Cross-layer Optimization for Ultra-reliable and Low-latency Radio Access Networks (1703.09575v2)

Abstract: In this paper, we propose a framework for cross-layer optimization to ensure ultra-high reliability and ultra-low latency in radio access networks, where both transmission delay and queueing delay are considered. With short transmission time, the blocklength of channel codes is finite, and the Shannon Capacity cannot be used to characterize the maximal achievable rate with given transmission error probability. With randomly arrived packets, some packets may violate the queueing delay. Moreover, since the queueing delay is shorter than the channel coherence time in typical scenarios, the required transmit power to guarantee the queueing delay and transmission error probability will become unbounded even with spatial diversity. To ensure the required quality-of-service (QoS) with finite transmit power, a proactive packet dropping mechanism is introduced. Then, the overall packet loss probability includes transmission error probability, queueing delay violation probability, and packet dropping probability. We optimize the packet dropping policy, power allocation policy, and bandwidth allocation policy to minimize the transmit power under the QoS constraint. The optimal solution is obtained, which depends on both channel and queue state information. Simulation and numerical results validate our analysis, and show that setting packet loss probabilities equal is a near optimal solution.

• The paper introduces a cross-layer optimization strategy that jointly addresses transmission delay, queueing delay, and block error probability to minimize power consumption in 5G networks.
• It incorporates a proactive packet dropping mechanism to tackle deep fading conditions and ensure ultra-reliable, low-latency performance under finite power constraints.
• Simulations demonstrate that optimizing packet loss probabilities significantly improves network reliability and reduces dropped packets, highlighting the framework's practical benefits.

Cross-layer Optimization for Ultra-reliable and Low-latency Radio Access Networks

The paper presents a detailed framework for cross-layer optimization intended to support Ultra-reliable and Low-latency Communications (URLLC) in Radio Access Networks (RANs). The focus of this paper is to address the challenge of ensuring ultra-high reliability and ultra-low latency specifically within the context of fifth-generation (5G) cellular networks, which are expected to support mission-critical applications such as autonomous vehicles, factory automation, and smart grids.

Key Contributions and Methodology

1. Challenge Identification: The authors first identify the key challenges in achieving URLLC within radio access networks. These include issues arising due to the shorter queueing and transmission delays compared to typical channel coherence times and the difficulty in ensuring even average transmit power under deep fading channel conditions. Furthermore, under constraints of finite blocklength channel codes, the achievable rate is neither convex nor concave concerning traditional resource parameters like power or bandwidth.
2. Proposed Framework: The paper proposes a cross-layer optimization strategy that incorporates transmission delay, queueing delay, and block error probability. The framework introduces a proactive packet dropping mechanism which is critical to maintaining the quality-of-service (QoS) requirements under finite transmit power constraints.
3. Optimization Problem: The approach is structured as an optimization problem where the goal is to minimize transmit power subject to QoS constraints. The optimal solution is derived through joint optimization of packet dropping, power allocation, and bandwidth distribution protocols that adapt based on both channel state information (CSI) and queue state information (QSI).
4. Numerical Results: The framework is validated through simulations that demonstrate the effectiveness of setting equal packet loss probabilities in minimizing power loss. Moreover, the results indicate that the proactive packet dropping mechanism significantly aids in handling cases of deep fading and reducing the number of dropped packets.

Implications and Future Directions

The research addresses practical challenges in resource-constrained environments typical of 5G networks with key results that show a combination of proactive strategies and cross-layer optimization can effectively meet the ultra-reliable and low-latency demands. Furthermore, by refining the packet loss probabilities and optimizing resource allocation dynamically, network reliability can be significantly enhanced without incurring excessive power consumption.

For future work, the paper suggests a need to evaluate the framework across different fading scenarios and to explore how the methodology could be extended or adapted for direct device-to-device (D2D) communications and other next-generation network topologies. Another potential area for development could be the extension of the framework to analyze joint uplink and downlink scenarios, possibly incorporating machine learning techniques for even more dynamic and adaptive optimization strategies.

Overall, the contributions of this paper lie in providing a comprehensive and adaptable approach to URLLC challenges, offering significant insights into resource management for next-generation wireless networks.