This paper investigates average area spectral efficiency (AASE) of the 3D flexible-load ultra-dense networks (FLUDNs) based on the multi-antenna sparse code multiple access (SCMA) scheme by leveraging spatial multiplexing technique and 3D stochastic-based models. A multiuser connectivity enabled by factor graph matrix (FGM) is proposed, as applying spatial multiplexing in SCMA-based FLUDNs necessitates a high-dimensional FGM design to ensure massive connectivity and reliable resource access for each multi-antenna base station (BS) with flexible-load (FL). A new modified moment generating function (modified-MGF) approach is introduced to derive the exact expression for the AASE in 3D multi-antenna SCMA-based FLUDNs. This method also facilitates an efficient reverse mapping, enabling the direct computation of area potential spectral efficiency (APSE) from the AASE. Additionally, to complement the modified-MGF approach, we derive both the exact expression and a lower bound for the AASE using the traditional MGF-based method. In addition to providing new insights into 3D multi-antenna SCMA-based FLUDNs under different parameter setups, simulation results under various system configurations validate the accuracy of the exact theoretical expressions from both the modified-MGF approach and the traditional MGF-based method for AASE. Moreover, these results confirm the effectiveness of the systematic reverse mapping process in our proposed framework for APSE, while the traditional MGF-based method fails even to predict APSE.

The Internet of Things (IoT) is undergoing significant advancements, driven by the emergence of backscatter communication (BC) and artificial intelligence (AI). BC is an energy-saving and cost-effective communication method where passive backscatter devices (BDs) communicate by modulating ambient radio-frequency (RF) carriers. AI has the potential to transform our way of communicating and interacting and represents a powerful tool for enabling the next generation of IoT devices and networks. By integrating AI with BC, we can create new opportunities for energy-efficient and low-cost communication and open the door to a range of innovative applications that were previously not possible. This article brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas, such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with reconfigurable intelligent surfaces (RISs), UAV, and vehicular networks. Finally, in the discussion section, we summarize the solutions, provide lessons learned and challenges, and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT.

The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks’ traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading’s category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.

Ensuring dependable quality of service (QoS) and quality of experience (QoE) for computation-intensive and delay-sensitive applications in vehicles can be a challenging task that impacts performance. While multi-access edge computing (MEC) based vehicular edge computing network (VECN) and vehicular cloudlets (VC) enable task offloading, but their prompt and optimal accessibility is another challenge. The conventional wireless technologies may not suffice to meet the stringent ultra-low latency and cost constraints of such applications. Nonetheless, the combination of different wireless technologies can enhance network performance and satisfy these requirements. Focusing on the computational efficacy of VECN, this paper proposes a mobility, contact, and computational load-aware (MCLA) task offloading scheme for heterogeneous VECN. The MCLA scheme dynamically considers the mobility, contact, and computational load of vehicles for making task offloading decisions. To optimize the performance, the MCLA scheme integrates the Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave communications. The MCLA scheme provides an opportunistic switching mechanism between these modes and heterogeneous radio access technologies (RATs) to reduce communication delays and costs. Moreover, the MCLA scheme leverages public vehicles (i.e., public buses), in proximity by using their computational power to manage computational latency and cost. Furthermore, it also considers the shareable computations from passengers’ mobile equipment within the public vehicle to improve the computation capacity of the public vehicles. Extensive evaluations and numerical results show that the proposed MCLA scheme significantly improves the task turnover ratio by 4%–15% with 4.7%–29.8% lower transmission and computation costs.

The last two decades have seen a clear trend toward crafting intelligent vehicles based on the significant advances in communication and computing paradigms, which provide a safer, stress-free, and more enjoyable driving experience. Moreover, emerging applications and services necessitate massive volumes of data, real-time data processing, and ultrareliable and low-latency communication (URLLC). However, the computing capability of current intelligent vehicles is minimal, making it challenging to meet the delay-sensitive and computation-intensive demand of such applications. In this situation, vehicular task/computation offloading toward the edge cloud (EC) and vehicular cloudlet (VC) seems to be a promising solution to improve the network’s performance and applications’ Quality of Service (QoS). At the same time, artificial intelligence (AI) has dramatically changed people’s lives. Especially for vehicular task offloading applications, AI achieves state-of-the-art performance in various vehicular environments. Motivated by the outstanding performance of integrating reinforcement learning (RL)/deep RL (DRL) to the vehicular task offloading systems, we present a survey on various RL/DRL techniques applied to vehicular task offloading. Precisely, we classify the vehicular task offloading works into two main categories: 1) RL/ DRL solutions leveraging EC and 2) RL/DRL solutions using VC computing. Moreover, the EC section-based RL/DRL solutions are further subcategorized into multiaccess edge computing (MEC) server, nearby vehicles, and hybrid MEC (HMEC). To the best of our knowledge, we are the first to cover RL/DRL-based vehicular task offloading. Also, we provide lessons learned and open research challenges in this field and discuss the possible trend for future research.

Vehicular edge computing (VEC) is an innovative computing paradigm with an exceptional ability to improve the vehicles’ capacity to manage computation-intensive applications with both low latency and energy consumption. Vehicles require to make task offloading decisions in dynamic network conditions to obtain maximum computation efficiency. In this article, we analyze computation efficiency in a VEC scenario, where a vehicle offloads its tasks to maximize computation efficiency as a tradeoff between computation time and energy consumption. Although, it is quite a challenge to ensure the quality of experience of the vehicle due to diverse task requirements and the dynamic wireless conditions caused by vehicle mobility. To tackle this problem, a computation efficiency problem is formulated by jointly optimizing task offloading decision and computation resource allocation. We propose a mobility-aware computational efficiency-based task offloading and resource allocation (MACTER) scheme and develop a distributed MACTER algorithm that provides the near-optimal solution. We further consider the fifth-generation new-radio vehicle-to-everything communication model, i.e., cellular link and millimeter wave, to enhance the system performance. The simulation outcomes demonstrate that the proposed algorithm can efficiently enhance computation efficiency while satisfying computing time and energy consumption constraints.