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<p>The rapid growth of data wireless traffic in cellular networks has intensified the need for energy efficient and power management schemes for maximising throughput without compromising the quality-of-service constraints. In this research work, an adaptive power control scheme for base stations (BSs) is proposed to enhance network throughput while satisfying predefined outage probability constraints in presence of co-channel interference. A multipath fading channel incorporating Rayleigh fading, lognormal shadowing, and pathloss is considered. The signal-to-interference-plus-noise ratio (SINR) is analytically characterised to evaluate the successful reception probability. Tight closed form upper and lower bounds on the outage probability are derived as a function of received SINR in presence of co-channel interference. Since broadcast transmission is the primary way of communication in cellular networks, optimal transmit power allocation is essential for improving reception performance and maximizing throughput. To identify, the optimal transmit power that maximises throughput while satisfying the outage constrains, a machine learning framework is employed that dynamically adjust BS transmit power. The proposed framework automatically converges to the optimal transmit power levels that maximize throughput while minimizing co-channel interference and maintaining a predefined outage probability constraint. The performance of the proposed scheme is validated through a comparative analysis between the actual transmit power and the predicted transmit power obtained after machine learning (ML) model convergence. The proposed model demonstrates strong predictive accuracy, achieving high <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:R^2\)</EquationSource> </InlineEquation> value and low MSE for outage and throughput, respectively. These findings confirm the effectiveness of the proposed approach in accurately capturing the relationship between system parameters and performance metrics.</p>

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Adaptive Power Control for Optimal Transmission Power for Throughput Maximization in Wireless Networks

  • 期刊论文

  • 发表日期：

    2026年7月6日

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  • Sunandita Debnath

摘要

The rapid growth of data wireless traffic in cellular networks has intensified the need for energy efficient and power management schemes for maximising throughput without compromising the quality-of-service constraints. In this research work, an adaptive power control scheme for base stations (BSs) is proposed to enhance network throughput while satisfying predefined outage probability constraints in presence of co-channel interference. A multipath fading channel incorporating Rayleigh fading, lognormal shadowing, and pathloss is considered. The signal-to-interference-plus-noise ratio (SINR) is analytically characterised to evaluate the successful reception probability. Tight closed form upper and lower bounds on the outage probability are derived as a function of received SINR in presence of co-channel interference. Since broadcast transmission is the primary way of communication in cellular networks, optimal transmit power allocation is essential for improving reception performance and maximizing throughput. To identify, the optimal transmit power that maximises throughput while satisfying the outage constrains, a machine learning framework is employed that dynamically adjust BS transmit power. The proposed framework automatically converges to the optimal transmit power levels that maximize throughput while minimizing co-channel interference and maintaining a predefined outage probability constraint. The performance of the proposed scheme is validated through a comparative analysis between the actual transmit power and the predicted transmit power obtained after machine learning (ML) model convergence. The proposed model demonstrates strong predictive accuracy, achieving high \(\:R^2\) value and low MSE for outage and throughput, respectively. These findings confirm the effectiveness of the proposed approach in accurately capturing the relationship between system parameters and performance metrics.