Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

Adaptation is one of the key capabilities of cognitive radio, which focuses on how to adjust the radio parameters to optimize the system performance based on the knowledge of the radio environment and its capability and characteristics. In this paper, we consider the cognitive radio adaptation problem for power consumption minimization. The problem is formulated as a constrained power consumption minimization problem, and the biogeography-based optimization （BBO） is introduced to solve this optimization problem. A novel habitat suitability index （HSI） evaluation mechanism is proposed, in which both the power consumption minimization objective and the quality of services （QoS） constraints are taken into account. The results show that under different QoS requirement settings corresponding to different types of services, the algorithm can minimize power consumption while still maintaining the QoS requirements. Comparison with particle swarm optimization （PSO） and cat swarm optimization （CSO） reveals that BBO works better, especially at the early stage of the search, which means that the BBO is a better choice for real-time applications.

Design of Clustering Techniques in Cognitive Radio Sensor Networks

In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)interfer-ence.The Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence time.In this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.In this research,multiple random Sec-ondary Users(SUs),and PUs are considered for implementation.Hence,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algo-rithms.Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing algorithms.Similarly,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing algorithms.Probability of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB values.The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection.Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.

Power Allocation for Cooperative Communications in Non-Orthogonal Cognitive Radio Vehicular Ad-Hoc Networks

To achieve the better system performance for cooperative communication in non-orthogonal cognitive radio vehicular adhoc networks(CR-VANETs),this paper investigates the power allocation considering the interference to the main system in a controllable range.We propose a three-slot one-way vehicle system model where the mobile vehicle nodes complete information interaction with the assistance of other independent nodes by borrowing the unused radio spectrum with the primary networks.The end-to-end SNR relationship in overlay and underlay cognitive communication system mode are analyzed by using two forwarding protocol,namely,decode-and-forward(DF)protocol and amplify-and-forward(AF)protocol,respectively.The system outage probability is derived and the optimal power allocation factor is obtained via seeking the minimum value of the approximation of system outage probability.The analytical results have been confirmed by means of Monte Carlo simulations.Simulation results show that the proposed system performance in terms of outage under the optimal power allocation is superior to that under the average power allocation,and is also better than that under other power allocation systems.

ADAPTIVE MODULATION AND POWER CONTROL FOR THROUGHPUT ENHANCEMENT IN COGNITIVE RADIOS

To regulate the transmit-power and enhance the total throughput, a novel Transmit Power Control Game (TPCG) algorithm and an adaptive Modulation TPCG (M-TPCG) algorithm which combine bandwidth allocation, adaptive modulation and transmit-power control based on Space Time Block Coding (STBC) OFDM-CDMA system are designed and a cross-layer framework of database sharing is proposed. Simulation results show that the TPCG algorithm can regulate their transmitter powers and enhance the total throughput effectively, M-TPCG algorithm can achieve maximal system throughput. The performance of the cognitive radio system is improved obviously.

Adaptive power control and beam-forming joint optimization in cognitive radio networks

A novel adaptive power control and beam-forming joint optimization algorithm is proposed in cognitive radio （CR） underlay networks, where cognitive network share spectrum with primary network which spectrum is licensed. In this paper, both primary base station （PBS） and cognitive base station （CBS） are all equipped with multi antennas, while each primary user （PU） and cognitive user （CU） has only one antenna. Different from traditional algorithms, an adaptive weight factor generating solution is supplied to different access users （both PUs and CUs） in this paper, and the different priority of users is also considered, because PUs have higher priority, the weight factor of PUs is fixed as constant and signal-to-interference and noise ratio （SINR） threshold is unchanged, while for CUs, it is set adaptively and SINR threshold is also changed accordingly. Using this algorithm, the transmit power is decreased, which relax the strict requirements for power amplifier in communication systems. And moreover, owing to PUS has fixed SINR threshold, the calculated SINR at receiver is nearly unchanged, but for CUs, the S1NR is changing with the adaptive weight factor. Under the assurance of quality of service （QoS） of PUs, the solution in this paper can enable CRs access to the CR network according to adaptive SINR threshold, therefore which supplies higher spectrum utilization efficiency.

Joint Adaptive Modulation and Power Allocation in Cognitive Radio Networks

Link adaptation is an important issue in the design of cognitive radio networks, which aims at making efficient use of system resources. In this paper, we propose and investigate a joint adaptive modulation and power allocation algorithm in cognitive radio networks. Specifically, the modulation scheme and transmit power are adjusted adaptively according to channel conditions, interference limit and target signal-to-interference-plus-noise ratio (SINR). As such the total power consumption of cognitive users (CUs) is minimized while keeping both the target SINR of CUs and interference to primary user (PU) at an acceptable level. Simulation results are provided to show that the proposed algorithm achieves a significant gain in power saving.

Location-aware downlink and uplink power control and throughput optimization in cellular cognitive radio networks

In this paper,we consider throughput maximization in cognitive radio systems with proper power control.In particular,we incorporate location-awareness into the power control design and maximize the average throughput of the cognitive system.As we shall show,the proposed approach effectively utilizes the“spatial opportunity”to maximize the system throughput,which clearly outperforms traditional power control methods.Further,the proposed approach still exhibits significant throughput gain even considering imperfect position information,with appropriate robust design modifications.

Price-Based Power Control Algorithm in Cognitive Radio Networks Based on Monotone Optimization

This paper considers a price-based power control problem in the cognitive radio networks(CRNs).The primary user(PU) can admit secondary users(SUs) to access if their interference powers are all under the interference power constraint. In order to access the spectrum, the SUs need to pay for their interference power.The PU first decides the price for each SU to maximize its revenue. Then, each SU controls its transmit power to maximize its revenue based on a non-cooperative game. The interaction between the PU and the SUs is modeled as a Stackelberg game. Using the backward induction, a revenue function of the PU is expressed as a non-convex function of the transmit power of the SUs. To find the optimal price for the PU, we rewrite the revenue maximization problem of the PU as a monotone optimization by variable substitution. Based on the monotone optimization, a novel price-based power control algorithm is proposed. Simulation results show the convergence and the effectiveness of the proposed algorithm compared to the non-uniform pricing algorithm.

Optimal Energy-Efficient Transmission for Hybrid Spectrum Sharing in Cooperative Cognitive Radio Networks

In order to improve the energy efficiency(EE)in cognitive radio(CR),this paper investigates the joint design of cooperative spectrum sensing time and the power control optimization problem for the secondary user systems to achieve the maximum energy efficiency in a cognitive network based on hybrid spectrum sharing,meanwhile considering the maximum transmit power,user quality of service(QoS)requirements,interference limitations,and primary user protection.The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem.The Dinkelbach’s method is adopted to solve this problem and to transform the non-convex optimization problem in fractional form into an equivalent optimization problem in the form of subtraction.Then,an iterative power allocation algorithm is proposed to solve the optimization problem.The simulation results show the effectiveness of the proposed algorithms for energy-efficient resource allocation in the cognitive network.

Optimal power allocation for multi-user OFDM and distributed antenna cognitive radio with RoF

In cognitive radio （CR）,power allocation plays an important role in protecting primary user from disturbance of secondary user. Some existing studies about power allocation in CR utilize ＇interference temperature＇ to achieve this protection,which might not be suitable for the OFDM-based CR. Thus in this paper,power allocation problem in multi-user orthogonal frequency division multiplexing （OFDM） and distributed antenna cognitive radio with radio over fiber （RoF） is firstly modeled as an optimization problem,where the limitation on secondary user is not ＇interference temperature＇,but that total throughput of primary user in all the resource units （RUs） must be beyond the given threshold. Moreover,based on the theorem about maximizing the total throughput of secondary user,equal power allocation algorithm is introduced. Furthermore,as the optimization problem for power allocation is not convex,it is transformed to be a convex one with geometric programming,where the solution can be obtained using duality and Karush-Kuhn-Tucker （KKT） conditions to form the optimal power allocation algorithm. Finally,extensive simulation results illustrate the significant performance improvement of the optimal algorithm compared to the existing algorithm and equal power allocation algorithm.

Joint channel selection and power control for video streaming over D2D communications based cognitive radio networks

A joint channel selection and power control scheme is developed for video streaming in device-to-device （D2D） communications based cognitive radio networks. In particular, physical queue and virtual queue models by applying ＇M/G/1 queue＇ and ＇M/G/1 queue with vacations＇ theories are built up, respectively, to evaluate the delays experienced by various video traffics. Such delays play a vital role in calculating the packet loss rate for video streaming, which reflects the video distortion. Based on the distortion model, a video distortion minimization problem is formulated, subject to the rate constraint, maximum power constraint, primary users＇ tolerant interference constraint, and secondary users＇ minimum data rate requirement constraint. The optimization problem turns out to be a mixed integer nonlinear programming （MINLP） , which is generally nondeterministic in polynomial time. A Lagrangian dual method is thus employed to reformulate the video distortion minimization problem, based on which the sub-gradient algorithm is used to determine a relaxed solution. Thereafter, applying the iterative user removal yields the optimal joint channel selection and power control solution to the original MINLP problem. Extensive simulations validate our proposed scheme and demonstrate that it significantly increases the peak signal- to-noise ratio （PSNR） compared with the existing schemes.

Cross-layer combining of power control and adaptive modulation with truncated ARQ for cognitive radios

To maximize throughput and to satisfy users＇ requirements in cognitive radios, a cross-layer optimization problem combining adaptive modulation and power control at the physical layer and truncated automatic repeat request at the medium access control layer is proposed. Simulation results show the combination of power control, adaptive modulation, and truncated automatic repeat request can regulate transmitter powers and increase the total throughput effectively.

Parameter adjustment based on improved genetic algorithm for cognitive radio networks

Multi-objective parameter adjustment plays an important role in improving the performance of the cognitive radio （CR） system. Current research focus on the genetic algorithm （GA） to achieve parameter optimization in CR, while general GA always fall into premature convergence. Thereafter, this paper proposed a linear scale transformation to the fitness of individual chromosome, which can reduce the impact of extraordinary individuals exiting in the early evolution iterations, and ensure competition between individuals in the latter evolution iterations. This paper also introduces an adaptive crossover and mutation probability algorithm into parameter adjustment, which can ensure the diversity and convergence of the population. Two applications are applied in the parameter adjustment of CR, one application prefers the bit error rate and another prefers the bandwidth. Simulation results show that the improved parameter adjustment algorithm can converge to the global optimal solution fast without falling into premature convergence.

公路隧道风光水储互补发电系统容量配置研究

为降低公路隧道的电力运营成本,探究可再生能源互补发电系统在公路隧道的应用前景,研究合适的容量配置求解方法。建立利用风、光、水和储能设备的互补发电系统为公路隧道提供电力资源。以特长公路隧道(总长7.1 km)为估算模型,采用改进后的粒子群优化算法,即离散型自适应粒子群优化算法,以全生命周期的建设成本和设备维护成本最小为目标函数,以缺电负荷率(LPSP)和储能电池的状态为约束,对风力发电设备、光伏发电设备、水力发电设备和储能设备的最优容量配置进行求解。结果表明:1)对比标准粒子群算法,离散型自适应粒子群优化算法的总投入成本更少,寻优能力更强;2)对比该隧道1年的用电成本,前期投入将在5年内回本;3)在风光水储互补发电系统的设备全生命使用周期的20年内,该隧道可节省1 920.39万元电费。

Radio resource management in energy harvesting cooperative cognitive UAV assisted IoT networks:A multi-objective approach

Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless systems.In this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous relays.Unmanned Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)facility.We adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)signals.In conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT devices.We developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio determination.We formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide emission.The MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to solve.To address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced complexity.The simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.

含分布式光伏的主动配电网供电能力自适应优化研究

为实现电网供电能力最大化,提出含分布式光伏的主动配电网供电能力自适应优化方法。构建配电网供电能力相关指标评估模型,建立最大年消纳能力计算模型,同时构建供电能力评价模型,获取实时供电能力效果;依据配电网供电的相关参数,构建配电网线路规划模型并求解,得到最大供应能力,实现主动配电网供电能力自适应优化。实验结果表明:在场景1和场景2中,所提方法得到了不同的配电网开关设备配置方案,最大供电量稳定在63 MW和80 MW,能够提升至少25%的供电能力;经过线路规划后,变电站建设数量大幅度减少,最多为5个,使投资成本减少43%以上。

一种有源配电网分布式光伏消纳能力评估方法

受太阳光照变化的影响,分布式光伏电源存在间歇性和波动性的问题,其接入有源配电网后给配电网稳定运行带来了一系列不利影响,因此研究有源配电网的光伏消纳能力问题对维持有源配电网安全运行至关重要。通过对分布式光伏电源接入电网对有源配电网电压偏差和系统网损的影响进行研究,设置的2个目标函数分别是分布式光伏电源接入功率最大量和系统网损,以电压偏差作为约束条件,建立关于IEEE33节点的有源配电网分布式光伏消纳能力评估模型,用于评估有源配电网的光伏消纳能力。根据评估结果,提出动态自适应粒子群算法,对有源配电网进行求解,并且与传统的粒子群算法进行比较,结果表明,该算法求解结果更加精确,误差较小。

下行卫星认知无线电门限与功率联合优化

用于卫星通信的频谱资源日益紧缺,但现存卫星系统却存在利用不足的问题。针对该问题,以最大化卫星通信中下行带内数据传输量为目标,提出了基于Underlay认知无线电的卫星信道检测门限与功率分配联合优化算法。首先分析了下行链路中信道融合检测误差、功率分配向量与数据传输量之间的数学关系,给出了信道数量及总功率均受限时信道与功率联合分配的可行性条件。之后,进行了目标模型可行性论证及融合误差分析,并将优化目标函数问题分解为检测门限与功率分配两个子优化问题。针对两个子问题,文中分别证明了加窗粒子群优化算法适用于数据融合后的门限优化,论证了信道与功率联合分配的可行性,在此基础上给出了信道与功率联合分配的最优解。引入中间量在两个子优化算法之间反复迭代,得到了本文目标函数的联合最优解。最后,仿真给出了检测门限优化性能,结果表明,改进型算法在准确度和迭代次数上均优于传统粒子群算法;比较了该联合优化算法与传统卫星通信方式及普通Underlay认知无线电用于卫星通信时的性能差异,结果表明,该联合算法能够有效提高频谱利用率;给出了算法复杂度。

认知网络中快速自适应功率控制算法

采用sigmoid函数设计了一种自适应效用函数,该函数仅与认知用户的信干噪比门限和当前获得的信干噪比相关,因此可以通过自适应改变信干噪比门限实现认知用户的机会频谱接入,并快速满足其服务质量要求.同时,兼顾用户公平性重新设计代价函数来改善纳什均衡功率解的帕累托有效性.在此基础上基于非合作博弈论提出一种功率控制模型,经证明该模型符合超模博弈,从理论上保证了纳什均衡解的存在性和惟一性.并提出一种自适应功率控制算法,仿真结果表明该算法可以保证约10次迭代收敛,与参考算法相比节省约8%的功耗,在获得的效用上有近15%改善.

认知OFDM网络中基于中断概率约束的功率分配

研究了认知正交频分复用(OFDM)系统的容量极限及最优功率分配策略.为限制主用户中断概率,建立了在主用户接收端中断概率受限下最大化认知用户传输速率的数学优化问题,并利用拉格朗日乘数法得到该问题的最优解.仿真结果表明,所提算法将主用户中断概率限制在其可容忍范围内的同时,最大化了认知用户的传输速率.