Information Theory and Reliable Communication

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Reversibility and Stochastic Networks

As one of the key communication scenarios in the fifth-generation and also the sixth-generation (6G) mobile communication networks, ultrareliable and low-latency communications (URLLCs) will be central for the development of various emerging mission-critical applications. State-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLCs. In particular, a holistic framework that takes into account latency, reliability, availability, scalability, and decision-making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLCs in future 6G networks. This tutorial illustrates how domain knowledge (models, analytical tools, and optimization frameworks) of communications and networking can be integrated into different kinds of deep learning algorithms for URLLCs. We first provide some background of URLLCs and review promising network architectures and deep learning frameworks for 6G. To better illustrate how to improve learning algorithms with domain knowledge, we revisit model-based analytical tools and cross-layer optimization frameworks for URLLCs. Following this, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLCs and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.

A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning

In this paper, the performance of cognitive radio systems is studied when the secondary users operate under statistical quality of service (QoS) constraints. In the cognitive radio channel model, secondary users initially perform channel sensing, and then engage in data transmission at two different average power levels depending on the channel sensing results. A state transition model is constructed to model this cognitive transmission channel. Statistical QoS constraints are imposed as limitations on buffer violation probabilities. Effective capacity of the cognitive radio channel, which provides the maximum throughput under such QoS constraints, is determined. This analysis is conducted for fixed-power/fixed-rate, fixed-power/variable-rate, and variable-power/variable-rate transmission schemes under different assumptions on the availability of channel side information (CSI) at the transmitter. The interactions and tradeoffs between the throughput, QoS constraints, and channel sensing parameters (e.g., sensing duration and threshold, and detection and false alarm probabilities) are investigated. The performances of fixed-rate and variable-rate transmission methods are compared in the presence of QoS limitations. It is shown that variable schemes outperform fixed-rate transmission techniques if the detection probabilities are high. Performance gains through adapting the power and rate are quantified and it is shown that these gains diminish as the QoS limitations become more stringent.

Effective Capacity Analysis of Cognitive Radio Channels for Quality of Service Provisioning

We address the fundamental tradeoffs among latency, reliability and throughput in a cellular network. The most important elements influencing the KPIs in a 4G network are identified, and the inter-relationships among them is discussed. We use the effective bandwidth and the effective capacity theory as analytical framework for calculating the maximum achievable rate for a given latency and reliability constraint. The analysis is conducted in a simplified LTE network, providing baseline — yet powerful — insight of the main tradeoffs. Guidelines to extend the theory to more complex systems are also presented, including a semi-analytical approach for cases with intractable channel and traffic models. We also discuss the use of system-level simulations to explore the limits of LTE networks. Based on our findings, we give some recommendations for the imminent 5G technology design phase, in which latency and reliability will be two of the principal KPIs.

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Fundamental tradeoffs among reliability, latency and throughput in cellular networks

Wireless systems offer a unique mixture of connectivity, flexibility, and freedom. It is therefore not surprising that wireless technology is being embraced with increasing vigor. For real-time applications, user satisfaction is closely linked to quantities such as queue length, packet loss probability, and delay. System performance is therefore related to, not only Shannon capacity, but also quality of service (QoS) requirements. This work studies the problem of resource allocation in the context of stringent QoS constraints. The joint impact of spectral bandwidth, power, and code rate is considered. Analytical expressions for the probability of buffer overflow, its associated exponential decay rate, and the effective capacity are obtained. Fundamental performance limits for Markov wireless channel models are identified. It is found that, even with an unlimited power and spectral bandwidth budget, only a finite arrival rate can be supported for a QoS constraint defined in terms of exponential decay rate

Jia Tang

Papers

Resource Allocation and Quality of Service Evaluation for Wireless Communication Systems Using Fluid Models