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In recent years, wireless communication has experienced a massive shift from a single service (i.e., voice) to an interconnected web of networks. Although many techniques have been developed improving the offered services to mobile users, still the demand for high-quality services cannot be reached. Therefore, this paper proposes a joint non-orthogonal multiple access (NOMA)-enabled optimization framework for small-cell network (SCNet) by utilizing the concepts of multi-objective problem. In particular, the transmit power of base station (BS) in each small-cell simultaneously optimizes to maximize the sum-capacity and total energy efficiency (EE) of SCNet. The multi-objective optimization problem is formulated as non-convex subject to several practical constraints, i.e., individual quality of service requirement, maximum power budget of small-cell BS, and efficient decoding of superimposed signal using successive interference cancellation. Based on the nature of the problem, the optimal solutions are provided using sequential quadratic programming, and Karush-Kuhn-Tucker approaches. The obtained results show significant performance gains over conventional orthogonal multiple access technique in terms of sum-capacity and total EE.

A new wireless era beckons, giving rise to novel communication techniques to support the services and demands foreseen for the coming decades. One such revolutionizing technique intended to enable the Internet of Things (IoT) is backscatter communication. Simply employing backscatter communication may not be enough to efficiently connect the massive number of devices in the IoT network. To achieve this feat, non-orthogonal multiple access (NOMA) techniques have been merged with backscatter communications. Although NOMA-enabled backscatter communication is expected to significantly improve the low-powered IoT system, the benefits come with several challenges. In this article, we show that NOMA-enabled backscatter communication has the potential to connect a large number of IoT devices in a battery-free manner. To begin, this article provides the basics on backscatter communication and NOMA techniques. Next, a taxonomy and gap analysis of the studies on backscatter communication is provided. Then novel use cases for NOMA-enabled backscatter communication are detailed, and a case study for smart farming using the overall data rate of backscatter sensor devices employing power-domain NOMA is presented. Finally, we discuss some interesting and potential research challenges to the realization of massive IoT networks using NOMA-enabled backscatter communication.

Non-orthogonal multiple access (NOMA) is a rapidly emerging paradigm with the capability to improve the spectral efficiency of data-driven, intelligence inspired, and highly digitized sixth-generation (6G) wireless networks. In the backdrop of ever-evolving NOMA techniques, this article presents a novel resource optimization framework for maximizing the spectral efficiency (SE) of the Internet-of-things (IoT) networks using power domain NOMA. The proposed framework considers a limited number of frequency blocks in the IoT network and provides an optimal power and frequency block allocation method. Different practical constraints like successive interference cancellation (SIC) complexity, ensuring the minimum gap of received power among different IoT equipment over the same frequency block for successful SIC operation, quality of services requirements, and IoT equipment's transmit powers have also been taken into account. Accordingly, a non-convex optimization problem has been formulated for resource management where the objective of spectral efficiency is coupled by both frequency block and power allocation. To effectively solve this problem, the resource optimization problem is decoupled into two subproblems for frequency block assignment and power allocation. A suboptimal algorithm has been designed for frequency block assignment and a new optimal sequential quadratic programming (SQP) approach is employed to solve the non-convex power control subproblem. For the sake of fair comparison, a low complexity suboptimal NOMA power allocation scheme, based on Karush-Kuhn-Tucker (KKT) conditions, and conventional orthogonal multiple access (OMA) scheme are also provided. The results demonstrate that the proposed optimal resource management scheme significantly improves the system performance compared to other schemes.

Heterogeneous backscatter networks are emerging as a promising solution to address the proliferating coverage and capacity demands of next-generation vehicular networks. However, despite its rapid evolution and significance, the optimization aspect of such networks has been overlooked due to their complexity and scale. Motivated by this discrepancy in the literature, this work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner. To evaluate the benefits of the proposed scheme, extensive simulation studies are conducted and a comparison is provided with benchmark techniques. The performance evaluation demonstrates the utility of the presented system architecture and learning-based optimization framework.

Non-orthogonal multiple access (NOMA) has emerged as a promising technology for 5G networks and beyond. In order to fully reap the benefits of NOMA, it is essential to characterize its performance for different channel conditions. In this paper, we carry out a performance analysis of downlink NOMA systems subject to $\kappa$ - $\mu$ shadowed fading channels. Specifically, ergodic capacity, outage probability, and average bit error rate expressions for a two-users NOMA scheme under $\kappa$ - $\mu$ shadowed fading links are derived. The accuracy of the analytical results has been validated by extensive simulations.

Backscatter communication is expected to help in revitalizing the domain of healthcare through its myriad applications. From on-body sensors to in-body implants and miniature embeddable devices, there are many potential use cases that can leverage the miniature and low-powered nature of backscatter devices. However, the existing literature lacks a comprehensive study that provides a distilled review of the latest studies on backscatter communications from the healthcare perspective. Thus, with the objective to promote the utility of backscatter communication in healthcare, this paper aims to identify specific applications of backscatter systems. A detailed taxonomy of recent studies and gap analysis for future research directions are provided in this work. Finally, we conduct measurements at 590 MHz in different propagation environments with the in-house designed backscatter device. The link budget results show the promise of backscatter devices to communicate over large distances for indoor environments which demonstrates its potential in the healthcare system., Comment: 10 Pages, 4 Figures, 2 Tables

Research efforts in vehicular communication networks have been driven by the need to achieve high data rates for value-added services and safety applications. The proliferation of billions of connected devices along with their ever growing data demands can potentially cause severe congestion in vehicular networks unless effective solutions are developed. In this context, the multiuser interference encountered in vehicular ad hoc networks is a major performance bottleneck. The interference among vehicles needs to be harnessed to meet the high data rate requirements of heterogeneous vehicular communication networks. This work proposes a novel paradigm of interference-aided vehicular networks in which the interference signals are exploited for communication-related operations such as message flooding, energy harvesting, channel estimation, signal alignment, and link security. We also highlight the technical and socio-economic challenges that must be addressed for successful implementation of interference- aided vehicular communication networks.

Device-to-Device (D2D) communication has emerged as a promising technology for optimizing spectral efficiency in future cellular networks. D2D takes advantage of the proximity of communicating devices for efficient utilization of available resources, improving data rates, reducing latency, and increasing system capacity. The research community is actively investigating the D2D paradigm to realize its full potential and enable its smooth integration into the future cellular system architecture. Existing surveys on this paradigm largely focus on interference and resource management. We review recently proposed solutions in over explored and under explored areas in D2D. These solutions include protocols, algorithms, and architectures in D2D. Furthermore, we provide new insights on open issues in these areas. Finally, we discuss potential future research directions.