Distributed cross‐layer optimization for wireless regional area network‐based cognitive radio networks

This paper presents a study of a cross‐layer design through joint optimization of spectrum allocation and power control for cognitive radio networks (CRNs). The spectrum of interest is divided into independent channels licensed to a set of primary users (PUs). The secondary users are activated only if the transmissions do not cause excessive interference to PUs. In particular, this paper studies the downlink channel assignment and power control in a CRN with the coexistence of PUs and secondary users. The objective was to maximize the total throughput of a CRN. A mathematical model is presented and subsequently formulated as a binary integer programming problem, which belongs to the class of non‐deterministic polynomial‐time hard problems. Subsequently, we develop a distributed algorithm to obtain sub‐optimal results with lower computational complexity. The distributed algorithm iteratively improves the network throughput, which consists of several modules including maximum power calculation, excluded channel sets recording, base station throughput estimation, base station sorting, and channel usage implementation. Through investigating the impacts of the different parameters, simulation results demonstrates that the distributed algorithm can achieve a better performance than two other schemes. Copyright © 2011 John Wiley & Sons, Ltd.     

Cross‐layer design of partial spectrum sharing for two licensed networks using cognitive radios

To utilize spectrum resources more efficiently, dynamic spectrum access attempts to allocate the spectrum to users in an intelligent manner. Uncoordinated sharing with cognitive radio (CR) users is a promising approach for dynamic spectrum access. In the uncoordinated sharing model, CR is an enabling technology that allows the unlicensed or secondary users to opportunistically access the licensed spectrum bands (belonging to the so‐called primary users), without any modifications or updates for the licensed systems. However, because of the limited resources for making spectrum observations, spectrum sensing for CR is bound to have errors and will degrade the grade‐of‐service performance of both primary and secondary users. In this paper, we first propose a new partial spectrum sharing policy, which achieves efficient spectrum sharing between two licensed networks. Then, a Markov chain model is devised to analyze the proposed policy considering the effects of sensing errors. We also construct a cross‐layer design framework, in which the parameters of spectrum sharing policy at the multiple‐access control layer and the spectrum sensing parameters at the physical layer are simultaneously coordinated to maximize the overall throughput of the networks, while satisfying the grade‐of‐service constraints of the users. Numerical results show that the proposed spectrum sharing policy and the cross‐layer design strategy achieve a much higher overall throughput for the two networks. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

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.     

A unified spectrum sensing and throughput analysis model in cognitive radio networks

Cognitive radio (CR) is a newly developed technology for increasing spectral efficiency in wireless communication systems. In the CR networks, there exist two traditional spectrum‐sharing technologies called spectrum overlay and spectrum underlay. A new hybrid overlay/underlay paradigm has also been discussed in the literature. In this work, we create a unified spectrum sensing and throughput analysis model, which is suitable for overlay, underlay, and hybrid overlay/underlay paradigms in the CR networks. In the proposed model, the energy detection scheme is employed for the spectrum sensing in the network in which the co‐channel interference is present among primary users and secondary users (SUs). The SUs' throughput in the proposed CR system model is then analyzed. The simulations are also carried out for demonstrating the performance of overlay, underlay, and hybrid overlay/underlay paradigms. Copyright © 2013 John Wiley & Sons, Ltd.     

感知无线电中频谱共享技术的研究

文章研究了感知无线电网络中联合频谱分配和调度的问题,给出了基于物理干扰模型的频谱分配算法。调度算法是对由频谱分配算法得到的一系列传输模式进行调度,从而实现频谱共享。本文基于提出的频谱分配算法通过仿真比较了三种调度算法,结果表明考虑了公平性的调度算法的网络吞吐量有稍微的降低（可达到最大吞吐量的96%）,但感知用户的整体需求满足度却得到了很大的提高,即达到了很好的公平性。     

Power allocation algorithm of full duplex cognitive relay network based on energy harvesting

To alleviate the shortage of spectrum resources and improve the power utilization of cognitive radio networks,a resource allocation algorithm of full duplex cognitive relay networks with energy harvesting was proposed.In the algorithm,the coefficient for power splitting of the relay and the transmit power of the secondary users were jointly optimized to maximize the throughput of the secondary users under the interference to primary users and energy harvesting constraints.Since the optimization of the algorithm was non-convex,it was transformed into two sub-optimizations,the sub-optimization of the coefficient for power splitting and the sub-optimization of the power transmitted of secondary users,which were the solvable convex sub-optimizations.Then,the final solution of the original optimization was obtained with the iterative algorithm.Simulation results show that the throughput of the proposed algorithm,can obtain 2 times throughput of the networks with half-duplex power splitting algorithm and 1.5 times throughput of the networks with full-duplex time switching algorithm.     

基于合作博弈的多信道认知无线网络中的频谱共享算法简

采用合作博弈对多信道认知无线网络中的频谱共享问题进行了建模分析,提出了次用户在各信道上的信干噪比乘积作为合作博弈的效用函数。次用户在各信道上保证对主用户的干扰小于一定门限的要求下,通过最大化各自效用函数的乘积来进行功率分配。由于最大化次用户效用函数的乘积问题是非凸的,通过变量替换将其转化为了一个等价的凸优化问题,利用该凸优化问题的对偶分解,提出了一种次用户间的频谱共享算法。仿真结果表明,所提算法在次用户和速率与公平性之间进行了有效折中。     

认知无线电网络中基于主系统有限反馈的频谱共享方案

摘 要：提出一种频谱共享方案,该方案适用于同时存在多个主用户和一对次用户的场景。各主用户依据接收机反馈的有限信道质量信息（CQI, channel quality information）分配发送功率及传输速率。次用户根据偷听到的主系统CQI有限反馈,以适当的功率及速率接入信道。次用户接入信道的行为对各主用户造成一定干扰,以致主系统传输速率遭受一定损失。本文在主系统速率损失约束条件下,研究得出了使次系统吞吐量最大化的次用户发送功率及传输速率最佳分配方案。数值结果表明,对于每个主用户仅需反馈3-4个量化比特,次系统的有效吞吐量就可堪比于主次发射端均拥有主系统链路完整CQI的情况。仿真结果显示,所提出的频谱共享方案能够满足主系统速率损失约束。     

Optimal distributed power allocation for a new spectrum sharing paradigm in cognitive radio networks

The problem of power allocation in cognitive radio networks plays an important role to improve the efficiency of spectrum utilization. However, most of previous works focus on the power allocation for secondary users in spectrum sharing overlay or spectrum sharing underlay, which needs to frequently handoff between the idle spectrum bands or considers the interference constraints in all spectrum bands respectively. In order to reduce the handoff and fully utilize the spectrum resource, we propose a new spectrum sharing paradigm which not only can just need to adjust the transmit power in spectrum bands instead of frequently handoff between idle spectrum bands, but can fully utilize the spectrum resource as we only consider the interference power constraints in active spectrum bands rather than in all spectrum bands. Then based on this new spectrum sharing paradigm and the constraint conditions, we study the distributed power allocation for secondary users and formulate the optimization problem as a non-cooperative game problem, after that the variational inequality approach is used to solve this game problem and a Nash equilibria solution is got, finally simulation results are illustrated to demonstrate the performance of the proposed scheme.     

基于PCN的水下认知网络动态频谱接入算法

水下网络可用频谱范围比较窄,且部分被水下生物占用,导致了水下传感器网络可用的频谱资源更为稀缺.针对上述问题,提出一种基于累积干扰预测(Predicted Cumulative Noise,PCN)的水下认知网络动态频谱接入算法.该算法把水下生物作为认知网络的主节点,水下传感器节点作为次节点;通过建立水下生物业务行为的马尔科夫模型预测累积干扰,次节点根据预测结果,采用合作的方式动态地接入授权频谱.仿真结果表明,该算法能够保护水下生物正常通信的同时,实现最优化的频谱共享,网络容量增益达到6.3dB.     

认知无线电网络用户合作机会接入方案的研究

认知无线电技术可以用来解决随着无线通信的迅速发展而突显出来的频谱实际利用率不高的问题,并缓解频谱分配紧张的局面.本文所关注的是多个认知无线电用户(SU)的机会频谱接入问题.分布式认知无线电网络中,为避免和授权用户(PU)发生冲突,通常采用周期性感知方法,这将使吞吐量降低.本文提出了一种认知无线电用户间高效的合作方案.文中分析了用户合作的最好和最坏两种情况下系统吞吐量的解析解.通过仿真验证了理论结果,并比较分析几种方案下每个用户和系统吞吐量随用户数量的变化,得出了用户间有效的合作可以提高各自以及整个系统的吞吐量的结论.     

分布式认知无线网络中的频谱接入凸规划算法

针对分布式认知无线网络中所可能存在的多频道隐终端问题,以及分布式协调方法(DCF)在随机冲突下性能下降的问题,该文以最优化网络的效用值为目标,将各个认知节点的接入概率作为求解对象,在分布式认知无线网络中建立了一个通用的凸规划模型。同时,基于凸规划算法,提出了一种分布式的信道接入 (DSAC) 算法对目标问题进行求解,从而决定认知网络中每个节点以及每条链路的接入概率。仿真结果表明,DSAC算法能够在较少的迭代次数内实现收敛;与普通的DCF方法相比,DSAC算法可以有效地提高网络中同时传输的链路个数,提高了频谱利用率以及网络吞吐量。     

认知网络中协作感知吞吐量的优化

为了提高集中式认知网络的吞吐量,提出了基于信任度的吞吐量优化算法.该算法在主用户充分保护的前提下,以认知用户的吞吐量为目标函数,融合中心采用双门限值对本地感知结果进行融合.从理论上证明了吞吐量是全局漏检概率的增函数,当全局漏检概率等于门限值时,吞吐量达到最大值.并利用牛顿迭代法求出单节点概率,然后采用遍历法可得到认知用户吞吐量最大值.仿真结果表明,当信噪比为-14 dB时认知用户融合优化算法相对"AND准则"OR准则"以及"HALF准则"归一化吞吐量分别提高了0.62、0.3和0.09.     

Fundamental Limits of Full-Duplex Spectrum Sharing Under Peak Interference Power Constraint

Cognitive radio (CR) and full-duplex (FD) have received extensive attention and research due to their high spectrum efficiency, which can effectively solve the problem of low spectrum efficiency in current communication systems. Based on CR and FD techniques, in this paper, a FD spectrum sharing CR networks is considered, in which both secondary users (SU1 and SU2) are each equipped with dual antennas, one antenna is used to transmit signals, and the other antenna is used to receive signals at the same time and frequency. Under peak interference power and peak transmit power constraints, we analysis the ergodic sum capacity and the outage probability based on the FD spectrum sharing CR networks and the conventional spectrum sharing CR networks. Furthermore, under no peak transmit power constrain and perfect self-interference cancellation (SIC), based on the FD spectrum sharing CR networks and the conventional spectrum sharing CR networks, the closed-form expressions of the theoretical performance upper bound of the ergodic sum capacity and the outage probability are derived by two lemmas and four propositions. Accurate mathematical analysis display, under the same bandwidth, the upper bound of ergodic sum capacity for the full-duplex spectrum sharing CR networks is twice as much as the traditional spectrum sharing CR networks, and the FD spectrum sharing CR networks based on SU1, also has better performance upper bound on the outage probability than the traditional spectrum sharing CR networks. Simulations results also validate that, the FD spectrum sharing CR networks obtains better communication performance than the conventional spectrum sharing CR networks, especially when the mean of self-interference channel power gain is small. Finally, we also can see that the simulation performance upper bound is completely consistent with the theoretical analysis performance upper bound, whether in the FD spectrum sharing CR networks or the conventional spectrum sharing CR networks. So also verifies the correctness of the theoretical performance upper bound derivation.

     

一种利用优先经验回放深度Q-Learning的频谱接入算法

针对认知无线传感器网络中频谱接入算法的频谱利用率不高、重要经验利用率不足、收敛速度慢等问题,提出了一种采用优先经验回放双深度Q-Learning的动态频谱接入算法。该算法的次用户对经验库进行抽样时,采用基于优先级抽样的方式,以打破样本相关性并充分利用重要的经验样本,并采用一种非排序批量删除方式删除经验库的无用经验样本,以降低能量开销。仿真结果表明,该算法与采用双深度Q-Learning的频谱接入算法相比提高了收敛速度;与传统随机频谱接入算法相比,其阻塞概率降低了6%~10%,吞吐量提高了18%~20%,提高了系统的性能。     

Distributed opportunistic scheduling for MIMO underlay cognitive radio networks

Cognitive radio networks have emerged to improve the utilization of the scarce spectrum. In this paper, we propose a distributed resource allocation algorithm that allocates resources opportunistically to the secondary users in a multiple‐input multiple‐output environment. In order to reduce the complexity and cost, antenna selection schemes are employed to allow the secondary communication using a single radio frequency chain. The proposed algorithm is proved theoretically and using simulations, to give a performance very close to that of a centralized one with lower delay and overhead. Furthermore, we introduce two techniques for the proposed algorithm based on the allowable data rates referred to as limited and maximum rates. We derive closed‐form expression for the consumed power and tight upper bounds for the average throughput achieved by each technique. A comparison between the proposed techniques is also provided. Both simulations and analytical results show that the proposed algorithm achieves high throughput with low complexity. Moreover, the results show that the tightness of the bounds improves with the diversity order. Finally, the proposed techniques are compared with two suggested random schemes to investigate their effectiveness. Copyright © 2016 John Wiley & Sons, Ltd.     

使次系统容量最大化的协作频谱感知性能分析及优化

针对认知无线电网络中SU数、检测门限、带宽、频谱利用率和次系统容量间关系的分析表明,一方面C-CSS不仅可降低虚警率、提高频谱利用率,还可利用较少的SU实现较高的次系统容量;另一方面在SU数一定且满足目标要求时,C-CSS与T-CSS实现最大次系统容量所要求的检测门限不同.提出协作频谱感知优化算法,利用NP准则通过直接对数学模型求解即可得到使次系统容量最大的SU数和相应检测门限.理论分析和仿真结果均表明,所提算法在保证目标检测率的前提下最大化了次系统容量.     

Joint resource allocation over licensed and unlicensed spectrum in U-LTE networks

Wireless Networks - By sharing unlicensed spectrum with Wi-Fi networks, unlicensed LTE (U-LTE) is one of the promising approaches to further improve LTE network throughput. However, the problem of...     

有限用户数多认知网络部分信道共享性能分析

认知网络中动态信道共享是提高频谱资源利用率的关键.针对有限用户数下多认知网络共存场景,该文建立3维马尔科夫链部分信道共享模型,仿真分析用户有/无信道切换功能下认知网络间部分信道共享的性能.分别就阻塞概率、强迫终止概率、切换概率和系统吞吐量等,与静态频谱分配策略和分级共享策略进行比较仿真测试,结果表明,采用部分信道共享策略的系统在容忍较小的切换概率和强迫终止概率下可以获得较大的系统吞吐量.