Scheduling In Wireless Networks Research Articles

A genetic scheduling strategy with spatial reuse for dense wireless networks

Novel networking technologies such as massive Internet-of-Things and 6G-and-beyond cellular networks are based on ultra-dense wireless communications. A wireless communication channel is a shared medium that demands access control, such as proper transmission scheduling. The SINR model can improve the performance of ultra-dense wireless networks by taking into consideration the effects of interference to allow multiple simultaneous transmissions in the same coverage area and using the same frequency band. However, scheduling in wireless networks under the SINR model is an NP-hard problem. This work presents a bioinspired solution based on a genetic heuristic to solve that problem. The proposed solution, called Genetic-based Transmission Scheduler (GeTS) produces a complete transmission schedule optimizing size, increasing the number of simultaneous transmissions (i.e., spatial reuse) thus allowing devices to communicate as soon as possible. Simulation results are presented for GeTS, including a convergence test and comparisons with other alternatives. Results confirm the ability of the solution to produce near-optimal schedules.

User Scheduling in Wireless Networks for Deterministic Service: An Efficient Genetic Algorithm Method

User Scheduling in Wireless Networks for Deterministic Service: An Efficient Genetic Algorithm Method

Wireless Network Scheduling With Discrete Propagation Delays: Theorems and Algorithms

Wireless Network Scheduling With Discrete Propagation Delays: Theorems and Algorithms

Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep Learning

Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep Learning

Fast Link Scheduling in Wireless Networks Using Regularized Off-Policy Reinforcement Learning

The centralized-link-scheduling problem in a wireless network graph involves solving the maximum-weighted-independent-set (MWIS) problem on the conflict graph. In this paper, we propose a novel regularized off-policy reinforcement learning-based MWIS solver and use for the scheduling problem. The proposed MWIS algorithm achieves 17% improvement over state-of-the-art heuristic solver KaMIS, 60% over greedy solver, 16% and 17% over RL-based solvers LwD and S2V-DQN, respectively. We show that our scheduler achieves stable throughput values 14% and 22% higher than LwD and a distributed greedy scheduler, respectively. We demonstrate the flexibility of our RL algorithm by modifying it to create a time-since-last-service-aware scheduler.

Reinforcement learning based flow and energy management in resource-constrained wireless networks

This paper aims at developing a learning-based framework for MAC sleep–listen–transmit scheduling in wireless networks. The Reinforcement Learning-based paradigm is shown to work in the absence of network time-synchronization and other complex hardware features, such as carrier-sensing, thus making it suitable for low-cost transceivers for IoT and wireless sensor nodes. The framework allows wireless nodes to learn policies that can support throughput-sustainable flows while minimizing node energy expenditure and sleep-induced packet drops and delays. Each node independently learns a scheduling policy without explicit communication with other network nodes. The trade-off between packet drops and energy efficiency is analyzed, and an application-specific solution is proposed for handling the trade-off. It is shown how this model allows users to prioritize between energy efficiency and packet drops depending on specific application requirements. An analytical model is developed for understanding the underlying system dynamics, which is also validated using extensive simulation experiments. Moreover, the developed mechanism is experimented with for heterogeneous network topologies and traffic patterns

Deep Reinforcement Learning Based Resource Allocation Approach for Wireless Networks Considering Network Slicing Paradigm

In this paper, we present an approach for resource scheduling in wireless networks based on the Network Slicing (NS) paradigm using Double Deep Q-Network (DDQN) Reinforcement Learning (RL) algorithm. More specifically, we propose a joint power and Scheduling Block (SB) allocation algorithm for networks with NS. The reinforcement learning algorithm applied to the resource allocation problem is formulated using state transitions regarding the system dynamics. We also present an algorithm, namely Network Slicing based on Reinforcement Learning (NSRL) that combines the proposed reinforcement learning based resource allocation with an approach based on reservation and sharing of resources among the slices where each RL agent acts in one slice. Simulations are carried out considering User Equipments (UEs) within a small cell coverage area - (Small Cells) with different Modulation and Coding Schemes (MCS) standardized by the 3rd Generation Partnership Project (3GPP) based on a simplified NS scenario with fifth generation wireless network (5G) characteristics. In the simulations, two slices are considered for the UEs: one considering Ultra-reliable and Low Latency Communications (URLLC) and other related to enhanced Mobile Broadband (eMBB) services. Simulation results show that the NSRL algorithm efficiently allocates power and SBs, outperforming other algorithms in the literature.

Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks

Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks

Secrecy Outage Analysis of Relay Aided Multiuser Scheduling for Wireless Networks in the Face of Co-Channel Interference

In this paper, we analyze the secrecy outage probability of a relay aided multiuser network which consists of a cluster head (CH), a decode-and-forward (DF) relay, and multiple users in the presence of an eavesdropper and co-channel interference (CCI). Firstly, we propose two relay aided multiuser scheduling (RMS) schemes to improve the secrecy outage performance of the network by considering the availability of channel state information (CSI) of CCI links, including the partial CSI based RMS (PCSI-RMS) scheme and the full CSI based RMS (FCSI-RMS) scheme. For comparison purposes, the relay aided round-robin scheduling (RRRS) scheme is also employed as a baseline. Then, we derive closed-form expressions for the proposed PCSI-RMS and FCSI-RMS schemes as well as the RRRS scheme in terms of their exact and asymptotic secrecy outage probabilities over Rayleigh fading channels in the face of CCI. Finally, numerical results show that the secrecy outage performance of the proposed PCSI-RMS and FCSI-RMS schemes is better than that of the RRRS scheme, where FCSI-RMS achieves the best secrecy outage performance. With an increase of the number of users, the secrecy outage performance of the PCSI-RMS and FCSI-RMS schemes is significantly improved, while that of the RRRS scheme keeps unchanged, which demonstrates the benefits of adopting the proposed PCSI-RMS and FCSI-RMS schemes against eavesdropping in CCI environments. Moreover, there is an optimal power allocation factor between the CH and the relay, which makes the network have the best secrecy outage performance.

Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning

In this paper, we study asynchronous federated learning (FL) in a wireless distributed learning network (WDLN). To allow each edge device to use its local data more efficiently via asynchronous FL, transmission scheduling in the WDLN for asynchronous FL should be carefully determined considering system uncertainties, such as time-varying channel and stochastic data arrivals, and the scarce radio resources in the WDLN. To address this, we propose a metric, called an effectivity score, which represents the amount of learning from asynchronous FL. We then formulate an Asynchronous Learning-aware transmission Scheduling (ALS) problem to maximize the effectivity score and develop three ALS algorithms, called ALSA-PI, BALSA, and BALSA-PO, to solve it. If the statistical information about the uncertainties is known, the problem can be optimally and efficiently solved by ALSA-PI. Even if not, it can be still optimally solved by BALSA that learns the uncertainties based on a Bayesian approach using the state information reported from devices. BALSA-PO suboptimally solves the problem, but it addresses a more restrictive WDLN in practice, where the AP can observe a limited state information compared with the information used in BALSA. We show via simulations that the models trained by our ALS algorithms achieve performances close to that by an ideal benchmark and outperform those by other state-of-the-art baseline scheduling algorithms in terms of model accuracy, training loss, learning speed, and robustness of learning. These results demonstrate that the adaptive scheduling strategy in our ALS algorithms is effective to asynchronous FL.

Shortest link scheduling in wireless networks under the Rayleigh fading model

Many shortest link scheduling algorithms adopt non-fading SINR interference model, which assumes that the received signal power will always remain determinate as long as the transmission power of the corresponding sender is fixed. In fact, because environment always influences the propagation of radio signals, the received signal power is by no means a certain value. Rayleigh fading is a statistical model for radio signals propagation. It assumes that the strength of a signal on a receiver is a random variable, varying with the Rayleigh distribution. This paper proposes a shortest link scheduling algorithm under the Rayleigh fading model (SLSRF). The SLSRF partitions the wireless network area into hexagons and colors the hexagons with three different colors such that two neighboring hexagons have different colors. The senders of the links scheduled simultaneously are arranged in hexagons with the same color. The correctness of the SLSRF is proved through theoretical analysis, and the efficiency is illustrated by elaborate simulations. Our simulation results demonstrate that the schedule delay of SLSRF is less than that of some results under the non-fading SINR interference model. Furthermore, we extend the SLSRF to a distributed version, which is suitable for large wireless networks.

Shortest link scheduling in wireless networks with oblivious power control

Link scheduling has always been a fundamental problem in wireless networks for its direct impacts on the performance of wireless networks such as throughput capacity, transmission delay, lifetime, etc. Existing work is mainly established under graph-based models, which are not only impractical but also incorrect due to the essentially fading characteristics of signals. In this paper, we study the shortest link scheduling problem under two more realistic models, namely the signal to interference plus noise ratio (SINR) model and the Rayleigh fading model. We propose a centralized square-based scheduling algorithm (CSSA) with oblivious power control under the SINR model and prove its correctness under both the SINR model and the Rayleigh fading model. Furthermore, we extend CSSA and propose a distributed square-based scheduling algorithm (DSSA). Note that DSSA adopts CSMA/CA so that a wireless node can compete for the wireless channel before starting its communication. We also show theoretical analysis and conduct extensive simulations to exhibit the correctness and efficiency of our algorithms.

Throughput Maximization in Uncooperative Spectrum Sharing Networks

Throughput-optimal transmission scheduling in wireless networks has been a well considered problem in the literature, and the method for achieving optimality, MaxWeight scheduling, has been known for several decades. This algorithm achieves optimality by adaptively scheduling transmissions relative to each user’s stochastic traffic demands. To implement the method, users must report their queue backlogs to the network controller and must rapidly respond to the resulting resource allocations. However, many currently-deployed wireless systems are not able to perform these tasks and instead expect to occupy a fixed assignment of resources. To accommodate these limitations, adaptive scheduling algorithms need to interactively estimate these uncooperative users’ queue backlogs and make scheduling decisions to account for their predicted behavior. In this work, we address the problem of scheduling with uncooperative legacy systems by developing algorithms to accomplish these tasks. We begin by formulating the problem of inferring the uncooperative systems’ queue backlogs as a partially observable Markov decision process and proceed to show how our resulting learning algorithms can be successfully used in a queue-length-based scheduling policy. Our theoretical analysis characterizes the throughput-stability region of the network and is verified using simulation results.

Distributed link scheduling in wireless networks

This work investigates distributed transmission scheduling in wireless networks. Due to interference constraints, “neighboring links” cannot be simultaneously activated, otherwise transmissions will fail. Here, we consider any binary model of interference. We use the model described by Bui et al. in [L. X. Bui, S. Sanghavi and R. Srikant, Distributed link scheduling with constant overhead, IEEE/ACM Trans. Netw. 17(5) (2009) 1467–1480; S. Sanghavi, L. Bui and R. Srikant, Distributed link scheduling with constant overhead, in Proc. ACM Sigmetrics (San Diego, CA, USA, 2007), pp. 313–324.]. We assume that time is slotted and during each slot there are two phases: one control phase in which a link scheduling algorithm determines a set of non-interfering links to be activated, and a data phase in which data is sent through these links. We assume random arrivals on each link during each slot, so that a queue is associated to each link. Since nodes do not have a global knowledge of the queues sizes, our aim (like in [L. X. Bui, S. Sanghavi and R. Srikant, Distributed link scheduling with constant overhead, IEEE/ACM Trans. Netw. 17(5) (2009) 1467–1480; S. Sanghavi, L. Bui and R. Srikant, Distributed link scheduling with constant overhead, in Proc. ACM Sigmetrics (San Diego, CA, USA, 2007), pp. 313–324.]) is to design a distributed link scheduling algorithm. To be efficient, the control phase should be as short as possible; this is done by exchanging control messages during a constant number of mini-slots (constant overhead). In this paper, we design the first fully distributed local algorithm with the following properties: it works for any arbitrary binary interference model; it has a constant overhead (independent of the size of the network and the values of the queues), and it does not require any knowledge of the queue-lengths. We prove that this algorithm gives a maximal set of active links, where for any non-active link there exists at least one active link in its interference set. We also establish sufficient conditions for stability under general Markovian assumptions. Finally, the performance of our algorithm (throughput, stability) is investigated and compared via simulations to that of previously proposed schemes.

Efficient Link Scheduling in Wireless Networks Under Rayleigh-Fading and Multiuser Interference

Link scheduling plays a key role in the network capacity and the transmission delay. In this paper, we study the problem of maximum link scheduling (MLS), aiming to characterize the maximum number of links that can be successfully scheduled simultaneously under Rayleigh-fading and multiuser interference. After analyzing the minimum distance between successful links in the existing GHW scheduling algorithm, we propose a DLS (Distance-based Link Scheduling) algorithm. Then, the global interference is characterized and bounded by introducing a separation distance between selected links, building on which we propose a distributed version of DLS (denoted by DDLS) that converges to a constant factor of the non-fading optimum within time complexity $O(n\ln n)$ , where $n$ is the number of links. Furthermore, we study the Shortest Link Scheduling (SLS) problem, which minimizes the number of time slots to successfully schedule each link for at least once. An algorithm for SLS with approximation factor of $O(\ln n)$ is obtained by executing DDLS. Extensive simulations show that DDLS greatly outperforms GHW and the other two popular algorithms.

Multi-channel scheduling analysis of dynamic data in wireless networks oriented big data

In order to reduce the running time of dynamic data multi-channel scheduling in wireless networks, a multi-channel scheduling analysis method based on partially observable Markov decision process (POMDP) for dynamic data of wireless networks is proposed. Firstly, the data type and dynamic data transmission process in wireless network are analysed. According to the node's request arrival rate and service rate, the network state transition probability and observation probability are calculated. Load balancing is used as the performance optimisation target of dynamic data multi-channel in wireless network, calculate its performance function. By calculating the observation probability and performance function, the dynamic data multi-channel scheduling analysis for big data wireless networks is finally realised. The experimental results show that the proposed method has high data transmission efficiency and low packet loss rate, and the scheduling operation time is relatively short. The effectiveness of the proposed method is verified.

Backbone colouring and algorithms for TDMA scheduling

We investigate graph colouring models for the purpose of optimizing TDMA link scheduling in Wireless Networks. Inspired by the BPRN-colouring model recently introduced by Rocha and Sasaki, we introduce a new colouring model, namely the BMRN-colouring model, which can be used to model link scheduling problems where particular types of collisions must be avoided during the node transmissions. In this paper, we initiate the study of the BMRN-colouring model by providing several bounds on the minimum number of colours needed to BMRN-colour digraphs, as well as several complexity results establishing the hardness of finding optimal colourings. We also give a special focus on these considerations for planar digraph topologies, for which we provide refined results.

Link Scheduling in Wireless Networks With RF Energy Harvesting Nodes

In a wireless network, a scheduler ensures links are given frequent transmission opportunities. A short schedule means links transmit frequently and thus the resulting network capacity is high. In this paper, we consider a novel aim: deriving a link schedule that supports both data and energy links; the latter type of links are used to fulfill the energy requirement of radio frequency energy harvesting (EH) devices. This aim is significant as we envisage future wireless routers to have dual roles: forwarding data and charging devices. To this end, we present a mixed integer linear program to derive the shortest link schedule for both types of links; each with a data rate or energy requirement. We consider transmission power control and the physical interference model. In addition, we present a heuristic algorithm called linear programming approximation algorithm (LPAA) to derive the schedule for large-scale topologies. Our results show that the schedule length is affected by the number of links, energy requirement and energy conversion rate of EH nodes, transmit power, and signal-to-interference-noise ratio threshold. Lastly, our experimental results indicate that LPAA is capable of producing schedules that are near optimal.

Machine-Learning-Based Parallel Genetic Algorithms for Multi-Objective Optimization in Ultra-Reliable Low-Latency WSNs

Different from conventional wireless sensor networks (WSNs), ultra-reliable and low-latency WSNs (uRLLWSNs), being an important application of 5G networks, must meet more stringent performance requirements. In this paper, we propose a novel algorithm to improve uRLLWSNs’ performance by applying machine learning techniques and genetic algorithms. Using the $K$ -means clustering algorithm to construct a 2-tier network topology, the proposed algorithm designs the fetal dataset, denoted by the population, and develops a clustering method of energy conversion to prevent overloaded cluster heads. A multi-objective optimization model is formulated to simultaneously satisfy multiple optimization objectives including the longest network lifetime and the highest network connectivity and reliability. Under this model, the principal component analysis algorithm is adopted to eliminate the various optimization objectives’ dependencies and rank their importance levels. Considering the NP-hardness of wireless network scheduling, the genetic algorithm is used to identify the optimal chromosome for designing a near-optimal clustering network topology. Moreover, we prove the convergence of the proposed algorithm both locally and globally. Simulation results are presented to demonstrate the viability of the proposed algorithm compared to state-of-the-art algorithms at an acceptable computational complexity.

Optimal Packet Aggregation Scheduling in Wireless Networks

One of the most critical emerging problems for 5G and Internet of Things is the handling of machine-to-machine communication. Wireless sensor networks are deployed every day, resulting in a more distributed infrastructure, where the communication and processing are handled by energy, bandwidth, and processing constrained devices. Aggregation of multiple packets flowing over the same path increases spectral efficiency, energy efficiency, and resource utilization. We address the problem of determining the optimal waiting time to maximize the utility within the network. We provide a general framework, where the utility function is user-defined for each individual application stream and packet. This allows the user to optimize for energy, delay, or expiration rate in the resolution of individual streams. Our algorithm calculates the optimal time for any given condition on-the-fly and can adapt to changing conditions with low computational complexity. We provide an optimal multi-hop distributed and scalable under congestion versions of our algorithm. Our simulations in ns3 show that we outperform state-of-the-art policies by 1.55x in terms of information freshness. Our solution reduces average power consumption by more than 60 percent. Our congestion-aware solution shows constant performance with increasing congestion levels, whereas state-of-the-art solutions degrade by up to 70 percent under the same conditions.