Distributed Uplink Scheduling in CDMA Networks

Sridharan, Ashwin; Subbaraman, Ramesh; Guérin, Roch

doi:10.1007/978-3-540-72606-7_43

Cited by 11 publications

(11 citation statements)

References 12 publications

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“…In addition, some [22,31] also discussed the influence of different timeout values on both service quality and energy consumption. Several other projects also studied network resource management [17,30]. However, all this prior work was evaluated based on simulation using particular traffic models.…”

Section: Related Workmentioning

confidence: 99%

Characterizing radio resource allocation for 3G networks

Qian

Wang

Gerber

et al. 2010

Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement

218

212

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ABSTRACT3G cellular data networks have recently witnessed explosive growth. In this work, we focus on UMTS, one of the most popular 3G mobile communication technologies. Our work is the first to accurately infer, for any UMTS network, the state machine (both transitions and timer values) that guides the radio resource allocation policy through a light-weight probing scheme. We systematically characterize the impact of operational state machine settings by analyzing traces collected from a commercial UMTS network, and pinpoint the inefficiencies caused by the interplay between smartphone applications and the state machine behavior. Besides basic characterizations, we explore the optimal state machine settings in terms of several critical timer values evaluated using real network traces. Our findings suggest that the fundamental limitation of the current state machine design is its static nature of treating all traffic according to the same inactivity timers, making it difficult to balance tradeoffs among radio resource usage efficiency, network management overhead, device radio energy consumption, and performance. To the best of our knowledge, our work is the first empirical study that employs real cellular traces to investigate the optimality of UMTS state machine configurations. Our analysis also demonstrates that traffic patterns impose significant impact on radio resource and energy consumption. In particular, We propose a simple improvement that reduces YouTube streaming energy by 80% by leveraging an existing feature called fast dormancy supported by the 3GPP specifications.

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Section: Related Workmentioning

confidence: 99%

Characterizing radio resource allocation for 3G networks

Qian

Wang

Gerber

et al. 2010

Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement

218

212

View full text Add to dashboard Cite

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“…Previous studies on cellular networks can be classified approximately into several categories, namely from ISP's view point of managing network resources [13,33], from end-user's perspectives of optimizing energy efficiency and network performance at the device [4,39,6], and finally developing infrastructure support for improving mobile application performance [8,28] and security [24]. Our work is complementary to them by exposing the internal design of the cellular data network structure that can be useful to guide such optimization efforts.…”

Section: Related Workmentioning

confidence: 99%

Cellular data network infrastructure characterization and implication on mobile content placement

Huang

Wang

et al. 2011

Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems

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Despite the tremendous growth in the cellular data network usage due to the popularity of smartphones, so far there is rather limited understanding of the network infrastructure of various cellular carriers. Understanding the infrastructure characteristics such as the network topology, routing design, address allocation, and DNS service configuration is essential for predicting, diagnosing, and improving cellular network services, as well as for delivering content to the growing population of mobile wireless users. In this work, we propose a novel approach for discovering cellular infrastructure by intelligently combining several data sources, i.e., server logs from a popular location search application, active measurements results collected from smartphone users, DNS request logs from a DNS authoritative server, and publicly available routing updates. We perform the first comprehensive analysis to characterize the cellular data network infrastructure of four major cellular carriers within the U.S. in our study.We conclude among other previously little known results that the current routing of cellular data traffic is quite restricted, as it must traverse a rather limited number (i.e., 4-6) of infrastructure locations (i.e., GGSNs), which is in sharp contrast to wireline Internet traffic. We demonstrate how such findings have direct implications on important decisions such as mobile content placement and content server selection. We observe that although the local DNS server is a coarse-grained approximation on the user's network location, for some carriers, choosing content servers based on the local DNS server is accurate enough due to the restricted routing in cellular networks. Placing content servers close to GGSNs can potentially reduce the end-to-end latency by more than 50% excluding the variability from air interface.

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“…Probabilistic transmission by mobile devices in the EV-DO system is described in [1], whereas the incorporation of token buckets in the EV-DO Rev A system is discussed in [2]. Consequently, mobile devices are given more autonomy in making transmission decisions, and thus more freedom of action ( [3], [4], and [5]). This differs from the traditional aspects of wireless networks, where the base stations maintain full and centralized control in the allocation of the system resources.…”

Section: Introductionmentioning

confidence: 98%

“…deciding when to transmit and at what rate. The price for this flexibility is potentially higher interference, and a corresponding degradation in performance [3], [4]. Consequently, base stations should maintain some control over the interference.…”

mentioning

confidence: 99%

“…They exercise this control by setting a Reverse-link Activity Bit (RAB) on when the load increases, and clearing it otherwise. The RAB is used in conjunction with a token bucket, where tokens are a function of transmission power, and the base station controls the token generation rate and the token bucket depth [3]. The reverse traffic channel rate control algorithm for cdma2000 is presented in [1], where transition from one rate to another is performed by mobile devices in a probabilistic manner based on the value of the RAB.…”

mentioning

confidence: 99%

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Reverse Link Rate Control in 1xEV-DO with Adaptive Antenna Arrays

Yaacoub

Dawy

Kabalan

et al. 2008

2008 International Wireless Communications and Mobile Computing Conference

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Rate control and distributed reverse link scheduling in CDMA systems are considered. Probabilistic rate allocation with and without token bucket constraints gives mobiles more autonomy in making transmission decisions. A system with adaptive antenna arrays deployed at the base stations is proposed as a generalization to the model with omnidirectional antennas. Uniform linear antenna arrays with a microstrip patch as their constituent element are presented. Their number of elements is varied and their performance is compared to the omnidirectional case, showing substantial throughput gains. Index Terms-Antenna arrays, distributed reverse link scheduling, rate control, 1xEV-DO, CDMA I. INTRODUCTIONIn recent years, distributed scheduling of mobile users has gained more importance. Probabilistic transmission by mobile devices in the EV-DO system is described in [1], whereas the incorporation of token buckets in the EV-DO Rev A system is discussed in [2]. Consequently, mobile devices are given more autonomy in making transmission decisions, and thus more freedom of action ([3], [4], and [5]). This differs from the traditional aspects of wireless networks, where the base stations maintain full and centralized control in the allocation of the system resources.In centralized schemes (e.g. maximum C/I [6] and proportional fair [7]), user scheduling is the responsibility of the base station, which normally allocates resources to a single user at a given time instant. Distributed schemes are allowed by several standards for modern 3G/4G cellular networks, e.g., 1xEV-DO Rev A [8]. These standards have introduced mechanisms that give devices greater independence in making transmission decisions best matched to their applications, e.g. deciding when to transmit and at what rate. The price for this flexibility is potentially higher interference, and a corresponding degradation in performance [3], [4]. Consequently, base stations should maintain some control over the interference. They exercise this control by setting a Reverse-link Activity Bit (RAB) on when the load increases, and clearing it otherwise. The RAB is used in conjunction with a token bucket, where tokens are a function of transmission power, and the base station controls the token generation rate and the token bucket depth [3]. The reverse traffic channel rate control algorithm for cdma2000 is presented in [1], where transition from one rate to another is performed by mobile devices in a probabilistic manner based on the value of the RAB. The reverse traffic channel MAC design of cdma2000 1xEV-DO Rev A system is discussed in [2], where an algorithm is presented to update the token generation rate and the token bucket depth based on the computation of the RAB. In this work, we generalize

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Distributed Uplink Scheduling in CDMA Networks

Cited by 11 publications

References 12 publications

Characterizing radio resource allocation for 3G networks

Characterizing radio resource allocation for 3G networks

Cellular data network infrastructure characterization and implication on mobile content placement

Reverse Link Rate Control in 1xEV-DO with Adaptive Antenna Arrays

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