Joint Source–Channel Codes for MIMO Block-Fading Channels

We consider transmission of a continuous amplitude source over an L-block Rayleigh-fading M_t x M_r multiple-input multiple-output (MIMO) channel when the channel state information is only available at the receiver. Since the channel is not ergodic, Shannon's source-channel separation theorem becomes obsolete and the optimal performance requires a joint source-channel approach. Our goal is to minimize the expected end-to-end distortion, particularly in the high signal-to-noise ratio (SNR) regime. The figure of merit is the distortion exponent, defined as the exponential decay rate of the expected distortion with increasing SNR. We provide an upper bound and lower bounds for the distortion exponent with respect to the bandwidth ratio among the channel and source bandwidths. For the lower bounds, we analyze three different strategies based on layered source coding concatenated with progressive superposition or hybrid digital/analog transmission. In each case, by adjusting the system parameters we optimize the distortion exponent as a function of the bandwidth ratio. We prove that the distortion exponent upper bound can be achieved when the channel has only one degree of freedom, that is L = 1, and min{M_t ,M_r} =1. When we have more degrees of freedom, our achievable distortion exponents meet the upper bound for only certain ranges of the bandwidth ratio. We demonstrate that our results, which were derived for a complex Gaussian source, can be extended to more general source distributions as well.     

Joint Source–Channel Coding Error Exponent for Discrete Communication Systems With Markovian Memory

We study the error exponent, $E_{J}$, for reliably transmitting a discrete stationary ergodic Markov (SEM) source ${mmb Q}$ over a discrete channel ${mmb W}$ with additive SEM noise via a joint source–channel (JSC) code. We first establish an upper bound for $E_{J}$ in terms of the RÉnyi entropy rates of the source and noise processes. We next investigate the analytical computation of $E_{J}$ by comparing our bound with Gallager's lower bound (1968) when the latter one is specialized to the SEM source–channel system. We also note that both bounds can be represented in CsiszÁr's form (1980), as the minimum of the sum of the source and channel error exponents. Our results provide us with the tools to systematically compare $E_{J}$ with the tandem (separate) coding exponent $E_{T}$. We show that as in the case of memoryless source–channel pairs $E_{J}leq 2E_{T}$ and we provide explicit conditions for which $E_{J}> E_{T}$. Numerical results indicate that $E_{J}thickapprox 2E_{T}$ for many SEM source–channel pairs, hence illustrating a substantial advantage of JSC coding over tandem coding for systems with Markovian memory.      

Joint Source–Channel Turbo Techniques for Discrete-Valued Sources: From Theory to Practice

The principles which have been prevailing so far for designing communication systems rely on Shannon's source and channel coding separation theorem. This theorem states that source and channel optimum performance bounds can be approached as close as desired by designing independently the source and channel coding strategies. However, this theorem holds only under asymptotic conditions, where both codes are allowed infinite length and complexity. If the design of the system is constrained in terms of delay and complexity, if the sources are not stationary, or if the channels are nonergodic, separate design and optimization of the source and channel coders can be largely suboptimal. For practical systems, joint source-channel (de)coding may reduce the end-to-end distortion. It is one of the aspects covered by the term cross-layer design, meaning a rethinking of the layer separation principle. This article focuses on recent developments of joint source-channel turbo coding and decoding techniques, which are described in the framework of normal factor graphs. The scope is restricted to lossless compression and discrete-valued sources. The presented techniques can be applied to the quantized values of a lossy source codec but the quantizer itself and its impact are not considered.     

Distributed Estimation in Energy-Constrained Wireless Sensor Networks

In this paper, we consider distributed estimation of a noise-corrupted deterministic parameter in energy-constrained wireless sensor networks from energy-distortion perspective. Given a total energy budget allowable to be used by all sensors, there exists a tradeoff between the subset of active sensors and the energy used by each active sensor in order to minimize the estimation MSE. To determine the optimal quantization bit rate and transmission energy of each sensor, a concept of equivalent unit-energy MSE function is introduced. Based on this concept, an optimal energy-constrained distributed estimation algorithm for homogeneous sensor networks and a quasi-optimal energy-constrained distributed estimation algorithm for heterogeneous sensor networks are proposed. Moreover, the theoretical energy-distortion performance bound for distributed estimation is addressed and it is shown that the proposed algorithm is quasi-optimal within a factor 2 of the theoretical lower bound. Simulation results also show that the proposed method can achieve a significant reduction in the estimation MSE when compared with other uniform schemes. Finally, the proposed algorithm is easy to implement in a distributed manner and it adapts well to the dynamic sensor environments.     

无人飞行器通信中联合信道估计和Turbo均衡

Turbo均衡技术能有效克服信道多径、消除符号间干扰(ISI),从而提高接收机整体性能。针对无人飞行器地空视距链路信道这种快时变多径信道,提出了一种联合信道估计和Turbo均衡的迭代接收算法。该算法采用单载波调制体制,通过信道估计和Turbo均衡之间相互反馈软信息来消除ISI。Matlab仿真结果表明,经过2次以上的迭代后,相比较于传统的单载波频域Turbo均衡算法,新算法的误码率性能得到了显著改善。     

Distributed Detection in Wireless Sensor Networks Using A Multiple Access Channel

Distributed detection in a one-dimensional (1-D) sensor network with correlated sensor observations, as exemplified by two problems-detection of a deterministic signal in correlated Gaussian noise and detection of a first-order autoregressive [AR(1)] signal in independent Gaussian noise, is studied in this paper. In contrast with the traditional approach where a bank of dedicated parallel access channels (PAC) is used for transmitting the sensor observations to the fusion center, we explore the possibility of employing a shared multiple access channel (MAC), which significantly reduces the bandwidth requirement or detection delay. We assume that local observations are mapped according to a certain function subject to a power constraint. Using the large deviation approach, we demonstrate that for the deterministic signal in correlated noise problem, with a specially chosen mapping rule, MAC fusion achieves the same asymptotic performance as centralized detection under the average power constraint (APC), while there is always a loss in error exponents associated with PAC fusion. Under the total power constraint (TPC), MAC fusion still results in exponential decay in error exponents with the number of sensors, while PAC fusion does not. For the AR signal problem, we propose a suboptimal MAC mapping rule which performs closely to centralized detection for weakly correlated signals at almost all signal-to-noise ratio (SNR) values, and for heavily correlated signals when SNR is either high or low. Finally, we show that although the lack of MAC synchronization always causes a degradation in error exponents, such degradation is negligible when the phase mismatch among sensors is sufficiently small     

Distributed Channel Allocation Using Kernel Density Estimation in Cognitive Radio Networks

Typical channel allocation algorithms for secondary users do not include processes to reduce the frequency of switching from one channel to another caused by random interruptions by primary users, which results in high packet drops and delays. In this letter, with the purpose of decreasing the number of switches made between channels, we propose a nonparametric channel allocation algorithm that uses robust kernel density estimation to effectively schedule idle channel resources. Experiment and simulation results demonstrate that the proposed algorithm outperforms both random and parametric channel allocation algorithms in terms of throughput and packet drops.     

Non-stationary Channel Estimation with Diffusion Adaptation Strategies Over Distributed Networks

In this paper we address non-stationary channel estimation problem with diffusion least mean square algorithm in distributed adaptive wireless sensor networks. Here we estimate channel coefficients or taps that are produced with Rayleigh fading models. All detailed explanations regarding to this fading channel type are presented and it is explained that how we can extend channel estimation with sensor networks to other newly presented channel types. We use the tracking performance analysis of diffusion cooperation over adaptive sensor networks to investigate the reliability of used algorithms and show the link between channel estimation problem and tracking a time varying entity. Theoretical analyzes are performed and the results are compared with simulation performance diagrams. It is proven that there is a reasonable match between these two outcomes. We present our results with the mean square deviation criteria.     

Cyclic Distributed Space–Time Codes for Wireless Relay Networks With No Channel Information

In this paper, we present a coding strategy for half duplex wireless relay networks, where we assume no channel knowledge at any of the transmitter, receiver, or relays. The coding scheme uses distributed space-time coding, that is, the relay nodes cooperate to encode the transmitted signal so that the receiver senses a space-time codeword. It is inspired by noncoherent differential techniques. The proposed strategy is available for any number of relays nodes. It is analyzed, and shown to yield a diversity linear in the number of relays. We also study the resistance of the scheme to relay node failures, and show that a network with R relay nodes and d of them down behaves, as far as diversity is concerned, as a network with R-d nodes. Finally, our construction can be easily generalized to the case where the transmitter and receiver nodes have several antennas.     

双向MIMO中继系统中一种低复杂度的联合信道估计方法

对于双向多输入多输出(MIMO)中继系统,如何在减少中继负担的情况下获得精确的信道状态信息(CSI)成为信道估计的一个难点。该文针对双向MIMO中继系统,提出一种低复杂度的联合信道估计方法。所提方法在两个用户端同时发送正交信道训练信号至中继,中继采用所设计的放大因子放大所接收的信号并转发至两个用户。每个用户对所接收的信号构造平行因子(PARAFAC)模型,并根据实际系统要求,分别设计了迭代和非迭代的两种拟合算法对PARAFAC模型进行拟合,从而联合估计出所有信道的CSI。所提信道估计方法无需在中继处进行信道估计,减轻了中继的负担。与已有信道估计方法相比,所提方法设计灵活,采用的拟合算法具有较低的复杂度,而且在使用较少信道训练信号的情况下具有较高的信道估计精度。     

无线传感器网络中基于多比特量化数据的滚动时域状态估计

该文基于多比特的量化策略,提出了无线传感器网络中多比特分布式滚动时域状态估计算法。每个传感器节点预先设定一个包含多个阈值的阈值簿,利用这个阈值簿将观测值量化成多比特,融合中心接收这些比特信息运用滚动时域的思想得到系统的状态估计值,与预期相同。仿真结果表明阈值簿中阈值个数越多则估计的结果会越精确。与单比特滚动时域状态估计方法相比,该方法避免了每一时刻传感器节点接收融合中心的反馈状态估计值用来设计阈值,并且在多比特信息下状态估计值的精度更高。     

基于分布式空频码的协作通信系统的信道估计及优化

该文研究了协作通信中分布式空频码系统的信道估计问题。为使信道可辨识,在中继节点处加入了循环卷积滤波器(CCF)。推导了训练模式中导频序列和中继节点处CCF的准最优设计方案,并由此得到了源节点与中继节点之间功率分配的闭合表达式。计算机仿真验证了本文工作的有效性。     

分布式机器学习在RIS辅助的无线信道估计中的应用

无线信道估计是部署可重构智能超表面(Reconfigurable Intelligent Surface, RIS)辅助通信系统的关键与前提,然而下行链路传输环境下信道估计困难且导频开销大是对智能超表面辅助通信的重大挑战。针对以上问题,提出了一种基于分布式机器学习(Distributed Machine Learning, DML)训练模型的区域交集切换方案。首先,建立了一个多用户共享的下行信道估计神经网络,通过DML技术协同用户与基站训练网络模型。其次,搭建分层次神经网络结构对用户区域信道进行分类和特征提取。最后,针对用户处于相邻信道交集位置问题采用特征区域模型融合。仿真结果表明,基于区域交集的DML模型方案能在减少信道训练导频开销的同时最大化信道估计的精准性能。     

多传感器分布式检测系统的跨层优化设计

目标检测就是判断被监测对象是否出现,是无线传感器网络应用的首要前提.由于传感器节点协作执行检测任务,因此,应用于通信网中的解耦和最大吞吐量的网络设计不能使系统检测性能最优.提出了集成物理层、MAC层和应用层的跨层设计,设计了感知信息质量、信道状态和有效能量协同的传输控制策略,该传输策略可以有效地调节节点和融合中心的通信使系统的检测性能最优.最后,通过解决非线性约束优化问题分别为ALOHA和TDMA传感器网络得到最优的设计变量,并与典型的解耦和最大吞吐量设计比较检测性能增益.     

无线传感器网络中的分布式压缩感知技术

本文对无线传感器网络中分布式压缩感知的几个关键技术进行了详细阐述。首先,简要论述了压缩感知方法的基本原理;其次,分析了无线传感器网络中的分布式压缩感知技术与单个信号的压缩感知技术的区别,针对无线传感器网络中联合稀疏模型的建立、分布式信源编码以及联合稀疏信号的重构技术等问题进行了详细讨论;分析了在无线传感器网络的实际应用中,联合稀疏模型、分布式信源编码方式及联合稀疏信号重构方法的性能。最后,对无线传感器网络中分布式压缩感知技术的未来研究方向进行了展望。      

一种传感器网络的分布式信任模型

传感器网络面临的攻击很多,尤其是内部节点进行的攻击带来的危险最大。同时,传感器网络特别是军用传感器网络不能采用建立信任中心的信任模型,导致其内部节点的恶意行为更难以控制。通过引入基于CPK认证的源地址认证模块,使用第三方推荐信任,并结合多种相关的网络行为属性,能够为用户提供更准确可信的信任度计算能力作为参考依据,使用户可以根据信任度对权限进行控制,以达到控制恶意行为的目的。     

基于点割集的无线带状传感网分布式寿命预测算法

无线带状传感网是一种典型传感网应用模式,现有的网络生存时间模型大多面向特定的布设模型和工作模式,不能直接应用于带状传感网寿命预测。该文提出一种基于点割集的带状网分布式寿命预测算法。该算法仅利用邻居节点的位置和剩余生存时间等局部信息求解点割集及局部网络生存时间,通过相邻节点间交互包含局部网络生存时间的信令,估计整个网络的剩余生存时间。仿真结果表明,与现有基于梯度的网络生命估计算法相比,该文算法能准确且实时地估计网络的生存时间。     

Multitarget Tracking in Distributed Sensor Networks

In this article, a survey of techniques for tracking multiple targets in distributed sensor networks is provided and introduce some recent developments. The single target tracking in distributed sensor networks is reviewed. The tracking and resource management issues can be readily extended to MTT. The MTT problem is also briefly reviewed and describe the traditional approaches in centralized systems. Then focus on MTT in resource-constrained sensor networks and present two distinct example methods demonstrating how limited resources can be utilized in MTT applications. Finally, the most important remaining problems are discussed and suggest future directions     

Distributed Active Sensor Scheduling for Target Tracking in Ultrasonic Sensor Networks

Active ultrasonic sensors for target tracking application may suffer from inter-sensor-interference if these highly dense deployed sensors are not scheduled, which can degrade the tracking performance. In this paper, we propose a dynamic distributed sensor scheduling (DSS) scheme, where the tasking sensor is elected spontaneously from the sensors with pending sensing tasks via random competition based on Carrier Sense Multiple Access (CSMA). The channel will be released immediately when sensing task is completed. Both simulation results and testbed experiment demonstrate the effectiveness of DSS scheme for large scale sensor networks in terms of system scalability and high tracking performance.     

非线性功放信道下联合信道估计研究

为了解决非线性放大器在60 GHz毫米波信道中造成的非线性影响,提出了基于马尔科夫蒙特卡洛（Markov Chain Monte Carlo,MCMC）算法的联合信道估计与信号检测技术。采用的是MCMC算法中的Metropolis-Hastings方法,在非线性放大器及信道参数未知的情况下,通过被非线性和噪声污染的输出信号（观测信号）来估计非线性放大器的参数,检测输入信号被称为盲均衡技术。仿真结果给出了非线性参数与真实值的对比图以及随SNR变化的误比特率,性能优越。