Automatic modulation recognition (AMR) is a key enabler for intelligent spectrum utilization in 5G-and-beyond wireless systems, requiring both high classification accuracy and low computational complexity. This paper proposes a lightweight hybrid deep learning framework, termed CBLGNet, that integrates convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and gated recurrent units (GRU) for efficient AMR from raw in-phase and quadrature (I/Q) samples. The CNN extracts compact spatial representations, while the BiLSTM–GRU structure captures bidirectional temporal dependencies with reduced parameter complexity. Unlike existing hybrid models that rely on deep recurrent stacks or heavy dense layers, the proposed architecture achieves effective feature fusion with a compact parameter budget. Evaluations on the RML2016.10a and RML2016.10b datasets demonstrate that CBLGNet achieves 93.39% classification accuracy, outperforming several state-of-the-art AMR methods while maintaining low computational cost.

Beamforming optimization in 6G networks is essential for ensuring energy-efficient and interference-aware transmission, where ultra-reliable low-latency communication (URLLC) and scalable antenna technologies play a key role. Classical optimization methods face scalability challenges, making real-time beamforming infeasible. This paper applies the quantum approximate optimization algorithm (QAOA) to minimize transmit power while ensuring signal quality constraints. The problem is formulated as a quadratic unconstrained binary optimization (QUBO) and solved using quantum simulation. Simulation results demonstrate that QAOA achieves lower transmit power compared to classical solvers, with faster convergence and improved efficiency. These findings suggest that quantum computing can significantly enhance beamforming optimization, paving the way for its integration into future wireless networks.

For Internet of Things (IoT) connectivity, visible light communication (VLC) can play an important role, compared to traditional radio frequency (RF) communication. VLC does not interfere with existing RF communication, and the frequency spectrum is unregulated. Additionally, in an indoor area, VLC can provide secure communication. This study proposes an authentication-based framework that enhances security at the user end. In addition, we have modified the multiple pulse position modulation (MPPM) to map digits and send particulate matter (PM) data in broad area coverage. To reduce communication channel usage, we take the difference between two consecutive values instead of sending the whole value, which enables us to transmit the same volume of data in fewer optical signals using the proposed modulation. The comparative analysis result shows that the proposed modulation can transmit PM data more efficiently than other modulation techniques. We also provided delay analysis for proper synchronization for connectivity between nodes. The modulation was tested in the case of multihop connectivity to send data on long distance about 36 m in real time.

Visible light communication (VLC) exhibits great potential in connecting Internet of Things (IoT) devices. The connection of a huge number of IoT devices by utilizing the existing radio-frequency spectrum is a challenging task. Therefore, a new frequency spectrum is required for a smooth operation. In this work, we investigate a multiple-input multiple-output (MIMO) VLC system capable of connecting IoT devices to achieve long-range indoor communication. To monitor the indoor environment, a monitoring system for collecting and transmitting particulate matter, temperature, and humidity data using MIMO VLC is proposed. Four different single-carrier techniques are tested, and their error performance is analyzed by experimental trials. Next, the maximum communication range, which is important for efficient network planning and uninterrupted connectivity, is evaluated. Two antenna configurations (2$\times $2 and 4$\times $4) are tested for MIMO VLC. In the 2$\times $2 configuration, a 14.5–21 m transmission distance is achieved by employing four different modulation techniques. In the 4$\times $4 communication, a 7–10 m transmission distance with a reliable error rate is achieved by employing three different modulation techniques. In addition, a lightweight encryption technique is used to enhance data security. The proposed system provides an efficient solution for monitoring different types of data using indoor IoT connectivity.

These particulate matter (PM) causes lethal diseases to humans, and both short term and long term exposure are known to have hazardous effects. PM10and PM2.5have diameters less 10 and 2.5 μm, respectively, which makes them more dangerous to the human body. Thus, information regarding PM concentrations in indoor environments of dust-sensitive places is essential for proper precaution. In this study, we measured PM values in a large area and transferred this information using visible light communication (VLC) to the monitoring node. VLC is considered as an efficient technique due to its unique advantages and is currently unregulated. We applied bidirectional VLC to transfer information to ensure two-way communication. We also applied a multihop strategy to make the system function as a query answering system at extended distances. At one end, we generated a request to relay the request to the proper node and received the response in the form of PM data. In our experiment, we implemented four nodes and conducted multihop communication utilizing only a VLC link. We achieved a distance of 13.5 m with a zero-error rate between the two nodes using nonreturn to zero on-off keying (NRZ-OOK) modulation. In case of multihop, we achieved a distance greater than 40 m using four nodes to send dust information with a minimal error rate. This can be applied to large indoor areas where radio frequency is restricted.