Gamma-Gamma大气湍流下相干光通信分集接收技术研究

大气湍流引起的光强闪烁使得自由空间光通信(FSO)系统性能恶化,而分集接收技术可有效改善这一影响。为进一步分析分集接收技术对相干接收系统性能的影响,基于二进制相移键控(BPSK)调制和外差相干接收技术,建立了Gamma-Gamma大气湍流信道模型下自由空间光通信分集接收系统模型。在不同大气湍流强度和接收天线数情况下,分别采取最大比合并(MRC)、选择合并(SC)和等增益合并(EGC),分析了对应的系统误码率(BER)和通信中断概率(OP),并与相同接收口径下的传统单天线接收系统的性能进行了比较。结果表明:MRC、EGC分集接收对大气湍流下的相干通信系统性能有明显改善,而SC分集接收仅当平均信噪比低于某一阈值时对相干通信系统的性能有所优化。     

相干水声通信幅相加权空间分集均衡算法

水声信道的典型特点为多普勒频移严重、可利用带宽窄以及强多径干扰。空间分集均衡技术是相干水声通信中克服信道多径干扰,消除码间干扰的一种有效手段。为了极大化地输出阵增益,结合无源相位共轭方法和多通道均衡算法,本文设计了组合信噪干比的全新信道评价方法。利用改进的Sigmoid函数对各通道接收信号的幅度进行加权处理;采用二阶锁相环跟踪各通道信号的相位变化,实现各通道信号同相累加。将各通道低通滤波后的信号能量归一化,采用了分数阶-判决反馈分集均衡器,加入各通道权重系数实现了水声通信系统的分集均衡接收。仿真结果和湖试数据处理结果均表明,优化的幅相加权分集均衡接收算法能抵消多径和噪声的干扰,性能优于等增益合并接收算法。湖试数据处理结果误码率降低了1.8%。      

湍流信道无线光通信中的分集接收技术

大气分子的吸收、散射和大气湍流等因素引起的光强闪烁严重影响无线光通信系统的性能,导致激光束能量衰减、信噪比下降,而分集接收技术能有效地克服这种影响。采用开关键控(OOK)调制,建立了强湍流模型-K分布模型下无线光通信空间接收分集系统模型,在不同信道参数和接收天线数下,分别对比分析了最大比合并(MRC)、等增益合并(EGC)和选择合并(SC)的差错性能。仿真结果表明,分集接收能在很大程度上改善大气激光通信的性能,具有较强的抗大气信道衰落能力。三种合并算法中,MRC性能最优,EGC次之,SC性能最差。     

无线携能通信系统中基于能量获取比例公平的波束成形设计

针对无线携能通信系统中存在能量获取不均衡的问题, 提出了一种基于能量获取比例公平的波束成形设计方案. 该方案在满足信息接收者的信干噪比以及发送端的最大发送功率等约束条件的基础上, 通过优化波束矢量实现能量获取的比例公平. 此设计在数学上是一个很难直接求解的非凸优化问题.为此, 本文首先利用半定松弛技术将其转换为半定规划问题, 然后结合二分法提出了可以获得最优波束矢量的迭代算法.此外, 在发送端仅知道部分信道状态信息且知道信道误差范围的情况下, 采用最差性能最优的方法对原优化问题进行了鲁棒波束成形设计, 并提出了相应的迭代算法. 仿真结果表明所提算法均可实现能量获取的比例公平且性能达到全局最优.     

室内LED可见光通信的分集接收系统

根据分集接收的原理,采用选择合并的方式设计了应用于LED可见光通信的信号选择接收装置.理论分析和测量结果表明:该装置能增加信道的数量并自动选择出信噪比最大的信号,可以有效地避免信道中断.     

基于水声信道传播时延排序的分层空时信号检测

基于分层空时编码的多输入多输出技术是一种极具潜力的高速水声通信技术, 但要实现这种潜力需要复杂的空时信号处理方法, 以抵消来自水声信道的多径干扰和异步到达干扰, 以及叠加在接收端的各层信号之间的干扰. 对低复杂度的空时信号处理方案进行了研究, 提出了一种基于子信道传播时延排序的有序连续干扰抵消信号检测算法, 利用子信道间的传播时延差, 实现可使差错概率最小的最佳检测排序; 给出了利用信道估计, 以极低的计算量确定排序的方法, 从而可以大幅降低信号检测的计算复杂度. 采用低复杂度的单载波频域均衡来抵消水声信道中的码间干扰和异步到达干扰. 仿真结果表明, 基于时延排序的信号处理算法可以获得检测性能的改善, 而且性能增益在高数据率时更加显著. 研究结果表明, 采用有效的信号处理方法可使水声信道中造成信号检测干扰的传播时延成为改善系统性能的有利因素.     

水声通信中后验符号概率密度分布及概率成形容量分析

考虑到实际水声信道复杂特性对水声通信的影响,对系统的通信容量进行计算分析,并研究了一种适用于水声信道的容量评估算法。文中通过对不同条件下的均衡器输出的后验符号特性进行统计分析,提出了一种基于后验高斯分布的符号方差分段转移函数模型。在此基础上,采用Maxwell-Boltzmann分布来改变均衡器输出符号的成形分布,即引入概率成形技术对实际的水声通信系统容量进行评估分析。最后通过对水池和湖试多径信道数据的处理,获得了不同调制方式下的通信容量。结果显示,在特定的信噪比区间内,概率成形技术比传统的等概率映射的容量提升最大可达0.5 bits/symbol。      

任意分集方式多输入多输出无线通信系统的统计信道建模

针对极化分集多输入多输出系统的传统统计模型存 在着对不同极化方向上的功率变化设置不合理的问题, 提出一种功率矩阵对其进行纠正; 对采用不同分集方式的多输入多输出系统信道建模的原理进行分析, 提出对功率矩阵和相关矩阵进行复合计算的方法, 建立了一种适用于任意分集方式多输入多输出系统的统计信道模型, 该模型能够确切反映出同时受到距离分集和极化分集影响的多输入多输出系统之统计特征.     

深海远程正交频分复用水声通信簇约束的分布式 压缩感知信道估计*

在深海远程正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)水声通信中,信道时延较长,导致信道频率选择性衰落严重,传统的压缩感知信道估计算法性能大幅下降。为此,本文利用深海远程信道在一定时间内具有相关性并呈较稳定簇状分布的特点,在分布式压缩感知信道估计中引入簇区域信息,进行簇约束的多数据块联合稀疏信道估计,提出一种簇约束的分布式压缩感知信道估计方法,并在深海开展了定点远程水声通信实验进行验证,实验结果表明,与传统的分布式压缩感知信道估计算法相比,本方法的能够降低50%左右的误码率。     

深海远程正交频分复用水声通信块间迭代稀疏信道估计方法

在深海远程正交频分复用(OFDM)水声通信中,信道时延长、频率选择性衰落严重,传统的块独立压缩感知稀疏估计需要较高导频插入密度才能保证一定的估计性能,通信频谱利用率较低。提出了一种基于信道稀疏时变建模的块间迭代信道估计方法,利用深海信道在两个相邻OFDM数据块之间的时间相关性建立块间信道稀疏多途结构的时变关系,在此基础上,对传统稀疏信道估计算法中的候选字典矩阵的字典原子进行删减并改进优化方程,实现了对前一数据块所估信道信息的有效利用,显著降低了信道估计所需的导频插入密度。在深海不同接收深度、不同距离条件下开展了海试验证,实验结果表明,与传统稀疏信道估计方法相比,本方法在导频插入密度减半的条件下可达到优于传统方法的估计性能。     

单载波频域均衡中的水声信道频域响应与噪声估计

本文对单载波频域均衡(SC-FDE)中的信道和噪声估计进行研究,理论分析了形成误码率平台的可能原因,提出了基于Chu序列的联合信道时频域响应和噪声功率估计算法.利用水声信道响应的稀疏特性和门限确定信道能量集中区域,进行信道估计的去噪处理和噪声功率计算.借助于水声射线模型对所提出的算法进行了仿真,分析了噪声估计对SC-FDE性能的重要影响.仿真结果表明,所提出的信道和噪声估计算法可以有效减缓或消除SC-FDE中的误码率平台.     

Acoustic communication in deep water exploiting multiple beams with a horizontal array

A recent experiment showed that coherent long-range acoustic communication (200-300?Hz) is feasible in deep water over a ～550-km range between a source and a horizontal towed array (～100?m aperture). The low input signal-to-noise ratio (SNR) (e.g., -10?dB) at an element level required conventional beamforming to enhance the SNR of the communication signals for subsequent channel equalization and decoding. This paper demonstrates that with a larger aperture (～200?m), multiple adjacent beams can be exploited to further improve the communication performance, achieving an almost error-free data rate of 100 bits/s for QPSK modulation at ～550?km range.     

Covert underwater acoustic communications

Low probability of detection (LPD) communications are conducted at a low received signal-to-noise ratio (SNR) to deter eavesdroppers to sense the presence of the transmitted signal. Successful detection at intended receiver heavily relies on the processing gain achieved by employing the direct-sequence spread-spectrum (DSSS) technique. For scenarios that lack a sufficiently low SNR to maintain LPD, another metric, referred to as low probability of interception (LPI), is of interest to protect the privacy of the transmitted information. If covert communications take place in underwater acoustic (UWA) environments, then additional challenges are present. The time-varying nature of the UWA channel prevents the employment of a long spreading waveform. Furthermore, UWA environments are frequency-selective channels with long memory, which imposes challenges to the design of the spreading waveform. In this paper, a covert UWA communication system that adopts the DSSS technique and a coherent RAKE receiver is investigated. Emphasis is placed on the design of a spreading waveform that not only accounts for the transceiver structure and frequency-selective nature of the UWA channel, but also possesses a superior LPI. The proposed techniques are evaluated using both simulated and SPACE'08 in-water experimental data.     

近程高速水声毫米波通信仿真与试验验证*

水声信道的严重双扩特性极大地限制了水声高速通信的稳健性。针对近程高速水声通信技术的需求,该文基于水声毫米波频段,提出了一种基于超奈奎斯特发射技术及高阶调制技术的单载波的水声毫米波通信技术。而超奈奎斯特发射技术以及高阶调制给接收机带来了极大的挑战:一是超奈奎斯特发射技术引入了严重的符号间干扰,二是高阶调制系统的符号检测对信道估计的精度要求很高。针对以上问题,该文提出了一种基于迭代信道估计技术的迭代软反馈DFE接收机技术。仿真实验表明:采用128QAM高阶调制时,在较为严重的多途衰落信道条件下,采用所提出的接收机可以在15 dB时实现无误码传输。信道水池试验证明:当通信带宽为300 kHz,通信符号率为300k符号/秒且采用64QAM调制时,在发射平台慢速运动的条件下可以实现900 kbps的净数据率,其相应的频谱利用率高达6 Bits/s/Hz。     

水声通信低复杂度最大似然联合均衡译码方法

现有Turbo均衡采用迭代联合均衡译码结构,这一迭代过程在信噪比高于某一门限后才能产生正向的迭代增益,当信道码间干扰较为严重或者采用高阶调制方式时,该信噪比门限较高;同时,现有Turbo均衡的性能还受限于其交织器长度,故当码块长度较短时,其性能并不理想。针对以上问题,提出了一种基于Spinal码的联合均衡译码方法。首先,该方法采用非迭代的最大似然联合均衡译码结构,有效避免了Turbo均衡迭代结构所带来的信噪比门限高问题;其次,该方法采用信息混合能力更强的Spinal码,有助于提高码块长度受限时的误码率性能;最后,该方法利用Spinal码可以有效增加相似信息序列间汉明距离的特点,将联合均衡译码时间复杂度降低为仅随信息序列长度与信道响应长度呈线性增长,解决了本方法难以实际应用于实时水声通信的难题。在抚仙湖湖试中,同样实现速率为3.34kbps数据通信,该方法的误码率优于Turbo均衡方法两个数量级以上。时变水声信道下的仿真以及湖试结果表明,该方法在码长受限的情况下仍在复杂水声信道中表现出更好性能,其所需的最低输入信噪比显著低于现有Turbo均衡方法。      

Long-range multi-carrier acoustic communication in deep water using a towed horizontal array

During a recent long-range acoustic communication experiment carried out in deep water, multi-carrier Orthogonal Frequency Division Multiplexing (OFDM) communication signals were transmitted with a 50 Hz bandwidth (225-275 Hz) at various source-receiver ranges from 100 to 700 km. The experiment consisted of two mobile components: (1) a source towed slowly at a speed of 2-3 knots at ～75 m depth and (2) a horizontal line array towed at 3.5 knots at a depth of ～200 m. In addition to beamforming, an interleaver gain is exploited to compensate for low signal-to-noise ratio at the expense of data rate while providing diversity in the frequency domain. Error-free performance is shown at effective data rates of 15 and 7.5 bits/s at ranges of 550 km and 700 km, respectively, by combining interleaved repetitions with low-density parity-check coding after beamforming, demonstrating the feasibility of multi-carrier OFDM communications in deep water using a towed horizontal array.     

Bandwidth-efficient frequency-domain equalization for single carrier multiple-input multiple-output underwater acoustic communications

This paper proposes a single carrier (SC) receiver scheme with bandwidth-efficient frequency-domain equalization (FDE) for underwater acoustic (UWA) communications employing multiple transducers and multiple hydrophones. Different from the FDE methods that perform FDE on a whole data block, the proposed algorithm implements an overlapped-window FDE by partitioning a large block into small subblocks. A decision-directed channel estimation scheme is incorporated with the overlapped-window FDE to track channel variations and improve the error performance. The proposed algorithm significantly increases the length of each block and keeps the same number of training symbols per block, hence achieving better data efficiency without performance degradation. The proposed scheme is tested by the undersea data collected in the Rescheduled Acoustic Communications Experiment (RACE) in March 2008. Without coding, the 2-by-12 MIMO overlapped-window FDE reduces the average bit error rate (BER) over traditional SC-FDE schemes by 74.4% and 84.6% for the 400 m and 1000 m range systems, respectively, at the same data efficiency. If the same BER performance is required, the proposed algorithm has only 8.4% transmission overhead, comparing to over 20% overhead in other existing UWA OFDM and SC-FDE systems. The improved data efficiency and/or error performance of the proposed FDE scheme is achieved by slightly increased computational complexity over traditional SC-FDE schemes.     

Experience-driven learning-based intelligent hybrid beamforming for massive MIMO mmWave communications

We have studied massive MIMO hybrid beamforming (HBF) for millimeter-wave (mmWave) communications, where the transceivers only have a few radio frequency chain (RFC) numbers compared to the number of antenna elements. We propose a hybrid beamforming design to improve the system’s spectral, hardware, and computational efficiencies, where finding the precoding and combining matrices are formulated as optimization problems with practical constraints. The series of analog phase shifters creates a unit modulus constraint, making this problem non-convex and subsequently incurring unaffordable computational complexity. Advanced deep reinforcement learning techniques effectively handle non-convex problems in many domains; therefore, we have transformed this non-convex hybrid beamforming optimization problem using a reinforcement learning framework. These frameworks are solved using advanced deep reinforcement learning techniques implemented with experience replay schemes to maximize the spectral and learning efficiencies in highly uncertain wireless environments. We developed a twin-delayed deep deterministic (TD3) policy gradient-based hybrid beamforming scheme to overcome Q-learning’s substantial overestimation. We assumed a complete channel state information (CSI) to design our beamformers and then challenged this assumption by proposing a deep reinforcement learning-based channel estimation method. We reduced hybrid beamforming complexity using soft target double deep Q-learning to exploit mmWave channel sparsity. This method allowed us to construct the analog precoder by selecting channel dominant paths. We have demonstrated that the proposed approaches improve the system’s spectral and learning efficiencies compared to prior studies. We have also demonstrated that deep reinforcement learning is a versatile technique that can unleash the power of massive MIMO hybrid beamforming in mmWave systems for next-generation wireless communication.     

稀疏贝叶斯学习水声信道估计与脉冲噪声抑制方法

水声信道具有显著的稀疏特性,利用稀疏贝叶斯学习(SBL)算法能够实现稀疏水声信道的有效估计。针对SBL计算复杂度较高的问题,将广义近似消息传递-稀疏贝叶斯学习(GAMP-SBL)引入水声信道估计。该方法在SBL的框架下结合GAMP以消息传递的方式计算信道冲激响应,能够有效降低SBL的计算复杂度。针对假设背景噪声服从高斯分布的信道估计方法在脉冲噪声环境下性能下降问题,提出了基于GAMP-SBL的脉冲噪声抑制水声信道估计方法:首先利用脉冲噪声时域稀疏特性,采用GAMP-SBL估计脉冲噪声并进行抑制,然后再次利用GAMP-SBL实现水声信道估计.基于第九次北极科考冰下脉冲噪声的两次仿真结果表明,所提出的方法在归一化均方误差上相对于未进行脉冲噪声抑制的GAMP-SBL最大分别降低了18.71%,6.61%,在信道解码前误码率上最大分别降低了1.66%,4.05%,并且相对于Clipping方法更加稳健。在信噪比为20 dB时,误码率可低于10-2。      

改进的时序多重稀疏贝叶斯学习冰下水声信道估计方法

针对时序多重稀疏贝叶斯学习信道估计方法计算复杂度高且在低信噪比时估计精度低的问题,本文提出了一种改进的时序多重稀疏贝叶斯学习正交频分复用冰下水声信道估计方法。首先,采用奇异值分解方法对接收导频矩阵进行去噪;随后利用去噪后的接收导频矩阵结合最小二乘信道估计方法获得时序多重稀疏贝叶斯信道估计的超参数矩阵、感知矩阵等先验知识;最后,利用冰下水声信道的稀疏特性和多途结构较为稳定的特点,采用时序多重稀疏贝叶斯信道估计对不同符号的冰下水声信道进行联合重建。仿真结果显示,在能量系数为0.03时,改进方法信道估计均方误差相比较于原始方法至少降低了约2.87×10-5,运算时间至少下降了约为90%。第11次北极科学考察冰下试验结果显示,改进方法的平均原始误码率略微低于原始方法,平均运算时间降低约75%。研究结果表明,利用冰下水声信道的特点,改进方法可以实现高精度冰下水声信道估计,并且有效降低系统计算复杂度。