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     

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.     

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。     

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。     

分布式协作通信网络中的MAC层协议

分布式协作网络通过节点间的相互合作来达到网络资源的共享,然而如何设计高效的媒体访问控制（MAC）层协议是分布式协作通信网络中的核心问题之一。文章基于MAC层协作的动机,探讨了分布式协作网络中MAC层协议设计所面临的问题和挑战,基于近年来涌现的典型协作MAC协议和协议性能的分析,得出结论：只有全面考虑分布式网络的特点和需求,合理地设计协议的各个环节,才能使协同通信技术在分布式网络中得到更好应用。     

MAC Protocols for Distributed Cooperative Communication Networks

Distributed cooperative networks use the cooperation among nodes to fulfill network resource sharing. However,designing an efficient Media Access Control (MAC) protocol is a key issue for the distributed cooperative network. Based on the principle of MAC-layer cooperation,this paper discusses problems and challenges for MAC protocol design in the distributed cooperative network. Through the analysis of typical cooperative MAC protocols and their performance,this paper concludes that only a reasonable MAC pr...     

Security and Privacy for Distributed Multimedia Sensor Networks

There is a critical need to provide privacy and security assurances for distributed multimedia sensor networking in applications including military surveillance and healthcare monitoring. Such guarantees enable the widespread adoption of such information systems, leading to large-scale societal benefit. To effectively address protection and reliability issues, secure communications and processing must be considered from system inception. Due to the emerging nature of broadband sensor systems, this provides fertile research ground for proposing more paradigm-shifting approaches. This paper discusses issues in designing for security and privacy in distributed multimedia sensor networks. We introduce the heterogeneous lightweight sensornets for trusted visual computing framework for distributed multimedia sensor networks. Protection issues within this architecture are analyzed, leading to the development of open research problems including secure routing in emerging free-space optical sensor networks and distributed privacy for vision-rich sensor networking. Proposed solutions to these problems are presented, demonstrating the necessary interaction among signal processing, networking, and cryptography.     

OFDM-UWB系统的自适应联合信道估计算法

提出了一种新的MB-OFDM-UWB自适应联合信道估计算法,该算法首先对基于DFT的信道估计算法进行改进,使其能自适应的调整滤波窗口的大小。然后利用改进后的算法与梳状导频估计相结合。算法的优点在于能根据不同的信道的统计特征,自适应调整滤波器窗口的大小,从而减少了噪声对信道估计的影响,并保留了梳状导频估计能快速跟踪信道变化的特性。仿真结果表明,该算法能很好地适用于UWB室内信道。     

Joint Synchronization and Channel Estimation for OFDM Systems

1Introduction Orthogonal Frequency Division Multiplexing(OFDM) has become a popular technique for transmis-sion of signals over wireless channels . OFDMhas beenadoptedinseveral wireless standards such as Digital Au-dio Broadcasting ( DAB) , Digital Video Broadcasting(DVB-T) ,the IEEE 802 .11a[1]Local Area Network(LAN) standard and high performance LAN type 2(HIPERLAN/2)[2]standard. OFDMis also being pur-sued for Dedicated Short-Range Communications(DSRC) for road side to …     

A Likelihood-Based Multiple Access for Estimation in Sensor Networks

In a wireless sensor network (WSN), the nodes collect independent observations about a nonrandom parameter thetas to be estimated, and deliver informations to a fusion center (FC) by transmitting suitable waveforms through a common multiple access channel (MAC). The FC implements some appropriate fusion rule and outputs the final estimate of thetas. In this paper, we introduce a new access/estimation scheme, here referred to as likelihood-based multiple access (LBMA), and prove it to be asymptotically efficient in the limit of increasingly large number of sensors , when the used bandwidth is allowed to scale as W ~cap^alpha,O.5 < alpha < 1 . The proposed approach is easy to implement, and simply relies upon the very basic property that the log likelihood is additive for independent observations, and upon the fact that the (noiseless) output of the MAC is just the sum of its inputs. Thus, the optimal fusion rule is automatically implemented by the MAC itself.     

Distributed Energy Optimization for Target Tracking in Wireless Sensor Networks

Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrics are calculated by grid exclusion algorithm and Dijkstra's algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combining the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection.