能量采集认知无线电系统中的最优频谱感知(英文)

In this paper,we consider a cognitive radio system with energy harvesting,in which the secondary user operates in a saving-sensing-transmitting(SST) fashion.We investigate the tradeoff between energy harvesting,channel sensing and data transmission and focus on the optimal SST structure to maximize the SU's expected achievable throughput.We consider imperfect knowledge of energy harvesting rate,which cannot be exactly known and only its statistical information is available.By formulating the problem of expected achievable throughput optimization as a mixed-integer non-linear programming one,we derive the optimal saveratio and number of sensed channels with indepth analysis.Simulation results show that the optimal SST structure outperforms random one and performance gain can be enhanced by increasing the SU's energy harvesting rate.     

The Optimal Spectrum Sensing Time for Maximizing Throughput of 802.11-Based MAC Protocol for Cognitive Radio Networks Under Unsaturated Traffic Conditions

Cognitive radio has attracted considerable attention as an enabling technology for addressing the problem of radio frequency shortages. In cognitive radio networks (CRNs), secondary users (SUs) are allowed to opportunistically utilize the licensed spectrum bands of primary users (PUs) when these bands are temporarily unused. Thus, SUs should monitor the licensed spectrum bands to detect any PU signal. According to the sensing outcomes, SUs should vacate the spectrum bands or may use them. Generally, the spectrum sensing accuracy depends on the sensing time which influences the overall throughput of SUs. That is, there is a fundamental tradeoff between the spectrum sensing time and the achievable throughput of SUs. To determine the optimal sensing time and improve the throughput of SUs, considerable efforts have been expended under the saturated traffic and ideal channel assumptions. However, these assumptions are hardly valid in practical CRNs. In this paper, we provide the framework of an 802.11-based medium access control for CRNs, and we analyze this framework to find the optimal spectrum sensing time under the saturated and unsaturated traffic condition. Through simulation, the proposed analytic model is verified and the fundamental problem of the sensing-throughput tradeoff for CRNs is investigated.     

Throughput optimization using simultaneous sensing and transmission in energy harvesting cognitive radio networks

A three‐dimensional continuous‐time Markov model is proposed for an energy harvesting cognitive radio system, where each secondary user (SU) harvests energy from the ambient environment and attempts to transmit data packets on spectrum holes in an infinite queuing buffer. Unlike most previous works, the SU can perform spectrum sensing, data transmission, and energy harvesting simultaneously. We determine active probability of the SU transmitter, where the average energy consumption for both spectrum sensing and data transmission should not exceed the amount of harvested energy. Then, we formulate achievable throughput of secondary network as a convex optimization problem under average transmit and interference energy constraints. The optimal pair of controlled energy harvesting rate and data packet rate is derived for proposed model. Results indicate that no trade‐off is available among harvesting, sensing/receiving, and transmitting. The SU capability for self‐interference cancelation affects the maximum throughput. We develop this work under hybrid channels including overlay and underlay cases and propose a hybrid solution to achieve the maximum throughput. Simulation results verify that our proposed strategy outperforms the efficiency of the secondary network compared to the previous works.     

非线性能量收集认知无线电网络的次用户吞吐量最大化方案

针对一对主用户和M对次用户构成的认知无线电网络（cognitive radio network,CRN），研究了非线性能量收集的认知无线电网络的次用户吞吐量最大化问题。具体来说，对于考虑次用户发射器（secondary transmitter,ST）电路功率的情况，首先将主用户吞吐量需求下的次用户吞吐量最大化（secondarythroughput maximization,STM）问题建模为一个非线性优化问题，然后将它转化成凸优化问题，最后提出一种联合使用黄金分割和二分法的低复杂度算法，获得主用户发射器（primarytransmitter,PT）能量传输和次用户信息传输的最优时间分配以及主用户发射器的最优发射功率。对于忽略次用户发射器电路功率的情况，首先证明次用户吞吐量最大化问题的凸特性，然后设计了一个更高效的算法来求解。仿真结果表明，相比等时间分配方案和链路增益优先级方案，提出的设计算法能显著提升次用户吞吐量。     

认知网络中的能量回收技术：性能分析及优化设计

通信网络中有限的能源和频带资源限制了网络容量的进一步提升.对能量回收技术在认知网络中的应用进行研究,量化评估用户可回收的能量以及可达吞吐率,并进行优化设计很有必要.在所分析的系统中,当授权用户进行通信时,非授权用户可回收无线信号中所带有的能量,并利用回收的能量进行频谱检测;当检测到授权用户空闲时,非授权用户将接入频谱,利用回收到的能量进行数据传输.采用马尔科夫链模型对通信场景进行描述分析,发现授权用户的活跃程度对非授权用户可回收的能量、获得的传输机会带来影响,进而决定了非授权用户的可达吞吐量.在此基础上,提出一种通过控制授权用户业务量,以最大化网络能量效用和频谱效用的优化方案,并通过仿真证实了理论分析的正确性.     

Energy Harvesting in Cognitive Networks with Cooperative Beamforming: Power Allocation and Stability Analysis

Energy harvesting (EH) is a promising technology to improve both energy efficiency and spectral efficiency in cognitive radio (CR) networks. However, due to the randomness of the harvested energy and the interference constraint at the primary users (PUs), the limited transmission power of secondary users (SUs) may reduce the service rate of SUs. To solve this problem, this paper investigates a cooperative transmission method where a zero-forcing beamforming method is used in the EH based secondary network. Considering the transmission power constraint and energy causality, we derive the closed-form solution of the optimal transmission power for the secondary source and relays, which achieves the maximal stable throughput of the secondary network. Numerical results show the impact of different system parameters to the maximal stable throughput. In addition, compared with the traditional decode-and-forward (DF) scheme, the cooperative beamforming method achieves higher stable throughput under an high quality source-to-relay channel.     

认知无线电网络中基于跨层设计的能效优化算法

为提高认知无线电网络中次用户节点的能量有效性,该文基于连续时间马尔科夫理论对次用户的频谱感知和接入过程进行联合建模,对影响次用户传输能效的主要因素进行了分析,提出了一种基于跨层设计的能量有效优化算法。该算法可有效减小主用户非时隙返回信道对次用户能量有效性的影响,并通过联合优化感知时间和接入概率,使次用户在感知性能和传输能效间实现了有效折衷。仿真结果表明,本文算法相对于仅考虑频谱感知或接入策略的单层优化算法,可使次用户的能量有效性得到较大提高。      

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.     

Sensing-Throughput Tradeoff in Cluster-Based Cooperative Cognitive Radio Networks with A TDMA Reporting Frame Structure

This paper proposes clustering schemes to solve the sensing throughput tradeoff problem in cooperative cognitive radio networks (CCRNs). The throughput of CCRNs extremely depends on the spectrum sensing performance and data transmission time. In CCRNs, the more secondary users (SUs) for cooperation, the better performance of spectrum sensing. However, the overhead consumption increases as the quantity of cooperative SUs becomes huge, which will lead to less time for data transmission. In this paper, we propose a frame structure that takes the sensing results reporting time into consideration. In order to reduce the reporting time consumption, a centralized cluster-based cooperative cognitive radio system model is created based on the frame structure. The sensing-throughput tradeoff problem under both the perfect reporting channel and imperfect reporting channel scenarios are formulated. The proposed clustering schemes reduce the reporting time consumption and ensure the maximum transmission time of each SU. Numerical results show that the proposed clustering schemes achieve satisfying performance.     

Optimization of Time-Domain Combining Cooperative Spectrum Sensing in Cognitive Radio Networks

In cognitive radio networks, the secondary users take chances to access the spectrum without causing interference to the primary users so that the spectrum access is dynamic and somewhat opportunistic. Therefore, spectrum sensing is of significant importance. In this paper, we propose a novel time-domain combining cooperative spectrum sensing framework, in which the time consumed by reporting for one secondary user is also utilized for other secondary users’ sensing. We focus on the optimal sensing settings of the proposed sensing scheme to maximize the secondary users’ throughput and minimize the average sensing error probability under the constraint that the primary users are sufficiently protected. Some simple algorithms are also derived to calculate the optimal solutions. Simulation results show that fundamental improvement of the achievable throughput and sensing performance can be obtained by optimal sensing settings. In addition, our proposed scheme outperforms the general frame structure on either achievable throughput or the performance of average sensing error probability.     

认知无线电网络中基于强化学习的智能信道选择算法

认知无线电系统不仅要具有自适应性,更应具备一定的智能性。该文将强化学习理论引入到认知无线电系统中,用于解决次用户在频谱感知过程中的信道选择问题,提出了一种基于强化学习的信道选择算法。该算法在未知主用户占用规律和动态特性的前提下,仅通过不断与环境进行交互学习,便能够引导次用户选择“较好”信道优先进行感知,使次用户吞吐量得到提高。仿真结果表明,相对于现有信道选择算法,所提算法可有效提高次用户的吞吐量,并且在主用户使用规律发生变化时,能够自动实现二次收敛,可作为认知无线电系统迈向智能化的一种尝试。      

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.     

Simultaneous spectrum sensing,data transmission and Energy harvesting in multi‐channel cognitive sensor Networks with imperfect signal cancellation

In multichannel cognitive sensor networks, the sensor users which have limited energy budgets sense the spectrum to determine the activity of the primary user. If the spectrum is idle, the sensor user can access the licensed spectrum. However, during the spectrum sensing, no data transmits. For improving the network throughput and saving more energy consumption, we propose the simultaneous spectrum sensing and data transmission scheme where the sensor receiver decodes the received signal, and from the remaining signal, the status of the channel (idle/busy) is determined. We also consider that the sensor users are powered by a radio‐frequency (RF) energy harvester. In this case, energy harvesting, data transmission, and spectrum sensing are done simultaneously. On the other hand, we select the proper sensor users for spectrum sensing and energy harvesting. We also allocate the best channels for data transmission simultaneously so that the network throughput maximizes and the constraints on the energy consumption and the detection performance are satisfied for each band. We formulate the problem and model it as a coalition game in which sensors act as game players and decide to make coalitions. Each coalition selects one of the channels to sense and transmit data, while the necessary detection probability and false alarm probability and also the energy consumption constraints are satisfied. The utility function of a coalition is proposed based on the energy consumption, false alarm probability, detection probability, and the network throughput. This paper proposes an efficient algorithm to reach a Nash‐stable coalition structure. It is demonstrated that the proposed method maximizes the network throughput and reduces the energy consumption while it provides sufficient detection quality, in comparison to other existent methods.     

Recent advances and future challenges of four key resources cooperation in cognitive radio network

Cognitive radio (CR) can improve spectrum utilization by spectrum sharing or cooperation between the primary user and secondary users.It is well known that energy,interference and relay are also three key resources in cognitive radio network (CRN).Energy cooperation or sharing between the primary user and secondary user will further promote energy efficiency.Energy harvesting from RF interference signal can turn bane (interference) into a boon (green energy).Secondary user relay data of the primary user can enhance QoS of the primary user,also get some opportunities for their own transmission.Thus,four resources cooperation (spectrum,energy,interference,relay) in CRN will improve simultaneously both spectrum efficiency and energy efficiency,and also increase throughput and QoS.The overviews for collaborative utilization problems of four key resources in CRN was given.Firstly,collaboration models of four key resources were analyzed.Then,recent research advances were summarized,including three kinds of resources collaborative utilization (both energy harvesting and relay transmission,both energy cooperation and energy harvesting) and four resources collaborative utilization (simultaneous relay transmission,energy harvesting and energy cooperation) in CRN.Further,some potential challenges of four key resources cooperation in CRN were discussed.Finally,some key future research directions was concluded.     

认知无线电网络中改进的拓扑控制算法

在对认知无线电网络进行信道分配时,网络的拓扑健壮性、对主用户的干扰和认知用户之间的干扰是衡量网络性能优劣的重要指标。文中在假设信道占用时间服从负指数分布的前提下,提出了一个权衡对主用户干扰和认知用户之间干扰的参数,并结合健壮的拓扑控制算法,给出了一种改进的拓扑控制算法。仿真结果表明,ITCA比CRTCA在网络吞吐量上有所增加,冲突率有所降低。     

Cross-Layer Based Opportunistic MAC Protocols for QoS Provisionings Over Cognitive Radio Wireless Networks

We propose the cross-layer based opportunistic multi-channel medium access control (MAC) protocols, which integrate the spectrum sensing at physical (PHY) layer with the packet scheduling at MAC layer, for the wireless ad hoc networks. Specifically, the MAC protocols enable the secondary users to identify and utilize the leftover frequency spectrum in a way that constrains the level of interference to the primary users. In our proposed protocols, each secondary user is equipped with two transceivers. One transceiver is tuned to the dedicated control channel, while the other is designed specifically as a cognitive radio that can periodically sense and dynamically use the identified un-used channels. To obtain the channel state accurately, we propose two collaborative channel spectrum-sensing policies, namely, the random sensing policy and the negotiation-based sensing policy, to help the MAC protocols detect the availability of leftover channels. Under the random sensing policy, each secondary user just randomly selects one of the channels for sensing. On the other hand, under the negotiation-based sensing policy, different secondary users attempt to select the distinct channels to sense by overhearing the control packets over the control channel. We develop the Markov chain model and the M/G^Y/1-based queueing model to characterize the performance of our proposed multi-channel MAC protocols under the two types of channel-sensing policies for the saturation network and the non-saturation network scenarios, respectively. In the non-saturation network case, we quantitatively identify the tradeoff between the aggregate traffic throughput and the packet transmission delay, which can provide the insightful guidelines to improve the delay-QoS provisionings over cognitive radio wireless networks.     

Optimized Reliable Data Combining Cooperative Spectrum Sensing Method in Cognitive Radio Networks

Spectrum sensing is one of the most important tasks of each cognitive radio network. Cooperation among secondary users, by increasing the sensing accuracy can improve the network throughput, but also increases the energy consumption of cognitive radio network. In this paper, we propose the reliable data combining method for cooperative spectrum sensing, according to which the fusion center by using two threshold values, makes the final decision only if it is confident enough in validity of received local data. Otherwise, an additional sensing will be performed. Throughput maximization problems under interference constraints are formulated for both soft and hard fusion schemes and the optimal sensing time and threshold values are obtained. Simulations show that for all SNRs, higher absolute throughput and also higher throughput per energy consumption are accessible, rather than conventional cooperative sensing. Moreover, for a large range of SNRs the less energy is consumed.     

Cyclostationarity-Based Decision Reporting Scheme for Cooperative Spectrum Sensing

Cooperative spectrum sensing has emerged as a promising solution to the hidden terminal problem in cognitive radio networks (CRNs). It could significantly promote the sensing capability of CRNs by exploiting space diversity gains in a fading environment. However, owing to the decision reporting overhead, there exists a tradeoff between the system throughput and performance of cooperative spectrum sensing. In this paper, we propose a cyclostationarity-based decision reporting scheme for cooperative spectrum sensing in CRNs with cyclic delay diversity orthogonal frequency division multiplexing (CDD-OFDM). Decision information would be embedded into the CDD-OFDM signals in terms of cyclostationary signatures and shared among cognitive radio (CR) users along with data transmissions. As a result, satisfied system throughput could be achieved without additional spectral or temporal resources consumption when the number of cooperative users increases. Numerical results are presented to show the system throughput enhancement.     

Power allocation algorithm for cognitive radio energy harvesting networks based on energy cooperation

An algorithm to optimize the power allocation by maximizing the system throughput in cognitive radio energy harvesting networks was proposed.The algorithm formulated the throughput optimization model subject to the causality constraints of the harvested energy within the two secondary users and the interference constraint of the primary user.In addition,by applying the variable-substitution method and problem equivalence transformation,the joint optimization problem of power and cooperative energy was decoupled into two problems:a power allocation problem and a cooperative energy one.The former problem could be solved by iterating the two decoupled problems.As shown in the simulation results,the energy cooperation can significantly improve the system throughput when the harvested energy difference between two nodes is rather large.     

Wireless powered underlay cognitive radio network with multiple primary transceivers: Energy constraint,node arrangement,and performance analysis

This paper considers a cognitive radio–assisted wireless information and power transfer system consisting of multipair of transceiver in primary network and 2‐hop relaying link in secondary network. In this investigation, a decoded‐and‐forward–assisted relay node and power splitting protocol are deployed to obtain ability of wireless energy transfer. The relay node harvests energy from the radio frequency signals of the secondary transmitter and primary transmitters in data transmission to the destination by reusing the licensed spectrum resource. We propose 2 policies for wireless power transfer at the relay, namely, (1) multisource power transfer and (2) single‐source power transfer. To evaluate performance under energy harvesting regime, we derive the closed‐form outage probability expressions and achievable throughput of the secondary network in delay‐limited transmission mode. In addition, we investigate the impact of various system parameters including number of primary transceivers, primary outage threshold, and position arrangement of nodes in primary transceivers on the outage performance of the proposed scheme. Furthermore, we evaluate the system energy efficiency to show trade‐off metric of energy consumption and throughput. Performance results are presented to validate our theoretical derivation and illustrate the impacts of various system parameters. An important result is that the secondary network is more beneficial than harmful from the primary interference under power constraint and reasonable node location arrangement.