面向复杂电磁环境的多天线信道估计技术

对于基于认知无线电架构的多天线信道估计技术,由于传统零相关序列是在全部频谱可用前提下设计的,在复杂电磁环境的频谱限制下,传统零相关序列部分频域元素发生变化,且不再满足原始序列的周期相关特性,因此不能直接应用于复杂电磁环境的多天线信道估计。该文介绍了多天线通信系统的信道估计算法并指出对理想序列的要求,然后针对复杂电磁环境,即在存在频谱空穴的条件下,联合优化频谱受限和良好周期相关程度两大评价指标,设计出适用的序列集合,作为训练序列应用到认知无线电系统的信道估计算法中,仿真结果验证了新序列集合的有效性。     

多径信道下MIMO系统发送天线数估计

为了解决认知无线电或信号截获中多径信道下MIMO系统发送天线数估计问题,首先分析了现有模型在多径信道下失效的原因,将MIMO多径信道模型等效变换出一种虚拟信道矩阵,从而建立多径信道下MIMO系统发送天线数估计模型;然后,利用随机矩阵理论中协方差矩阵最小特征值分布的相关研究结果,证明了时不变瑞利信道的协方差矩阵最小特征值收敛于第二类Tracy-Widom分布,分析了该特点对发送天线数估计的影响,并提出一种改进的RMT估计算法来估计多径信道下MIMO系统发送天线数.最后对改进算法进行了仿真验证,结果表明在低信噪比和小数据条件下,改进算法的估计性能相比RMT算法有较大提升.     

瑞利衰落环境中一种能量有效的多无线电信道搜索机制研究

针对当前频谱搜索机制在能量有效方面的不足,该文提出了一种在瑞利衰落条件下基于部分可测马尔科夫决策过程(POMDP)的多无线电信道搜索机制MRCSS。该机制通过对瑞利衰落环境中信道状态建立POMDP模型来分析和推导出能效最佳信道,并以此指导用户的信道选择。仿真结果表明该机制能较传统搜索机制更有效地减少感知时间和节省能耗,从而达到提高频谱感知性能的目的。     

并行多信道软件无线电接收机仿真

讨论了软件无线电接收机的基本理论:信道划分和解调算法。在此基础上研究了下变频技术和低通滤波技术在并行多信道接收机中的应用,提出了采用软件无线电中的FM和AM解调方式解调信号,并完成了软件无线电接受机的系统仿真。     

基于信道缩短的多频带超宽带信道估计

针对多频带超宽带系统,提出了一种基于信道缩短的信道估计方法.利用循环前缀(CP)结构,在接收机前端设计信道缩短均衡器,解决了循环前缀长度小于信道最大多径延迟时难于估计信道参数的问题.根据均衡器输出序列估计出复合信道,通过反卷积解出原信道参数.计算仿真表明该算法具有良好性能.     

同信道干扰下相关多输入多输出信道容量分析

在Rayleigh衰落环境下,研究了具有同信道干扰的多输入多输出(MIMO)信道容量问题,分析了通信用户发送端带有空间相关性的情况。假设接收端完美地知道信道状态信息而发送端不知道,基于矩阵变量分布理论,推导出MIMO信道互信息之矩生成函数的精确闭式表达式。利用该表达式进一步推导出MIMO遍历信道容量的精确表达式。用数值结果验证了分析结果的正确性,并给出各种参数对遍历信道容量的影响。     

基于确定性观测的压缩感知MIMO信道参数反馈

在实际资源受限(带宽受限或功率受限)的无线通信系统中,多径信道具有很强的稀疏特性,如何利用这一特点更加高效地将接收端获得的多径信道状态信息( CSI)进行压缩、反馈,是目前信道状态信息反馈技术的研究热点。针对现有多入多出( MIMO)信道状态信息反馈方法中随机观测矩阵需要较大存储空间的问题,引入了确定性Golay测量矩阵,提出了一种基于确定性观测的压缩感知MIMO多径信道参数反馈方法。在接收端对由信道估计得出的信道状态信息利用确定性Golay测量矩阵进行观测,将较少数目的观测值反馈到发送端,在发送端通过重构算法恢复出完全信道状态信息。仿真实验表明,与随机观测相比,采用确定性Golay观测矩阵的方法虽然需要的观测值数目会有所增加,但所需存储空间远小于随机观测矩阵,且利用确定性观测反馈信道状态信息的重构性能与随机观测矩阵相当。     

QoS保证的多信道混合接入CRN频谱共享策略设计

根据既不对主用户(Primary Radio user,PR)产生干扰,又对认知无线电用户(Cognitive Radio user,CR)提供QoS保证的原则,将认知无线电网络(Cognitive Radio Network,CRN)中多信道接入频谱共享问题建模为混合整数非线性规划。采用多级功率限制保证PR不被干扰,以CR物理层能够提供的速率作为信道和功率分配的依据,提出将能够为CR提供最大传输速率的信道分配给该CR的分配策略,将混合整数非线性规划转化为整数线性规划,并给出集中式启发算法和基于功率速率比(Power Rate Ratio,PRR)最小的分布式启发算法求解该整数线性规划。仿真结果表明,文中给出的算法能够提供较高的CRN吞吐量和较好的CR用户QoS保证。     

时变多径MIMO信道中一种带自适应信道追踪的序列检测方法

该文针对时变多径MIMO信道,各MISO子系统首先分别采用混合MLSE(H-MLSE)处理,然后结合-幸存状态选择,提出了一种复杂度可控的、带自适应信道追踪的序列检测方法。与传统的MLSE算法相比,该方法具有3个显著特征:通过参数选择,可实现对算法复杂度的可控调节;通过嵌入的判决导向/LMS(DD/LMS)算法,对各幸存状态转移对应的幸存路径上的信道参数可实现接近零时延追踪;可部分采用并行处理技术来实现。对具有两条多径的2X4 MIMO时变信道通过数值仿真表明:当2时,该方法可获得满意的检测性能。     

多天线认知无线电网络多信道联合频谱感知策略

当前,以单天线认知用户组成的认知无线电网络同时协作检测多个信道的分配策略得到了广泛研究,而在认知用户可以灵活选取自身天线进行空间分集接收的条件下,当联合感知多信道时,多天线认知无线电网络如何获取最优的天线分配策略仍有待进一步研究。为解决这一问题,在限制各信道最大虚警概率的前提下,以最小化所有信道漏检概率之和为目标,建立了优化模型,并提出了基于分支定界的算法和基于贪婪思想的启发式算法。前者可以获得最优策略,但复杂度较高,后者以牺牲较小检测性能为代价,明显降低了复杂度,有效实现了检测性能与复杂度的平衡,并且在保护各个信道上主用户免受认知用户干扰层面,一定程度上兼顾了公平性。      

A novel spectrum sharing scheme for cognitive radios

A novel approach, which combines spectrum adaptation and orthogonal frequency division multiplexing (OFDM), is proposed to share the licensed spectrum dynamically for cognitive radio systems. Given spectrum sensing and channel estimation information by the receiver, an improved model due to signal power thresholds is adopted to achieve spectrum adaptation for unlicensed users. In order to efficiently allocate the unlicensed signal power, a dynamic power allocation algorithm is also proposed. Simulation results indicate that the propositional scheme solves the partial interference problem of interference temperature model (ITM) and improves the spectrum utilization.     

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.     

Channel status prediction for cognitive radio networks

The cognitive radio (CR) technology appears as an attractive solution to effectively allocate the radio spectrum among the licensed and unlicensed users. With the CR technology the unlicensed users take the responsibility of dynamically sensing and accessing any unused channels (frequency bands) in the spectrum allocated to the licensed users. As spectrum sensing consumes considerable energy, predictive methods for inferring the availability of spectrum holes can reduce energy consumption of the unlicensed users to only sense those channels which are predicted to be idle. Prediction‐based channel sensing also helps to improve the spectrum utilization (SU) for the unlicensed users. In this paper, we demonstrate the advantages of channel status prediction to the spectrum sensing operation in terms of improving the SU and saving the sensing energy. We design the channel status predictor using two different adaptive schemes, i.e., a neural network based on multilayer perceptron (MLP) and the hidden Markov model (HMM). The advantage of the proposed channel status prediction schemes is that these schemes do not require a priori knowledge of the statistics of channel usage. Performance analysis of the two channel status prediction schemes is performed and the accuracy of the two prediction schemes is investigated. Copyright © 2010 John Wiley & Sons, Ltd.     

SBT (Sense Before Transmit) Based LTE Licenced Assisted Access for 5 GHz Unlicensed Spectrum

Utilization of unlicensed spectrum under licensed assisted access ensuring fair co-existence with Wi-Fi networks is a good solution to address immense usage of mobile data. Radio communication operation of LTE in unlicensed frequency band is referred as LTE-unlicensed (LTE-U) or LTE-licensed assisted access. In this paper, we consider a HGNW in which coverage area of Wireless-Fidelity (Wi-Fi)’s Access Point is integrated within the LTE-U small base station’s cellular network coverage area. To overcome the disadvantages of existing LTE-U technics like carrier sense adaptive transmission and listen before talk, we proposed a new methodology i.e., sense before transmit in this paper by adopting a transmit power control mechanisms using reciprocity theorem based on the channel state information to assign the secondary carriers in the uplink as well as in the downlink directions in the unlicensed spectrum to carry the traffic. In our proposal, LTE-U users are allowed to use the unlicensed spectrum provided that the interference produced at Wi-Fi users due to LTE-U activities is remained below a certain threshold. We evaluated the performance of proposed network model in terms of outage probability and achievable throughputs.

     

基于多智能体深度强化学习的D2D通信资源联合分配方法

设备对设备(D2D)通信作为一种短距离通信技术,能够极大地减轻蜂窝基站的负载压力和提高频谱利用率。然而将D2D直接部署在授权频段或者免授权频段必然导致与现有用户的严重干扰。当前联合部署在授权和免授权频段的D2D通信的资源分配通常被建模为混合整数非线性约束的组合优化问题,传统优化方法难以解决。针对这个挑战性问题,该文提出一种基于多智能体深度强化学习的D2D通信资源联合分配方法。在该算法中,将蜂窝网络中的每个D2D发射端作为智能体,智能体能够通过深度强化学习方法智能地选择接入免授权信道或者最优的授权信道并发射功率。通过选择使用免授权信道的D2D对(基于“先听后说”机制)向蜂窝基站的信息反馈,蜂窝基站能够在非协作的情况下获得WiFi网络吞吐量信息,使得算法能够在异构环境中执行并能够确保WiFi用户的QoS。与多智能体深度Q网络(MADQN)、多智能体Q学习(MAQL)和随机算法相比,所提算法在保证WiFi用户和蜂窝用户的QoS的情况下能够获得最大的吞吐量。     

Q-Learning-Based Dynamic Spectrum Access in Cognitive Industrial Internet of Things

In recent years, Industrial Internet of Things (IIoT) has attracted growing attention from both academia and industry. Meanwhile, when traditional wireless sensor networks are applied to complex industrial field with high requirements for real time and robustness, how to design an efficient and practical cross-layer transmission mechanism needs to be fully investigated. In this paper, we propose a Q-learning-based dynamic spectrum access method for IIoT by introducing cognitive self-learning technical solution to solve the difficulty of distributed and ordered self-accessing for unlicensed terminals. We first devise a simplified MAC access protocol for unlicensed users to use single available channel. Then, a Q-learning-based multi-channels access scheme is raised for the unlicensed users migrating to other lower cells. The channel with most Q value will be considered to be selected. Every mobile terminals store and update their own channel lists due to distributed network mode and non-perfect sensing ability. Numerical results are provided to evaluate the performances of our proposed method on dynamic spectrum access in IIoT. Our proposed method outperforms the traditional simplified accessing methods without self-learning capability on channel usage rate and conflict probability.     

Hopping control channel MAC protocol for opportunistic spectrum access networks

Opportunistic spectrum access （OSA） is considered as a promising approach to mitigate spectrum scarcity by allowing unlicensed users to exploit spectrum opportunities in licensed frequency bands. Derived from the existing channel-hopping multiple access （CHMA） protocol,we introduce a hopping control channel medium access control （MAC） protocol in the context of OSA networks. In our proposed protocol,all nodes in the network follow a common channel-hopping sequence; every frequency channel can be used as control channel and data channel. Considering primary users＇ occupancy of the channel,we use a primary user （PU） detection model to calculate the channel availability for unlicensed users＇ access. Then,a discrete Markov chain analytical model is applied to describe the channel states and deduce the system throughput. Through simulation,we present numerical results to demonstrate the throughput performance of our protocol and thus validate our work.     

Efficient error detection in soft data fusion for cooperative spectrum sensing

The primary objective of cooperative spectrum sensing (CSS) is to determine whether a particular spectrum is occupied by a licensed user or not, so that unlicensed users called secondary users (SUs) can utilize that spectrum, if it is not occupied. For CSS, all SUs report their sensing information through reporting channel to the central base station called fusion center (FC). During transmission, some of the SUs are subjected to fading and shadowing, due to which the overall performance of CSS is degraded. We have proposed an algorithm which uses error detection technique on sensing measurement of all SUs. Each SU is required to re-transmit the sensing data to the FC, if error is detected on it. Our proposed algorithm combines the sensing measurement of limited number of SUs. Using Proposed algorithm, we have achieved the improved probability of detection (PD) and throughput. The simulation results compare the proposed algorithm with conventional scheme.     

A decision theoretic approach for channel ranking in crowded unlicensed bands

Interference is disruptive to the operation of wireless sensor networks (WSNs) in unlicensed bands as wireless systems proliferate on the spectrum. The design of a spectrum sharing scheme for WSNs to enable coexistence with geographically collocated heterogeneous wireless systems having multiple parallel interfering channels is a persistent challenge. In this context, interference identification and channel ranking in terms of spectrum access opportunities are addressed in this paper. The goal is to develop a low complexity channel ranking algorithm from channel energy measurements at sensors when a packet-reception-ratio to signal-to-interference-and-noise-ratio (PRR-SINR) interference model is unavailable at network initialization phase. The interference characterizing estimators, temporal occupancy and strength level of a channel, are proposed for interference identification. The effectiveness of the estimators is tested on a sensor platform at 2.4 GHz ISM band under interference from WLAN. Subsequently, the impact of the interference estimators on a channel quality from a receiver perspective is determined with a decision theoretic approach. The estimators are weighted according to their influence on the fitness of a channel and channel ranking is established. The proposed channel ranking achieves a significant gain over heuristic channel ranking (HCR) and gives an accurate interference profile of the channels.     

A MAC protocol by applying staggered channel model for cognitive radio networks

Cognitive radio (CR) is a novel and promising spectrum management technique, which aims to cope with the spectrum scarcity problem occurring in unlicensed bands and alleviate the inefficient spectrum utilization of licensed bands. To ensure that the operation of licensed users will not be adversely affected and that the licensed bands can be efficiently utilized by unlicensed users, this paper proposes a cognitive radio MAC protocol called SMC-CR-MAC. When any primary user is detected, the proposed SMC-CR-MAC protocol applies Contiguous Channel Switching and Sender-Receiver Channel Swap rules to cope with the rendezvous, packet collision, and channel congestion problems. Simulation results show that the proposed SMC-CR-MAC protocol can significantly improve the network performance in terms of utilization of licensed bands, standard deviation of traffic load on each channel, and probability of successful rendezvous.