Joint optimal fair cooperative spectrum sensing and transmission in cognitive radio

Since the sensing power consumption of cooperative spectrum sensing (CSS) will decrease the throughput of secondary users (SU) in cognitive radio (CR), a joint optimal model of fair CSS and transmission is proposed in this paper, which can compensate the sensing overhead of cooperative SUs. The model uses the periodic listen-before-transmission method, where each SU is assigned a portion of channel bandwidth, when the primary user (PU) is estimated to be free by the coordinator. Then, a joint optimization problem of local sensing time, number of cooperative SUs, transmission bandwidth and power is formulated, which can compensate the sensing overhead of cooperative SUs appropriately through choosing suitable compensating parameter. The proposed optimization problem can be solved by the Polyblock algorithm. Simulation results show that compared with the traditional model, the total system throughput of the fairness cooperation model decreases slightly, but the total throughput of the cooperative SUs improves obviously.     

Traffic-Aware Green Cognitive Radio

Cognitive Radio Network (CRN) has emerged as an effective solution to the spectrum under-utilization problem, by providing secondary users (SUs) an opportunistic access to the unoccupied frequency bands of primary users (PUs). Most of the current research on CRN are based on the assumption that the SU always has a large amount of data to transmit. This leads to the objective of SU throughput maximization with a constraint on the allowable interference to the PU. However, in many of the practical scenarios, the data arrival process of the SU closely follows an ON–OFF traffic model, and thus the usual throughput optimization framework may no longer be suitable. In this paper, we propose an intelligent data scheduling strategy which minimizes the average transmission power of the SU while maintaining the transmission delay to be sufficiently small. The data scheduling problem has been formulated as a finite horizon Markov Decision Process (MDP) with an appropriate cost function. Dynamic programming approach has been adopted to arrive at an optimal solution. Our findings show that the average transmitted power for our proposed approach can be as small as 36.5% of the power required for usual throughput maximization technique with insignificant increase in average delay.     

Distributed dynamic spectrum access through multi-agent deep recurrent Q-learning in cognitive radio network

This paper addresses the problem of distributed dynamic spectrum access in a cognitive radio (CR) environment utilizing deep recurrent reinforcement learning. Specifically, the network consists of multiple primary users (PU) transmitting intermittently in their respective channels, while the secondary users (SU) attempt to access the channels when PUs are not transmitting. The problem is challenging considering the decentralized nature of CR network where each SU attempts to access a vacant channel, without coordination with other SUs, which result in collision and throughput loss. To address this issue, a multi-agent environment is considered where each of the SUs perform independent reinforcement learning to learn the appropriate policy to transmit opportunistically so as to minimize collisions with other users. In this article, we propose two long short-term memory (LSTM) based deep recurrent Q-network (DRQN) architectures for exploiting the temporal correlation in the transmissions by various nodes in the network. Furthermore, we investigate the effect of the architecture on success rate with varying number of users in the network and partial channel observations. Simulation results are compared with other existing reinforcement learning based techniques to establish the superiority of the proposed method.     

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.     

Performance evaluation of improved double-threshold energy detector over Rayleigh-faded sensing and imperfect reporting channels

Cognitive radio (CR) has been viewed as a promising solution to spectrum scarcity. In order to design a reliable CR system, many improvements have been proposed to enhance spectrum sensing performance of secondary users (SUs) in a CR network (CRN). Sensing reliability and transmission throughput of SUs are two important performance criteria, which should be optimized to enhance signal protection of primary user (PU) as well as spectrum utilization rate. In this paper, we consider Rayleigh-faded sensing channels and SUs use improved energy detector (IED) to make their local decisions. The final decision is made in a fusion center (FC) through the cooperative spectrum sensing (CSS) scheme with erroneous reporting channels. We show that the improved double-threshold energy detector (IDED) outperforms the conventional energy detector (CED) in terms of the total error rate. Furthermore, we evaluate the transmission throughput of the CRN through various ED schemes with detection constraints over both perfect and imperfect reporting channels. We show that the IDED has the highest achievable throughput among different ED schemes over imperfect reporting channels.     

A spectrum auction algorithm based on joint power control and beamforming in small cell networks

Spectrum auction is considered as a suitable approach to efficiently allocate spectrum among unlicensed users. However, in previous studies of spectrum auction, competition can hardly be reflected in the traditional spectrum allocation and the spectrum efficiency is still not high after the allocation. In this paper, we enhance the factor of competition in the auctions, in which bidders need to pay for the competition and the interference to macro cell users (MUs). We consider a communication system with one macro cell and several small cells, thus a licensed radio spectrum is shared by both MUs and small cell users (SUs). A truthful auction algorithm is proposed for spectrum allocation and the spectrum is divided into multiple channels in different time slots, so that SUs can make their choice for bidding under the guidance of history. In order to raise the communication quality, we propose a power control and beamforming algorithm in the heterogeneous network to enhance the system performance. Simulation results are presented to verify the effectiveness of the proposed algorithm in the small cell network.     

System throughput maximization in IRS-assisted phase cooperative NOMA networks

This work investigates performance of system throughput in intelligent reflecting surfaces (IRSs)-enabled phase cooperative non-orthogonal multiple access (NOMA) framework. By exploiting heterogeneous cognitive radio networks concept the aim is to maximize the sum rate of secondary users in the proposed phase cooperative downlink network configuration via optimization solutions. However, the optimization problem comes out to be NP-hard and precludes direct solution. Hence, an alternating optimization is applied at the primary network to solve the maximization problem by exploiting the transmit beamforming (BF) at the power station (PS) and phase shift optimization at the IRS. Later, sum rate maximization for secondary network is performed by utilizing phase shifts of primary network via phase cooperation. In order to find global optimal solutions for active beamformers at both PSs, a branch-reduce-and-bound (BRnB) method is used whereas, passive phase shift optimization at the primary PS is performed via a simple iterative solution, i.e., the element-wise block coordinate descent method. For the proposed framework, Monte-Carlo simulations are performed where the optimality of the global solution is compared with heuristic BF methods including minimum-mean-square-error/regularized zero-forcing-beamforming (ZFBF) and ZFBF. The BRnB algorithm sets an upper performance bound by improving the sum rate of users in comparison with the conventional heuristic BF schemes. This work signifies the utilization of phase cooperation in IRS-assisted NOMA networks for a multi-user environment.     

Optimal Channel Selection algorithm for CRahNs

As the data traffic is increasing, the spectrum bands are getting congested. It causes low latency and unreliable communication. Additional spectrum can be utilized to solve this problem but moving towards higher frequency means higher power requirement and increased cost. Cognitive radio network is another solution to this problem. It helps the nodes of a network to use the channels of the nearby bands which are not being used at that time. However, it has several challenges. One of these challenges is the transmission collision with the primary users of the network. Researchers have been working on this problem. However, it is still a major concern for the researchers. This paper proposes an algorithm that selects the optimal cognitive channel for the data transmission by the secondary user in such a way so that the transmission collision with the PU is minimized. After comparison with the existing latest similar protocol, the proposed protocol has shown 5.6% improvement in the throughput, 5.3% improvement in PDR. The delay is decreased by 0.6% and the transmission collision with PUs is reduced by 2.5%.     

A spectrum handoff scheme for optimal network selection in Cognitive Radio vehicular networks: A game theoretic auction theory approach

The recent strides in vehicular networks have emerged as a convergence of multi radio access networks having different user preferences, multiple application requirements and multiple device types. In future Cognitive Radio (CR) vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. Hence, it becomes a challenge for CR vehicular node to select the optimal network for the spectrum handoff decision. A game theoretic auction theory approach is interdisciplinary effective approach to select the optimal network for spectrum handoff. The competition between different CR vehicular node and access networks can be formulated as multi-bidder bidding to provide its services to CR vehicular node. The game theory is the branch of applied mathematics which make intelligent decision to select the optimal alternative from predetermined alternatives. Hence, this paper investigates a spectrum handoff scheme for optimal network selection using game theoretic auction theory approach in CR vehicular networks. The paper has also proposed a new cost function based multiple attribute decision making method which outperforms other existing methods. Numerical results revel that the proposed scheme is effective for spectrum handoff for optimal network selection among multiple available networks.     

3D beamforming in Intelligent Reflecting Surface (IRS)-assisted multi-user cognitive radio networks

In this paper, we consider a multi-user cognitive radio network (CRN) equipped with an intelligent reflecting surface (IRS). We examine the network performance by evaluating the fairness of the secondary system, which is satisfying the minimum required signal to interference and noise ratio (SINR) for each secondary user (SU). The minimum SINR of the SUs is maximized by joint optimization of the beamforming vector and three-dimensional beamforming (3DBF) angles at the secondary base station (SBS) and also the phase shifts of the IRS elements. This optimization problem is highly non-convex. To solve this problem, we utilize Dinkelbach’s algorithm along with an alternating optimization (AO) approach to achieve some sub-problems. Accordingly, by further applying a semi-definite relaxation method, we convert these sub-problems to equivalent convex forms and find a solution. Furthermore, analytically we propose an algorithm for optimizing 3DBF angles at the SBS. Through numerical results, the improvement of the sum SINR of the secondary system using the proposed method is illustrated. Moreover, it is shown that as the number of reflecting elements of IRS increases, the sum SINR significantly augments while satisfying fairness. Also, the convergence of the proposed algorithm is verified utilizing numerical results.     

Optimal sensing energy and transmission interval in hybrid overlay–underlay cognitive radio networks with energy harvesting

In this work, we present a new concept called “transmission interval” in a hybrid overlay/underlay cognitive radio network. A transmission interval consists of a sequence of time slots during which the secondary user (SU) transmits its data using the optimal mode based on its current state. After the transmission interval ends, the SU has to choose between staying idle for a single time slot to save energy for future possible transmission, transmitting using the underlay mode without sensing to optimize the usage of the limited amount of available energy, or sensing the channel and transmitting using either overlay or underlay mode depending on the primary user (PU) state. The energy harvesting technology is also considered in the presence of multiple PUs and multiple SUs. For the SU network, a sequential decision problem is formulated using the mixed observable Markov decision process to determine the optimal sensing energy and the optimal transmission interval length that maximize the SU network throughput and minimize both the consumed energy and the interference to the PUs. Numerical results show that applying the transmission interval concept increases the SU network throughput and decreases the interference to the PUs compared to conventional models. Moreover, adding the action of underlay transmission without sensing increases the SU network throughput.     

Optimal satisfaction degree in energy harvesting cognitive radio networks

A cognitive radio(CR) network with energy harvesting(EH) is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model(HMM) is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree(WSD) of the cognitive radio network, a tradeoff between the average throughput of the secondary user(SU) and the interference to the primary user(PU) is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming(MINLP) problem. The satisfaction degree optimization problem is solved by using differential evolution(DE) algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service(Qos). Numerical results are given to verify our analysis.     

Computation rate maximization for IRS-enhanced dynamic spectrum access assisted MEC system

Intelligent reflecting surface (IRS)-enhanced dynamic spectrum access (DSA) is a promising technology to enhance the performance of the mobile edge computing (MEC) system. In this paper, we consider the integration of the IRS enhanced DSA technology to a MEC system, and study the pertinent joint optimization of the phase shift coefficients of the IRS, the transmission powers, the central processing unit (CPU) frequencies, as well as the task offloading time allocations of the secondary users (SUs) to maximize the average computation bits of the SUs. Due to the non-convexity, the formulated problem is difficult to solve. In order to tackle this difficulty, we decompose the optimization problem into tractable subproblems and propose an alternating optimization algorithm to optimize the optimization variables in an iterative fashion. Numerical results are provided to show the effectiveness and the correctness of the proposed algorithm.     

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.     

一种深度学习的立体阵波达方向估计方法

针对常规波束形成主瓣宽且目标分辨能力低的问题,提出一种基于深度卷积神经网络的波达方向估计方法。算法使用常规波束形成计算二维空间功率谱,将预处理后的空间功率谱图输入深度卷积神经网络。该文利用神经网络学习解卷积映射关系,输出主瓣宽度更窄的空间功率谱图,从而实现高分辨率二维波达方向估计。该算法对阵列结构没有限制,适用于立体阵。仿真结果表明该文方法在不同目标个数、快拍数及信噪比参数下均能准确估计目标方向。该文方法目标分辨能力优于常规波束形成方法。在低快拍情况下,目标方向估计误差低于自适应波束形成方法。     

Interference and priority aware resource allocation in coexisting WBANs using game models

Wireless body area networks (WBANs) have become an integral part of our health monitoring system. However, the specific challenges offered by a dense co-existence of heterogeneous WBANs have not been properly addressed. The interference between the coexisting WBANs which cause throughput degradation and energy wastage can be mitigated by suitable channel and slot allocations. In this work, a Stackelberg game model with pricing is used for resource scheduling among multi-class WBANs. The access points (APs) act as leaders who decide the prices to be paid by the normal WBAN users for reusing the resources allocated to the critical WBAN users. The normal WBAN users act as followers which use the pricing information and a distributed learning algorithm that guarantees Pareto-optimal solution. As a benchmark for comparison purpose, we propose a centralized resource scheduling which is formulated as an optimization problem where the objective is to maximize system throughput while guaranteeing the quality of service (QoS) requirements of multi-class WBANs. To efficiently solve this problem, a column generation method is proposed. Numerical results are provided to show the efficiency and effectiveness of the proposed game model.     

Quadratic covariance matrix-based cooperative spectrum sensing method by using an evolutionary algorithm

Cognitive radio (CR) is a practical technology to solve the current low utilization of spectrum resources, and spectrum sensing is the most critical technique in a CR network. In this paper, a genetic simulated annealing algorithm based on quadratic covariance matrix and information geometry is proposed for cooperative spectrum sensing (CSS) to enhance the performance in the low signal-noise ratio (SNR). Firstly, the quadratic covariance matrix of cooperative secondary users (SUs) is used as the characteristic matrix to perform feature extraction. Secondly, based on the information geometry, the characteristic matrix is mapped on the statistical manifold to avoid information loss. Furthermore, the genetic simulated annealing algorithm is used to obtain a classifier on the statistical manifold, and the mutation process is improved by a new mutation operator to accelerate the convergence speed of the whole algorithm. Finally, the classifier is employed to implement spectrum sensing. In the simulation analysis, the proposed method has better spectrum sensing performance than the popular various methods under low SNR and faster convergence speed.     

Macrocell–femtocells resource allocation with hybrid access motivational model

Femtocell technology has emerged as an efficient cost-effective solution not only to solve the indoor coverage problem but also to cope with the growing demand requirements. This paper investigates two major design concerns in two tier networks: resource allocation and femtocell access. Base station selection together with dual bandwidth and power allocation among the two tiers is investigated under shared spectrum usage. To achieve fair and efficient resource optimization, our model assumes that the hybrid access mode is applied in the femtocells. The hybrid access mode is beneficial for system performance as (1) it lessens interference caused by nearby public users, (2) it allows public users to connect to near femtocells and get better Quality of Service (QoS) and (3) it increases system capacity as it allows the macrocell to serve more users. However, femtocells’ owners can behave selfishly by denying public access to avoid any performance reduction in subscribers’ transmissions. Such a problem needs a motivation scheme to assure the cooperation of femtocells’ owners. In this paper, we propose a game-theoretical hybrid access motivational model. The proposed model encourages femtocells’ owners to share resources with public users, thus, more efficient resource allocation can be obtained. We optimize the resource allocation by means of the Genetic Algorithm (GA). The objective of the formulated optimization problem is the maximization of network throughput that is calculated by means of Shannon’s Capacity Law. Simulations are conducted where a modified version of the Weighted Water Filling (WWF) algorithm is used as a benchmark. Our proposed model, compared to WWF, achieves more efficient resource allocation in terms of system throughput and resources utilization.     

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.