Multi-channel sensing and resource allocation in energy constrained cognitive radio networks

We consider a cognitive radio network in a multi-channel licensed environment. Secondary user transmits in a channel if the channel is sensed to be vacant. This results in a tradeoff between sensing time and transmission time. When secondary users are energy constrained, energy available for transmission is less if more energy is used in sensing. This gives rise to an energy tradeoff. For multiple primary channels, secondary users must decide appropriate sensing time and transmission power in each channel to maximize average aggregate-bit throughput in each frame duration while ensuring quality-of-service of primary users. Considering time and energy as limited resources, we formulate this problem as a resource allocation problem. Initially a single secondary user scenario is considered and solution is obtained using decomposition and alternating optimization techniques. Later we extend the analysis for the case of multiple secondary users. Simulation results are presented to study effect of channel occupancy, fading and energy availability on performance of proposed method.     

Deep Q-learning based optimal resource allocation method for energy harvested cognitive radio networks

In this article, we propose a deep Q-learning based algorithm for optimal resource allocation in energy harvested cognitive radio networks (EH-CRN). In EH-CRN, channel resources of primary users (PU) networks are shared with secondary users (SU) and energy harvesting allows nodes of the CRN to acquire energy from the environment for operation sustainability. However, amount of energy harvested from the environment is not fixed and requires dynamic allocation of resources for obtaining optimum network and throughput capacity. In this work, we overcome the limitations of existing Q-learning based resource allocation schemes which are constrained by large state-space systems and have slow convergence. Proposed deep Q-learning based algorithm improves the resource allocation in EH-CRN, while considering quality of service (QoS), energy and interference constraints. Simulation results show that proposed algorithm provide improved convergence and better resource utilization compared to other techniques in literature.     

Outlier-aware cooperative spectrum sensing in cognitive radio networks

This paper considers the problem of cooperative spectrum sensing in cognitive radio networks (CRN). Communication in CRNs may be disrupted due to the presence of malicious secondary users (SU) or channel impairments such as shadowing. This paper proposes a spatio-frequency framework that can detect and track malicious users and anomalous measurements in CRNs. The joint problem of spectrum sensing and malicious user identification is posed as an optimization problem that aims to exploit the sparsity inherent to both, spectrum occupancy and malicious user occurrence. Proposed scheme obtains improved performance by utilizing node location information, and can handle missing or inaccurate location information, and noisy SU reports. A distributed block-coordinate descent-based algorithm is proposed that is shown to outperform the state-of-the-art PCA-based approach, and is flexible enough to defeat a variety of attacks encountered in SU networks. An online algorithm, that can handle incorporate multiple SU readings sequentially and adapt to time-varying channels, primary user, and malicious user activity, is also proposed and shown to be consistent. Simulation results demonstrate the efficacy of the proposed algorithms.     

Robust secrecy rate maximization in IRS-assisted cognitive radio networks

In this paper, we focus on the secrecy rate maximization problem in intelligent reflecting surface (IRS)-assisted cognitive radio (CR) networks. In order to improve the security, there is a common scheme to add artificial noise (AN) to the transmitted signal, which is also applied in this paper. Further, in CR networks, the secondary users always cannot obtain accurate channel state information (CSI) about the primary user and eavesdropper. By taking jointly design for the IRS phase shift matrix, the transmitted beamforming of the secondary base station (BS), and the covariance matrix of AN, our objective is to maximize the minimal secrecy rate of all secondary users. Due to the serious coupling among the designed variables, it cannot be solved by conventional methods. We propose an alternating optimization (AO) algorithm. In simulation results, we apply primary users and secondary users randomly distributed in the communication area, which numerically demonstrate the superiority of our proposed scheme.     

Opportunistic spectrum access for TDMA-based cognitive radio networks

In this paper, opportunistic spectrum access is proposed for TDMA-based cognitive radio networks. In TDMA-based networks, the time is divided into slots with fixed length one by one. If a primary user (PU) needs to transmit data, one or several slots will be used. Otherwise, the slots are idle and can be utilized by secondary users (SUs). When SUs want to use the licensed channel, they should sense the channel at the beginning period of each slot. Then SUs exchange their sensing results and make the same decision about the channel state (idle or used by PUs), which could reduce the probability of false sensing. The aforementioned duration is called spectrum sensing phase. When SUs decide there is an idle channel, they contend to use the channel at the rest time of the slot. The duration is called access phase. In this period, SUs contend the channel with backoff counters. When the remaining time is less than one data transmission duration, SUs cannot transmit data packets. Therefore, the remaining time is wasted. To solve this problem, SUs transmit control packets with small length in the remaining time instead. The SU who exchange control packets successfully reserves the channel and sends a data packet prior to other SUs in access phase of the next idle slot. Obviously, this reserved transmission is without collision. The independent spectrum sensing, channel state decision and control packets reservation influence the performance of SUs. The proposed scheme is formulated with all above factors. Simulations which consist with the numerical results show the proposed access scheme achieve higher throughput than the existed scheme without channel reservation.     

A virus spreading model for cognitive radio networks

Since cognitive radio (CR) networks could solve the spectrum scarcity problem, they have drawn much research in recent years. Artificial intelligence(AI) is introduced into CRs to learn from and adapt to their environment. Nonetheless, AI brings in a new kind of attacks specific to CR networks. The most powerful one is a self-propagating AI virus. And no spreading properties specific to this virus have been reported in the literature. To fill this research gap, we propose a virus spreading model of an AI virus by considering the characteristics of CR networks and the behavior of CR users. Several important observations are made from the simulation results based on the model. Firstly, the time taken to infect the whole network increases exponentially with the network size. Based on this result, CR network designers could calculate the optimal network size to slow down AI virus propagation rate. Secondly, the anti-virus performance of static networks to an AI virus is better than dynamic networks. Thirdly, if the CR devices with the highest degree are initially infected, the AI virus propagation rate will be increased substantially. Finally, it is also found that in the area with abundant spectrum resource, the AI virus propagation speed increases notably but the variability of the spectrum does not affect the propagation speed much.     

Optimal radio access technology selection on heterogeneous networks

The joint management of radio resources in heterogeneous networks is considered to improve their capacity. We propose joint schemes for admission control and access technology selection with vertical handoffs. Optimal policies are found for wireless networks that support two access techniques and cover the same geographical area. In addition, the system under study also supports heterogeneous traffic of two types: streaming and elastic. We explore the optimization of different functions expressed in terms of blocking probabilities and throughput. An exhaustive numerical analysis allows us to characterize the optimal admission policies according to the arrival type and system state. Based on this characterization, heuristic policies are designed and their performance is compared to the one obtained by previously proposed schemes. This analysis is also done when constraints, expressed in terms of blocking probability bounds, are added. An extension of the previous system that includes vertical handoffs, in order to evaluate their impact on the system performance, is also studied. For the four types of vertical handoffs considered, we determine and characterize the optimal policies according to the arrival type, system state and vertical handoff action. Since it is not computationally feasible to calculate the optimal policies online, new heuristic policies with vertical handoffs are design and evaluated. It is found that the heuristic policies scale with the system size without requiring any adjustment, their performance is very close to the one obtained by the optimal policies and they are simple to implement, and, therefore, can be used online. In addition, we find that the heuristic policies are insensitive to the service time of the voice sessions and the elastic flow sizes beyond the mean. Finally, in order to take into account the cost of performing vertical handoffs, a new optimization problem is formulated that relates the costs of voice and data blocking with the costs of vertical handoffs.     

Power management in heterogeneous networks with energy harvesting base stations

In this paper, heterogeneous cellular networks (HCNs) with base stations (BSs) powered from both renewable energy sources and the grid power are considered. Based on a techno-economic analysis, we demonstrate that by controlling both transmit power and stored energy usage of BSs, energy costs can be effectively reduced. Specifically, we propose a two-stage BS operation scheme where an optimization and control subproblem is solved at each stage, respectively. For the first subproblem, transmit power of BSs is adjusted while quality of service (QoS) experienced by users is preserved. In the second subproblem, we consider the strategic scheduling of renewable energy used to power the BSs. That is, harvested energy may be reserved in the battery for future use to minimize the cost of on-grid power that varies in real-time. We propose: (1) an optimization approach built on a lattice model with a method to process outage rate constraint, and (2) a control algorithm based on nonlinear model predictive control (NMPC) theory to solve the two subproblems, respectively. Simulation results include a collection of case studies that demonstrate as to how operators may manage energy harvesting BSs to reduce their electricity costs.     

CEAR: A cooperation based energy aware reward scheme for next generation green cognitive radio networks

Cognitive Radio (CR) networks are envisioned as a key empowering technology of the fifth-generation (5G) wireless communication networks, which solves the major issues of 5G, like high-speed data transmission, seamless connectivity, and increased demand for mobile data. Another significant characteristic of the 5G network is green communications, as energy consumption from the communication field is predicted to rise remarkably by the year 2030. In this work, we are concerned about energy-related issues and propose a cooperation-based energy-aware reward scheme (CEAR) for next-generation green CR networks. The proposed CEAR scheme is based on the antenna and temporal diversity of the primary users (PUs). For providing the service to the PUs, the users of another network called cognitive users (CUs) work as a cooperative relay node, and, in return, they get more spectrum access opportunities as a reward from the primary network. The CUs with delay-tolerant data packets take a cooperative decision by recognizing the availability and traffic load of PUs, channel state information, and data transmission requirements. We utilize the optimal stopping protocol for solving the decision-making problem and use the backward induction method to obtain the optimal cooperative solution. The simulation results reveal notable enhancements in energy efficiency (EE) of CUs compared with other cooperative schemes. The proposed CEAR scheme is more energy-efficient for ultra-dense network deployment because results show that the CU’s EE, spectral efficiency (SE), and throughput improved with the increase of PUs.     

Analysis of mobility impact on interference in cognitive radio networks

Cognitive radio (CR) technology seems to be a promising candidate for solving the radio frequency (RF) spectrum occupancy problem. CRs strive to utilize the white holes in the RF spectrum in an opportunistic manner. Because interference is an inherent and a very critical design parameter for all sorts of wireless communication systems, many of the recently emerging wireless technologies prefer smaller size coverage with reduced transmit power in order to decrease interference. Prominent examples of short-range communication systems trying to achieve low interference power levels are CR relays in CR networks and femtocells in next generation wireless networks (NGWNs). It is clear that a comprehensive interference model including mobility is essential especially in elaborating the performance of such short-range communication scenarios. Therefore, in this study, a physical layer interference model in a mobile radio communication environment is investigated by taking into account all of the basic propagation mechanisms such as large- and small-scale fading under a generic single primary user (PU) and single secondary user (SU) scenario. Both one-dimensional (1D) and two-dimensional (2D) random walk models are incorporated into the physical layer signal model. The analysis and corresponding numerical results are given along with the relevant discussions.     

On spectrum sensing in cognitive radio CDMA networks with beamforming

In this paper, the performance of cognitive radio (CR) code division multiple access (CDMA) networks is analyzed in the presence of receive beamforming at the base stations (BSs). More precisely, we analyze, through simulations, the performance achievable by a CR user, with and without spectrum sensing, in a three-cell scenario. Uplink communications are considered. Three different schemes for spectrum sensing with beamforming are presented, together with a scheme without spectrum sensing. CR users belong to a cognitive radio network (CRN) which is coexisting with a primary radio network (PRN). Both the CRN and the PRN are CDMA based. The CRN is assumed to utilize beamforming for its CR users. Soft hand-off (HO) and power control are considered in both the CRN and the PRN. The impact of beamforming on the system performance is analyzed, considering various metrics. In particular, we evaluate the performance of the proposed systems in terms of outage probability, blocking probability, and average data rate of CR users. The results obtained clearly indicate that significant performance improvements can be obtained by CR users with the help of beamforming. The impact of several system parameters on the performance of the three considered spectrum sensing schemes with beamforming is analyzed. Our results, in terms of probability of outage, show that the relative improvement brought by the use of beamforming is higher in the absence of spectrum sensing (reduction of 80%) than in the presence of spectrum sensing (reduction of 42%).     

Adaptive multimode transmission in wireless sensor networks with energy harvesting

Limited energy has always been an important factor restricting the development of wireless sensor networks. The unbalanced energy consumption of nodes will accelerate the death of some nodes. To solve the above problems, an adaptive routing algorithm for energy collection sensor networks based on distributed energy saving clustering (DEEC) is proposed. In each hop of data transmission, the optimal mode is adaptively selected from four transmission modes: single-hop cooperative, multi-hop cooperative, single-hop non-cooperative and multi-hop non-cooperative, so as to reduce and balance the energy consumption of nodes. The performance of the proposed adaptive multi-mode transmission method and several benchmark schemes are evaluated and compared by computer simulation, where a few performance metrics such as the network lifetime and throughput are adopted. The results show that, the proposed method can effectively reduce the energy consumption of the network and prolong the network lifetime; it is superior to various benchmark schemes.     

Spectrum sensing in cognitive radio networks: Performance evaluation and optimization

This paper studies cooperative spectrum sensing in cognitive radio networks where secondary users collect local energy statistics and report their findings to a secondary base station, i.e., a fusion center. First, the average error probability is quantitively analyzed to capture the dynamic nature of both observation and fusion channels, assuming fixed amplifier gains for relaying local statistics to the fusion center. Second, the system level overhead of cooperative spectrum sensing is addressed by considering both the local processing cost and the transmission cost. Local processing cost incorporates the overhead of sample collection and energy calculation that must be conducted by each secondary user; the transmission cost accounts for the overhead of forwarding the energy statistic computed at each secondary user to the fusion center. Results show that when jointly designing the number of collected energy samples and transmission amplifier gains, only one secondary user needs to be actively engaged in spectrum sensing. Furthermore, when the number of energy samples or amplifier gains are fixed, closed form expressions for optimal solutions are derived and a generalized water-filling algorithm is provided.     

认知无线电网络中提高传输层端到端吞吐率的跨层参数配置

在认知无线电网络中, 传输层端到端(TCP)吞吐率是衡量网络性能的重要指标. 前期相关研究大都具有以下两方面缺点: 第一, 大部分研究只考虑了协议底层参数来优化物理链路性能, 对传输层性能有所忽略; 第二, 目前的研究大都基于马尔可夫决策过程建模, 这需要网络具有完全知识, 使得这类模型的应用受到很大限制. 针对以上问题, 本文提出一种新的算法: 网络中每个节点通过联合配置物理层调制方式、发射功率、 链路层信道接入和TCP拥塞控制因子来找到传输层端到端近似最优吞吐率. 由于无线设备对环境感知存在误差, 本文将网络模型建模为部分可观测马尔可夫决策过程, 并将其转换成信念状态马尔可夫决策过程, 采用Q值迭代找到近似最优策略. 仿真分析表明, 提出的算法能在动态无线环境下以一定的误差限收敛于最优策略, 能在功率受限条件下, 有效提高传输层端到端吞吐率.     

基于博弈论的认知无线电网络跨层资源分配

针对多跳认知无线电网络的多层资源分配问题,提出了协作去耦合方法和跨层联合方法,协作去耦合方法首先单独完成路径选择任务,随后进行信道与功率的博弈分配;跨层联合方法则通过博弈直接对路径、信道、功率三层资源进行同时分配,两种方法都综合考虑网络层、介质访问控制层、物理层的启发原则,引入了节点被干扰度信息和节点主动干扰度信息来辅助路径选择,设计了基于功率允许宽度信息的Boltzmann探索来完成信道与功率选择,设计了长链路和瓶颈链路替换消除机制以进一步提高网络性能,从促进收敛角度,选择序贯博弈并设计了具体的博弈过程,此外还分析了博弈的纳什均衡,讨论了两种算法的复杂度,仿真结果表明,协作去耦合方法和跨层联合方法在成功流数量、流可达速率、发射功耗性能指标上均优于简单去耦合的链路博弈、流博弈方法。     

Spectral efficiency maximization for IRS-assisted wireless communication in cognitive radio networks

In this paper, we consider the Intelligent reflecting surface (IRS)-assisted cognitive radio (CR) network, in which the IRS is applied to improve the spectral efficiency of secondary users. In specific, the advantages of applying IRS is double: it can not only improve the transmission rate of the secondary users, but also reduce the interference from the secondary users to the primary users, which allow the secondary users to increase the transmission power correspondingly. The original problem is formulated by simultaneously consider the maximum power limitation of the secondary users and the Quality of Service (QoS) requirement of the primary users, which expressed by the interference temperature (IT). The formulated problem is non-convex and difficult to solve directly. We apply the alternating optimization (AO) algorithm to split the original problem into two sub-problems. For the first sub-problem, we transform it into a convex problem and for the second sub-problem, we use the successive convex approximation method to obtain the corresponding results. The simulation experiments show that the performance of our proposed scheme is better than existing schemes.     

认知无线电网络中基于抢占式排队论的频谱切换模型

针对认知无线电网络中认知用户广义传输时间的优化问题, 提出了一种基于抢占式续传优先权M/G/m排队理论的频谱切换模型. 在该排队模型中, 为了最小化认知用户广义传输时间, 采用混合排队-并列式服务的排队方式. 在此基础上, 深入分析多个认知用户、多个授权信道、多次频谱切换条件下认知用户信道使用情况, 从而推导出广义传输时间表达式. 最后探讨了该模型下自适应频谱切换策略. 仿真结果表明, 相比于已有的频谱切换模型, 该模型不仅能够更加完整地描述认知用户频谱切换行为, 而且使得认知用户传输时延更小, 广义传输时间更短. 此外, 认知无线电网络允许的认知用户服务强度增加, 能够容纳的认知用户数量增多. 因此, 该模型提升了认知用户频谱切换的性能, 更好地实现了认知用户与授权用户的频谱共享.     

Group mobility assisted network selection framework in 5G vehicular cognitive radio networks

The heterogeneity nature of networks is the most eminent characteristic in 5G vehicular cognitive radio networks across complex radio environments. Since multiple communicating radios may be in motion at the same time in a vehicle. So, group mobility is the most prominent characteristic that requires to be a deep investigation. Therefore, different communication radios that are moving on a train/bus needed to select the networks simultaneously. Without considering the group mobility feature, there is a possibility that the same network may be selected by each moving node and cause congestion in a particular network. To overcome this problem, a novel network selection technique considering the group mobility feature is proposed to improve the throughput of the network. In this work, a 5G vehicular cognitive radio network scenario is also realized using USRP-2954 and LabVIEW communications system design suite testbed. The performance metrics like transmission delay, packet loss rate, reject rate and, channel utilization for vehicular nodes, are gained to analyze the proposed technique in vehicular cognitive radio networks environment. The proposed technique demonstrates a remarkable improvement in channel utilization for vehicular nodes and outperformed conventional schemes.