Scheduling In Multihop Wireless Networks Research Articles

HITLinQ: Improving path capacity through hybrid information-theoretic link scheduling in multi-hop wireless networks

By taking advantage of full-duplex radios, a one-sided interference channel model in the case of co-directional transmission on two links having a common node is introduced. Analysis of this model shows that asymmetric increase of transmitter power or decrease of distance will change the path capacity. When the signal noise ratio is high in this channel, using full-duplex radios for simultaneous co-directional transmission will worsen the path capacity, while direct transmission is better. On this basis, a scheduling strategy, called the hybrid information-theoretic link scheduling (HITLinQ) algorithm, which combines self-interference and inter-link interference management with link reconstruction, is proposed. The simulation results show that HITLinQ can provide capacity of not less than several baseline algorithms on a long multi-hop path under given setting.

A learning-based distributed algorithm for scheduling in multi-hop wireless networks

We address the joint problem of learning and scheduling in multi-hop wireless network without a prior knowledge on link rates. Previous scheduling algorithms need the link rate information, and learning algorithms often require a centralized entity and polynomial complexity. These become a major obstacle to develop an efficient learning-based distributed scheme for resource allocation in large-scale multi-hop networks. In this work, by incorporating with learning algorithm, we develop provably efficient scheduling scheme under packet arrival dynamics without a priori link rate information. We extend the results to distributed implementation and evaluation their performance through simulations.

Joint BATS Code and Periodic Scheduling in Multihop Wireless Networks

Batched sparse (BATS) code is a promising technology for reliable data transmission in error-prone environments. A BATS code consists of an outer code and an inner code. It has been shown that a well-designed inner code is critical to the performance of BATS. Though great efforts have been made in the design of inner codes, the impact of scheduling (that determines when inner codes take place at nodes in the path) on inner codes is still unclear. In this paper, we study the joint design of inner code and scheduling in multihop wireless networks. We first introduce a new network utility from which to associate inner codes with scheduling, and formulate the coding constraint and the scheduling constraint using independent sets of a graph. With the utility, the joint design problem is then transformed to a network utility maximization problem under the constraints. We next exploit the property of the proposed optimization problem and reveal the relation between the expected batch transfer matrix rank and maximal independent sets. In the light of their relationship, we propose joint coding and scheduling rules and show that a periodic scheduling can be used to achieve provable performance guarantees. However, under most realistic coding settings, the proposed coding and scheduling problem is NP-hard. In order to meet the practical needs of the implementation, we develop greedy algorithms that attempt to iteratively improve the current best solution. Numerical results show that our algorithms enables us to approach the utilization of multi-hop wireless networks with a relatively low end-to-end delay.

Asynchronous Distributed Greedy Link Scheduling in Multihop Wireless Networks

We consider the problem of link scheduling in multihop wireless networks subject to interference constraints. We consider a graph-based representation of wireless networks where scheduled links adhere to the K-hop link interference model. For this scheduling problem, we develop an asynchronous and distributed greedy heuristic. Furthermore, we show that this distributed heuristic computes the exact same schedule as the well-known centralized greedy link scheduling algorithm.

A generalized quorum system for asynchronous sleep scheduling in multi-hop wireless networks

It has been shown that quorum systems with rotation closure property can be utilized to enable asynchronous power saving in wireless ad-hoc networks. Moreover, the quorum system characteristics (e.g., cycle length, activity ratio, overlap size) can be exploited to provide for a variety of sleep-scheduling paradigms which differ in terms of their adaptability and symmetry properties. To enable these paradigms, generally requires that the quorum schedules available to the nodes be heterogeneous so that they may pick their schedules in accordance with their roles or instantaneous conditions. Motivated by this heterogeneity requirement, in this paper, we generalize the basic notion of a quorum system into what we refer to as aMulti-Class Quorum System(MCQS).MCQS consists of a set ofquorum classes, each of which is basically a quorum system with a guaranteed intra-class overlap size. Also, every two quorum classes have a guaranteed inter-class overlap size. We extend the classical rotation closure property of the quorum systems to guarantee any desirable number of intra- and inter-class overlaps between asynchronous nodes.We show that MCQS gives rise to a unified framework for quorum-based scheduling which can be easily tailored to enable all types of sleep-scheduling paradigms. We propose a binary integer programming (BIP) model to compute instances of MCQS which are optimal in terms of activity ratio. Also, unlike comparable quorum systems in the literature, MCQS is relieved of many restrictions on cycle length and overlap size. As a proof of concept, we study the application of MCQS in the joint problem of Quorum-based Sleep-scheduling and Routing (QSR) in wireless sensor networks (WSNs). We propose a centralized energy-optimal formulation as well as a decentralized algorithm for the QSR problem, and compare their performance through simulations.

Design of Scheduling Algorithms for End-to-End Backlog Minimization in Wireless Multi-Hop Networks Under -Hop Interference Models

In this paper, we study the problem of link scheduling for multi-hop wireless networks with per-flow delay constraints under the $K$ -hop interference model. Specifically, we are interested in algorithms that maximize the asymptotic decay-rate of the probability with which the maximum end-to-end backlog among all flows exceeds a threshold, as the threshold becomes large. We provide both positive and negative results in this direction. By minimizing the drift of the maximum end-to-end backlog in the converge-cast on a tree, we design an algorithm, Largest-Weight-First (LWF), that achieves the optimal asymptotic decay-rate for the overflow probability of the maximum end-to-end backlog as the threshold becomes large. However, such a drift minimization algorithm may not exist for general networks. We provide an example in which no algorithm can minimize the drift of the maximum end-to-end backlog. Finally, we simulate the LWF algorithm together with a well known algorithm (the back-pressure algorithm) and a large-deviations optimal algorithm in terms of the sum-queue (the P-TREE algorithm) in converge-cast networks. Our simulation shows that our algorithm performs significantly better not only in terms of asymptotic decay-rate, but also in terms of the actual overflow probability.

Distributed Interference-Aware Cooperative Random Access in Multi-Hop Wireless Networks

Cooperative communication has emerged as a promising technique to improve transmission reliability and spectral efficiency by utilizing virtual antenna arrays formed by multiple nodes in wireless networks. In this paper, we study distributed interference-aware random access strategies based on cooperative link selection scheduling for multi-hop wireless networks. In order to fully exploit the broadcast feature of wireless channels, improve spatial reuse, and achieve diversity gains, the cooperative random access is formulated as a network utility optimization problem with probabilistic model and interference avoidance. In the multi-hop wireless networks, the nodes near the destination node in the current hop that can decode the data packets from the source node are able to serve as candidate relays to take part in the cooperative transmission in the next hop. Simulation results show that our proposed spectrum access strategy significantly improves the system average throughput and spectrum efficiency compared with the existing solutions.

Scheduling in Multihop Wireless Networks Without Back-Pressure

This paper focuses on scheduling in multihop wireless networks where flows are associated with fixed routes. The well-known back-pressure scheduling algorithm is throughput-optimal, but requires constant exchange of queue length information among neighboring nodes for calculating the back-pressure. Moreover, previous research shows that the total queue length along a route increases quadratically as the route length under the backpressure algorithm, resulting in poor delay performance. In this paper, we propose a self-regulated MaxWeight scheduling, which does not require back-pressure calculation. We prove that the self-regulated MaxWeight scheduling is throughput-optimal (an algorithm is said to be throughput-optimal if it can stabilize any traffic that can be stabilized by any other algorithm). In the simulation part, we show that the self-regulated MaxWeight scheduling has a much better delay performance than the back-pressure algorithm.

OPTIMAL SCHEDULING ALGORITHM FOR THROUGHPUT MAXIMIZATION IN MULTIHOP WIRELESS NETWORKS

In this paper, focus on designing a scheduling scheme for achieve maximum throughput. Although, it does not require per-flow or perdestination information and also, this paper consider the problem of link scheduling in Multihop Wireless Networks under general interference constraints. The main goal is to achieve maximum throughput and better delay performance at low complexity. Previously, we use Max Weight Scheduling Algorithm (Per-Hop Queue and Per-Link Queue Scheduling) for Throughput maximization. These algorithms does not require per-flow information but it need local hop count information and use simple data Queue for each link in the network. Some time occurring buffer overflow (that is data queue overflow) because each link maintain simple data Queue. Our proposed scheme overcomes this problem and produces better delay performance and throughput at low complexity. Our proposed scheme combines MaxWeightScheduling and Greedy Algorithm, which reduce buffer overflow and achieve maximum throughput at low complexity. The performance of the proposed scheme is analyzed by various setting NS2 simulation. Prove that proposed scheme is optimal one and produce efficient throughput at low complexity.

Delay-Guaranteed Cross-Layer Scheduling in Multihop Wireless Networks

In this paper, we propose a cross-layer scheduling algorithm that achieves a throughput “ ε-close” to the optimal throughput in multihop wireless networks with a tradeoff of O([1/(ε)]) in average end-to-end delay guarantees. The algorithm guarantees finite buffer sizes and aims to solve a joint congestion control, routing, and scheduling problem in a multihop wireless network while satisfying per-flow average end-to-end delay constraints and minimum data rate requirements. This problem has been solved for both backlogged as well as arbitrary arrival rate systems. Moreover, we discuss the design of a class of low-complexity suboptimal algorithms, effects of delayed feedback on the optimal algorithm, and extensions of the proposed algorithm to different interference models with arbitrary link capacities.

Throughput-Optimal Scheduling in Multihop Wireless Networks Without Per-Flow Information

In this paper, we consider the problem of link scheduling in multihop wireless networks under general interference constraints. Our goal is to design scheduling schemes that do not use per-flow or per-destination information, maintain a single data queue for each link, and exploit only local information, while guaranteeing throughput optimality. Although the celebrated back-pressure algorithm maximizes throughput, it requires per-flow or per-destination information. It is usually difficult to obtain and maintain this type of information, especially in large networks, where there are numerous flows. Also, the back-pressure algorithm maintains a complex data structure at each node, keeps exchanging queue-length information among neighboring nodes, and commonly results in poor delay performance. In this paper, we propose scheduling schemes that can circumvent these drawbacks and guarantee throughput optimality. These schemes use either the readily available hop-count information or only the local information for each link. We rigorously analyze the performance of the proposed schemes using fluid limit techniques via an inductive argument and show that they are throughput-optimal. We also conduct simulations to validate our theoretical results in various settings and show that the proposed schemes can substantially improve the delay performance in most scenarios.

Throughput Optimizing Localized Link Scheduling for Multihop Wireless Networks under Physical Interference Model

We study throughput-optimum localized link scheduling in wireless networks. The majority of results on link scheduling assume binary interference models that simplify interference constraints in actual wireless communication. While the physical interference model reflects the physical reality more precisely, the problem becomes notoriously harder under the physical interference model. There have been just a few existing results on link scheduling under the physical interference model, and even fewer on more practical distributed or localized scheduling. In this paper, we tackle the challenges of localized link scheduling posed by the complex physical interference constraints. By cooperating the partition and shifting strategies into the pick-and-compare scheme, we present a class of localized scheduling algorithms with provable throughput guarantee subject to physical interference constraints. The algorithm in the linear power setting is the first localized algorithm that achieves at least a constant fraction of the optimal capacity region subject to physical interference constraints. The algorithm in the uniform power setting is the first localized algorithm with a logarithmic approximation ratio to the optimal solution. Our extensive simulation results demonstrate correctness and performance efficiency of our algorithms.

Delay-Based Back-Pressure Scheduling in Multihop Wireless Networks

Scheduling is a critical and challenging resource allocation mechanism for multihop wireless networks. It is well known that scheduling schemes that favor links with larger queue length can achieve high throughput performance. However, these queue-length-based schemes could potentially suffer from large (even infinite) packet delays due to the well-known last packet problem, whereby packets belonging to some flows may be excessively delayed due to lack of subsequent packet arrivals. Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay the packet has encountered. However, characterizing throughput optimality of these delay-based schemes has largely been an open problem in multihop wireless networks (except in limited cases where the traffic is single-hop.) In this paper, we investigate delay-based scheduling schemes for multihop traffic scenarios with fixed routes. We develop a scheduling scheme based on a new delay metric and show that the proposed scheme achieves optimal throughput performance. Furthermore, we conduct simulations to support our analytical results and show that the delay-based scheduler successfully removes excessive packet delays, while it achieves the same throughput region as the queue-length-based scheme.

Distributed Scheduling in Multihop Wireless Networks with Maxmin Fairness Provisioning

Fair allocation of resources is an important consideration in the design of wireless networks. In this paper, we consider the setting of multihop wireless networks with multiple routing paths and develop an online flow control and scheduling algorithm for packet admission and link activation that achieves high aggregate throughput while providing different data flows with a fair share of network capacity. For fairness provisioning, we seek to maximize the minimum throughput provided to flows in the network. To cope with different degrees of data reliability among the different links in the network, we use different channel code rates as appropriate. While we expect performance improvement using channel coding and multipath routing, the main contribution of our work is a joint treatment of network stability, multipath routing and link-level reliability in meeting the overarching goal of maxmin fairness. We develop a decentralized, and hence practical, scheduling policy that addresses various concerns and demonstrate, via simulations, that it is competitive with respect to an optimal centralized rate allocator. We also evaluate the fairness provisioning under the proposed algorithm and show that channel coding improves the performance of the network significantly. Finally, we show through simulations that the proposed algorithm outperforms a class of existing approaches on fairness provisioning, which are developed based on utility maximization.

Local Greedy Approximation for Scheduling in Multihop Wireless Networks

In recent years, there has been a significant amount of work done in developing low-complexity scheduling schemes to achieve high performance in multihop wireless networks. A centralized suboptimal scheduling policy, called Greedy Maximal Scheduling (GMS) is a good candidate because its empirically observed performance is close to optimal in a variety of network settings. However, its distributed realization requires high complexity, which becomes a major obstacle for practical implementation. In this paper, we develop simple distributed greedy algorithms for scheduling in multihop wireless networks. We reduce the complexity by relaxing the global ordering requirement of GMS, up to near zero. Simulation results show that the new algorithms approximate the performance of GMS, and outperform the state-of-the-art distributed scheduling policies.

A Greedy Link Scheduler for Wireless Networks With Gaussian Multiple-Access and Broadcast Channels

Information-theoretic broadcast channels (BCs) and multiple-access channels (MACs) enable a single node to transmit data simultaneously to multiple nodes, and multiple nodes to transmit data simultaneously to a single node, respectively. In this paper, we address the problem of link scheduling in multihop wireless networks containing nodes with BC and MAC capabilities. We first propose an interference model that extends protocol interference models, originally designed for point-to-point channels, to include the possibility of BCs and MACs. Due to the high complexity of optimal link schedulers, we introduce the Multiuser Greedy Maximum Weight algorithm for link scheduling in multihop wireless networks containing BCs and MACs. Given a network graph, we develop new local pooling conditions and show that the performance of our algorithm can be fully characterized using the associated parameter, the multiuser local pooling factor. We provide examples of some network graphs, on which we apply local pooling conditions and derive the multiuser local pooling factor. We prove optimality of our algorithm in tree networks and show that the exploitation of BCs and MACs improve the throughput performance considerably in multihop wireless networks.

Weighted Sum-Rate Maximization for a Set of Interfering Links via Branch and Bound

We consider the problem of weighted sum-rate maximization (WSRMax) for a set of interfering links. It plays a central role in resource allocation, link scheduling or in finding achievable rate regions for both wireline and wireless networks. This problem is known to be NP-hard. We propose a solution method, based on the branch and bound technique, which solves globally the nonconvex WSRMax problem with an optimality certificate. Efficient analytic bounding techniques are introduced and their impact on the convergence is numerically evaluated. The considered link-interference model is general enough to model a wide range of network topologies with various node capabilities, e.g., single- or multipacket transmission (or reception), simultaneous transmission and reception. Several applications, including cross-layer network utility maximization and maximum weighted link scheduling for multihop wireless networks as well as finding achievable rate regions for singlecast/multicast wireless networks, are presented. The proposed algorithm can be further used to provide other performance benchmarks by back-substituting it into any network design method which relies on WSRMax. It is also very useful for evaluating the performance loss encountered by any heuristic algorithm.

On Optimal SINR-Based Scheduling in Multihop Wireless Networks

In this paper, we revisit the problem of determining the minimum-length schedule that satisfies certain traffic demands in a wireless network. Traditional approaches for the determination of minimum-length schedules are based on a collision channel model, in which neighboring transmissions cause destructive interference if and only if they are within the “interference region” of the receiving nodes. By contrast, we adopt here a more realistic model for the physical layer by requiring that a threshold be exceeded by the signal-to-interference-plus-noise ratio (SINR) for a transmission to be successful. We present a novel formulation of the problem that incorporates various power and rate adaptation schemes while seamlessly integrating the generation of “matchings” (i.e., sets of links that can be activated simultaneously) by taking into consideration the SINR constraints at the receivers. For the formulated problem, we propose a column-generation-based solution method and show that it theoretically converges to a globally optimal solution, with a potential advantage of not having to enumerate all the feasible matchings a priori. We also discuss the influence of power control, spatial reuse, and variable transmission rates on network performance. Furthermore, we include aspects of the routing problem and provide computational results for our proposed column-generation-based solution procedure.

Minimum energy scheduling in multi-hop wireless networks with retransmissions

MaxWeight algorithm, a.k.a., back-pressure algorithm [1]-[4], has received much attention as a viable solution for dynamic link scheduling in multi-hop wireless networks. The basic principle of the MaxWeight algorithm is to select a set of interference-free links with the maximum overall link weights in the network, where the link weight is determined by the queue difference between the transmitter and the receiver. While the throughput-optimality of the MaxWeight algorithm is well understood in the literature, the energy consumption induced by the MaxWeight algorithm is less studied, which is of great interest in energy-constrained wireless networks such as wireless sensor networks. In this paper, we propose a minimum energy scheduling (MES) algorithm for multi-hop wireless networks with stochastic traffic arrivals and time-varying channel conditions. We show that our algorithm is energy optimal in the sense that the proposed MES algorithm can achieve an energy consumption which is arbitrarily close to the global minimum solution. Moreover, the energy efficiency of the MES algorithm is achieved without losing the throughput-optimality. In other words, the proposed MES algorithm is still throughput optimal whereas the average consumed energy in the network is significantly reduced, as compared to the traditional MaxWeight algorithm. The theoretical results are substantiated via simulations.

Distributed Link Scheduling With Constant Overhead

This paper proposes a new class of simple, distributed algorithms for scheduling in multihop wireless networks under the primary interference model. The class is parameterized by integers k ges 1. We show that algorithm k of our class achieves k/(k + 2) of the capacity region, for every k ges 1. The algorithms have small and constant worst-case overheads. In particular, algorithm k generates a new schedule using a) time less than 4k + 2 round-trip times between neighboring nodes in the network and b) at most three control transmissions by any given node for any k. The control signals are explicitly specified and face the same interference effects as normal data transmissions. Our class of distributed wireless scheduling algorithms are the first ones guaranteed to achieve any fixed fraction of the capacity region while using small and constant overheads that do not scale with network size. The parameter k explicitly captures the tradeoff between control overhead and throughput performance and provides a tuning-knob protocol designers can use to harness this tradeoff in practice.