Sixth-generation (6G) networks must deliver safe, reliable and ubiquitous connectivity for consumer applications such as connected and automated vehicles (CAVs), broadband access, and emergency response. This paper proposes a vision-assisted, AI-optimized aerial cell-free massive MIMO framework under the concept of Integrated Sensing Assisted Communications (IS-AC). Unlike Joint Sensing and Communication (JSAC) or Integrated Sensing and Communications (ISAC), ISAC emphasizes how sensing actively assists communication performance. In our design, unmanned aerial vehicles (UAVs) act as distributed access points, coordinated by a high-altitude platform station (HAPS) serving as the CPU and equipped with the MACSHAP optical sensor. A YOLOv8 vision model extracts vehicle context, enabling the proposed IS-AC algorithm to optimize the mmWave beam selection using a contextual multi-armed bandit (CMAB) strategy with minimal CSI. Compared to baselines without vision, IS-AC improves spectral efficiency by 46.4% and localization accuracy by 18%. These results show that IS-AC with vision sensing can directly benefit consumers by enabling safer autonomous driving, resilient broadband in underserved regions, and rapidly deployable emergency response networks. Beyond connected vehicles, the framework also supports autonomous robots and drones for critical 6G applications in disaster recovery and public safety.

This study examines a cell-free massive MIMO architecture for unmanned aerial vehicles (UAVs), It evaluates aerial access points (APs) coverage, performance, and data rate of the aerial cell-free network. It proposes deploying an aerial cell-free massive MIMO architecture to mitigate the effects of path loss and interference in aerial cellular networks. The analysis includes a 2-dimensional multi-armed bandit (MAB) model for beam selection optimized with machine learning and using millimeter-wave technology to analyze an aerial cell-free network that connects a HAPS (CPU/data network) with ground vehicles through UAV-based APs. The multi-armed bandit model incorporates 3GPP blockage stochastics, water-filling power allocation, and optimization of multi-user capacity. The results include the aerial cell-free model's comprehensive geometric and radio link simulation analysis. The simulation outcomes demonstrate that the suggested cell-free network outperforms aerial cellular networks and NLOS terrestrial cell-free networks. Finally, we present a comparative study between our MAB model-based AI technique and a conventional non-AI technique, highlighting the significant performance improvements achieved by our approach.

Physical Layer Security (PLS) as a native signaling enhancement to Layer-l security guarantees consumer privacy at the air interface. This paper proposes applying AIIML integration to Radio Access Networks (RAN) for enhanced 6G Internet of Things (loT) security. The paper defines a AIIML based PLS system model that can guarantee security against an eavesdropper. The paper also proposes an operational overview of AIIML integrated PLS with shared key agreement protocol in an O-RAN architecture for 6G IoT, and proposed sApps, that will lead the way of a new paradigm in low latency communication scheme for the consumers. Simulations of Key Bit Error rate (BER) detection in the presence of Rayleigh fading plus noise demonstrate that the AIIML-based PLS model jointly improves security and consumer communication performance.

The growth in wireless traffic and mobility of devices have congested the core network significantly. This bottleneck, along with spectrum scarcity, made the conventional cellular networks insufficient for the dissemination of large contents. The ability of content-centric networking (CCN) and device-to-device (D2D) communication in offloading the network and huge unlicensed spectrum at millimeter-wave (mmWave) band, make the integration of CCN with D2D communication in the mmWave band a viable solution to improve the network's throughput. In this paper, we propose a novel scheme that enables efficient initialization of CCN-based D2D networks in the mmWave band through addressing decentralized D2D peer association and antenna beamwidth selection. The proposed scheme considers mmWave characteristics such as directional communication and blockage susceptibility. We propose a heuristic peer association algorithm to associate D2D users using context information, including link stability time and content availability. We model the beamwidth selection problem as a potential game and propose a synchronous log-linear learning algorithm to obtain the game's optimal Nash equilibrium. The performance of the proposed scheme in terms of data throughput and transmission efficiency is evaluated through extensive simulations. Simulation results show that the proposed scheme improves network performance significantly and outperforms other methods in the literature.

Due to its scale and largely interconnected nature, the Internet of Things (IoT) will be vulnerable to a number of security threats that range from physical layer attacks to network layer attacks. In this paper, a novel anti jamming strategy for OFDM-based IoT systems is proposed which enables an IoT controller to protect the IoT devices against a malicious radio jammer. The interactions between the controller node and the jammer are modeled as a Colonel Blotto game with continuous and asymmetric resources. In this game, the IoT controller, acting as defender, seeks to thwart the jamming attack by distributing its power among the subcarries in a smart way so as to decrease the aggregate bit error rate (BER) caused by the jammer. The jammer, on the other hand, aims at disrupting the system performance by allocating its jamming power to different frequency bands. To solve the game, an evolutionary algorithm is proposed which can find a mixed-strategy Nash equilibrium of the Blotto game. Simulation results show that the proposed algorithm enables the IoT controller to maintain the BER above an acceptable threshold, thereby preserving the IoT network performance in the presence of malicious jamming.