Visible light communication (VLC) can achieve high data rate transmission with multicarrier modulation techniques. The common variant is direct current (DC) optical orthogonal frequency division multiplexing (DCO-OFDM) which offers a spectral efficient modulation solution for VLC. However, similar to other multicarrier modulation schemes, DCO-OFDM suffers from a high peak-to-average power ratio (PAPR). In this paper, the efficacy of pilot-assisted (PA) PAPR reduction system in DCO-OFDM based VLC is demonstrated experimentally. Wavelength division multiplexing (WDM) is applied using three off-the-shelf light emitting diodes (LEDs). PA DCO-OFDM is compared to the conventional DCO-OFDM based on achievable data rate and bit error rate (BER). The available modulation bandwidth of each LED is utilised by adaptive bit and power loading in both systems. The proposed system reduces the high PAPR values of the system, hence, reduces the clipping noise and minimises the nonlinearity effect of each wavelength. Thus, the PA DCO-OFDM has achieved more than 7% data rate higher than that of the conventional DCO-OFDM with no PAPR reduction.

This paper presents an analytical study of signal clipping that leads to the noise/distortion in the waveform of DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM)-based visible light communication (VLC) systems. The pilot-assisted (PA) technique is used to reduce the high peak-to-average power ratio (PAPR) of the time-domain waveform of the DCO-OFDM system. The bit error rate (BER) performance of the PA DCO-OFDM system is investigated analytically at three different clipping levels as well as without any clipping. The analytical BER performance is verified through simulation and then compared to that of the conventional DCO-OFDM without PAPR reduction at the selected clipping levels. The PA DCO-OFDM system shows improved BER performance at all three clipping levels.

The expansion of 6G-enabled IoT networks demands integrated solutions for optimizing computational, communication, and energy resources. Existing methods often treat power efficiency and resource allocation independently, limiting overall performance gains. This study introduces FLARE, a federated learning (FL)-based framework for adaptive resource management and optimization in 6G-IoT. FLARE integrates an adaptive learning rate scheduler $\eta _{t}=\eta _{0}/(1+\alpha t)$ , gradient sparsification (Top- $k$ ) and uniform quantization to minimize communication overhead, and model pruning with weight quantization for energy reduction. A novel dynamic route optimization algorithm jointly minimizes delay and power consumption via continuous flow-based updates, supported by a virtualization-aware server activation model. An economic optimization module applies dynamic pricing $P_{\text {dynamic}}(t)$ and incentive mechanisms to balance demand and cost. NS-3 simulations on Edge-IIoTset, N-BaIoT, and IoT-23 show that FLARE improves resource utilization by up to 5%, power efficiency by 4%, and network performance by 3%–5% compared to leading baselines under varied demand scenarios.

In this paper, a pilot-assisted scheme is used to reduce the peak-to-average power ratio of multicarrier signalling based visible light communication systems and the system throughput is maximised with bit and power loading.