接入系统中的从用户数量的最大化,并且还动态考虑了主用户的数量、信道条件、发送功率以及正常通信时的信干噪比（SINR）阈值等情况,最大可接入从用户的数量还会随着主用户各参数的变化而变化。仿真结果表明,在主用户数量、主从用户SINR阈值设置相同的情况下,所提功率分配方法与FTPC方法相比复杂度相同,但是本文的分配方法可以比FTPC多接入近一半的从用户数量。      

A game approach for cooperative spectrum sharing in cognitive radio networks

We consider the problem of cooperative spectrum sharing among primary users (PUs) and secondary users (SUs) in cognitive radio networks. In our system, each PU selects a proper set of SUs to serve as the cooperative relays for its transmission and in return, leases portion of channel access time to the selected SUs for their own transmission. PU decides how to select SUs and how much time it would lease to SUs, and the cooperative SUs decide their respective power levels in helping PU's transmission, which are proportional to their access times. We assume that both PUs and SUs are rational and selfish. In single‐PU scenario, we formulate the problem as a noncooperative game and prove that it converges to a unique Stackelberg equilibrium. We also propose an iterative algorithm to achieve the unique equilibrium point. We then extend the proposed cooperative mechanism to a multiple‐PU scenario and develop a heuristic algorithm to assign proper SUs to each PU considering both performance and fairness. The simulation results show that when the competition among SUs is fierce, the performance gap between our heuristic algorithm and the optimal one is smaller than 3%. Copyright © 2013 John Wiley & Sons, Ltd.     

Fair and Efficient Spectrum Splitting for Unlicensed Secondary Users in Cooperative Cognitive Radio Networks

In cooperative cognitive radio networks (CCRNs), a licensed primary-user (PU) is allowed to leverage several unlicensed secondary-users (SUs) to relay its traffic. In this paper, a staged dynamic spectrum allocation (DSA) scheme is proposed for CCRNs. In the first stage, the network is uncongested. A simple pricing based DSA scheme is proposed for the PUs to lease their idled frequency bands to the SUs. And, hence, the initial quality of service (QoS) demands (in terms of the minimum rate requirements) of the PUs and the SUs are both satisfied through direct transmission on the allocated frequency bands. In the second stage, the network reaches the full-loaded situation. Therefore, a cooperative relaying based DSA scheme is proposed to stimulate the PUs to split more extra frequency bands to fulfill the increased QoS demands of the SUs, on condition that the QoS of the PUs are well maintained. By applying the cooperative bargaining game theory in the proposed cooperative relaying based DSA, on the one hand, the SUs can get fairness rate-rewards from the PUs according to the level of contribution that they can make to compensate the PUs for the rate-losses. Hence, the increased QoS demands of the SUs can be accommodated in short term. On the other hand, the PUs could retain the SUs successfully to obtain the long-term revenue, on condition that their QoS constraints are still satisfied. Finally, the analysis results of the proposed bargaining game theoretic DSA scheme (in the second stage) are testified through computer simulations.