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971.
Uplink scheduling in wireless systems is gaining importance due to arising uplink intensive data services (ftp, image uploads
etc.), which could be hampered by the currently in-built asymmetry in favor of the downlink. In this work, we propose and
study algorithms for efficient uplink packet-data scheduling in a CDMA cell. The algorithms attempt to maximize system throughput
under transmit power limitations on the mobiles assuming instantaneous knowledge of user queues and channels. However no channel
statistics or traffic characterization is necessary. Apart from increasing throughput, the algorithms also improve fairness
of service among users, hence reducing chances of buffer overflows for poorly located users.
The major observation arising from our analysis is that it is advantageous on the uplink to schedule “strong” users one-at-a-time,
and “weak” users in larger groups. This contrasts with the downlink where one-at-a-time transmission for all users has shown
to be the preferred mode in much previous work. Based on the optimal schedules, we propose less complex and more practical
approximate methods, both of which offer significant performance improvement compared to one-at-a-time transmission, and the
widely acclaimed Proportional Fair (PF) algorithm, in simulations. When queue content cannot be fed back, we propose a simple modification of PF, Uplink PF (UPF), that offers similar improvement.
Hereafter, we refer to users with low recieved power at the base even when transmitting at peak transmit power as “weak” users,
and the strongly recieved users at the base as “strong” users.
Krishnan Kumaran is currently a member of the Complex Systems Modeling section in the Corporate Strategic Research of ExxonMobil Corp., Clinton,
NJ. Formerly, he was a Member of Technical Staff in the Mathematics of Networks and Systems Research Department at Bell Labs
in Murray Hill, NJ, where his research interests were in modeling, analysis and simulation of design, resource management
and scheduling issues in telecommunication networks.
Lijun Qian is an assistant professor in the Department of Electrical Engineering at Prairie View A&M University. He received his B.S.
from Tsinghua University in Beijing, M.S. from Technion-Israel Institute of Technology, and Ph.D. from WINLAB, Rutgers University,
all in electrical engineering. Before joining PVAMU, he was a researcher at Networks and Systems Research Department of Bell
Labs in Murray Hill, NJ. His major research interests are in wireless communications and networking technologies, especially
in radio resource management, protocol design, TCP/RLP optimization and MPLS traffic engineering. 相似文献
972.
基于混合智能的多Agent个性化信息推荐 总被引:1,自引:0,他引:1
在基于Agent的个性化网页推荐中,目前主要有两种过滤方法:基于内容的过滤和基于多Agent合作的过滤。在分析了单独使用这两种方法存在的不足之后,提出了基于改进蚁群算法的聚类分析建立用户模型的算法,并给出了结合两种方法优点的用户模型主动学习算法,最后给出了个性化信息推荐模型及相关算法。 相似文献
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980.
MPTA型原油脱金属剂的工业应用 总被引:2,自引:2,他引:0
在对高金属含量原油进行初步评价的基础上,利用SH—Ⅰ型电脱盐试验仪对自主开发的MPTA型原油脱金属剂进行了原油脱金属的实验室研究。结果表明,当MPTA型脱金属剂加入量为250μg/g时,钙的脱除率可达97.4%,并且对其它金属元素,如镍、铁、钠、锰、铝和钒也有明显的脱除效果。该剂在山东恒源石油化工股份有限公司重交沥青车间500kt/a的电脱盐装置上进行原油脱金属工业试验的结果表明,经过二级电脱盐处理后,钙的脱除率达到99.1%,钠的脱除率为94.6%,铁的脱除率为82.9%,并可在一定程度上降低原油中镍和铜的含量。使用MPTA型原油脱金属剂显著降低了一、二级电脱盐装置的电场电流,有利于炼油厂的节能降耗。 相似文献