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.     

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.     

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.     

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.     

Route selection for interference minimization to primary users in cognitive radio ad hoc networks: A cross layer approach

An opportunistic routing problem in a cognitive radio ad hoc network is investigated with an aim to minimize the interference to primary users (PUs) and under the constraint of a minimum end-to-end data rate for secondary users (SUs). Both amplify-and-forward (AF) and decode-and-forward (DF) relaying techniques are considered for message forwarding by SU nodes in the network. Unlike popular transmit power control based solutions for interference management in cognitive radio networks, we adopt a cross layer approach. The optimization problem is formulated as a joint power control, channel assignment and route selection problem. Next, closed form expression for transmission power is derived and corresponding channel selection scheme and routing metric are designed based on this solution. The proposed route selection schemes are shown to depend not only on gains of the interference channels between SUs and PUs but also on the values of the spectrum sensing parameters at the SU nodes in the network. Two distributed routing schemes are proposed based on our analysis; (i) optimal_DF and (ii) suboptimal_AF. The routing schemes could be implemented using existing table driven as well as on demand routing protocols. Extensive simulation results are provided to evaluate performance of our proposed schemes in random multihop networks. Results show significant reduction in PUs’ average interference experience and impressive performance as opportunistic routing schemes can be achieved by our schemes compared to traditional shortest path based routing schemes. Performance improvement is also reported over prominent recent schemes.     

一种面向中继协作频谱感知系统的自适应全局最优化算法

该文研究了冗余中继, 次用户及中继用户数目, 检测门限, 信道传输错误率等因素对中继协作频谱感知系统性能的影响, 并提出一种新的自适应全局最优化算法.该算法基于获得最大无干扰功率的自适应中继选择方法, 确定备选认知中继集合;单个次用户以信道传输错误率最小为准则, 从备选认知中继集合中自适应选择最佳中继, 使总体检测率最大;在给定目标检测率的条件下, 以系统吞吐量最大为准则, 给出了自适应全局最优化算法.仿真实验结果表明新算法信道传输精度高, 信道吞吐量大, 节约带宽资源.     

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.     

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.     

Analysis of degrees of freedom under mixture Gaussian model in cognitive radio systems

We propose a mixture-Gaussian model for a cognitive radio channel to analyze the interplay between the interference in the system and the degrees-of-freedom (DOF), i.e., the average number of channel uses per transmission frame, used by the secondary user (SU) for communications in the long run. In contrast to the conventional studies, we assume that the SU receiver (SU-RX) does not precisely know whether the primary-user (PU) transmitter is on or off. Due to this assumption the resulting interference channel is mixture-Gaussian. Our objective is to find the optimal sensing threshold and sensing time for the signal detector used by the SU transmitter (SU-TX). Our formulation of the optimization problem reflects the trade-off between SU-TX’s DOF for communications and that for detection. Both the DOFs affect PU’s interference to SU, and SU’s interference to PU. The latter interference causes PU performance degradation, which is kept within tolerable range as a constraint. As a further contribution, we define interference regimes for SU performance on the basis of PU transmission power level. We also address the scenario when PU receiver uses the nearest neighbor decoding while wrongly anticipating that the channel is Gaussian. Finally, we demonstrate that even if SU-TX’s signal detector performs suboptimally, SU can still achieve the optimal detector’s performance in the high interference regime by adjusting the sensing parameters.     

Power allocation for cognitive underlay networks with spectrum band selection

In this paper, we study the cooperative communication of a cognitive underlay network by utilizing the diversity of multiple spectrum bands. In particular, we assume that the transmission power of the secondary user (SU) is subject to different joint constraints, such as peak interference power of the multiple primary users (PUs), peak transmission power of the SU, outage tolerate interference, and outage probability threshold. Accordingly, two power allocation schemes are considered on the basis of the minimum interference channel from the SU to the PU and the channel state information of the primary user link. Furthermore, the SU can select one of the three transmission modes following the channel state conditions, namely as cellular, device-to-device, or switching mode, to transmit the signal to the secondary user receiver. Given this setting, two power allocation schemes over a spectrum band selection strategy are derived. In addition, closed-form expressions for the outage probability of three modes are also obtained to evaluate the performance of the secondary network. Most importantly, a closed-form expression for the peak interference power level of the PU, which is considered as one of the most important parameters to control the SU’s transmission power, is derived by investigating the relation of two considered power allocation schemes in the practise. Finally, numerical examples show that the outage performance of secondary network in the switching mode outperforms the one of the cellular and device-to-device (D2D) mode for all considered power allocation schemes.     

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.     

A novel power allocation strategy for cooperative PDMA systems

Cooperative communication technology is of great importance for increasing the user reachable rate, further improving throughput and reducing the outage probability of non-orthogonal multiple access (NOMA) systems. This paper mainly studies the power allocation optimization method based on amplify-and-forward (AF) pattern division multiple access (PDMA) to obtain the maximum achievable throughput. We formulate an optimization problem of user power allocation in a downlink PDMA system with cooperative relaying, the exact expressions of system throughput and user outage probability of the AF-PDMA system are derived, and a novel power allocation optimization method based on uniform distribution and restricted constraints is proposed. The effectiveness of the restricted constraints and optimization method is verified by theoretical analysis and simulation. The studies we have performed showed that the proposed scheme with uniform distribution and restricted constraints can be significantly improved in terms of the system throughput in comparison to the case with a genetic algorithm (GA) and fixed power allocation scheme. Concerning the proposed method, the search space is reduced to 1/3 of the original feasible region, and the runtime of the algorithm accounts for only 20% of the GA runtime.     

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.     

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.     

Performance Analysis and Optimization of a Cooperative Transmission Protocol in NOMA-Assisted Cognitive Radio Networks with Discrete Energy Harvesting

In this paper, we propose a spectrum-sharing protocol for a cooperative cognitive radio network based on non-orthogonal multiple access technology, where the base station (BS) transmits the superimposed signal to the primary user and secondary user with/without the assistance of a relay station (RS) by adopting the decode-and-forward technique. RS performs discrete-time energy harvesting for opportunistically cooperative transmission. If the RS harvests sufficient energy, the system performs cooperative transmission; otherwise, the system performs direct transmission. Moreover, the outage probabilities and outage capacities of both primary and secondary systems are analyzed, and the corresponding closed-form expressions are derived. In addition, one optimization problem is formulated, where our objective is to maximize the energy efficiency of the secondary system while ensuring that of the primary system exceeds or equals a threshold value. A joint optimization algorithm of power allocation at BS and RS is considered to solve the optimization problem and to realize a mutual improvement in the performance of energy efficiency for both the primary and secondary systems. The simulation results demonstrate the validity of the analysis results and prove that the proposed transmission scheme has a higher energy efficiency than the direct transmission scheme and the transmission scheme with simultaneous wireless information and power transfer technology.     

5G-based wideband cognitive radio system design with cooperative spectrum sensing

Since the 5G bandwidth is very large, there are a large number of non-continuous idle spectrum in 5G communication. In this paper, we have designed transmitter and receiver of a 5G-based wideband cognitive radio (CR) system with cooperative spectrum sensing, in order to improve transmission performance and avoid interference signals. Each CR user marks the spectrum availability for getting the sub-basis function through doing Inverse Fast Fourier Transform (IFFT) with the product of spectrum marker vector and random phase vector. The cooperative spectrum sensing can be realized by cascading the sub-basis functions of all the users. Multiple access of the CR system is also proposed to access much non-continuous idle spectrum. The simulation results have shown that the proposed CR system can avoid the interference effectively and outperform the spread spectrum system obviously.     

高速光纤通信系统中抑制偏振模色散的新机制

重点研究了偏振模色散、群速度色散、自相位调制三者之间在高速光纤通信系统中的相互作用，从时域角度分析脉冲的演变，从频域角度分析频谱的变化，提出一定条件下，啁啾、色散、自相位调制可以部分补偿偏振模色散的思想。通过对40Gbit／s系统进行偏振模色散、群速度色散和自相位调制共同作用的仿真，从统计意义上验证了它们之间的相互影响，并找到最佳传输方案，对系统设计提供了参考。     

Design and implementation of spectrum sensing for cognitive radios with a frequency-hopping primary system

In this work, spectrum sensing for cognitive radios is considered in the presence of multiple Primary Users (PU) using frequency-hopping communication over a set of frequency bands. The detection performance of the Fast Fourier Transform (FFT) Average Ratio (FAR) algorithm is obtained in closed-form, for a given FFT size and number of PUs. The effective throughput of the Secondary Users (SU) is formulated as an optimization problem with a constraint on the maximum allowable interference on the primary network. Given the hopping period of the PUs, the sensing duration that maximizes the SU throughput is derived. The results are validated using Monte Carlo simulations. Further, an implementation of the FAR algorithm on the Lyrtech (now, Nutaq) small form factor software defined radio development platform is presented, and the performance recorded through the hardware is observed to corroborate well with that obtained through simulations, allowing for implementation losses.     

一种基于延时和带宽约束的纳米级互连线优化模型

基于RLC互连线延时模型,通过缓冲器插入和改变互连线宽及线间距,提出了一种基于延时和带宽约束的互连功耗-缓冲器面积的乘积优化模型.基于90 nm,65 nm和45 nm CMOS工艺验证了互连线优化模型,在牺牲1/3和1/2的带宽的前提下,平均能够节省46%和61%的互连功耗,以及65%和83%的缓冲器面积,能应用于纳米级SOC的计算机辅助设计. 关键词： 纳米互连功耗 缓冲器面积 延时 带宽     

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.