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

Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight (LoS) connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains including physical layer, network layer, and joint communication, computing and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.

UAV Communications for 5G and Beyond: Recent Advances and Future Trends

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.

A Survey of Device-to-Device Communications: Research Issues and Challenges

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

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Federated Learning for Internet of Things: A Comprehensive Survey

The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems. In this article, we explore the emerging opportunities brought by 6G technologies in IoT networks and applications, by conducting a holistic survey on the convergence of 6G and IoT. We first shed light on some of the most fundamental 6G technologies that are expected to empower future IoT networks, including edge intelligence, reconfigurable intelligent surfaces, space-air-ground-underwater communications, Terahertz communications, massive ultra-reliable and low-latency communications, and blockchain. Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely Healthcare Internet of Things, Vehicular Internet of Things and Autonomous Driving, Unmanned Aerial Vehicles, Satellite Internet of Things, and Industrial Internet of Things. Finally, we highlight interesting research challenges and point out potential directions to spur further research in this promising area.

6G Internet of Things: A Comprehensive Survey

Data dissemination in Internet of Vehicles (IoVs) is attracting more and more attention. However, few efforts have been made on mixed data dissemination in urban scenario. The rigid Quality of Service (QoS) requirements of Emergency Messages (EMs), the varied QoS tolerant degrees of Service Messages (SMs), and the complex transmission environment in urban scenario pose great challenges. In this paper, a Cluster-based Vehicle-to-Vehicle (V2V) approach for Mixed Data Dissemination (CMDD) is proposed to tackle the aforementioned challenges in urban scenario of IoVs. The proposed approach consists of two main algorithms: the bus-based clustering and the mixed data scheduling. To deal with the complex and harsh transmission environment in urban scenario, we propose to use buses, which have fixed driving routes and schedules as well as sufficient resources, to act as cluster heads. Meanwhile, multiple characteristics are deeply analyzed and artfully utilized in the design of the clustering algorithm, to achieve efficient data dissemination from buses to ordinary vehicles. To meet the rigid and personalized QoS requirements of EMs/SMs under stringent resources, a new priority calculation method is proposed and mixed data are scheduled accordingly. Simulation results show that the proposed approach achieves remarkable performance advantage in terms of data download delay, emergency warning ratio and service response ratio.

A Cluster-Based V2V Approach for Mixed Data Dissemination in Urban Scenario of IoVs

In this paper, a joint transmitter and receiver design for pattern division multiple access (PDMA) is proposed. At the transmitter, pattern mapping utilizes power allocation to improve the overall sum rate, and beam allocation to enhance the access connectivity. At the receiver, hybrid detection utilizes a spatial filter to suppress the inter-beam interference caused by beam domain multiplexing, and successive interference cancellation to remove the intra-beam interference caused by power domain multiplexing. Furthermore, we propose a PDMA joint design approach to optimize pattern mapping based on both the power domain and beam domain. The optimization of power allocation is achieved by maximizing the overall sum rate, and the corresponding optimization problem is shown to be convex theoretically. The optimization of beam allocation is achieved by minimizing the maximum of the inner product of any two beam allocation vectors, and an effective dimension reduction method is proposed through the analysis of pattern structure and proper mathematical manipulations. Simulation results show that the proposed PDMA approach outperforms the orthogonal multiple access and power-domain non-orthogonal multiple access approaches even without any optimization of pattern mapping, and that the optimization of beam allocation yields a significant performance improvement than the optimization of power allocation.

Joint Transmitter and Receiver Design for Pattern Division Multiple Access

In this paper, the cooperative caching problem with security guarantees in fog radio access networks (F-RANs) is investigated. To avoid file retransmission, we propose a security prediction model by training a long short-term memory (LSTM) network, which leverages the file integrity and malicious requests to determine which access points can be trusted. To minimize the file transmission delay, we formulate a cooperative caching optimization problem under the consideration of security risk. In view of the complexity of the formulated problem, we employ a soft-actor-critic (SAC) based deep reinforcement learning (DRL) algorithm. Simulation results show that our proposed scheme achieves significant performance improvement over the baseline.

Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks

In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs) is investigated. On account of the non-deterministic polynomial hard (NP-hard) property of this problem, a federated deep deterministic policy gradient learning (FDDPG) based caching policy is proposed. By considering the dynamic content popularity and time varying requested content information, deep deterministic policy gradient learning (DDPG) is adopted to make optimal caching decisions. As the action selection is directly generated by policy network, DDPG can effectively solve the high dimensional action space caching problem. To address the over-consumption of computational resources, slow network convergence and leakage of user sensitive data, we apply reward weighted horizontal federated learning (RWHFL) in the training of DDPG network. Simulation results show the superiority of our proposed policy compared with the baselines in reducing the average content request delay.

Yanxiang Jiang

Papers

A Cluster-Based V2V Approach for Mixed Data Dissemination in Urban Scenario of IoVs

Joint Transmitter and Receiver Design for Pattern Division Multiple Access

Security-aware Cooperative Caching via Deep Reinforcement Learning in Fog Radio Access Networks

Cooperative Edge Caching via Federated Deep Deterministic Policy Gradient Learning in Fog-RANs