Stochastic Delay Management in Wireless Communication Systems
Summary
Wireless communication systems increasingly support applications with stringent timing requirements, from industrial automation and virtual-reality services to safety-critical sensor swarms and vehicular networks. Stochastic delay management addresses the inherent randomness of packet arrivals, channel conditions and scheduling, aiming to provide probabilistic guarantees on end-to-end latency. Key approaches include stochastic network calculus, which uses statistical envelopes to bound queueing and service processes; martingale techniques, which capture the evolution of delay violations over time; and information-freshness metrics such as the Age of Information. Recent research has extended these tools to multi-hop and mobile scenarios, incorporating mobility models and contention-based protocols, and to next-generation network paradigms including terahertz 6G links and edge computing offload. Advances have been made in deriving tight delay bounds under diverse scheduling policies, in unifying latency and freshness in a statistical framework, and in developing decentralised algorithms that adapt to local state information. Together, these developments underpin design principles for ultra-reliable low-latency communication (URLLC), the Internet of Things and emerging time-sensitive networks, ensuring robust performance even under unpredictable traffic and channel dynamics.
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Stochastic Delay Management in Wireless Communication Systems publication trend
The graph below shows the total number of articles in stochastic delay management in wireless communication systems across all publications each year (not limited to Nature Index journals).
Technical terms
Stochastic Network Calculus: A set of mathematical tools for deriving probabilistic delay and backlog bounds in queueing systems based on statistical envelopes.
Moment Generating Function: A transform that characterises the distribution of a random variable and enables tail-bound analysis in delay studies.
Discrete-Time Markov Chain: A stochastic process model with a finite set of states and transition probabilities, used to represent time-slotted channel behaviour or mobility.
Age of Information (AoI): A metric quantifying the freshness of received data by measuring the time elapsed since the most recent packet was generated.
Martingale: A sequence of random variables whose future expectation equals its current value, applied to derive tight bounds on delay-violation probabilities.
References
- Delay Guarantees for a Swarm of Mobile Sensors in Safety-Critical Applications. IEEE Open Journal of the Communications Society (2024).
- End-to-End Delay Bound Analysis for VR and Industrial IoE Traffic Flows under Different Scheduling Policies in a 6G Network. Computers (2023).
- A Perspective on Time Toward Wireless 6G. Proceedings of the IEEE (2022).
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