Survey on Low-Complexity Precoding Techniques for PAPR Reduction in Massive MIMO Systems
Keywords:
Massive MIMO, Peak-to-Average Power Ratio (PAPR), Precoding, Low-Complexity Algorithms, Power Amplifier EfficiencyAbstract
Massive Multiple-Input Multiple-Output (MIMO) systems are a cornerstone of modern wireless communications, offering significant improvements in spectral efficiency and network capacity. However, one of the key challenges in massive MIMO deployment is the high Peak-to-Average Power Ratio (PAPR) of transmitted signals, which adversely affects power amplifier efficiency and overall system performance. This review paper provides a comprehensive overview of recent advances in low-complexity PAPR-aware precoding techniques tailored for massive MIMO systems. Emphasis is placed on methods that effectively reduce PAPR while maintaining manageable computational complexity, making them suitable for practical implementation. The paper categorizes various precoding strategies including optimization-based, iterative, and heuristic approaches, highlighting their strengths and limitations in terms of complexity, PAPR reduction capability, and impact on system throughput. Additionally, the review discusses the trade-offs involved between computational efficiency and signal quality, as well as the challenges associated with scalability and hardware constraints. By synthesizing current research trends, this review aims to guide future developments toward more efficient and robust precoding schemes that address PAPR concerns without compromising massive MIMO’s inherent benefits. The insights provided will be valuable for researchers and engineers working on next-generation wireless systems seeking to enhance energy efficiency and signal integrity in massive MIMO deployments.
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