In this chapter we deal with the following nonlinear fractional programming problem: 

$$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$

where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.

Nonlinear Fractional Programming

In this article, we consider the massive nonorthogonal multiple access (NOMA) with a hybrid automatic repeat request (HARQ) for short packet communications. To reduce the latency, each user can perform one retransmission provided that the previous packet was not decoded successfully. The system performance is evaluated for both coordinated and uncoordinated transmissions. We first develop a Markov model (MM) to analyze the system dynamics and characterize the packet error rate (PER) and throughput of each user in the coordinated scenario. The power levels are then optimized for two scenarios, including the power constrained and reliability constrained scenarios. A simple yet efficient dynamic cell planning is also designed for the uncoordinated scenario. Numerical results show that both coordinated and uncoordinated NOMA-HARQ with a limited number of retransmissions can achieve the desired level of reliability with the guaranteed latency using a proper power control strategy. The results also show that NOMA-HARQ achieves a higher throughput compared to the orthogonal multiple access scheme with HARQ under the same average received power constraint at the base station.

Performance Analysis and Optimization of NOMA With HARQ for Short Packet Communications in Massive IoT

Recently, ultra-reliable and low-latency communications (URLLC) using short-packets has been proposed to fulfill the stringent requirements regarding reliability and latency of emerging applications in 5G and beyond networks. In addition, multiple-input multiple-output non-orthogonal multiple access (MIMO NOMA) is a potential candidate to improve the spectral efficiency, reliability, latency, and connectivity of wireless systems. In this paper, we investigate short-packet communications (SPC) in a multiuser downlink MIMO NOMA system over Nakagami- m fading, and propose two antenna-user selection methods considering two clusters of users having different priority levels. In contrast to the widely-used long data-packet assumption, the SPC analysis requires the redesign of the communication protocols and novel performance metrics. Given this context, we analyze the SPC performance of MIMO NOMA systems using the average block error rate (BLER) and minimum blocklength, instead of the conventional metrics such as ergodic capacity and outage capacity. More specifically, to characterize the system performance regarding SPC, asymptotic (in the high signal-to-noise ratio regime) and approximate closed-form expressions of the average BLER at the users are derived. Based on the asymptotic behavior of the average BLER, an analysis of the diversity order, minimum blocklength, and optimal power allocation is carried out. The achieved results show that MIMO NOMA can serve multiple users simultaneously using a smaller blocklength compared with MIMO OMA, thus demonstrating the benefits of MIMO NOMA for SPC in minimizing the transmission latency. Furthermore, our results indicate that the proposed methods not only improve the BLER performance, but also guarantee full diversity gains for the respective users.

/pdf/short-packet-communications-for-mimo-noma-systems-over-g5avwe9546.pdf

Short-Packet Communications for MIMO NOMA Systems Over Nakagami- m Fading: BLER and Minimum Blocklength Analysis

In recent times, the rapid growth in mobile subscriptions and the associated demand for high data rates fuels the need for a robust wireless network design to meet the required capacity and coverage. Deploying massive numbers of cellular base stations (BSs) over a geographic area to fulfill high-capacity demands and broad network coverage is quite challenging due to inter-cell interference and significant rate variations. Cell-free massive MIMO (CF-mMIMO), a key enabler for 5G and 6G wireless networks, has been identified as an innovative technology to address this problem. In CF-mMIMO, many irregularly scattered single access points (APs) are linked to a central processing unit (CPU) via a backhaul network that coherently serves a limited number of mobile stations (MSs) to achieve high energy efficiency (EE) and spectral gains. This paper presents key areas of applications of CF-mMIMO in the ubiquitous 5G, and the envisioned 6G wireless networks. First, a foundational background on massive MIMO solutions-cellular massive MIMO, network MIMO, and CF-mMIMO is presented, focusing on the application areas and associated challenges. Additionally, CF-mMIMO architectures, design considerations, and system modeling are discussed extensively. Furthermore, the key areas of application of CF-mMIMO such as simultaneous wireless information and power transfer (SWIPT), channel hardening, hardware efficiency, power control, non-orthogonal multiple access (NOMA), spectral efficiency (SE), and EE are discussed exhaustively. Finally, the research directions, open issues, and lessons learned to stimulate cutting-edge research in this emerging domain of wireless communications are highlighted.

/pdf/application-of-cell-free-massive-mimo-in-5g-and-beyond-5g-18njzzv3rm.pdf

Application of cell-free massive MIMO in 5G and beyond 5G wireless networks: a survey

Various advanced and mission-critical applications are enabled by the emerging technologies in fifth-generation (5G) mobile communication systems. To ensure improved quality of experience (QoE) of users, 5G and beyond networks require ultra-reliable low-latency communications (URLLC). The successful realization of the URLLC entails the advent of new technological concepts. Therefore, this article presents an overview of the enabling techniques for the URLLC. Classification of the enabling techniques is done and an extensive review of the literature is presented to identify the state-of-the-art techniques, limitations, and the potential approaches for alleviating the limitations. It is observed that artificial intelligence (AI)-enabled edge computing and caching solutions are widely explored as promising techniques to effectively guarantee low latency and reliable content acquisition while reducing redundant network traffic and improving the QoE. Therefore, we present a classification of the AI-enabled edge caching solutions and discuss various mechanisms of the caching agents. In particular, we investigate the use of deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) algorithms. Subsequently, we analyze the performance of the state-of-the-art edge caching schemes and demonstrate the performance gains of FL frameworks over conventional centralized and decentralized DL and DRL frameworks. We confirm that FL edge caching is a viable mechanism in 5G and beyond networks. On the other hand, it is shown that the IEEE 802.1 time sensitive networking and the emerging IETF deterministic networking standards present effective mechanisms when deterministic networks with bounded ultra-low latency are considered. Finally, we present the open issues and opportunities for further research.

/pdf/a-classification-of-the-enabling-techniques-for-low-latency-1gu0o32xep.pdf

A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

This letter considers an uplink short-packet ultra-reliable low latency communications (URLLC) system with finite blocklength (FBL). A quasi-static Rayleigh fading channel is adopted in this letter, and we employ wireless power transfer (WPT) to guarantee the data packets transmission of user equipment (UE). A novel non-orthogonal multiple access (NOMA) scheme with WPT under FBL is first proposed to improve reliability and reduce latency. Furthermore, we adopt outage probability (OP) as an important URLLC metric, and deduce two closed-form approximate bounds of system OP for finite and infinite battery capacities to characterize the relationship between reliability and latency. Simulation results verify that the derived bounds are tight with theoretical values, and the proposed scheme outperforms traditional OMA methods.

Outage Performance of URLLC NOMA Systems With Wireless Power Transfer

In multicell massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks, base stations (BSs) with multiple antennas deliver their radio frequency energy in the downlink, and Internet-of-Things (IoT) devices use their harvested energy to support uplink data transmission. This paper investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer (WPT). To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information (CSI) are considered, and their corresponding EE performance is analyzed. Under quality-of-service (QoS) requirements, an EE maximization problem is formulated, which is non-trivial due to non-convexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex, and then, develop a distributed alternating direction method of multipliers (ADMM)- based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.

Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks with Wireless Power Transfer: A Distributed ADMM Approach.

In multicell massive multiple-input–multiple-output (MIMO) nonorthogonal multiple access (NOMA) networks, base stations with multiple antennas deliver their radio-frequency energy in the downlink, and Internet-of-Things devices use their harvested energy to support uplink data transmission. This article investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer. To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information are considered and their corresponding EE performance is analyzed. Under the Quality-of-Service requirements, an EE maximization problem is formulated, which is nontrivial due to nonconvexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex and then develop a distributed alternating direction method of multipliers-based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.

Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks With Wireless Power Transfer: A Distributed ADMM Approach

This paper investigates the energy-efficient resource allocation problem in massive multiple input and multiple output (MIMO) non-orthogonal multiple access (NOMA) networks with practical non-linear energy harvesting (NEH). In the considered system, a multi-antenna base station (BS) transfers power to the Internet-of-Things (IoT) devices via energy beamforming in the downlink, followed by the IoT devices sending their data simultaneously in the uplink by consuming the harvested energy. A time division protocol is designed to adequately allocation the energy harvesting (EH) time and wireless information transmission time. To improve the energy efficiency (EE), we propose a joint power, time and antenna selection allocation scheme under the NEH model. An EE maximization problem is formulated to effectively determine the optimal resource allocation strategies. As the formulated problem is non-trivial, a non-linear fraction programming method is applied to convert the problem into a convex optimization problem, and then solve it by Lagrange dual decomposition approach. Simulation results demonstrate the effectiveness of the proposed solution.

Energy Efficiency Maximization in Massive MIMO-NOMA Networks with Non-linear Energy Harvesting

This paper investigates the energy-efficient resource allocation problem in multicell massive multiple input and multiple output (MIMO) non-orthogonal multiple access (NOMA) networks with wireless power transfer (WPT). In considered networks, Internet-of-Things (IoT) devices who have data packets to transmit in the uplink are charged by the wireless power harvested from the multi-antenna base stations (BSs) in the downlink. To adequately allocate the time of energy harvesting and data transmission, a time division protocol is proposed to divide downlink WPT and uplink wireless information transmission (WIT) time into two separate time slots. Aiming to obtain the optimal energy efficiency (EE) performance, we propose a joint power, time, subcarrier and antenna selection (AS) allocation scheme. An EE maximization problem is formulated to effectively determine the optimal resource allocation strategies. As the problem is nonconvex, we first invoke the nonlinear programming method to convert the problem into a convex optimization problem, and then solve it by taking an alternating direction method of multipliers (ADMM)-based distributed resource allocation algorithm. Simulation results demonstrate performance enhancement of the proposed algorithm over the benchmark schemes.

Zhongyu Wang

Papers

Outage Performance of URLLC NOMA Systems With Wireless Power Transfer

Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks with Wireless Power Transfer: A Distributed ADMM Approach.

Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks With Wireless Power Transfer: A Distributed ADMM Approach

Energy Efficiency Maximization in Massive MIMO-NOMA Networks with Non-linear Energy Harvesting

Energy-Efficient and Distributed Resource Allocation for WPT Enabled Multicell Massive MIMO-NOMA Networks