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

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

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems are expected to provide faster and more robust connections to user equipments by cooperation of a massive number of distributed access points (APs), and to be one of the key technologies for beyond 5G (B5G). CF-mMIMO systems with multiple-antenna APs have been investigated from various viewpoints recently. However, no comprehensive analysis of the impact of antenna distribution on CF-mMIMO system performance has been done so far, which is important for practical deployment. Besides spectral efficiency, in B5G, energy efficiency of user equipments is one of the key indicators because various kinds of battery-limited devices connect to the network. Thus, this paper provides a comprehensive performance analysis of the impact of antenna distribution on the performance indicators, while considering several combining/precoding schemes and transmit power control algorithms. For uplink maximal-ratio combining, the concentrated deployment has the best performance thanks to the channel hardening and favorable propagation phenomena. On the other hand, the concentrated deployment prominently suffers from shadowing effects. For uplink/downlink minimum mean-square error combining with transmit power control, the semi-distributed deployments show the best performance, and it implies that we can reduce the number of APs to 1/4 for uplink and to 1/2 for downlink while keeping the same performance as the fully-distributed deployment.

https://ieeexplore.ieee.org/ielx7/8782661/8901158/09903845.pdf

Impact of Antenna Distribution on Spectral and Energy Efficiency of Cell-Free Massive MIMO With Transmit Power Control Algorithms

Cellular vehicle-to-everything (C-V2X) has been continuously evolving since Release 14 of the 3rd Generation Partnership Project (3GPP) for future autonomous vehicles. Apart from automotive safety, 5G NR further bring new capabilities to C-V2X for autonomous driving, such as real-time local update, and coordinated driving. These capabilities rely on the provision of low latency and high reliability from 5G NR. Among them, a basic demand is broadcasting or multicasting environment update messages, such as cooperative perception data, with high reliability and low latency from a Road Side Unit (RSU) or a base station (BS). In other words, broadcasting multiple types of automotive messages with high reliability and low latency is one of the key issues in 5G NR C-V2X. In this work, we consider how to select Modulation and Coding Scheme (MCS), RSU/BS, Forward Error Correction (FEC) code rate, to maximize the system utility, which is a function of message delivery reliability. We formulate the optimization problem as a nonlinear integer programming problem. Since the optimization problem is NP-hard, we propose an approximation algorithm, referred to as the Hyperbolic Successive Convex Approximation (HSCA) algorithm, which uses the successive convex approximation to find the optimal solution. In our simulations, we compare the performance of HSCA with those of three algorithms respectively, including the baseline algorithm, the heuristic algorithm, and the optimal solution. Our simulation results show that HSCA outperforms the baseline and the heuristic algorithms and is very competitive to the optimal solution.

Optimizing Resource Allocation With High-Reliability Constraint for Multicasting Automotive Messages in 5G NR C-V2X Networks

Vehicle-to-everything (V2X) communication is one of the key technologies of 5G New Radio to support emerging applications such as autonomous driving. Due to the high density of vehicles, Remote Radio Heads (RRHs) will be deployed as Road Side Units to support V2X. Nevertheless, activation of all RRHs during low-traffic off-peak hours may cause energy wasting. The proper activation of RRH and association between vehicles and RRHs while maintaining the required service quality are the keys to reducing energy consumption. In this work, we first formulate the problem as an Integer Linear Programming optimization problem and prove that the problem is NP-hard. Then, we propose two novel algorithms, referred to as “Least Delete (LD)” and “Largest-First Rounding with Capacity Constraints (LFRCC).” The simulation results show that the proposed algorithms can achieve significantly better performance compared with existing solutions and are competitive with the optimal solution. Specifically, the LD and LFRCC algorithms can reduce the number of activated RRHs by 86 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 89 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in low-density scenarios. In high-density scenarios, the LD algorithm can reduce the number of activated RRHs by 90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> . In addition, the solution of LFRCC is larger than that of the optimal solution within 7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on average. 

Efficient RRH Activation Management for 5G V2X

This paper proposes hybrid signal processing schemes for the uplink cell-free massive MIMO; these schemes serve to reduce fronthaul loads to obtain a scalable centralized processing architecture. In this architecture, received signals of multiple receive antennas at the access points (APs) are compressed into fewer streams by local spatial signal processing and then the streams are forwarded to a central processing unit (CPU) via fronthaul, and the CPU performs scalable processing for channel estimation and signal detection based on partial minimum mean squared error (PMMSE). We propose two kinds of concrete local signal processing methods for this hybrid processing architecture: one is based on MMSE, and the other is based on principal component analysis (PCA) with eigenvalue decomposition (EVD). For the EVD, a local vector selection based EVD (LVS-EVD) that selects uniform number of eigenvectors for each AP in a standalone way, and a global vector selection based EVD (GVS-EVD) that determines the dimensions of the weight vector of each AP in the CPU, are further considered. Computer simulations verify the approaches and compare their effectiveness. In addition, we show that the GVS-EVD scheme can be operated with significantly reduced fronthaul loads without severe performance degradation.

Wei-Yu Chen

Papers

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

Impact of Antenna Distribution on Spectral and Energy Efficiency of Cell-Free Massive MIMO With Transmit Power Control Algorithms

Optimizing Resource Allocation With High-Reliability Constraint for Multicasting Automotive Messages in 5G NR C-V2X Networks

Efficient RRH Activation Management for 5G V2X

Fronthaul Load-Reduced Scalable Cell-Free massive MIMO by Uplink Hybrid Signal Processing