Capacity and Energy Efficiency of Multi-User Spectrum Sharing Systems with Opportunistic Scheduling

This paper investigates the capacity and energy efficiency of spectrum sharing systems with opportunistic user selection where a secondary network utilizes spectrum bands licensed to a primary network under interference regulation. In spectrum sharing systems, secondary users consume a fraction of their resources in sensing the channels to the primary users to comply with the interference constraints. Although more resources for sensing improve reliability and performance, the throughput loss due to time overhead and energy loss due to power overhead should be properly incorporated in performance evaluation. In this context, we define and derive a new metric ? average capacity normalized by the total energy consumption ? reflecting time and power overhead for spectrum sensing. Based on the developed framework, the optimal normalizedcapacity is investigated. We also propose a simple and practical suboptimal best-n scheme motivated by the infeasibility and high computational complexity of the optimal strategy, where n denotes the number of sensing secondary users. Our analytical and simulation results show that the proposed best-1 scheme is an energy-efficient technique with near optimality in terms of the capacity normalized by the energy consumption.     

A new sensing‐throughput tradeoff scheme in cooperative multiband cognitive radio network

A conventional cognitive radio network (CRN) uses the spectrum of the licensed primary network (PN) on the premise of detecting the absence of the PN by the spectrum sensing of the sensor node (SN). In this paper, a cooperative multiband CRN is considered, wherein the SNs are allowed to use some time of the transmission slot to relay PN data by cooperative communication, while using the remaining time of the transmission slot to forward its own data, over multiple sub‐bands during each frame, if the presence of PN is detected by cooperative spectrum sensing of the SNs in the sensing slot. A new sensing–throughput tradeoff scheme is formulated as a multi‐variable optimization problem, which maximizes the average aggregate throughput of the CRN over all the sub‐bands by jointly optimizing spectrum sensing time and sub‐band transmission power, subject to the constraints on the average aggregate throughput of the PN, the maximal aggregate power of each SN, and the false alarm and detection probabilities of each sub‐band. The bi‐level optimization method is adopted to obtain the optimal solution by dividing the multi‐variable optimization problem into two convex single‐variable sub‐optimization problems. The simulations show that there exists the optimal sensing time and sub‐band transmission power that maximize the average aggregate throughput of the CRN and, compared with the conventional scheme, the throughput obtained by the proposed scheme is outstanding. Copyright © 2014 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.     

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.     

Cluster‐based adaptive multispectrum sensing and access in cognitive radio networks

Spectrum sensing and access have been widely investigated in cognitive radio network for the secondary users to efficiently utilize and share the spectrum licensed by the primary user. We propose a cluster‐based adaptive multispectrum sensing and access strategy, in which the secondary users seeking to access the channel can select a set of channels to sense and access with adaptive sensing time. Specifically, the spectrum sensing and access problem is formulated into an optimization problem, which maximizes the utility of the secondary users and ensures sufficient protection of the primary users and the transmitting secondary users from unacceptable interference. Moreover, we explicitly calculate the expected number of channels that are detected to be idle, or being occupied by the primary users, or being occupied by the transmitting secondary users. Spectrum sharing with the primary and transmitting secondary users is accomplished by adapting the transmission power to keep the interference to an acceptable level. Simulation results demonstrate the effectiveness of our proposed sensing and access strategy as well as its advantage over conventional sensing and access methods in terms of improving the achieved throughput and keeping the sensing overhead low. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Cognitive radio network with coordinated multipoint joint transmission

Cognitive radio (CR) is considered to be a promising technology for future wireless networks to make opportunistic utilization of the unused or underused licensed spectrum. Meanwhile, coordinated multipoint joint transmission (CoMP JT) is another promising technique to improve the performance of cellular networks. In this paper, we propose a CR system with CoMP JT technique. We develop an analytical model of the received signal‐to‐noise ratio at a CR to determine the energy detection threshold and the minimum number of required samples for energy detection–based spectrum sensing in a CR network (CRN) with CoMP JT technique. The performance of energy detection–based spectrum sensing under the developed analytical model is evaluated by simulation and found to be reliable. We formulate an optimization problem for a CRN with CoMP JT technique to configure the channel allocation and user scheduling for maximizing the minimum throughput of the users. The problem is found to be a complex mixed integer linear programming. We solve the problem using an optimization tool for several CRN instances by limiting the number of slots in frames. Further, we propose a heuristic‐based simple channel allocation and user scheduling algorithm to maximize the minimum throughput of the users in CRNs with CoMP JT technique. The proposed algorithm is evaluated via simulation and found to be very efficient.     

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.     

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.     

Access Control Engine with Dynamic Priority Resource Allocation for Cognitive Radio Networks

An access control engine with dynamic priority resource allocation (ACE-DPRA) is proposed for unlicensed users to utilize free spectrum of wireless communication systems. A cognitive radio (CR) network with sensing and learning abilities is essential for unlicensed users to achieve ACE-DPRA. Three algorithms are included in ACE-DPRA to improve the spectral efficiency. While requesting to set up connection, unlicensed CR users generate excessive interferences to licensed users. The proposed ACE-DPRA with an admission control scheme allows the connection of unlicensed CR users without degrading the communication quality of licensed users. The priority algorithm for utilizing the unused spectrum is designed according to the location information of unlicensed users. A transmitted power control method is achieved by a fuzzy-learning mechanism. The spectral efficiency of wireless communication systems can be increased after adopting the proposed ACE-DPRA algorithm. Simulation results show that licensed users keep the advantages of high transmission data rate, low interference power, and low average outage probability after the connection of unlicensed CR users.     

Semi‐distributed resource management for underlay D2D communication with user's cooperation

Device‐to‐device (D2D) communication is a viable solution proposed by the Third Generation Partnership Project (3GPP) to handle the enormous number of devices and expected data explosion in 5G. It is competent in enhancing the system performances such as increased data rate, reduced delay, and less power consumption while maintaining a low load on the base station (BS). In this paper, channel assignment and power control scheme is proposed for underlay D2D system where one cellular channel is allowed to be shared among multiple D2D pairs. This will lead to enhanced spectral efficiency on the cost of additional interferences introduced among the D2D and cellular users (CUs). Our aim is to maximize the D2D throughput without degrading the performance of existing CU that is sharing the channel with D2D. This is achieved by maintaining a threshold signal‐to‐interference‐plus‐noise ratio (SINR) for each CU. A centralized channel assignment algorithm based on the well‐known two‐sided preference Gale‐Shapley algorithm is proposed, named as RAbaGS‐HR. Further, suboptimal distributed power control (DPC) algorithms are proposed for both uplink and downlink D2D. The novelty of the work lies in the facts that a channel is shared among multiple D2D users and the optimal power is calculated for all the users sharing the same channel under the full consideration of all kinds of interferences unlike most of the existing work that either assumed the fixed CU power or ignored the interferences among the D2D users. Numerical results show the efficacy of the proposed algorithms in terms of significant gain in throughput with a very low computational cost. In addition to this, the energy efficiency (EE) is also analyzed for different D2D user density, with respect to average circuit power consumption and D2D maximum transmit power.     

基于信噪比墙的协作能量检测算法

认知无线电技术可有效地检测到授权频段的频谱空洞,从而提高频谱效率.能量检测由于不需要授权用户的先验信息而被广泛应用.然而由于接收的噪声存在不确定性,使得在信噪比低于某一闸值时,无论观测时间多长,都无法保证检测结果满足要求的检测性能,这一闸值被称作“信噪比墙”.本文通过信噪比墙这一现象进行分析,同时由于协作感知算法在确定噪声下在提高检测性能方便表现出的优势,提出一种基于信噪比墙的协作能量检测算法,通过仿真结果分析,表明本文算法在检测性能和节能上较已有的协作算法具有优势.     

Prediction‐based MAC‐layer sensing in cognitive radio networks

We consider a cognitive radio network which coexists with multiple primary users (PUs) and secondary users (SUs) transmit over time‐varying channels. In this scenario, one problem of the existing work is the poor performances of throughput and fairness due to variances of SUs' channel conditions and PUs' traffic patterns. To solve this problem, we propose a novel prediction‐based MAC‐layer sensing algorithm. In the proposed algorithm, the SUs' channel quality information and the probability of the licensed channel being idle are predicted. Through the earlier predicted information, we schedule the SUs to sense and transmit on different licensed channels. Specifically, multiple significant factors, including network throughput and fairness, are jointly considered in the proposed algorithm. Then, we formulate the prediction‐based sensing scheduling problem as an optimization problem and solve it with the Hungarian algorithm in polynomial time. Simulation results show that the proposed prediction‐based sensing scheduling algorithm could achieve a good tradeoff between network throughput and fairness among SUs. Copyright © 2014 John Wiley & Sons, Ltd.     

Distributed power control algorithm for cognitive radios with primary protection via spectrum sensing under user mobility

Substantial spectrum gains have been demonstrated with the introduction of cognitive radio however; such gains are usually short lived due to the increased level of interference to licensed users of the spectrum. The interference management problem is herein tackled from the transmitter power control perspective so that transmissions by cognitive radio network does not violate the interference threshold levels at the primary users as well as maintain the QoS requirements of cognitive radio users. We model the cognitive radio network for mobile and immobile users and propose algorithms exploiting primary radio environment knowledge (spectrum use), called power control with primary protection via spectrum sensing. The algorithm is briefly introduced for time invariant systems and demonstrated that it has the ability to satisfy tight QoS constraints for cognitive radios as well as meet the interference constraints for licensed users. We, however, further show that such assumption of terminal immobility in the power control algorithm would fail in cases where user mobility is considered, resulting in increased levels of interference to the primary as well as increased outages in cognitive radio network. We model the link gain evolution process as a distance dependent shadow fading process and scale-up the target signal to interference ratio to cope with user mobility. Since mobility driven power control algorithms for cognitive radios have not been investigated before, we therefore, propose a mobility driven power control framework for cognitive radios based on spectrum sensing, which ensures that the interference limit at the primary receiver is unperturbed at all times, while concurrently maintaining the QoS within the cognitive radio network as compared to static user cases. We also corroborate our algorithms with proof of convergence.     

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.     

概率反馈动态频谱分配策略及性能分析

为了折衷认知用户吞吐量与平均时延,并适应多种网络业务需求,在认知无线电网络中引入概率反馈机制和能量检测阈值,提出一种新的动态频谱分配策略。针对认知用户的非理想感知结果,建立一种2类用户可能相互干扰的优先级排队模型,并构造状态转移概率矩阵。采用矩阵几何解方法求出系统的稳态分布,给出信道利用率、认知用户吞吐量、认知用户平均延迟及授权用户干扰率等性能指标的表达式。通过数值实验和系统仿真验证所提动态频谱分配策略性的有效性,并给出能量检测阈值的优化设置方案。     

Utility driven cooperative spectrum sensing scheduling for heterogeneous multi-channel cognitive radio networks

Owing to the spectrum scarcity and energy constrained devices in wireless networks arises the demand for an efficient spectrum sensing technique which improves both sensing performance and energy efficiency for cognitive radio networks. This paper proposes a cooperative spectrum sensing scheduling (CSSS) scheme for heterogeneous multi-channel cognitive radio networks with the objective of finding an efficient sensing schedule to enhance network utility while keeping the energy depletion at a lower level. We start with formulating the CSSS problem as an optimization problem, which captures both the energy-performance and performance opportunity trade-offs. We prove that the formulated CSSS problem is non-deterministic polynomial hard (NP-hard). To tackle the higher computational complexity of the formulated problem, we propose a greedy-based heuristic solution, which produces a sub-optimal result in polynomial time. To reduce energy consumption during spectrum sensing, we make secondary users to adaptively decide on the sensing duration based on the received signal-to-noise ratio (SNR), where higher SNR leads to lower sensing duration and vice-versa. For enhancing network throughput, SUs sense multiple channels in the order of their suitability for data transmission to explore as many numbers of channels as possible within the permitted maximum sensing time. We consider erroneous nature of reporting channel to make the cooperative decision robust against errors during reporting. Simulation based results show the effectiveness of the proposed scheme in terms of utility, energy overhead, and the number of channels explored compared to similar schemes from literature.

     

认知Ad Hoc网络能量有效频谱接入策略

针对认知无线Ad Hoc网络中次用户能量受限问题,提出一种基于能量有效性的机会频谱接入策略。联合考虑信道状态的时变性和次用户的频谱感知准确性,基于部分可观测马尔科夫决策过程（POMDP）建立了一种最大化能量有效性的分析架构,指导次用户选择能效最佳信道,并根据信念状态、信道增益和检测概率,自适应控制传输功率。仿真结果表明,该策略能够有效提高次用户传输的能量有效性,通过对传输功率的有效控制,实现了传输性能和能量开销的有效折中。      

一种基于传输成功率的自适应传输策略

在认知无线电网络中,识别用户可以机会接入分配给主用户的频谱。在满足感知时间需求的情况下,如何能让识别用户跟踪主用户的活动状况,从而减少对主用户的干扰,并同时最大化识别用户的数据吞吐量。针对这个问题提出了一种基于传输成功率的自适应传输策略(ATS-SR),识别用户以自适应帧为单位去监测信道状况和传输数据。仿真结果表明,与文献[2]相比,ATS-SR在不降低吞吐量的情况下可以显著减少对主用户的干扰。     

考虑恶意节点的CRN合作频谱感知方法

基于带有恶意节点的更为实际的频谱感知环境,研究了基于合作感知的频谱共享网络模型,次级用户将会根据合作感知结果动态地调整其发射功率。为了防止恶意节点对感知系统的感知性能造成严重影响,研究了如何进行合作感知以提高感知性能。在一定的检测概率和相关功率约束下,建立了一个以最大化次级网络的吞吐量为目标函数的优化问题。仿真实验首先突出说明了恶意节点数目对频谱感知影响重大,同时还表明无论是否存在恶意节点,提出的算法均可有效地计算出最优的感知时间和发射功率,且在降低最大干扰功率限制和最大发射功率限制时,网络的吞吐量是增大的。