Queue-aware Scheduling Research Articles

Simple Bounds on Delay-Constrained Capacity and Delay-Violation Probability of Joint Queue and Channel-Aware Wireless Transmissions

The emerging Ultra-Reliable and Low-Latency Communication (URLLC) is expected to meet a hard or probabilistic delay constraint that plays a key role in Time Sensitive Networking (TSN) and Deterministic Networking (DetNet). In this paper, we are interested in simple bounds for delay-constrained wireless communications with deterministic or even random packet arrivals. More specifically, we present a sufficient condition and derive a legitimate data arrival rate, with which the bounded delay can be guaranteed deterministically with an average power constraint. Our derived results will show that the delay-bounded data rate increases with the average power and the tolerated latency normalized to the coherent time. Furthermore, we derive the upper and lower bounds of the delay-violation probability (DVP) when the bounded delay condition cannot be satisfied. It is revealed that in the log coordinate, the DVP as a function of the normalized tolerated latency may enjoy a non-linear decay, with a decay rate that increases with the latency bound. This is in contrast to the large deviation based performance analysis that focuses on the low-latency transmission policies achieving linearly decayed DVP only. Our result demonstrates that the joint channel-aware and queue-aware scheduling may significantly reduce the DVP, compared to the single-layer approaches.

A reinforcement learning approach to queue-aware scheduling in full-duplex wireless networks

Full-duplex communications promise to double the throughput of a wireless network, so long as the resulting interferences can be combated. Nonetheless, already dealing with the intricacy of determining base station-to-user radio channels, full duplex wireless networks need additional information on the channels in between all the user equipment. This information is necessary to determine user radio conditions, and thereafter efficiently allocate resources. A signaling overhead is likely to burden the user equipment, which already lack any methods to estimate and convey such user-to-user channel states. In this paper, we aim to circumvent the complexities and requirements of traditional scheduling techniques by introducing a reinforcement learning based scheduling algorithm for full-duplex wireless networks. This scheduling approach does not need to estimate user-to-user channels, and rather learns how to best allocate the network’s radio resources. We show that our proposal can match scheduling with complete channel state information in terms of user equipment throughput, and that it performs well under multiple testing scheduling scenarios: increased user equipment numbers, randomized user equipment demand, and user equipment clustering among others.

Joint Channel and Queue Aware Scheduling for Latency Sensitive Mobile Edge Computing With Power Constraints

Mobile edge computing (MEC) is a promising technique to improve the quality of computation experience for mobile devices by providing computation resources in their close proximity. However, the design of scheduling policies for MEC systems inevitably encounters a challenging optimization problem that should take both transmissions and computations into consideration. In particular, how to jointly schedule transmissions and computations should adapt to the cross-layer system dynamics, i.e., random task arrivals and channel state variations. We formulate this scheduling problem as a joint optimization problem for both transmissions and computations in order to minimize the power consumption of mobile devices, while meeting the latency requirement. With given distributions of the system dynamics, Markov decision process (MDP) is used to model the system operations. Based on this model, the power-optimal scheduling policy can be obtained by converting the joint optimization problem to linear programming (LP) by using variable substitutions and thus the optimal power-latency tradeoff can be achieved. When the distribution information of the system dynamics is unknown, we exploit the Lyapunov optimization to present a low complexity scheduling policy. Our theoretical analysis and extensive simulation studies show that our approach can offer a good tradeoff between power consumption and latency.

Queue-aware scheduling in full-duplex wireless networks

Contemporary progress in telecommunication technologies have made full-duplex wireless communications feasible. The latter promise to double the capacity of wireless networks by allowing devices to concurrently transmit and receive on the same radio resources. In this paper, we devise mathematical optimal formulations for scheduling in full-duplex and hybrid full-duplex/half-duplex orthogonal frequency division multiple access networks. Our optimal models are queue-aware and address the new interferences that arise from working with full-duplex wireless networks: self-interference and intra-cell co-channel interference. We apply these models with different scheduling objectives, tackling issues such as signal-to-interference-plus-noise ratio (SINR) maximization and user fairness. Accordingly, we first propose an optimal full-duplex Max-SINR algorithm and an optimal full-duplex Proportional Fair algorithm. Additionally, and since full-duplex communications may not always be profitable, we introduce an optimal hybrid Max-SINR algorithm and an optimal hybrid Proportional Fair algorithm. These algorithms switch between full-duplex and half-duplex transmissions, so as to enhance network performance. Moreover, to avoid possible intractability with the optimization problems, we propose heuristic versions of our algorithms. We simulate these proposals, showing that they achieve near optimal performances, and asserting the different gains they attain with respect to their half-duplex counterparts: more than a 50% increase in user equipment throughput values alongside a three fold decrease in the average user equipment waiting delay.

A Channel and Queue-Aware Scheduling for the LTE Downlink Based on Service Curve and Buffer Overflow Probability

In this letter, we propose a joint channel and queue-aware resource block allocation algorithm for the long-term evolution (LTE) downlink based on buffer overflow probability estimation. To this end, we first derive an equation for the buffer overflow probability bound. Furthermore, we propose a novel service curve in order to estimate maximum delay for LTE system users considering the Effective exponential signal-to-noise ratio mapping to the SNR values of the LTE sub-bands. Simulation results reveal that in general, compared to other approaches, the proposed scheduling algorithm provides lower delay and buffer overflow rate while maximizing throughput.

Queue based scheduling in single and multi channel dynamic spectrum access networks

One of the ways to harness the radio bands that are not used by their primary owners, is to allow opportunistic use of the bands by secondary users (SUs) such that they do not interfere with the primary users. In a dynamic spectrum access (DSA) network, multiple SUs contend to acquire the bands that are available and proper scheduling techniques can resolve such contentions. However, all scheduling techniques have their own pros and cons. In this paper, we propose scheduling algorithms with different objectives for secondary users in a DSA network. Scheduling is performed at the beginning of each super-frame where appropriate channel(s) are assigned to the SUs based on the expected throughput for each channel on specific time slots. Throughput is computed considering: (i) primary channel occupancy, (ii) signal-to-interference-and-noise ratio, and (iii) back-logged queue length of the SUs. We consider two DSA environments: single channel and multi-channel. We propose two scheduling algorithms—the first one focuses on maximizing the expected throughput and the second one focuses on fairness. We model the state of the channel for every user using a discrete-time Markov chain (DTMC) and compute their state transition probabilities. Multiple SUs are allocated the same channel on the same time slot as long as there is no interference between them. Such non-interfering sets of SUs are found by considering the expected throughput of each user on all candidate channels. In order to maximize spatial and temporal reuse of channels, the non-interfering sets are assigned as many channels as possible. Moreover, the channel is reused among the non-interfering sets in the same super-frame. Performances of the proposed scheduling schemes are validated using simulations where we measure metrics such as throughput, number of slots allocated, fairness, delay, and blocking probability. We also show the efficiency of the queue-aware scheduling by comparing with one that does not consider the SU queues.

Queue-aware energy-efficient scheduling and power allocation with feedback reduction in small-cell networks

Queue-aware energy-efficient scheduling and power allocation with feedback reduction in small-cell networks

Scheduling and Resource Allocation in Downlink Multiuser MIMO-OFDMA Systems

Multiuser MIMO (MU-MIMO) in general, and block diagonalization (BD) in particular, are playing a prominent role toward the achievement of higher spectral efficiencies in modern OFDMA-based wireless networks. The utilization of such techniques necessarily has implications in the scheduling and resource allocation processes taking care of assigning subcarriers, power, and transmission modes to the different users. In this paper, a framework for channel- and queue-aware scheduling and resource allocation for BD-based MU-MIMO-OFDMA wireless networks is introduced. In particular, using an SNR-based abstraction of the physical layer, the proposed design is able to cater for different BD-MU-MIMO processing schemes [co-ordinated Tx-Rx (CTR) or receive antenna selection (RAS)], uniform or adaptive power allocation (UPA/APA), continuous or discrete rate allocation (CRA/DRA), and many different scheduling rules. Additionally, the different strategies are complemented by a new greedy user/stream selection algorithm that is shown to perform very close to the optimal user/stream selection policy at a much lower complexity. Results using system parameters typically found in 4G networks reveal that, in most cases, low-complexity solutions (RAS-, UPA-based) achieve a performance close to the one attained by their more complex counterparts (CTR-, APA-based).

Queue-aware uplink scheduling with stochastic guarantees

Adaptive resource allocation arises naturally as a technique to optimize resource utilization in communication networks with scarce resources under dynamic conditions. One prominent example is cellular communication where service providers seek to utilize the costly resources in the most effective way. In this work, we investigate an uplink resource allocation scheme that takes into account the buffer occupation at the transmitter to retain a given level of quality of service (QoS). First, we regard exact results for the class of Poisson traffic where we investigate the sensitivity of the resource adaptation and QoS level to the actuating variables. We show relevant resource savings in comparison with a static allocation. Further, we regard a queueing setting with general random arrival and service processes. In particular, we consider the service of wireless fading channels. We show two different resource adaptation mechanisms that depend on the strictness of different assumptions. Finally, we present simulation results that show substantial resource savings using the queue-aware scheduling scheme, where we provide insight on the implementation and operation of such an adaptive system.

Throughput Enhancements for Ieee802.16j Networks Using Queue Aware Scheduling with Directional Antennas

WiMAX Cellular Networks with Multi-hop Relays(MR), standardized as IEEE 802.16j networks, have been proposed for the improvement of overall throughput by providing last mile high speed broadband service to the users. The scheduling algorithm at Base Station(BS) plays a vital role in optimal resource allocation. In Queue aware scheduling, Concurrent Transmission Scenarios(CTS) are identified based on high back pressure links, which are used as input for linear programming model that determines the transmission schedules to maximize the overall throughput with simultaneous transmissions over Independent Links(IL). As shown in the results of Queue aware scheduling algorithm, the increase in the number of Concurrent Transmission Scenarios, improves the throughput and reaches a steady state after a level because of the limited number of independent links. In this paper, the installation of Directional Antennas at Base Station(BS), Relay Stations(RS) is proposed and studied the impact of increase in independent links, so as the concurrent scenarios with Directional Antennas, and observed some improvement in throughput. Simulation results showed that the proposed Queue aware scheduling with Directional Antennas outperforms even when the Mobile Stations(MSs) are at the edge of the cell coverage.

Adaptive Q-aware Scheduling Algorithm for Multi-Service Flows in 802.16j Networks

The IEEE 802.16j standard defines Mobile Multi-hop Relay Wireless Cellular Networks. Introduction of Relays in WiMAX networks pose new challenges along with the advantages of High Throughput and Improved Coverage Area. Thus Scheduling plays a crucial role in Mobile Multihop Relay(MMR) Wireless Networks. The Queue aware scheduling algorithm [1] maximizes the throughput by considering Queues at Base Station(BS) and Relay Station(RS) as well along with Concurrent Transmission Technique. In Queue aware scheduling, as all the arrived packets are queued up in FCFS manner, the high priority packets are obviously delayed. In this paper, QOS guarantee is incorporated by considering multiple Queues for different service flows at Base Station for Queue aware scheduling algorithm. Real Time Adaptive Scheme (RTAS) [2] is modified to improve throughput and used for picking the packets from service class sub queues(SCSQ) to include them in BS input queue. The results show that high priority packets are given proper importance while considering low priority packets as well. Throughput is also increased because of Modified RTAS and Concurrent Transmission Technique of Queue aware scheduling. General Terms WiMAX, Scheduling, Algorithm, Throughput, Queue, Packet

Queue-Aware Transmission Scheduling for Cooperative Wireless Communications

Queue-aware transmission scheduling for cooperative wireless communications with sub-fading-block scheduling to better balance load and capacity in low mobility environments is investigated. The scheduling problem for joint cooperation scheduling and resource allocation is formulated as a constrained nonlinear integer optimization problem over an integer convex set based on a source buffer queueing analysis. It is shown that with queue-aware scheduling, the state transition matrix of the source buffer queue has a highly dynamic form. As a result, the objective function of the optimization problem does not have an analytic form in general. The constrained discrete Rosenbrock search algorithm, which is a gradient-free directed discrete search algorithm, is employed to solve the nonlinear integer problem. The output of the directed integer search algorithm is used for queue-aware transmission scheduling for the cooperative system.

Queue-Aware Channel-Adapted Scheduling and Congestion Control for Best-Effort Services in LTE Networks

In this paper, we study the performance of long-term evolution (LTE) for various types of channel-adapted scheduling for nonreal-time flows, while an end-to-end congestion control algorithm controls the rate of elastic traffic at the end users. First, we propose a new type of queue-aware channel-adapted scheduling at a base station, and explain how it allocates resources to competing nonreal-time flows where channel conditions are time-varying. We also introduce a new congestion measure function for a minimum cost flow control (MCFC) algorithm in the LTE and call it an individual flow-based congestion measure. We show that using different combinations of channel-adapted scheduling at the base station and congestion control algorithms can lead to major differences in the obtained throughput and fairness for the best-effort traffic. The results clearly show that the transport protocol and scheduling algorithm can cause significant conflict in some situations. We show the advantages of the proposed queue-aware channel-adapted scheduling in performance improvement and we also show that the combination of an MCFC algorithm (in which the new individual flow-based congestion measure is applied), with queue-aware proportional fair scheduling, leads to a better tradeoff between overall throughput and fairness compared with the other studied combinations.

Generalized Queue-Aware Resource Management and Scheduling for Wireless Communications

The general problem of a queue-aware radio resource management and scheduling design is investigated for wireless communications under quasi-static fading channel conditions. Based on an analysis of the source buffer queuing system, the problem is formulated as a constrained nonlinear discrete programming problem. The state transition matrix of the queuing system determined by the queue-aware scheduler is shown to have a highly dynamic structure, so that the conventional matrix analysis and optimization tools are not applicable. By reformulating the problem into a nonlinear integer programming problem on an integer convex set, a direct search approach is considered. Two types of search algorithms, gradient based and gradient-free, are investigated. An integer steepest-descent search with a sub-sequential interval search algorithm and a constrained discrete Rosenbrock search (CDRS) algorithm is proposed to solve the nonlinear integer problem. Both algorithms are shown to have low complexity and good convergence. The numerical results for a single user resource allocation are presented, which show that both algorithms outperform equal partitioning and random partitioning queue-aware scheduling. The dynamic programming (DP) solution given by the relative value iteration algorithm, which provides the true optima but has high complexity, is used as a benchmark. In the majority of the numerical examples, the performance of the CDRS algorithm is almost identical to that of the DP approach in terms of both the average queue length minimization and the average packet blocking plus packet retransmission minimization, but it is less complex, and thus has better scalability.

Traffic-based queue-aware scheduling for 3GPP LTE system

Although the medium access control (MAC) signaling has been well-defined in the 3rd generation partnership project (3GPP) long term evolution (LTE) specifications, the scheduling algorithm crucial to guarantee QoS performance, still remains as open issues. In this article, a traffic-based queue-aware scheduling (TQS) algorithm is proposed for evolved nodeB's (eNB's) MAC scheduler in 3GPP LTE broadband wireless networks. The proposed TQS is divided into three sub-algorithms: firstly, the authors propose a traffic model construction (TMC) algorithm which can construct a discrete-time Markov-modulated Poisson process (dMMPP) to represent each flow. Secondly, a newly traffic state estimation (TSE) algorithm is designed to obtain the queue's analytical statistics. Thirdly, based on the derived results of TSE and the channel states, a scheduling action decision (SAD) algorithm is presented that can adaptively allocate bandwidth to flows by considering both queue states and spectrum efficiency. Simulation shows that the TMC and TSE algorithm can capture the fluctuation of traffic and queue accurately. Moreover, compared with a widely accepted traffic-based scheduling algorithm, the proposed TQS has better average queue length and overflow probability performance.

On the Value of Coordination and Delayed Queue Information in Multicellular Scheduling

We study limited-coordination scheduling in a wireless downlink network with multiple base stations, each serving a collection of users over shared channel resources. When neighboring base stations simultaneously schedule users on the same channel resource, collisions occur due to interference, leading to loss of throughput. Full coordination to avoid this problem requires each base station to have complete “instantaneous” channel-state information for all its own users, as well as the ability to communicate on the same timescale as channel fluctuations with neighboring base stations. As such a scheme is impractical, if not impossible, to implement, we consider the setting where each base station has only limited instantaneous channel-state information for its own users, and can communicate with other base stations with a significant lag from the channel state variations to coordinate scheduling decisions. In this setting, we first characterize the throughput capacity of the system. A key insight is that sharing delayed queue-length information enables coordination on a slow timescale among the base stations, and this permits each base station to use limited and local channel-state along with global delayed queue-state to stabilize its users' packet queues. Based on this, we develop a distributed, queue-aware scheduling (and information exchange) algorithm that is provably throughput-optimal. Finally, we study the effect of inter-base-station coordination delay on the system packet delay performance under the throughput-optimal algorithm.

Large Deviation Delay Analysis of Queue-Aware Multi-User MIMO Systems With Two-Timescale Mobile-Driven Feedback

Multi-user multi-input-multi-output (MU-MIMO) systems transmit data to multiple users simultaneously using the spatial degrees of freedom with user feedback channel state information (CSI). Most of the existing literatures on the reduced feedback user scheduling focus on the throughput performance, while the user queueing delay is usually ignored. As the delay is very important for real-time applications, a low feedback queue-aware user scheduling algorithm is desired for the MU-MIMO system. This paper proposes a two-stage queue-aware user scheduling algorithm, which consists of a queue-aware mobile-driven feedback filtering stage and a user scheduling stage, where the feedback filtering policy is obtained from optimizations. We evaluate the queueing performance of the proposed scheduling algorithm by using the sample path large deviation analysis. We show that the large deviation decay rate for the proposed algorithm is much larger than that of the CSI-only user scheduling algorithm. The numerical results also demonstrate that the proposed algorithm performs much better than the CSI-only algorithm, while requiring only a small amount of feedback.

Utility maximisation in channel-aware and queue-aware orthogonal frequency division multiple access scheduling based on arrival rate control

A channel-aware and queue-aware (CAQA) scheduling scheme is proposed for the downlink of orthogonal frequency division multiple access (OFDMA) networks. The scheduling scheme cooperates with a rate control policy, which controls arrival rates to the base station queues. The network resource allocation is formulated as a utility maximisation problem, which is decomposed into a CAQA OFDMA scheduling problem and a rate control problem. The authors decompose the scheduling problem into subproblems of rate allocation to subcarriers and propose a joint subcarrier assignment and rate allocation (JSARA) solution for the problem. Simulation is conducted to evaluate the performance of JSARA in terms of throughput, queue length and dropping probability. In addition, JSARA performance gain in allocating users’ arrival rates and achieved aggregate utility, in cooperation with a rate control policy, is investigated.

Optimal Buffer Partitioning on a Multiuser Wireless Link

A finite buffer shared by multiple packet queues is considered. Partitioning the buffer to maximize total throughput is formulated as a resource allocation problem, the solution is shown to be achieved by a greedy incremental algorithm in polynomial time. The optimal buffer allocation strategy is applied to different models for a wireless downlink. First, a set of parallel M/M/1/mi queues, corresponding to a downlink with orthogonal channels is considered. It is verified that at high load, optimal buffer partitioning can boost the throughput significantly with respect to complete sharing of the buffer. Next, the problem of optimal combined buffer allocation and channel assignment problems are shown to be separable in an outage scenario. Motivated by this observation, buffer allocation is considered in a system where users need to be multiplexed and scheduled based on channel state. It is observed that under finite buffers in the high load regime, scheduling simply with respect to channel state with a simply partitioned buffer achieves comparable throughput to combined channel and queue-aware scheduling.

Cross-Layer Joint Rate Control and Scheduling for OFDMA Wireless Mesh Networks

We consider the utility-maximization problem in the downlink of wireless mesh networks with orthogonal frequency-division multiple access (OFDMA). We formulate this problem as a cross-layer design of joint rate control and OFDMA scheduling to efficiently utilize the scarce radio resources. The problem is decoupled into a rate-control problem at the transport layer and a channel-aware and queue-aware scheduling problem at the Media Access Control/physical layer (MAC/PHY). The rate-control problem adjusts arrival rates to the base station (BS) queues, and the scheduling problem determines link rates, i.e., departure rates from the BS and other network nodes, through subcarrier and modulation rate assignment. Although the rate-control problem is locally solved at the BS, we propose a greedy algorithm that solves the scheduling problem in a distributed manner, at network nodes. Furthermore, we propose a heuristic algorithm for fast execution of the scheduling scheme at individual nodes. Numerical results show that the heuristic algorithm performs comparably with the greedy algorithm, whereas it has lower computational complexity. In addition, our proposed scheduling scheme, when it cooperates with the rate-control mechanism, improves the network performance in terms of end-to-end delay, aggregate utility, and fairness.