天津大学学报
天津大學學報
천진대학학보
JOURNAL OF TIANJIN UNIVERSITY SCIENCE AND TECHNOLOGY
2013年
10期
910-916
,共7页
认知无线视觉传感网络%机会传输%跨层设计%随机次梯度法
認知無線視覺傳感網絡%機會傳輸%跨層設計%隨機次梯度法
인지무선시각전감망락%궤회전수%과층설계%수궤차제도법
cognitive wireless visual sensor network%opportunistic transmission%cross-layer design%stochastic sub-gradient method
为了充分利用空闲授权无线电频段和增强视觉信息的端到端传输质量,研究了认知无线视觉传感网络机会传输的跨层设计问题。在分析信道随机性和网络模型的基础上,把跨层设计问题表达为一个视觉信息峰值信噪比和网络平均传输时延的权衡优化问题。通过对该问题进行对偶分解和基于随机次梯度的求解方法,提出了一个分布式跨层传输优化算法。该算法不需要预先知道可用授权频段的静态概率分布,而通过节点在每个时隙中进行独立计算和局部信息交换使得上层视觉感知信息的压缩速率与底层链路机会传输自适应匹配,达到权衡优化问题的最优解,因此可以作为认知无线视觉传感网络的实用传输协议。仿真结果表明,该分布式算法能够快速收敛,并能获得与集中式最优化算法相似的性能。
為瞭充分利用空閒授權無線電頻段和增彊視覺信息的耑到耑傳輸質量,研究瞭認知無線視覺傳感網絡機會傳輸的跨層設計問題。在分析信道隨機性和網絡模型的基礎上,把跨層設計問題錶達為一箇視覺信息峰值信譟比和網絡平均傳輸時延的權衡優化問題。通過對該問題進行對偶分解和基于隨機次梯度的求解方法,提齣瞭一箇分佈式跨層傳輸優化算法。該算法不需要預先知道可用授權頻段的靜態概率分佈,而通過節點在每箇時隙中進行獨立計算和跼部信息交換使得上層視覺感知信息的壓縮速率與底層鏈路機會傳輸自適應匹配,達到權衡優化問題的最優解,因此可以作為認知無線視覺傳感網絡的實用傳輸協議。倣真結果錶明,該分佈式算法能夠快速收斂,併能穫得與集中式最優化算法相似的性能。
위료충분이용공한수권무선전빈단화증강시각신식적단도단전수질량,연구료인지무선시각전감망락궤회전수적과층설계문제。재분석신도수궤성화망락모형적기출상,파과층설계문제표체위일개시각신식봉치신조비화망락평균전수시연적권형우화문제。통과대해문제진행대우분해화기우수궤차제도적구해방법,제출료일개분포식과층전수우화산법。해산법불수요예선지도가용수권빈단적정태개솔분포,이통과절점재매개시극중진행독립계산화국부신식교환사득상층시각감지신식적압축속솔여저층련로궤회전수자괄응필배,체도권형우화문제적최우해,인차가이작위인지무선시각전감망락적실용전수협의。방진결과표명,해분포식산법능구쾌속수렴,병능획득여집중식최우화산법상사적성능。
To fully utilize the vacant licensed radio bands to enhance the transmission quality of visual information, a cross-layer design for the opportunistic transmission of cognitive wireless visual sensor network was studied. A trade-off optimiza-tion between peak signal-to-noise ratio and the average transmission delay of visual information was formulated based on the analysis of stochastic channel model and network model. By applying dual decomposition and stochastic sub-gradient method to the dual problem, a distributed cross-layer optimization algorithm was proposed. Without the knowledge of the stationary probability distribution of the licensed bands, the algorithm can achieve an adaptive matching between the com-pression rate of the perceived visual information in the upper layer and the opportunistic transmission of the links in the lower layer through independent computation and local information exchanging in relevant nodes in each slot, thus obtaining the optimal solution to the trade-off optimization problem. The proposed algorithm can be used as a practical transmission protocol in cognitive wireless visual sensor networks. Simulation results show that the distributed algorithm is able to con-verge quickly and achieve the performance similar to that of the optimal centralized algorithm.