Recent Advances in Joint Power and Delay Optimization-Based Resource Allocation in MIMO-OFDM System Using Deep Convolutional Red Piranha Pyramid-Dilated Neural Network: A Systematic Review

Abstract

The rapid growth of next-generation wireless communication systems has increased the demand for efficient resource allocation strategies in MIMO-OFDM systems. Joint optimization of power and delay has emerged as a critical challenge due to dynamic channel conditions, user mobility, and quality-of-service (QoS) requirements. Traditional optimization techniques often suffer from high computational complexity and limited adaptability. Recently, deep learning-based approaches, particularly convolutional neural networks (CNNs) and hybrid optimization frameworks, have demonstrated significant potential in addressing these challenges. This paper presents a systematic review of recent advances in joint power and delay optimization for resource allocation in MIMO-OFDM systems, focusing on deep learning architectures such as pyramid-dilated CNNs and hybrid optimization algorithms. Special attention is given to the Deep Convolutional Pyramid-Dilated Neural Network (DCPDNN) integrated with Red Piranha Optimization (RPO), which enables efficient power allocation and delay scheduling in multi-user environments.  The review covers studies published in recent years, highlighting key developments in deep learning-based optimization, reinforcement learning approaches, and hybrid AI frameworks. Additionally, challenges such as computational complexity, scalability, and real-time implementation are discussed. The paper provides comparative insights and future research directions for intelligent and energy-efficient wireless systems.