Delay analysis of the approximate maximum weight scheduling in wireless networks

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.

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.

We consider the stability of the longest-queue-first (LQF) scheduling policy in wireless networks with multihop traffic under the one-hop interference model. Although it is well known that the back-pressure (BP) algorithm achieves the maximal stability, its computational complexity is very high. In this paper, we consider LQF, a low-complexity scheduling algorithm, which has been shown to have near optimal throughput performance in many networks with single-hop traffic flows. We are interested in the performance of LQF for multihop traffic flows. In this scenario, the analysis of local-pooling factors for LQF does not carry through because of the complicated coupling between queues due to multihop traffic flows. Using fluid limit techniques, we show that LQF achieves the maximal stability for linear networks with multihop traffic and a single destination under the one-hop interference.

We consider a system consisting of N parallel queues, served by one server. Time is slotted, and the server serves one of the queues in each time slot, according to some scheduling policy. In the first part of the paper, we characterize the buffer overflow exponents and the likeliest overflow trajectories under the Longest Queue First (LQF) scheduling policy. Under statistically identical arrivals to each queue, we show that the buffer overflow exponent can be simply expressed in terms of the total system occupancy exponent of m parallel queues, for some m ≤ N. We next turn our attention to the rate of queue length information needed to operate a scheduling policy, and its relationship to the buffer overflow exponents. It is known that LQF scheduling has superior overflow exponents compared to queue blind policies such as processor sharing (PS) and random scheduling. However, we show that the overflow exponent of the LQF policy can be preserved under arbitrarily infrequent queue length information.

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

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.

The regularity of inter-service time is essential for processing real-time traffic in wireless networks. Bounding the size of inter-service time for every single user is a precondition to support the normal operating of the network. Motivated by this, we design a framework for inter-service time guaranteed scheduling. We first define a new capacity region of networks with a strict inter-service time constraint. Then we propose a novel scheduling policy that is throughput-optimal and guarantees a bounded inter-service time for every user. Simulation results shows the policy performs well on inter-service time and throughput.

Message scheduling in wireless networks addresses the question of which links should transmit at what time, in order to maximize the transmitting data volume without interference. Many algorithms have been proposed to solve this problem, both centralized and distributed. The distributed approach is more attractive since the networks usually lack a centralized processor. Aiming to solve the message scheduling problem for wireless networks, in this paper, we present a fully distributed scheduling policy based on the Ising model.

Downlink data transmission scheduling algorithms in wireless networks

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.

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