Throughput-optimal Scheduling Research Articles

Joint Throughput-Optimal Scheduling and Energy Efficiency Optimization for NOMA Systems With Flow-Level Dynamics

Joint Throughput-Optimal Scheduling and Energy Efficiency Optimization for NOMA Systems With Flow-Level Dynamics

Combination of Throughput-optimal Scheduling and Network Utility Maximization in NOMA Systems with Flow-Level Dynamics

In wireless networks, the network queue stability (throughput-optimality) and the network utility maximization are two crucial tasks for network operators. Besides, new techniques of mobile communications, e.g. the non-orthogonal multiple access (NOMA), have emerged endlessly to meet coming challenges. In this paper, we for the first time, investigate a downlink NOMA system with flow-level dynamics, where long-lived flows and short-lived flows exist simultaneously, and formulate an optimization problem of user selection and power allocation. We aim to maximize the network utility at each time slot and simultaneously guarantee the throughput-optimality, under the quality of service (QoS) constraints. Considering the practicability of algorithm, the suboptimal algorithm containing two stages, is proposed. And we analyze the complexity of proposed algorithm. In the performance evaluation, the benchmark algorithm is provided for comparison. And we take the energy efficiency (EE) as an example of the network utility and define the scheduling delay (SD). Simulation results show that the suboptimal algorithm is throughput-optimal, and understandably, its performances could be better than those in the benchmark algorithm in terms of EE and SD, as long as the appropriate parameter is set.

Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach

In wireless communication networks, the network provider serves certain licensed primary users who pay for a dedicated use of the frequency channels. However, not all the channels are occupied by the primary users at all times. For efficient spectrum utilization, in centralized cognitive radio networks (CRNs), a cognitive base station (CBS) dynamically identifies the spectrum holes and allocates the frequency channels to the on-demand unlicensed secondary users known as cognitive radios (CRs). Although existing literature has developed various dynamic spectrum access mechanisms for CBS, there is still a dearth of studies due to the wide range of assumptions made in the solutions. Most of the existing works study the CBS scheduling problem scheme by adopting optimization-based methods and rely on the prior knowledge of the network parameters such as primary users’ activity. Moreover, the impact of channel switching costs on the network throughput has not been well studied. In this paper, we aim to maximize the CRNs total throughput, and we formulate the CBS scheduling problem as a non-stochastic (i.e., adversarial) combinatorial multi-armed bandit problem with semi-bandit feedback and arm switching costs. We propose two novel online learning algorithms for CBS scheduling with and without channel switching costs, where their regret performances are proved sublinear order-optimal in time as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{1/2}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2/3}$ </tex-math></inline-formula> , respectively, offering throughput-optimal scheduling for CRNs. Experiments on the synthetic and real-world spectrum measurement data complement and validate our theoretical findings.

Learning to Schedule Network Resources Throughput and Delay Optimally Using Q+-Learning

As network architecture becomes complex and the user requirement gets diverse, the role of efficient network resource management becomes more important. However, existing throughput-optimal scheduling algorithms such as the max-weight algorithm suffer from poor delay performance. In this paper, we present reinforcement learning-based network scheduling algorithms for a single-hop downlink scenario which achieve throughput-optimality and converge to minimal delay. To this end, we first formulate the network optimization problem as a Markov decision process (MDP) problem. Then, we introduce a new state-action value function called Q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -function and develop a reinforcement learning algorithm called Q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -learning with UCB (Upper Confidence Bound) exploration which guarantees small performance loss during a learning process. We also derive an upper bound of the sample complexity in our algorithm, which is more efficient than the best known bound from Q-learning with UCB exploration by a factor of γ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> where γ is the discount factor of the MDP problem. Finally, via simulation, we verify that our algorithm shows a delay reduction of up to 40.8% compared to the max-weight algorithm over various scenarios. We also show that the Q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -learning with UCB exploration converges to an ε-optimal policy 10 times faster than Q-learning with UCB.

A Novel Method for Scheduling of Wireless Ad Hoc Networks in Polynomial Time

In this article, we address the scheduling problem in wireless ad hoc networks by exploiting the computational advantage that comes when scheduling problems can be represented by claw-free conflict graphs where we consider a wireless broadcast medium. It is possible to formulate a scheduling problem of broadcast transmissions as finding the maximum weighted independent set (MWIS) in the conflict graph of the network. Finding the MWIS of a general graph is NP-hard leading to an NP-hard complexity of scheduling. In a claw-free conflict graph, MWIS may be found in polynomial time leading to a throughput-optimal scheduling. We show that the conflict graphs of certain wireless ad hoc networks are claw-free. In order to obtain claw-free conflict graphs in general networks, we suggest introducing additional conflicts (edges) with the aim of keeping the decrease in MWIS size minimal. To this end, we introduce an iterative optimization problem to decide where to introduce edges and investigate its efficient implementation. We conclude that the claw breaking method by adding extra edges can perform very close to optimal scenario and better than the polynomial time maximal independent set scheduling benchmark under the necessary assumptions.

Reduced-State, Optimal Scheduling for Decentralized Medium Access Control of a Class of Wireless Networks

Motivated by medium access control for resource-challenged wireless Internet of Things (IoT), we consider the problem of queue scheduling with reduced queue state information. In particular, we consider a time-slotted scheduling model with $N$ sensor nodes, with pair-wise dependence, such that Nodes $i$ and $i + 1,~0 < i < N$ cannot transmit together. We develop new throughput-optimal scheduling policies requiring only the empty-nonempty state of each queue that we term Queue Nonemptiness-Based (QNB) policies. We propose a Policy Splicing technique to combine scheduling policies for small networks in order to construct throughput-optimal policies for larger networks, some of which also aim for low delay. For $N = 3,$ there exists a sum-queue length optimal QNB scheduling policy. We show, however, that for $N > 4,$ there is no QNB policy that is sum-queue length optimal over all arrival rate vectors in the capacity region. We then extend our results to a more general class of interference constraints that we call cluster-of-cliques (CoC) conflict graphs. We consider two types of CoC networks, namely, Linear Arrays of Cliques (LAoC) and Star-of-Cliques (SoC) networks. We develop QNB policies for these classes of networks, study their stability and delay properties, and propose and analyze techniques to reduce the amount of state information to be disseminated across the network for scheduling. In the SoC setting, we propose a throughput-optimal policy that only uses information that nodes in the network can glean by sensing activity (or lack thereof) on the channel. Our throughput-optimality results rely on two new arguments: a Lyapunov drift lemma specially adapted to policies that are queue length-agnostic, and a priority queueing analysis for showing strong stability.

Energy-Optimal Data Aggregation and Dissemination for the Internet of Things

Established approaches to data aggregation in wireless sensor networks (WSNs) do not cover the variety of new use cases developing with the advent of the Internet of Things (IoT). In particular, the current push toward fog computing, in which control, computation, and storage are moved to nodes close to the network edge, induces a need to collect data at multiple sinks, rather than the single sink typically considered in WSN aggregation algorithms. Moreover, for machine-to-machine communication scenarios, actuators subscribing to sensor measurements may also be present, in which case data should be not only aggregated and processed in-network but also disseminated to actuator nodes. In this paper, we present mixed-integer programming formulations and algorithms for the problem of energy-optimal routing and multiple-sink aggregation, as well as joint aggregation and dissemination, of sensor measurement data in IoT edge networks. We consider optimization of the network for both minimal total energy usage, and min-max per-node energy usage. We also provide a formulation and algorithm for throughput-optimal scheduling of transmissions under the physical interference model in the pure aggregation case. We have conducted a numerical study to compare the energy required for the two use cases, as well as the time to solve them, in generated network scenarios with varying topologies and between 10 and 40 nodes. Although aggregation only accounts for less than 15% of total energy usage in all cases tested, it provides substantial energy savings. Our results show more than 13 times greater energy usage for 40-node networks using direct, shortest-path flows from sensors to actuators, compared with our aggregation and dissemination solutions.

Reduced-complexity delay-efficient throughput-optimal distributed scheduling with heterogeneously delayed network-state information

We consider the problem of distributed scheduling in wireless communication networks where heterogeneously delayed queue lengths and channel states of all links are available at all the transmitters. In an earlier work (by Reddy et al. in Queueing Systems, 2012), a throughput-optimal scheduling policy (which we refer to henceforth as the R policy) for this setting was proposed. We study the R policy, and examine its two drawbacks — (i) its huge computational complexity, and (ii) its non-optimal average per-packet queueing delay. We show that the R policy unnecessarily constrains itself to work with information that is more delayed than that afforded by the system. We propose a new distributed scheduling policy that fully exploits the common state information available to all transmitters, thereby greatly improving upon the computational complexity and the delay performance relative to those of the R policy. We also establish the throughput optimality of our policy analytically. We evaluate the performance of the proposed policy and validate our analytical results through extensive numerical simulation. Thus, our work enlarges the ambit of networks for which throughput-optimal scheduling is practicable.

Complexity reduction of throughput optimal link scheduling algorithm through topology control in wireless networks

In single channel wireless networks, concurrent transmissions at different links may interfere with each other. To improve system throughput, a scheduling algorithm is necessary to choose a subset of links for data transmission. Throughput optimal link scheduling discipline is generally an NP-hard problem. In this paper, we utilise the concept of line graph and extend it to line multigraph to cope with the complexity issue of the maximum weight scheduling (MWS) algorithm. The necessary and sufficient conditions for reducing the complexity of MWS in terms of network topology are derived. We prove that the complexity of eLehot is polynomial time provided that conflict graph does not contain seven derived forbidden graphs as induced subgraphs. We also propose eLehot algorithm for detecting whether a graph is line multigraph and output its root graph. The results of this paper introduce a new approach in wireless topology control where the target is complexity reduction.

Asymptotically Throughput Optimal Scheduling for Energy Harvesting Wireless Sensor Networks

In this paper, we investigate a single-hop wireless sensor network in which a fusion center (FC) collects data packets from $M$ energy harvesting (EH) sensor nodes. Energy harvested by each node is stored without battery overflow and leakage at that node. The FC schedules $K$ nodes over its mutually orthogonal channels to receive data from them in each time slot. The FC knows neither the statistics of EH processes nor the battery states of nodes. The FC solely has information on consequences of previous transmission attempts. We aim for obtaining an efficient and simple policy achieving maximum throughput in this network. The nodes are data backlogged and the data transmission only depends on the harvested energy of the scheduled nodes. A node can transmit data whenever it is scheduled, provided that it has sufficient energy. We propose a simple policy, uniforming random ordered policy (UROP) , for the problem. We exhibit that the UROP is nearly throughput-optimal over finite time horizons for a broad class of EH processes. We also prove that for general EH processes, UROP achieves asymptotically optimal throughput over the infinite time horizon under infinite capacity battery assumption. Numerical results indicate that even with finite-capacity batteries, UROP achieves near-optimal throughput over finite time horizons. We believe that UROP is applicable to much wider area than EH wireless sensor networks.

Bandwidth Aggregation Protocol and Throughput-Optimal Scheduler for Hybrid RF and Visible Light Communication Systems

In this paper, we propose a bandwidth aggregation protocol for hybrid visible light communication (VLC) and radio frequency (RF) communication systems. By efficiently combining the RF communication with the VLC, the proposed protocol compensates for the disadvantages of the VLC, e.g., unstable communication quality and a small coverage area, while enjoying the additional bandwidth provided by the VLC. In the hybrid VLC and RF communication system, packets which arrive at the system are scheduled to be transmitted via the VLC or RF communication. This scheduling decision has a profound impact on the overall performance of the system. Our proposed protocol employs a scheduling algorithm based on queue lengths, the goal of which is the throughput optimality. We define a Lyapunov function as a function of queue lengths and obtain the optimal scheduling policy by minimizing the drift of the Lyapunov function. We implement a real-life prototype of the hybrid VLC and RF communication system and conduct the experiments of the proposed protocol. The experimental results show that the proposed protocol is capable of successfully aggregating the bandwidth of the VLC and RF communication.

Near‐optimal packet scheduling scheme in satellite LTE networks

This study proposes a near-optimal packet scheduling scheme in the satellite long term evolution (LTE) network which uses multiuser multiple-in multiple-out technique. For the available resources in the satellite LTE network to be fully utilised and in order to meet the set targets of 4G standard, the need for an optimal scheduling scheme that will maximise the throughput without compromising the quality of service and fairness of users is very crucial. The aim of this study is to propose near-optimal scheduling schemes that have been obtained using Karush-Kuhn-Tucker multipliers and compare their performances with other throughput-optimal scheduling schemes. A new metric that can be used to compute the overall performance of the scheduling schemes is also presented. This work has adopted the use of a land mobile dual-polarised GEO satellite system.

Throughput-Optimal Scheduling and Rate Adaptation for Reduced Feedback Best- $M$ Scheme in OFDM Systems

In orthogonal frequency division multiplexing systems, reduced feedback schemes provide essential channel state information from the users to the base station (BS) without overwhelming the uplink. For the practically important best- $M$ scheme, in which each user feeds back only its $M$ strongest subchannels and their indices to the BS, we derive a novel, throughput-optimal scheduling and rate adaptation policy that enables the BS to schedule the best user and its data rate for all the subchannels. The policy exploits the structure of the information fed back by the best- $M$ scheme and the correlation among subchannel gains. We present it in closed-form for the widely studied exponential correlation model. Using insights gleaned from the optimal policy, we propose a novel, low-complexity two subchannel reduction approach, which is seen empirically to be near-optimal and easily handles practically important general channel correlation models, quantized feedback, and co-channel interference in multi-cell scenarios. Compared with several ad hoc approaches, the proposed approaches improve the cell throughput without any additional feedback. A modified gradient-based opportunistic scheduler is also proposed to ensure user fairness.

On Achieving Fair and Throughput-Optimal Scheduling for TCP Flows in Wireless Networks

Throughput-optimal scheduling has been heavily investigated given its ability to fully utilize network resources and maintain network stability. Most of the existing throughput-optimal algorithms, including the classic queue-length based MaxWeight algorithm and flow-delay-based MaxWeight algorithm, however, may bring a severe unfairness problem when scheduling transmission control protocol (TCP) controlled flows. As TCP is the dominant transport layer protocol in the Internet and it controls the majority of Internet traffic, we study how to design the scheduling algorithm that can ensure both throughput optimality and be compatible to TCP flows. In this paper, we analyze the reason behind the incompatibility between the existing scheduling algorithms and TCP, and then investigate the properties of the head-of-line access delay-based scheduling algorithm (HOLD) we proposed. We prove that the proposed HOLD can fairly schedule TCP flows in wireless networks with time-varying channel conditions and achieve throughput optimality with flow-level dynamics. Simulations using OMNeT++ 4 have been conducted to validate our analytical results, and compare the performance of different scheduling algorithms comprehensively.

Distributed Timely Throughput Optimal Scheduling for the Internet of Nano-Things

Nanotechnology is enabling the development of miniature devices able to perform simple tasks at the nanoscale. The interconnection of such nano-devices with traditional wireless networks and ultimately the Internet enables a new networking paradigm known as the Internet of Nano-Things (IoNT). Despite their promising applications, nano-devices have constrained power, energy, and computation capabilities along with very limited memory on board, which may only be able to hold one packet at once and, thus, requires packets to be delivered before certain hard deadlines. Toward this goal, a fully-distributed computation-light provably-correct scheduling/MAC protocol is introduced for bufferless nano-devices, which can maximize the network throughput, while achieving perpetual operation. More specifically, the proposed scheduling algorithm allows every nano-device to make optimal transmission decisions locally based on its incoming traffic rate, virtual debts, and channel sensing results. It is proven that the proposed algorithm is timely throughput optimal in the sense that it can guarantee reliable data delivery before deadlines as long as the incoming traffic rates are within the derived maximum network capacity region. This feature not only can lead to high network throughput for the IoNT, but also guarantees that the memory of each device is empty before the next packet arrives, thus addressing the fundamental challenge imposed by the extremely limited memory of nano-devices. In addition, the optimal deadline is derived, which guarantees that all the nano-devices can achieve perpetual communications by jointly considering the energy consumption of communications over the terahertz channel and energy harvesting based on piezoelectric nano-generators.

Wireless Scheduling Design for Optimizing Both Service Regularity and Mean Delay in Heavy-Traffic Regimes

We consider the design of throughput-optimal scheduling policies in multihop wireless networks that also possess good mean delay performance and provide regular service for all links—critical metrics for real-time applications. To that end, we study a parametric class of maximum-weight-type scheduling policies, called Regular Service Guarantee (RSG) Algorithm, where each link weight consists of its own queue length and a counter that tracks the time since the last service, namely Time-Since-Last-Service (TSLS). The RSG Algorithm not only is throughput-optimal, but also achieves a tradeoff between the service regularity performance and the mean delay, i.e., the service regularity performance of the RSG Algorithm improves at the cost of increasing mean delay. This motivates us to investigate whether satisfactory service regularity and low mean-delay can be simultaneously achieved by the RSG Algorithm by carefully selecting its design parameter. To that end, we perform a novel Lyapunov-drift-based analysis of the steady-state behavior of the stochastic network. Our analysis reveals that the RSG Algorithm can minimize the total mean queue length to establish mean delay optimality under heavily loaded conditions as long as the design parameter weighting for the TSLS scales no faster than the order of $ {{1}\over {\root{5}\of {\epsilon }}}$ , where $\epsilon $ measures the closeness of the network load to the boundary of the capacity region. To the best of our knowledge, this is the first work that provides regular service to all links while also achieving heavy-traffic optimality in mean queue lengths.

Maximal throughput scheduling based on the physical interference model using learning automata

Wireless link scheduling is one of the major challenging issues in multi-hop wireless networks when they need to be designed in distributed fashion. This paper improves the general randomized scheduling method by using learning automata based framework that allows throughput optimal scheduling algorithms to be developed in a distributed fashion. A distributed scheduling algorithm that operates on more realistic conflict graph was proposed based on the physical interference model. This model uses a combination of a distributed learning automata based on the pick algorithm and an algorithm that compares successive scheduling solutions. Comparison was made by creating spanning tree on the conflict graph of the two consecutive schedules. Briefly, a distributed scheduling schemes was proposed, that: (i) is throughput optimal, (ii) intelligently choose links for new schedule, and (iii) message and time complexity is in O(n3).

Throughput-Optimal Scheduling Design With Regular Service Guarantees in Wireless Networks

Motivated by the regular service requirements of video applications for improving Quality-of-Experience (QoE) of users, we consider the design of scheduling strategies in multi-hop wireless networks that not only maximize system throughput but also provide regular inter-service times for all links. Since the service regularity of links is related to the higher-order statistics of the arrival process and the policy operation, it is highly challenging to characterize and analyze directly. We overcome this obstacle by introducing a new quantity, namely the time-since-last-service (TSLS), which tracks the time since the last service. By combining it with the queue-length in the weight, we propose a novel maximum-weight type scheduling policy, called Regular Service Guarantee (RSG) Algorithm. The unique evolution of the TSLS counter poses significant challenges for the analysis of the RSG Algorithm. To tackle these challenges, we first propose a novel Lyapunov function to show the throughput optimality of the RSG Algorithm. Then, we prove that the RSG Algorithm can provide service regularity guarantees by using the Lyapunov-drift based analysis of the steady-state behavior of the stochastic processes. In particular, our algorithm can achieve a degree of service regularity within a factor of a fundamental lower bound we derive. This factor is a function of the system statistics and design parameters and can be as low as two in some special networks. Our results, both analytical and numerical, exhibit significant service regularity improvements over the traditional throughput-optimal policies, which reveals the importance of incorporating the metric of time-since-last-service into the scheduling policy for providing regulated service.

Efficient and Fair Throughput-Optimal Scheduling in Buffer-Aided Relay-Based Cellular Networks

In this letter, we study throughput-optimal scheduling policies in single-path buffer-aided relay networks, where the packets of a user travel one fixed path from the source to the destination and do not require routing. We modify the well-known Max-Weight (MW) policy and propose a version of throughput-optimal algorithms, which we refer to as Modified MW (MMW). In MMW, by defining a suitably large threshold, a link's weight is proportional to just the corresponding local queue size either in the base station or the relay station, in most of the time slots. Moreover, a parameter can be adjusted for improving delay fairness between the relayed and direct users. MMW is suitable for use in both shared and independent channel scenarios and facilitates decentralized network implementations. Numerical results confirm that MMW is better able to provide similar performance for direct and relayed users in terms of queueing delay, compared with the existing algorithms.

Complexity Reduction of Throughput Optimal Link Scheduling Algorithm through Topology Control in Wireless Networks

In single channel wireless networks, concurrent transmissions at different links may interfere with each other. To improve system throughput, a scheduling algorithm is necessary to choose a subset of links for data transmission. Throughput optimal link scheduling discipline is generally an NP-hard problem. In this paper, we utilise the concept of line graph and extend it to line multigraph to cope with the complexity issue of the maximum weight scheduling (MWS) algorithm. The necessary and sufficient conditions for reducing the complexity of MWS in terms of network topology are derived. We prove that the complexity of eLehot is polynomial time provided that conflict graph does not contain seven derived forbidden graphs as induced subgraphs. We also propose eLehot algorithm for detecting whether a graph is line multigraph and output its root graph. The results of this paper introduce a new approach in wireless topology control where the target is complexity reduction.