This paper proposes an adaptive joint source and channel coding scheme for H.264 video multicast over wireless LAN which takes into account the user topology changes and varying channel conditions of multiple users, a...This paper proposes an adaptive joint source and channel coding scheme for H.264 video multicast over wireless LAN which takes into account the user topology changes and varying channel conditions of multiple users, and dynamically allocates available bandwidth between source coding and channel coding, with the goal to optimize the overall system performance. In particular, source resilience and error correction are considered jointly in the scheme to achieve the optimal performance. And a channel estimation algorithm based on the average packet loss rate and the variance of packet loss rate is proposed also. Two overall performance criteria for video multicast are investigated and experimental results are presented to show the improvement obtained by the scheme.展开更多
To achieve much efficient multimedia transmission over an error-prone wireless network, there are still some problem must to be solved, especially in energy limited wireless sensor network. In this paper, we propose a...To achieve much efficient multimedia transmission over an error-prone wireless network, there are still some problem must to be solved, especially in energy limited wireless sensor network. In this paper, we propose a joint detection based on Schur Algorithm for image wireless transmission over wireless sensor network. To eliminate error transmissions and save transmission energy, we combine Schur algorithm with joint dynamic detection for wireless transmission of JPEG 2000 encoded image which we proposed in [1]. Schur algorithm is used to computing the decomposition of system matrix to decrease the computational complexity. We de-scribe our transmission protocol, and report on its performance evaluation using a simulation testbed we have designed for this purpose. Our results clearly indicate that our method could approach efficient images transmission in wireless sensor network and the transmission errors are significantly reduced when compared to regular transmissions.展开更多
Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correlation structures and enables new distributed coding algorithms for multiple signal ensembles in wireless senso...Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correlation structures and enables new distributed coding algorithms for multiple signal ensembles in wireless sensor networks. The DCS theory rests on the joint sparsity of a multi-signal ensemble. In this paper we propose a new mobile-agent-based Adaptive Data Fusion (ADF) algorithm to determine the minimum number of measurements each node required for perfectly joint reconstruction of multiple signal ensembles. We theoretically show that ADF provides the optimal strategy with as minimum total number of measurements as possible and hence reduces communication cost and network load. Simulation results indicate that ADF enjoys better performance than DCS and mobile-agent-based full data fusion algorithm including reconstruction performance and network energy efficiency.展开更多
无线图像传输面临着带宽和计算资源的双重挑战,在节点计算能力有限的物联网等应用场景中尤为突出.联合信源信道编码(Joint Source-Channel Coding,JSCC)能够同时优化信源和信道编码,逐渐成为无线图像传输中一个重要研究方向.近年来,基...无线图像传输面临着带宽和计算资源的双重挑战,在节点计算能力有限的物联网等应用场景中尤为突出.联合信源信道编码(Joint Source-Channel Coding,JSCC)能够同时优化信源和信道编码,逐渐成为无线图像传输中一个重要研究方向.近年来,基于深度学习的JSCC方法受到广泛关注,其通过端到端训练实现编码器与解码器的联合优化.然而,大多数基于深度学习的JSCC方法的编码器涉及大量的线性与非线性运算,导致计算复杂度较高,难以应用于物联网边缘计算节点等计算资源受限的设备.为实现轻量化的编码过程,本文提出了一种基于深度压缩感知的联合信源信道编码方法BCS-JSCC(Block Compressive Sensing-Joint Source Channel Coding),实现对于编解码器的端到端优化.该方法在编码端设计可学习尺度二值化测量的压缩感知采样,实现噪声环境下匹配解码器的轻量化编码方法;在解码端,基于MMSE(Minimum Mean Squared Error)准则求解测量值传输的线性逆问题,获得信道噪声敏感的初始重建,抑制噪声对参数复用重建网络的影响.与现有的基于深度学习的JSCC方法相比,在保持编码端每像素浮点计算次数(FLOating Point operations per pixel,FLOPs per pixel)相同的条件下,本文所提出的BCS-JSCC方法在高信噪比条件下可以取得更好的传输性能.在低算力(0.10 K FLOPs/pixel)情况下,优势更为明显.本文提出的BCS-JSCC方法编码器构造简单、计算量低,适用于物联网边缘计算节点等低算力设备部署.展开更多
文摘This paper proposes an adaptive joint source and channel coding scheme for H.264 video multicast over wireless LAN which takes into account the user topology changes and varying channel conditions of multiple users, and dynamically allocates available bandwidth between source coding and channel coding, with the goal to optimize the overall system performance. In particular, source resilience and error correction are considered jointly in the scheme to achieve the optimal performance. And a channel estimation algorithm based on the average packet loss rate and the variance of packet loss rate is proposed also. Two overall performance criteria for video multicast are investigated and experimental results are presented to show the improvement obtained by the scheme.
文摘To achieve much efficient multimedia transmission over an error-prone wireless network, there are still some problem must to be solved, especially in energy limited wireless sensor network. In this paper, we propose a joint detection based on Schur Algorithm for image wireless transmission over wireless sensor network. To eliminate error transmissions and save transmission energy, we combine Schur algorithm with joint dynamic detection for wireless transmission of JPEG 2000 encoded image which we proposed in [1]. Schur algorithm is used to computing the decomposition of system matrix to decrease the computational complexity. We de-scribe our transmission protocol, and report on its performance evaluation using a simulation testbed we have designed for this purpose. Our results clearly indicate that our method could approach efficient images transmission in wireless sensor network and the transmission errors are significantly reduced when compared to regular transmissions.
文摘Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correlation structures and enables new distributed coding algorithms for multiple signal ensembles in wireless sensor networks. The DCS theory rests on the joint sparsity of a multi-signal ensemble. In this paper we propose a new mobile-agent-based Adaptive Data Fusion (ADF) algorithm to determine the minimum number of measurements each node required for perfectly joint reconstruction of multiple signal ensembles. We theoretically show that ADF provides the optimal strategy with as minimum total number of measurements as possible and hence reduces communication cost and network load. Simulation results indicate that ADF enjoys better performance than DCS and mobile-agent-based full data fusion algorithm including reconstruction performance and network energy efficiency.
文摘无线图像传输面临着带宽和计算资源的双重挑战,在节点计算能力有限的物联网等应用场景中尤为突出.联合信源信道编码(Joint Source-Channel Coding,JSCC)能够同时优化信源和信道编码,逐渐成为无线图像传输中一个重要研究方向.近年来,基于深度学习的JSCC方法受到广泛关注,其通过端到端训练实现编码器与解码器的联合优化.然而,大多数基于深度学习的JSCC方法的编码器涉及大量的线性与非线性运算,导致计算复杂度较高,难以应用于物联网边缘计算节点等计算资源受限的设备.为实现轻量化的编码过程,本文提出了一种基于深度压缩感知的联合信源信道编码方法BCS-JSCC(Block Compressive Sensing-Joint Source Channel Coding),实现对于编解码器的端到端优化.该方法在编码端设计可学习尺度二值化测量的压缩感知采样,实现噪声环境下匹配解码器的轻量化编码方法;在解码端,基于MMSE(Minimum Mean Squared Error)准则求解测量值传输的线性逆问题,获得信道噪声敏感的初始重建,抑制噪声对参数复用重建网络的影响.与现有的基于深度学习的JSCC方法相比,在保持编码端每像素浮点计算次数(FLOating Point operations per pixel,FLOPs per pixel)相同的条件下,本文所提出的BCS-JSCC方法在高信噪比条件下可以取得更好的传输性能.在低算力(0.10 K FLOPs/pixel)情况下,优势更为明显.本文提出的BCS-JSCC方法编码器构造简单、计算量低,适用于物联网边缘计算节点等低算力设备部署.
