In this paper, we consider the problem of location-dependent opportunistic bandwidth sharing between static and mobile (i.e., moving) downlink users in a cellular network. Each cell of the network has some fixed number of static users. Mobile users enter the cell, move inside the cell for some time, and then leave the cell. In order to provide higher data rate to the highly mobile users whose fast fading channel variation is difficult to track, we propose location dependent bandwidth sharing between the two classes of static and mobile users; the idea is to provide higher bandwidth to the mobile users at favourable locations, and provide higher bandwidth to the static users in other times. Our approach is agnostic to the way the bandwidth is further shared within the same class of users; it can be combined with any particular bandwidth allocation policy employed for one of these two classes of users. We formulate the problem as a long run average reward Markov decision process (MDP) where the per-step reward is a linear combination of instantaneous data volumes received by static and mobile users, and find the optimal policy. The optimal policy is binary in nature; it allocates the entire bandwidth either to the static users or to the mobile users at any given time. The reward structure of this MDP is not known in general, and it may change with time. To alleviate these issues, we propose a learning algorithm based on single timescale stochastic approximation. Also, noting that the MDP problem can be used to maximize the long run average data rate for mobile users subject to a constraint on the long run average data rate of static users, we provide a learning algorithm based on multi-timescale stochastic approximation. We prove asymptotic convergence of the bandwidth sharing policies under these learning algorithms to the optimal policy. The results are extended to address the issue of fair bandwidth sharing between the two classes of static and mobile users, where the notion of fairness is motivated by the popular notion of @math α-fairness in the literature. Numerical results exhibit significant performance improvement by our scheme, as well as fast convergence, and also demonstrate the trade-off between performance gain and fairness requirement.
There has been a vast literature on the impact of user mobility in wireless networks. The authors in @cite_13 have shown that mobility increases the capacity. @cite_5 has explored the trade-off between delay and throughput in ad-hoc networks in presence of mobility. The papers @cite_8 , @cite_2 , @cite_9 , @cite_19 , @cite_3 study the impact of inter and intra cell mobility on capacity, and also the trade-off between throughput and fairness; these results show that mobility increases the capacity of cellular networks when base stations cooperate among themselves.
@cite_8 @cite_9 @cite_3 @cite_19 @cite_2 @cite_5 @cite_13
2121141468 2007089224 2153757920 2055124819
Abstract—The performance of wireless data systems has been thoroughly studied in the context of a single base station. In the present paper we analyze networks with several interacting base stations, and specifically examine the capacity impact of intraand inter-cell mobility. We consider a dynamic setting where users come and go over time as governed by random finite-size data transfers, and explicitly allow for users to roam around over the course of their service. We show that mobility tends to increase the capacity, not only in case of globally optimal scheduling, but also when each of the base stations operates according to a fair sharing policy. The latter approach offers the advantages that it avoids complex centralized control, and grants each user a fair share of the resources, preventing the potential starvation that may occur under a globally optimal strategy. An important implication is that a simple, conservative capacity estimate is obtained by ‘ignoring’ mobility, and assuming that users remain stationary for the duration of their service. We further demonstrate that the capacity region for globally optimal scheduling is in general strictly larger than the stability region for a fair sharing discipline. However, if the users distribute themselves so as to maximize their individual throughputs, thus enabling some implicit coordination, then a fair sharing policy is in fact guaranteed to achieve stability whenever a globally optimal strategy is able to do so. The performance evaluation of wireless networks is severely complicated by the specific features of radio communication, such as highly variable channel conditions, interference issues, and possible hand-offs among base stations. The latter elements have no natural counterparts in wireline scenarios, and create a need for novel performance models that account for the impact of these characteristics on the service rates of users. Motivated by the above issues, we review several models for characterizing the capacity and evaluating the flow-level performance of wireless networks carrying elastic data transfers. We first examine the flow-level performance and stability of a wide family of so-called ?-fair channel-aware scheduling strategies. We establish that these disciplines provide maximum stability, and describe how the special case of the Proportional Fair policy gives rise to a Processor-Sharing model with a state-dependent service rate. Next we turn attention to a network of several base stations with inter-cell interference. We derive both necessary and sufficient stability conditions and construct lower and upper bounds for the flow-level performance measures. Lastly we investigate the impact of user mobility that occurs on a slow timescale and causes possible hand-offs of active sessions. We show that the mobility tends to increase the capacity region, both in the case of globally optimal scheduling and local ?-fair scheduling. It is additionally demonstrated that the capacity and user throughput improve with lower values of the fairness index ?. During crowded events, cellular networks face voice and data traffic volumes that are often orders of magnitude higher than what they face during routine days. Despite the use of portable base stations for temporarily increasing communication capacity and free Wi-Fi access points for offloading Internet traffic from cellular base stations, crowded events still present significant challenges for cellular network operators looking to reduce dropped call events and improve Internet speeds. For effective cellular network design, management, and optimization, it is crucial to understand how cellular network performance degrades during crowded events, what causes this degradation, and how practical mitigation schemes would perform in real-life crowded events. This paper makes a first step towards this end by characterizing the operational performance of a tier-1 cellular network in the United States during two high-profile crowded events in 2012. We illustrate how the changes in population distribution, user behavior, and application workload during crowded events result in significant voice and data performance degradation, including more than two orders of magnitude increase in connection failures. Our findings suggest two mechanisms that can improve performance without resorting to costly infrastructure changes: radio resource allocation tuning and opportunistic connection sharing. Using trace-driven simulations, we show that more aggressive release of radio resources via 1-2 seconds shorter RRC timeouts as compared to routine days helps to achieve better tradeoff between wasted radio resources, energy consumption, and delay during crowded events; and opportunistic connection sharing can reduce connection failures by 95 when employed by a small number of devices in each cell sector. This paper presents an empirical study on the performance of mobile High Speed Packet Access (HSPA, a 3.5G cellular standard) networks in Hong Kong via extensive field tests. Our study, from the viewpoint of end users, covers virtually all possible mobile scenarios in urban areas, including subways, trains, off-shore ferries and city buses. We have confirmed that mobility has largely negative impacts on the performance of HSPA networks, as fast-changing wireless environment causes serious service deterioration or even interruption. Meanwhile our field experiment results have shown unexpected new findings and thereby exposed new features of the mobile HSPA networks, which contradict commonly held views. We surprisingly find out that mobility can improve fairness of bandwidth sharing among users and traffic flows. Also the triggering and final results of handoffs in mobile HSPA networks are unpredictable and often inappropriate, thus calling for fast reacting fallover mechanisms. We have conducted in-depth research to furnish detailed analysis and explanations to what we have observed. We conclude that mobility is a double-edged sword for HSPA networks. To the best of our knowledge, this is the first public report on a large scale empirical study on the performance of commercial mobile HSPA networks.