The sixth-generation (6G) wireless network promises unprecedented enhancements in terms of system throughput, energy efficiency, traffic capacity per area, spectral efficiency, and low latency. To meet these demands, the radio interface must demonstrate the adaptability and efficient utilization of scarce frequency resources, necessitating novel multiple access techniques and new waveforms. Furthermore, in large-bandwidth multiuser networks, intersymbol interference (ISI) and inter-user interference (IUI) represent major design challenges. In this regard, time reversal (TR) has emerged as a promising 6G waveform candidate, concentrating signal energy in both the time and space domains in multipath environments. On the other hand, non-orthogonal multiple access (NOMA) promises high spectral efficiency and enhanced connectivity, serving multiple users over the same time-frequency-code resources. In this paper, we investigate the integration of NOMA and TR to mitigate these challenges and propose, for the first time in the literature, a novel receiver architecture for downlink NOMA-based TR communications, which does not require precoding at the transmitter. In more detail, power-domain NOMA is employed by the transmitter, while TR filtering is carried out at each receiving end. We derive novel approximated expressions for the pairwise error probability (PEP), a fundamental component in establishing the union bound on the bit error rate (BER), to characterize users' performance. We extensively employ Monte Carlo simulations and numerical analyses to verify the analytical expressions, providing significant insights into the error rate performance for each user. Also, we investigate the performance gain of the proposed NOMA-based TR receiver, over the orthogonal multiple access scheme, namely time-reversal multiple access (TRMA). Results demonstrate the superiority of our scheme, in terms of the bit error rate (BER), particularly in sparse multi-path environments compared to TRMA, with a percent of improvement in the average BER between 73.5%−98.31%. This improvement is also accompanied by a reduced overhead compared to traditional TRMA, which necessitates users' channel state information feedback to the base station for TR precoding. Moreover, our findings indicate that at high signalto-noise ratio values, the diversity gain for a particular user is proportional to the product of the user's order, determined by its channel strength, and the number of its channel taps.

Sensing and localization are envisioned to play a key role in shaping the future of the sixth-generation (6G) of wireless networks by enhancing their ability to make intelligent decisions. Existing research efforts have primarily focused on utilizing radio-frequency (RF) signals to facilitate sensing tasks, thereby contributing to the heightened strain on the already congested spectrum, due to shared hardware and frequency bands. Additionally, with the explosive growth in the number of connected devices and the diverse sensing scenarios envisioned, exploring alternate frequency bands becomes of paramount importance. Within this context, it has been demonstrated that Light Fidelity (LiFi), which utilizes existing lighting infrastructure, stands out as a promising technology due to its highly accurate 3D sensing and localization capabilities. Motivated by this, in this paper, we explore a forward-looking perspective wherein LiFi-empowered wireless networks integrate illumination, communication, and sensing capabilities. Specifically, we thoroughly review LiFi-based sensing and localization principles, highlighting technologies set to enhance its performance. Subsequently, we introduce the concept of LiFi-empowered multi-modal sensing as a promising technological advancement achieved by integrating LiFi with other sensory data sources. This fusion empowers sensing systems to effectively adapt to environmental conditions. Finally, we shed light on potential research directions and challenges that are set to realize the full potential of LiFi-empowered multi-modal sensing.

Ensuring the security and reliability of cooperative vehicle-to-vehicle (V2V) communications is an extremely challenging task, due to the dynamic nature of vehicular networks as well as the delay-sensitive wireless medium. The moving target defense (MTD) paradigm has been proposed to overcome the challenges of conventional solutions, based on static network services and configurations. Specifically, the MTD approach involves the dynamic altering of the network configurations to improve resilience to cyberattacks. Nevertheless, the current MTD solution for cooperative networks has several limitations, such as they are not well-suited for highly dynamic environments; they require high synchronization modules that are resource-intensive and difficult to implement; and finally, they rely heavily on the attack-defense models, which may not always be accurate or comprehensive to use. In this paper, we propose an intelligent spatiotemporal diversification MTD scheme to defend against eavesdropping attacks in cooperative V2V networks. Specifically, we design benign random data injection patterns to meet the security and reliability requirements of the vehicular network. Our methodology involves modeling the configuration of vehicular relays and data injection patterns as a Markov decision process, followed by applying deep reinforcement learning to determine the optimal configuration. We then iteratively evaluate the intercept probability and the percentage of transmitted real data for each configuration until convergence is achieved. In order to optimize the security-real data percentage (S-RDP), we developed a two-agent framework, namely MTD-DQN-RSS & MTD-DQN-RSS-RDP. The first agent, MTD-DQN-RSS, tries to minimize the intercept probability by injecting additional fake data, which in turn reduces the overall RDP, while the second agent, MTD-DQN-RSS-RDP, attempts to inject a sufficient amount of fake data to achieve a target S-RDP. Finally, extensive simulation results are conducted to demonstrate the effectiveness of our proposed solution where they improved the system security by almost 28% and 49%, respectively compared to the conventional relay selection approach.

The transition from 5G to 6G networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), leveraging AI and ML, have emerged as a promising solution for enhancing automation in 5G/6G networks but face significant challenges. Specifically, they are vulnerable to cyber-attacks, and the development of AI/ML-based cybersecurity mechanisms requires substantial specialized expertise and encounters model drift issues. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public ORACLE RF fingerprinting and CICIDS2017 datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.

The sixth generation (6G) of wireless networks are envisioned to support a plethora of human-centric applications and offer connectivity to a massive number of devices with diverse requirements, thus enabling massive Machine Type Communications. Nevertheless, with the rapid growth of the number of connected devices as well as the ever-increasing network traffic, network energy consumption has become a major challenge. Additionally, 6G is expected to catalyze the emergence of new applications that are characterized by their harsh environmental conditions, with ultra-small and low-cost wireless devices. Therefore, there is a pressing need for developing sustainable solutions that take into consideration all these requirements in order to realize the full potential of 6G networks. Within this context, zero-energy devices (ZEDs) have emerged as a prominent solution for the next generation green communication architecture. Such devices eliminate the need for recharging plugins and replacing batteries by integrating disruptive technologies, such as radio frequency energy harvesting, backscatter communications, low power computing, and ultra-low power receivers. Motivated by this, this article provides an in-depth review of the existing literature on the newly emerging ZEDs for future networks. We further identify different relevant use cases and provide an extensive overview on the key enabling technologies and their requirements for realizing ZED-empowered networks. Finally, we discuss potential future research directions and challenges that are envisioned to enhance the performance and efficiency of ZED-based networks.