Low-latency Adaptive Communication Protocols for Ultra-dense Network Environments

Abstract

Ultra-dense networks (UDNs) face serious latency fluctuations and throughput degradation issues under high concurrency access and resource competition conditions. Traditional transmission protocols struggle to balance low latency and high stability in dynamic scenarios. In response to this challenge, this paper proposes a low latency adaptive communication protocol (MLACP), which constructs a multi-layer control system consisting of a physical access layer, a resource scheduling layer, and an adaptive decision layer. Through a cross layer feedback mechanism combined with RNN based short-term state prediction and DQN based strategy optimization, dynamic adjustment of resource slicing, distributed collaboration, and path selection is achieved. The protocol design is implemented in the system level simulation environment of a 3GPP UMi SC channel model and a Poisson cluster process, and integrated with ZeroMQ and PyTorch on the NS-3.36 platform. The experiment covered different user densities and link states, with each scenario running independently 10 times and taking the average. The results showed that under high-density conditions of 1500 UE/km², MLACP outperformed TCP Reno, QUIC, and the URLLC simplification scheme in terms of end-to-end latency, peak throughput, packet loss rate, path stability, and energy consumption. Moreover, it maintained controllable performance degradation in robustness tests such as link interruption, prediction bias, and base station failure. This result validates the feasibility and adaptability of the proposed protocol in dynamic and interference complex UDN environments, providing methodological references and an experimental basis for the design of low latency and intelligent communication systems.