Results 1  10
of
89
EndtoEnd Performance and Fairness in Multihop Wireless Backhaul Networks
 In Proceedings of ACM MOBICOM
, 2004
"... Wireless IEEE 802.11 networks in residences, small businesses, and public "hot spots" typically encounter the wireline access link (DSL, cable modem, T1, etc.) as the slowest and most expensive part of the endtoend path. Consequently, network architectures have been proposed that employ ..."
Abstract

Cited by 132 (4 self)
 Add to MetaCart
(Show Context)
Wireless IEEE 802.11 networks in residences, small businesses, and public "hot spots" typically encounter the wireline access link (DSL, cable modem, T1, etc.) as the slowest and most expensive part of the endtoend path. Consequently, network architectures have been proposed that employ multiple wireless hops in route to and from the wired Internet. Unfortunately, use of current media access and transport protocols for such systems can result in severe unfairness and even starvation for flows that are an increasing number of hops away from a wired Internet entry point. Our objective is to study fairness and endtoend performance in multihop wireless backhaul networks via the following methodology. First, we develop a formal reference model that characterizes objectives such as removing spatial bias (i.e., providing performance that is independent of the number of wireless hops to a wire) and maximizing spatial reuse. Second, we perform an extensive set of simulation experiments to quantify the impact of the key performance factors towards achieving these goals. For example, we study the roles of the MAC protocol, endtoend congestion control, antenna technology, and traffic types. Next, we develop and study a distributed layer 2 fairness algorithm which targets to achieve the fairness of the reference model without modification to TCP. Finally, we study the critical relationship between fairness and aggregate throughput and in particular study the fairnessconstrained system capacity of multihop wireless backhaul networks.
Optimal Power Control, Scheduling and Routing in UWB Networks
, 2004
"... UltraWide Band (UWB) is an emerging wireless physical layer technology that uses a very large bandwidth. We are interested in finding the design objectives of the medium access (MAC, namely, power control and scheduling) and routing protocols of a multihop, besteffort, UWB network. Our objective ..."
Abstract

Cited by 82 (5 self)
 Add to MetaCart
UltraWide Band (UWB) is an emerging wireless physical layer technology that uses a very large bandwidth. We are interested in finding the design objectives of the medium access (MAC, namely, power control and scheduling) and routing protocols of a multihop, besteffort, UWB network. Our objective is to maximize flow rates (more precisely, logutility of flow rates) given node power constraints. The specificity of UWB is expressed by the linear dependence between rate and signaltonoise ratio at the receiver. It is known that, in wireless networks, different routing strategies can imply differences in MAC protocol design. Hence we search for the jointly optimal routing, scheduling and power control.
Complexity in geometric sinr
 In MobiHoc
, 2007
"... In this paper we study the problem of scheduling wireless links in the geometric SINR model, which explicitly uses the fact that nodes are distributed in the Euclidean plane. We present the first NPcompleteness proofs in such a model. In particular, we prove two problems to be NPcomplete: Scheduli ..."
Abstract

Cited by 77 (2 self)
 Add to MetaCart
(Show Context)
In this paper we study the problem of scheduling wireless links in the geometric SINR model, which explicitly uses the fact that nodes are distributed in the Euclidean plane. We present the first NPcompleteness proofs in such a model. In particular, we prove two problems to be NPcomplete: Scheduling and OneShot Scheduling. The first problem consists in finding a minimumlength schedule for a given set of links. The second problem receives a weighted set of links as input and consists in finding a maximumweight subset of links to be scheduled simultaneously in one shot. In addition to the complexity proofs, we devise an approximation algorithm for each problem.
How Optimal are Wireless Scheduling Protocols?
 IN: PROC. OF THE 26 TH ANNUAL JOINT CONF. OF THE IEEE COMPUTER AND COMMUNICATIONS SOCIETIES (INFOCOM
, 2007
"... In wireless networks mutual interference impairs the quality of received signals and might even prevent the correct reception of messages. It is therefore of paramount importance to dispose of power control and scheduling algorithms, coordinating the transmission of communication requests. We propo ..."
Abstract

Cited by 34 (1 self)
 Add to MetaCart
(Show Context)
In wireless networks mutual interference impairs the quality of received signals and might even prevent the correct reception of messages. It is therefore of paramount importance to dispose of power control and scheduling algorithms, coordinating the transmission of communication requests. We propose a new measure disturbance in order to comprise the intrinsic difficulty of finding a short schedule for a problem instance. Previously known approaches suffer from extremely bad performance in certain network scenarios even if disturbance is low. To overcome this problem, we present a novel scheduling algorithm for which we give analytical worstcase guarantees on its performance. Compared to previously known solutions, the algorithm achieves a speed up, which can be exponential in the size of the network.
Maxmin fair scheduling in wireless ad hoc networks
 IEEE J. SEL. AREAS COMMUN
, 2005
"... We investigate from an algorithmic perspective the maxmin fair allocation of bandwidth in wireless ad hoc networks. We formalize the maxmin fair objective under wireless scheduling constraints, and present a necessary and sufficient condition for maxmin fairness of a bandwidth allocation. We propos ..."
Abstract

Cited by 32 (4 self)
 Add to MetaCart
(Show Context)
We investigate from an algorithmic perspective the maxmin fair allocation of bandwidth in wireless ad hoc networks. We formalize the maxmin fair objective under wireless scheduling constraints, and present a necessary and sufficient condition for maxmin fairness of a bandwidth allocation. We propose an algorithm that assigns weights to the sessions dynamically such that the weights depend on the congestion in the neighborhood, and schedules the sessions that constitute a maximum weighted matching. We prove that this algorithm attains the maxmin fair rates, even though it does not use any information about the statistics of the packet arrival process.
Optimal CWmin Selection for Achieving Proportional Fairness in MultiRate 802.11e WLANs: Testbed Implementation and Evaluation
 in Proc. of 1st ACM Int’l Workshop on Wireless Network Testbeds, Experimental evaluation and CHaracterization (WiNTECH
, 2006
"... We investigate the optimal selection of minimum contention window values to achieve proportional fairness in a multirate IEEE 802.11e testbed. Unlike other approaches, the proposed model accounts for the contentionbased nature of 802.11’s MAC layer operation and considers the case where stations c ..."
Abstract

Cited by 25 (2 self)
 Add to MetaCart
(Show Context)
We investigate the optimal selection of minimum contention window values to achieve proportional fairness in a multirate IEEE 802.11e testbed. Unlike other approaches, the proposed model accounts for the contentionbased nature of 802.11’s MAC layer operation and considers the case where stations can have different weights corresponding to different throughput classes. Our testbed evaluation considers both the longterm throughput achieved by wireless stations and the shortterm fairness. When all stations have the same transmission rate, optimality is achieved when a station’s throughput is proportional to its weight factor, and the optimal minimum contention windows also maximize the aggregate throughput. When stations have different transmission rates, the optimal minimum contention window for high rate stations is smaller than for low rate stations. Furthermore, we compare proportional fairness with timebased fairness, which can be achieved by adjusting packet sizes so that low and high rate stations have equal successful transmission times, or by adjusting the transmission opportunity (TXOP) limit so that high rate stations transmit multiple backtoback packets and thus occupy the channel for the same time as low rate stations that transmit a single packet. The testbed experiments show that when stations have different transmission rates and the same weight, proportional fairness achieves higher performance than the timebased fairness approaches, in terms of both aggregate utility and throughput.
Maximizing Capacity in MultiHop Cognitive Radio Networks Under the SINR Model
, 2010
"... Cognitive radio networks (CRNs) have the potential to utilize spectrum efficiently and are positioned to be the core technology for the nextgeneration multihop wireless networks. An important problem for such networks is its capacity. We study this problem for CRNs in the SINR (signaltointerfer ..."
Abstract

Cited by 17 (3 self)
 Add to MetaCart
Cognitive radio networks (CRNs) have the potential to utilize spectrum efficiently and are positioned to be the core technology for the nextgeneration multihop wireless networks. An important problem for such networks is its capacity. We study this problem for CRNs in the SINR (signaltointerferenceandnoiseratio) model, which is considered to be a better characterization of interference (but also more difficult to analyze) than disk graph model. The main difficulties of this problem are twofold. First, SINR is a nonconvex function of transmission powers; an optimization problem in the SINR model is usually a nonconvex program and NPhard in general. Second, in the SINR model, scheduling feasibility and the maximum allowed flow rate on each link are determined by SINR at the physical layer. To maximize capacity, it is essential to follow a crosslayer approach; but joint optimization at physical (power control), link (scheduling), and network (flow routing) layers with the SINR function is inherently difficult. In this paper, we give a mathematical characterization of the joint relationship among these layers. We devise a solution procedure that provides a (1 − ε) optimal solution to this complex problem, where ε is the required accuracy. Our theoretical result offers a performance benchmark for any other algorithms developed for practical implementation. Using numerical results, we demonstrate the efficacy of the solution procedure and offer quantitative understanding on the interaction of power control, scheduling, and flow routing in a CRN.
Lexicographic Maxmin Fairness for Data Collection in Wireless Sensor Networks
"... Abstract—The ad hoc deployment of a sensor network causes unpredictable patterns of connectivity and varied node density, resulting in uneven bandwidth provisioning on the forwarding paths. When congestion happens, some sensors may have to reduce their data rates. It is an interesting but difficult ..."
Abstract

Cited by 16 (2 self)
 Add to MetaCart
(Show Context)
Abstract—The ad hoc deployment of a sensor network causes unpredictable patterns of connectivity and varied node density, resulting in uneven bandwidth provisioning on the forwarding paths. When congestion happens, some sensors may have to reduce their data rates. It is an interesting but difficult problem to determine which sensors must reduce rates and how much they should reduce. This paper attempts to answer a fundamental question about congestion resolution: What are the maximum rates at which the individual sensors can produce data without causing congestion in the network and unfairness among the peers? We define the maxmin optimal rate assignment problem in a sensor network, where all possible forwarding paths are considered. We provide an iterative linear programming solution, which finds the maxmin optimal rate assignment and a forwarding schedule that implements the assignment in a lowrate sensor network. We prove that there is one and only one such assignment for a given configuration of the sensor network. We also study the variants of the maxmin fairness problem in sensor networks. Index Terms—Multipath maxmin fairness, wireless sensor networks, data collection applications, iterative linear programming. 1
Scheduling multiple partially overlapped channels in wireless mesh networks
 In IEEE ICC
, 2007
"... Abstract — In this paper, we explore the use of partially overlapped channels in wireless mesh networks that consist of multiple 802.11based access points. We propose novel channel allocation and link scheduling algorithms in the MAC layer to enhance network performance. Due to different traffic ch ..."
Abstract

Cited by 12 (2 self)
 Add to MetaCart
Abstract — In this paper, we explore the use of partially overlapped channels in wireless mesh networks that consist of multiple 802.11based access points. We propose novel channel allocation and link scheduling algorithms in the MAC layer to enhance network performance. Due to different traffic characteristics in multihop WMNs compared to those in onehop 802.11 networks, we perform our optimization based on endtoend flow requirement, instead of the sum of link capacity. In addition, we discuss other factors affecting the performance of P OC, including topology, node density, and distribution. I.
Joint Networkwide Opportunistic Scheduling and Power Control in Multicell Networks
, 2008
"... We present a unified analytical framework that maximizes generalized utilities of a wireless network by networkwide opportunistic scheduling and power control. That is, base stations in the network jointly decide mobile stations to be served at the same time as the transmission powers of base stati ..."
Abstract

Cited by 12 (2 self)
 Add to MetaCart
We present a unified analytical framework that maximizes generalized utilities of a wireless network by networkwide opportunistic scheduling and power control. That is, base stations in the network jointly decide mobile stations to be served at the same time as the transmission powers of base stations are coordinated to mitigate the mutually interfering effect. Although the maximization at the first glance appears to be a mixed, twofold and nonlinear optimization requiring excessive computational complexity, we show that the maximization can be transformed into a pure binary optimization with much lower complexity. To be exact, it is proven that binary power control of base stations is necessary and sufficient for maximizing the networkwide utilities under a physical layer regime where the channel capacity is linear in the signaltointerferencenoise ratio. To further reduce the complexity of the problem, a distributed heuristic algorithm is proposed that performs much better than existing opportunistic algorithms. Through extensive simulations, it becomes clear that networkwide opportunistic scheduling and power control is most suitable for fairnessoriented networks and underloaded networks. We believe that our work will serve as a cornerstone for networkwide scheduling approaches from theoretical and practical standpoints.