Energy‐Efficient Power Allocation for Cognitive Radio Networks with Joint Overlay and Underlay Spectrum Access Mechanism

Traditional designs of cognitive radio (CR) focus on maximizing system throughput. In this paper, we study the joint overlay and underlay power allocation problem for orthogonal frequency‐division multiple access–based CR. Instead of maximizing system throughput, we aim to maximize system energy efficiency (EE), measured by a “bit per Joule” metric, while maintaining the minimal rate requirement of a given CR system, under the total power constraint of a secondary user and interference constraints of primary users. The formulated energy‐efficient power allocation (EEPA) problem is nonconvex; to make it solvable, we first transform the original problem into a convex optimization problem via fractional programming, and then the Lagrange dual decomposition method is used to solve the equivalent convex optimization problem. Finally, an optimal EEPA allocation scheme is proposed. Numerical results show that the proposed method can achieve better EE performance.     

Energy‐Efficiency Power Allocation for Cognitive Radio MIMO‐OFDM Systems

This paper studies energy‐efficiency (EE) power allocation for cognitive radio MIMO‐OFDM systems. Our aim is to minimize energy efficiency, measured by “Joule per bit” metric, while maintaining the minimal rate requirement of a secondary user under a total power constraint and mutual interference power constraints. However, since the formulated EE problem in this paper is non‐convex, it is difficult to solve directly in general. To make it solvable, firstly we transform the original problem into an equivalent convex optimization problem via fractional programming. Then, the equivalent convex optimization problem is solved by a sequential quadratic programming algorithm. Finally, a new iterative energy‐ efficiency power allocation algorithm is presented. Numerical results show that the proposed method can obtain better EE performance than the maximizing capacity algorithm.     

Robust energy efficiency power allocation for relay-assisted uplink cognitive radio networks

In this paper, we investigate a worst-case robust power allocation scheme to improve energy efficiency (EE) for an amply-and-forward relaying uplink underlay OFDM cognitive radio system with imperfect channel situation information about the channel between primary user (PU) and secondary user (SU) and the channel between SU and corresponding relay. Specifically, a max–min problem is formulated to transform the original optimization problem into maximum EE on minimum throughout channel, and an epigraph problem is introduced to obtain analytical expressions of objective power allocation. Simulation results show that the proposed EE power allocation scheme is valid and effective in EE and robustness.     

多无人机协作的认知通信网络中能/谱效折中优化

在无人机（Unmanned Aerial Vehicle,UAV）认知通信网络中,其能量受限和通信高吞吐量问题备受关注。然而,能量效率（Energy Efficiency,EE）的提升可能会导致频谱效率（Spectrum Efficiency,SE）的下降。针对此问题,对UAV协作认知通信网络中EE和SE的折中优化进行了研究。首先,进行了感知时间、UAV通信的发射功率和判决门限各自对SE与EE两者的优化;其次,通过二分法求解使得EE和SE最大化的感知时间值,并通过穷尽搜索法分别求解感知时间、UAV通信的发射功率和判决门限对EE和SE折中优化问题的最优参数值。在此基础上,提出一种联合参数迭代优化算法,求解EE和SE的折中优化问题。仿真实验表明,SE和EE之间存在折中的权衡,并验证了所提优化方案的有效性。     

Optimised fusion rule in cluster-based energy-efficient CSS for cognitive radio networks

Cooperative spectrum sensing (CSS) can solve the problem of the hidden terminal in cognitive radio (CR). Energy consumption and sensing time are high in CSS due to a large number of users. In CSS, the parameters that affect the energy efficiency (EE) of the system are fusion rule, sensing time, transmission power and number of users. In this paper, four fusion rules OR-OR, AND-OR, OR-AND and AND-AND are proposed for cluster-based CSS and optimum fusion rule is determined. It is found that in terms of EE metric, AND-OR rule outperforms the other fusion rules. An iterative algorithm has been proposed which finds the optimum number of CR users in a cluster and total number of clusters that maximises the EE. Quantitatively at SNR = ?20 dB, EE is maximum when number of clusters equals to 4 and number of users in the cluster equals to 3 for 12 fixed number of users. The maximum EE is 3.69 Mbits/Hz/joule at sensing time 1.5 ms.     

Energy-efficient full-duplex UAV relaying networks: Trajectory design for channel-model-free scenarios

In this paper, we propose an energy-efficient unmanned aerial vehicle (UAV) relaying network. In this network, the channels between UAVs and ground transceivers are model-free. A UAV acting as a flying relay explores better channels to assist in efficient data delivery between two ground nodes. The full-duplex relaying mode is applied for potential energy efficiency (EE) improvements. With the genetic algorithm, we manage to optimize the UAV trajectory for any arbitrary radio map scenario. Numerical results demonstrate that compared to other schemes (eg, fixed trajectory/speed policies), the proposed algorithm performs better in terms of EE. Additionally, the impact of self-interference on average EE is also investigated.     

Joint impact of sensing time and IED parameter on the performance of an energy efficient cognitive radio system

Joint impact of sensing time and improved energy detector (IED) parameter is evaluated for an energy efficient cooperative cognitive radio (CR) system where the CR users use IED. The aim of this work is to design the CR system in such a way that it can achieve two objectives for a given level of protection on primary user: (i) optimization of sensing time to make balance between detection performance and throughput and (ii) appropriate allocation of energy between sensing time and transmission time so as to enhance the energy efficiency of the CR system. The key parameters such as sensing time and IED parameter are set appropriately to meet the objectives. The performance is assessed in terms of throughput and energy efficiency of the system. The effect of the sensing time and the IED parameter on the performance is evaluated under a collision constraint. Furthermore, the optimal sensing time and IED parameter are investigated jointly for which the higher throughput as well as maximum energy efficiency can be obtained, and at the same time, a desired detection probability can also be maintained by the CR system. Copyright © 2016 John Wiley & Sons, Ltd.     

衰落环境下一种高能效协作频谱感知算法

在认知无线网络中,对于电池供电的认知设备,如何高效地利用其能量资源极为重要。在将能量效率(能效)定义为认知网络频谱利用效率和平均功率消耗之比的基础上,提出了一种高能效优化算法,在加性高斯白噪声(AWGN)信道、Rayleigh衰落信道和Nakagami衰落信道条件下使融合中心门限达到最优,并求得了最佳的参与协作的用户数。通过蒙特卡洛仿真对认知网络的能效进行了性能评估,结果表明所提算法能有效提升认知网络的能量效率。     

Combined spatial-temporal energy harvesting and relay selection for cognitive wireless powered networks

In order to improve the Energy Efficiency (EE) and spectrum utilization of Cognitive Wireless Powered Networks (CWPNs), a combined spatial-temporal Energy Harvesting (EH) and relay selection scheme is proposed. In the proposed scheme, for protecting the Primary User (PU), a two-layer guard zone is set outside the PU based on the outage probability threshold of the PU. Moreover, to increase the energy of the CWPNs, the EH zone in the two-layer guard zone allows the Secondary Users (SUs) to spatially harvest energy from the Radio Frequency (RF) signals of temporally active PUs. To improve the utilization of the PU spectrum, the guard zone outside the EH zone allows for the constrained power transmission of SUs. Moreover, the relay selection transmission is designed in the transmission zone of the SU to improve the EE of the CWPNs. In addition to the EE of the CWPNs, the outage probabilities of the SU and PU are derived. The results reveal that the setting of a two-layer guard zone can effectively reduce the outage probability of the PU and improve the EE of CWPNs. Furthermore, the relay selection transmission decreases the outage probabilities of the SUs.     

Tradeoff between energy efficiency and spectral efficiency in a delay constrained wireless system

Because of the inevitable trend of green networking, energy efficiency (EE) is quickly becoming one of the key performance metrics to evaluate wireless communication systems, together with spectrum efficiency (SE) and quality of service (QoS) that have been traditionally used. This paper studies the fundamental tradeoff between EE and SE in the presence of statistical QoS requirements in wireless transmission systems. Earlier studies have shown that the performance with QoS requirements in the wireless transmission can be measured through effective capacity, which can capture the physical layer fading channel characteristics in the link layer QoS requirements, such as delay and data rate. Under this context, SE is defined as effective capacity per unit frequency bandwidth, and EE is defined as energy consumed per effective capacity bit. Both circuit power and transmission power are considered in the energy model, based on which we derive the quasi‐convex generalized EE formulation. To exploit the tradeoff between EE and SE with QoS considerations, we propose a generic close‐form approximation for EE–SE formulation by employing a curve fitting approach. The impacts of QoS and circuit power consumption on EE–SE tradeoff are respectively analyzed. QoS requirement and circuit power consumption affect the EE–SE tradeoff differently. In the low‐SNR regime, circuit power shows more impact on the EE–SE tradeoff, whereas QoS impacts EE–SE tradeoff more in the high‐SNR regime. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimal Energy Harvesting-based Weighed Cooperative Spectrum Sensing in Cognitive Radio Network

In cognitive radio (CR) network, to improve spectrum sensing performance to primary user (PU) and decrease energy wastage of secondary user (SU) in cooperative spectrum sensing, an energy harvesting-based weighed cooperative spectrum sensing is proposed in this paper. The SU harvests the radio frequency (RF) energy of the PU signal and then converts the RF energy into the electric energy to supply the power used for energy detection and cooperation. The time switching model and power splitting model are developed to realize the notion. In the time switching model, the SU performs either spectrum sensing or energy harvesting at any time, while in the power splitting model, the received PU signal is split into two signal streams, one for spectrum sensing and the other one for energy harvesting. A joint optimization problem is formulated to maximize the spectrum access probability of the SU by jointly optimizing sensing time, number of cooperative SUs and splitting factor. The simulation results have shown that compared to the traditional cooperative spectrum sensing, the proposed energy harvesting-based weighed cooperative spectrum sensing can decrease the energy wastage obviously while guaranteeing the maximum spectrum access probability.     

A novel approach for energy‐efficient resource allocation in double threshold‐based cognitive radio network

This paper mainly focuses on solving the energy efficiency (EE) maximization problem in double threshold‐based soft decision fusion (SDF) cooperative spectrum sensing (CSS) in the cognitive radio network (CRN). The solution to this objective problem starts with the selection of suitable secondary users (SUs) both for the spectrum sensing and data transmission. Here, energy efficiency is maximized under the constraints of interference to the primary user (PU), an acceptable outage of SUs, the transmission power of the SUs and the probability of false alarm. We propose a novel algorithm called iterative Dinkelbach method (IDM) which jointly optimizes the sensing time and transmission power allocation to the SUs. Further, Lagrangian duality theorem is employed to find the exact power assigned to the SUs. Finally, simulation results are carried out to validate the effectiveness of our proposed scheme by comparing with the other existing schemes. The performance is also analyzed for different system parameters.     

Throughput analysis in censoring-based cooperative cognitive radio network with energy harvesting

An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied.     

Spectral efficiency and energy efficiency of distributed antenna systems with virtual cells

In this paper, we consider user centric virtual cells model in distributed antenna systems (DAS). We investigate different power allocation optimization problems with interferences in DAS with and without user centric virtual cells model, respectively. The first objective problem is maximizing spectral efficiency (SE) of the DAS with user centric virtual cells model under the constraints of the minimum SE requirements of each user equipment (UE), maximum transmit power of each remote access unit (RAU). We firstly transform this non-convex objective function into a difference of convex functions (D.C.) problem, and then we obtain the optimal solutions by using the concave-convex procedure (CCCP) algorithm. The second objective problem is maximizing energy efficiency (EE) of the DAS with user centric virtual cells model under the same constraints as the first objective problem. Firstly, we exploit fractional programming theory to obtain the equivalent objective function of the second problem with subtract form, and then transform it into a D.C. problem and use CCCP algorithm to obtain the optimal power allocation. In each part, we propose the corresponding optimal power allocation algorithm and also use similar method to obtain optimal solutions of the same optimization problems in DAS without using user centric virtual cells model. Simulation results are provided to demonstrate the effectiveness of the DAS with user centric virtual cells model, which can significantly improve the SE and the EE of the communication systems.     

Energy efficiency and spectral efficiency tradeoff in downlink OFDMA systems with imperfect CSI

In this paper, we investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in downlink orthogonal frequency division multiplexing access (OFDMA) systems, whilst considering the channel estimation cost and the corresponding effect of imperfect channel state information (CSI) on SE and EE. The problem is formulated as a multi-objective optimization to determine the optimal pilot transmission power, data transmission power and subcarrier assignment, and then transformed into a single-objective optimization problem, which is a non-convex mixed-integer nonlinear programming (NCMINP) and NP-hard. To address it, we propose an efficient algorithm by adopting alternating optimization and convex optimization methods in lower power region as well as approximate conversion and branch-and-bound methods in high power region. Simulation results analyze and validate the performance of EE-SE tradeoff.     

合作认知无线网络中能效最优资源分配

针对能量受限的合作认知网络,该文研究在保证主用户服务质量要求下,认知用户能量效率最大化问题。认知用户利用信能同传技术接收主用户信号,并采用解码转发协议协助主用户通信。基于分式规划和引入辅助变量将原始非凸问题转换为凸优化问题进行求解,并提出一种迭代的资源分配算法。仿真结果表明,所提算法能够快速收敛于最优解。与能量合作方案相比,该文所采用方案能量效率显著提高,同时能更好地保证主用户服务质量要求。     

Joint impact of sensing time and SPRF parameter on the performance of a continuous energy harvesting cooperative cognitive radio network

Joint impact of sensing time and signal power raise factor is studied for an improved energy detector–based energy harvesting cooperative cognitive radio network. All the cognitive radio nodes harvest energy either from radio frequency resources or from non–radio frequency resources. The probability density function of harvested energy from both the sources is exponentially distributed. Novel theoretical expressions for harvested energy and throughput are derived. Impact of several sensing parameters and a device constraint on the outage is studied. Optimal values of sensing time and signal power raise factor parameter pair are estimated for maximum harvested energy and maximum throughput. Energy efficiency of the network is also evaluated, and impact of sensing time on it is indicated.     

PLC-RF无线传输网络中的能效最优设计方法

针对电力线通信（PLC）和射频（RF）无线通信混合传输的室内通信场景,提出了一种基于角度信息的信道状态信息（AI-CSI）的能效优先传输方案。首先,Wi-Fi无线网络和PLC网络分别作为主网络和次级网络,并且采用认知无线电技术来提高频谱效率的情况下,建立次级网络总能效最大化为目标函数的优化问题。其次,为了求解该问题,通过基于AI-CSI的迫零波束成形方法,获得波束成形权矢量,并进一步提出Dinkelbach与拉格朗日乘子法相结合的优化方法,进行最优的功率分配。最后,计算机仿真结果不仅验证了所提方案的有效性和优越性,而且分析了中继天线数和用户个数等典型参数对系统能效带来的影响,从而为实际系统设计提供了参考和依据。     

Low‐complexity energy efficient power allocation scheme for DAS with maximum power constraint

In this paper, a low‐complexity optimal power allocation (PA) scheme is developed to maximize energy efficiency (EE) in a distributed antenna system (DAS) under maximum power constraint and target bit error rate (BER) requirement. Composite Rayleigh fading, multiple receive antennas, and dynamic circuit power consumption are all considered in the system. Unlike conventional schemes, the presented scheme provides a closed‐form expression of PA. Firstly, the optimization problem is formulated according to the definition of EE. Using the Karush‐Kuhn‐Tucker conditions, a general form of the optimal PA, in which the number of active antennas and corresponding power allocation are required only, is then proposed. With this general form, an effective algorithm is presented to yield the closed‐form PA. The proposed scheme can be applied to the system with static circuit power consumption and/or without target BER constraint to obtain optimal PA. Simulation results corroborate the effectiveness of the developed scheme, and the scheme can achieve the same EE performance as the existing optimal schemes with lower complexity. Moreover, the distributed antenna system with multiple receive antennas has higher EE than that with single receive antenna.     

Multi-channel energy detection under phase noise: analysis and mitigation

For the development of highly integrated, flexible and low-cost cognitive radio (CR) devices, simple transceiver architectures, like direct-conversion receiver, are expected to be deployed and provide viable radio frequency (RF) spectrum sensing solutions for practical implementation. Yet, this can be very challenging task especially if spectrum sensing and down-conversion are conducted over multiple RF channels simultaneously for improved efficiency in channel scans. Then, the so-called dirty RF problem that degrades link performance of traditional transmission systems starts to be influential from spectrum sensing perspective as well. The unavoidable RF impairments, e.g., oscillator phase noise in direct-conversion receiver, could generate crosstalk between multiple channels that are down-converted simultaneously, and thus considerably limit the spectrum sensing capabilities. Most of the existing spectrum sensing studies in literature assume an ideal RF receiver and have not considered such practical RF hardware problem. In this article, we study the impact of oscillator phase noise on energy detection (ED) based spectrum sensing in multi-channel direct-conversion receiver scenario. With complex Gaussian primary user (PU) signal models, we first derive the detection and false alarm probabilities in closed-form expression. The analytical results, verified through extensive simulations, show that the wideband multi-channel sensing receiver is very sensitive to the neighboring channel crosstalk induced by oscillator phase noise. More specifically, it is shown that the false alarm probability of multi-channel energy detection increases significantly, compared to the ideal RF receiver case. The exact performance degradation depends on the power of neighboring channels as well as statistical characteristics of the phase noise in the deployed receiver. In order to prevent such performance degradation in spectrum identification, an enhanced energy detection technique is proposed. The proposed technique calculates the leakage power from neighboring channels for each channel and improves the sample energy statistics by subtracting this leakage power from the raw values. An analytical expression is derived for the leakage power which is shown to be a function of power spectral levels of neighboring channels and 3-dB bandwidth of phase noise process. Practical schemes for estimating these two quantities are discussed. Extensive computer simulations show that the proposed enhanced detection yields false alarm rates that are very close to those of an ideal RF receiver and hence clearly outperforms classical energy detection.