A Distributed Transmission Scheduling Algorithm for Wireless Networks Based on the Ising Model

Fractional Link scheduling is one of the most fundamental problems in wireless networks. The prevailing approach for shortest fractional link scheduling is based on a reduction to the maximum-weighted independent set problem, which itself may not admit efficient approximation algorithms. In addition, except for the wireless networks under the protocol interference model, none of the existing scheduling algorithms can produce a link schedule with explicit upper bounds on its length in terms of the link demands. As the result, the polynomial approximate capacity regions in these networks remain blank. This paper develops a purely combinatorial paradigm for fractional link scheduling in wireless networks. In addition to the superior efficiency, it is able to provide explicit upper bounds on the lengths of the produced link schedule. By exploiting these upper bounds, polynomial approximate capacity regions are derived. The effectiveness of this new paradigm is demonstrated by its applications in wireless networks under the physical interference model and wireless MIMO networks under the protocol interference model.

Complicated collisions and spectrum uncertainty constrain the usage of Cognitive Radio Networks (CRNs) on heavy transmission and time sensitive applications. On the other hand, data aggregation has been considered as an essential operation in wireless networks. A large amount of effort has been dedicated to the investigation of CRNs and data aggregation in wireless networks. However, the existing literatures rarely concentrate on how to use cognitive radio technique to promote the performance of data aggregation in conventional wireless networks. In this paper, we investigate the Minimum Latency Data Aggregation Scheduling in wireless networks with Cognitive Radio capability (MLDAS-CR) problem. As the first try, an approximation scheduling algorithm based on Integer Linear Programming (ILP) and Linear Programming (LP) is proposed. According to the simulation results, this method performances great, however, it is difficult to theoretically evaluate the solution. Therefore, a heuristic scheduling algorithm with guaranteed latency bound is presented in our further investigation. The performance of the proposed solutions are evaluated through extensive simulations.

The paper studies throughput-optimal scheduling in wireless networks when there are file arrivals and departures. In the case of single-hop traffic, the well-studied Max Weight algorithm provides soft priorities to links with larger queue lengths. If packets arrive in bursts during file arrival instants, then large variances in file sizes would imply that some links will have very large queue lengths while others will have small queue lengths. Thus, links with small queue lengths may be starved for long periods of time. An alternative is to use only MAC-layer queue lengths in making scheduling decisions; in fact, typically only this information is available since scheduling is performed at the MAC layer. Therefore the questions we ask in this paper are the following: (i) is scheduling using only MAC-layer queue length information throughput-optimal? and (ii) does it improve delay performance compared to the case where scheduling is performed using the total number of packets waiting at a link? We affirmatively answer both questions in the paper (the first theoretically and the second using simulations), making minimal assumptions on the transport-layer window control mechanism.

Downlink data transmission scheduling algorithms in wireless networks

Future wireless networks such as 5G and upcoming 6G require highly efficient resource management to handle massive devices, high data rates, and low-latency applications. Traditional optimisation techniques struggle due to complex traffic patterns and unpredictable wireless environments. This paper explores innovative approaches that use Generative Artificial Intelligence (GAI) — including diffusion models, GANs, and transformers — to optimise spectrum, power, routing, and network scheduling. Generative AI can simulate real network scenarios, create synthetic datasets, design optimal configurations, and support autonomous decision-making. The study highlights how GAI significantly reduces optimisation time, improves network reliability, and lays the foundation for intelligent, self- adaptive wireless systems. Keywords: 6G, generative AI, diffusion models, GANs, VAEs, digital twin, resource allocation, routing, spectrum, ISCC, edge intelligence, intent-driven networking

This paper studies joint scheduling and network coding in wireless multicast networks with independent sources. Designing a network coding solution for wireless networks involves scheduling interference-free transmissions and optimizing a performance measure for the predetermined scheduling sets, followed by constructing network codes for the specific solution. In such a design process, the results of prior steps need to provide enough information to the subsequent steps. In this paper, we formulate a linear optimization problem whose results can be used to design a coding solution. We build our work on previous works and present statistics that show the importance of incorporating unequal timeshares in designing network codes. In particular, our statistics show a throughput improvement of about 35% in maximum flow problems and energy savings between 13% and 30%, depending on the network size, in minimum energy problems. We also present the requirements of code construction algorithms for wireless networks that capture the broadcast property of these networks and can design proper codes when timeshares are unequal. In particular, the adaptation of a centralized network coding scheme for wireless networks is discussed. Overall, the work reported here provides a three-step solution to derive network codes that optimize a performance criterion of interest while also solving the scheduling problem in multihop wireless networks.

We provide bounds for the expectations of the stationary delay and the sum of the stationary queue sizes under the approximate maximum weight scheduling (MWS) policy and under the longest-queue-first (LQF) policy in one-hop wireless networks. For MWS, our results improves the previously provided bounds; for LQF, the results are new. In the derivation of the new bounds, a connection with certain graph theory quantities has been established. We also derive bounds for the sum of the second moments of the stationary queue sizes, which are also bounds for the sum of the queue-size variances. The method is general and it can be used to derive bounds for the sum of even higher moments of the stationary queue sizes. However, for the approximate MWS policy, the bounds for the second or higher moments are only valid when the average arrival rate vector is confined to a subset of the capacity region that depends on the order of the moment.

In this letter, we propose a scheme for the guaranteed end-to-end delay with the proportional fair scheduling in wireless networks. With the newly introduced delay parameter, we adjust the queue length of each link. Specifically, we formulate a new optimization problem which guarantees the average end-to-end delay of each flow in wireless networks. Through simulation, we show that network throughput is improved while guaranteeing the average end-to-end delay compared to the existing results.

ABSTRACTIn this paper, we study an opportunistic scheduling problem in an OFDMA system, in which sub‐carriers of the system are allocated to each user in each time slot considering the time‐varying channel condition and QoS requirement of each user. We consider two different classes of services that are represented with different types of utility functions. The utility function for a user in one class is defined as a function of its average data rate, which can be applicable to best‐effort services and the utility function for a user in the other class is defined as a function of its instantaneous data rate, which can be applicable to rate‐sensitive services. Those two types of utility functions have been extensively considered in opportunistic scheduling in wireless networks. However, in most of the previous work, they are considered separately in different problems. In this paper, we formulate a stochastic optimization problem that can treat those two types of utility functions in a single problem, which enables us to implement an opportunistic scheduling algorithm that can consider those two classes of services in a single system in a unified way. Through simulations, we first show that our algorithm provides a good approximation to the optimal solution. In addition, we also verify the appropriateness of our utility models. Copyright © 2010 John Wiley & Sons, Ltd.

We consider a general class of low complexity distributed scheduling algorithms in wireless networks, prioritized maximal scheduling, which compute a maximal set of transmitting links in each time slot according to certain pre-specified static priorities. The proposed scheduling scheme has low complexity, and is easily amendable for distributed implementation, such as adjusting inter-frame space (IFS) parameters under the ubiquitous 802.11 protocols. We provide throughput guarantees on the proposed scheduling scheme, by proving tight lower bound on the stability region and scheduling efficiency associated with a fixed priority vector. We next propose an online low complexity priority assignment algorithm, which can stabilize any arrival rate that is in the union of the lower bound regions of all priorities. The throughput guarantees are proved by fluid limits, and therefore can be applied to very general stochastic arrival processes. Finally, the performance of the proposed prioritized maximal scheduling scheme is verified by simulation results.

We consider the fundamental delay tradeoffs for utility optimal scheduling in a general network with time-varying channels. A network controller acts on randomly arriving data and makes flow control, routing, and resource allocation decisions to maximize a fairness metric based on a concave utility function of network throughput. A simple set of algorithms are constructed that yield total utility within O(1/V) of the utility-optimal operating point, for any control parameter V>0, with a corresponding end-to-end network delay that grows only logarithmically in V. This is the first algorithm to achieve such super-fast performance. Furthermore, we show that this is the best utility-delay tradeoff possible. This work demonstrates that the problem of maximizing throughput utility in a data network is fundamentally different than related problems of minimizing average power expenditure, as these latter problems cannot achieve such performance tradeoffs

In this paper, we propose a cognitive radio based Medium Access Control (MAC) protocol for packet scheduling in wireless networks. Cognitive MAC protocols allow a class of users, called secondary users, to identify the unused frequency spectrum and to communicate without interfering with the primary users. In our proposed MAC protocol, each secondary user is equipped with two transceivers. One of the transceivers is used for control messages while the other periodically senses and dynamically utilizes the unused data channel. The secondary users report the status of channels on control channel and negotiate on the selected data channel itself for onward data transmission. Each channel is used by different set of secondary users. Contrary to existing protocols, data transmission takes place in the time slot in which spectrum opportunity is found. We develop a new analytical model, while taking into account the backoff mechanism. Our simulation results show that throughput increases with the increase in the number of channels.

Link scheduling is a fundamental problem in wireless ad hoc and sensor networks. In this paper, we focus on the shortest link scheduling (SLS) under Signal-to-Interference-plus-Noise-Ratio and hypergraph models, and propose an approximation algorithm $$SLS_{pc}$$SLSpc (A link scheduling algorithm with oblivious power assignment for the shortest link scheduling) with oblivious power assignment for better performance than GOW* proposed by Blough et al. [IEEE/ACM Trans Netw 18(6):1701---1712, 2010]. For the average scheduling length of $$SLS_{pc}$$SLSpc is 1 / m of GOW*, where $$m=\lfloor \varDelta _{max}\cdot p \rfloor $$m=źΔmax·pź is the expected number of the links in the set V returned by the algorithm HyperMaxLS (Maximal links schedule under hypergraph model) and $$0

Joint routing and scheduling in multi-Tx/Rx wireless mesh networks with random demands

A new channel access protocol for ad-hoc networks based on topology-dependent transmission scheduling, named collision-avoidance time allocation (CATA), is introduced. CATA allows nodes to contend for and reserve time slots by means of a distributed reservation and handshake mechanism. Contention is limited among nodes within two hops of one another, which provides a very efficient spatial reuse of the bandwidth available. CATA ensures that no collisions occur in successfully reserved time slots, even when hidden terminals exist. Reservations in CATA support unicasting, multicasting and broadcasting simultaneously, and adapt to dynamic service time. The throughput achieved by CATA is analyzed for the case of a fully-connected network topology. Numerical results show that CATA can achieve very high throughput.