Residual radio resources are abundant in wireless networks due to dynamic traffic load, which can be exploited to support high throughput for serving non-real-time (NRT) traffic. In this paper, we investigate how to achieve this by resource allocation with predicted time-average rate, which can be obtained from predicted average residual bandwidth after serving real-time traffic and predicted average channel gains of NRT mobile users. We show the connection between the statistics of their prediction errors. We formulate an optimization problem to make a resource allocation plan within a prediction window for NRT users that randomly initiate requests, which aims to fully use residual resources with ensured quality of service (QoS). To show the benefit of knowing the contents to be requested and the request arrival time in advance, we consider two types of NRT services, video on demand and video on reservation. The optimal solution is obtained, and an online policy is developed that can transmit according to the plan after instantaneous channel gains are available. Simulation and numerical results validate our analysis and show a dramatic gain of the proposed method in supporting high arrival rate of NRT requests with given tolerance on QoS.
For RT traffic such as phone calls, predictive wireless access has been extensively investigated to improve the admission-level quality of service (QoS), say reducing the call dropping rates during handover among adjacent cells @cite_24 . Considering that the information bits are generated randomly by each user and the RT service is with high priority, the major mechanism is to reserve resources for the RT traffic. Mobility prediction has long been used for mobility management to assist handover and for other location-based services, where the prediction granularity is in cell level or even more coarse (say, the next location) @cite_21 @cite_0 . With the predicted next-cell connection and hand-off time, dynamical resource reservation and call admission control can be used to improve the QoS @cite_21 @cite_20 .
@cite_24 @cite_21 @cite_0 @cite_20
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In cellular networks, an important practical issue is how to limit the handoff dropping probability efficiently. One possible approach is to perform dynamic bandwidth reservation based on mobility predictions. With the rapid advances in mobile positioning technology, and the widespread availability of digital road maps previously designed for navigational devices, we propose a predictive bandwidth reservation scheme built upon these timely opportunities. In contrast to the common practice of utilizing only incoming handoff predictions at each cell to compute the reservations, our scheme is more efficient as it innovatively utilizes both incoming and outgoing handoff predictions; it can meet the same target handoff dropping probability by blocking fewer new calls. The individual base stations are responsible for the computations, which are shown to be simple enough to be performed in real-time. We evaluate the scheme via simulation, along with five other schemes for comparison. Simulation results show that those schemes that rely on positioning information are significantly more efficient than those that do not. Our scheme's additional use of the road topology information further improves upon this advantage, bringing the efficiency closer to the bound set by a benchmark scheme that assumes perfect knowledge about future handoffs. How to keep the probability of hand-off drops within a prespecified limit is a very important quality-of-service (QoS) issue in cellular networks because mobile users should be able to maintain ongoing sessions even during their hand-off from one cell to another. We design and evaluate predictive and adaptive schemes for bandwidth reservation for the hand-offs of ongoing sessions and the admission control of new connections. We first develop a method to estimate user mobility based on an aggregate history of hand-offs observed in each cell. This method is then used to probabilistically predict mobiles' directions and hand-off times in a cell. For each cell, the bandwidth to be reserved for hand-offs is calculated by estimating the total sum of tractional bandwidths of the expected hand-offs within a mobility-estimation time window. Three different admission-control schemes for new connection requests using this bandwidth reservation are proposed. We also consider variations that utilize the path location information available from the car navigation system or global positioning system. Finally, we evaluate the performance of the proposed schemes extensively to show that they meet our design goal and outperform the static reservation scheme under various scenarios. Bandwidth is an extremely valuable and scarce resource in mobile networks; therefore, efficient mobility-aware bandwidth reservation is necessary to support multimedia applications (e.g., video streaming) that require quality of service (QoS). In this paper, we propose a distributed bandwidth reservation scheme called the mobility-prediction-aware bandwidth reservation (MPBR) scheme. The objective of MPBR is to reduce handoff call dropping rate and maintain acceptable new call blocking rate while providing efficient bandwidth utilization. MPBR consists of 1) a handoff time estimation (HTE) scheme that aims to estimate the time windows when a user will perform handoffs along the path to his destination, 2) an available bandwidth estimation (ABE) scheme that aims to estimate in advance available bandwidth, during the computed time windows in the cells to be traversed by the user to his destination, and 3) an efficient call admission control (ECaC) scheme that aims to control bandwidth allocation in the network cells. The simulation results show that MPBR outperforms existing schemes in terms of reducing handoff call dropping rate.