Abstract

The fifth-generation (5G) wireless communication system promises to revolutionize global connectivity by offering ultra-high-speed data transfer, massive device connectivity, and low latency. However, achieving these objectives demands efficient spectrum allocation and error-correcting mechanisms. The scarcity of radio spectrum and interference management are key challenges, necessitating optimized solutions for spectrum allocation. Additionally, reliable communication over noisy channels requires robust error-correcting codes. Optimized turbo codes, known for their iterative decoding capabilities, have emerged as a viable solution to enhance error resilience and throughput in 5G networks. This research proposes an integrated approach to optimize spectrum allocation and turbo code performance. The spectrum allocation employs a dynamic multi-objective optimization model based on machine learning algorithms, prioritizing fairness, quality of service (QoS), and interference minimization. Simultaneously, an improved turbo coding algorithm utilizing adaptive puncturing and interleaving strategies enhances data integrity. Simulations conducted in a heterogeneous 5G environment demonstrate significant performance improvements. The optimized turbo codes achieve a Bit Error Rate (BER) of 10-5 at a Signal-to-Noise Ratio (SNR) of 2.5 dB, outperforming conventional turbo codes by 40%. The proposed spectrum allocation strategy enhances spectral efficiency by 25%, ensuring equitable resource distribution and improved QoS. This integrated framework highlights the potential for scalable and efficient 5G systems, addressing the dual challenges of spectrum scarcity and error correction.