6G virtualized beamforming: a novel framework for optimizing massive MIMO in 6G networks

(2025) 2025:23 Alwakeel J Wireless Com Network https://doi.org/10.1186/s13638-025-02451-2 RESEARCH EURASIP Journal on Wireless Communications and Networking Open Access 6G virtualized beamforming: a novel framework for optimizing massive MIMO in 6G networks Ahmed M. Alwakeel1,2*    *Correspondence: 1 Faculty of Computers and Information Technology, University of Tabuk, 71491 Tabuk, Saudi Arabia 2 The Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, 71491 Tabuk, Saudi Arabia Abstract The advent of 6G networks promises unprecedented connectivity, with massive multiple-input multiple-output (MIMO) technology playing a critical role in achieving high throughput, low latency, and enhanced spectral efficiency. However, optimizing massive MIMO for 6G present’s challenges related to beamforming efficiency, power consumption, and dynamic network environments. This paper introduces a novel framework for 6G Virtualized Beamforming that leverages virtualization techniques to enhance beam management in massive MIMO systems. Our framework employs a combination of advanced machine learning algorithms and software-defined networking to dynamically allocate beamforming resources, improving adaptability in high-density environments and optimizing signal-to-noise ratios. By virtualizing beamforming control, the proposed framework reduces the overhead of hardware dependencies and facilitates seamless integration with existing 6G infrastructure. Furthermore, the system incorporates predictive analytics for proactive beam steering and user allocation, enhancing network performance while minimizing power consumption. Simulation results show a 22% reduction in power consumption and a 19% increase in spectral efficiency compared to traditional beamforming approaches. This study provides a foundation for scalable, virtualized MIMO systems that can meet the demands of next-generation wireless communications. Our research opens avenues for further exploration into the interplay between virtualization and beamforming in 6G, with implications for the future design of more flexible, costeffective network architectures. Keywords: 6G networks, Virtualized beamforming, Massive MIMO, Software-defined networking (SDN), Machine learning in telecommunications 1 Introduction The exponential growth of data traffic driven by the proliferation of the Internet of Things (IoT), smart cities, and immersive applications is pushing current communication networks to their limits. Recent projections estimate that by 2030, over 125 billion IoT devices will be connected globally, generating zettabytes of data annually [1]. This rapid increase in data demand highlights the urgency of developing © The Author(s) 2025. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Alwakeel J Wireless Com Network (2025) 2025:23 next-generation wireless systems, such as 6G, to meet the needs of highly dynamic and data-intensive environments. 5G networks, while revolutionary, fall short in providing the scalability and adaptability required for future applications. Specifically, hardware-based beamforming in 5G is limited in its flexibility to handle dynamic environments where user mobility and dense device connections rapidly fluctuate. These limitations are particularly challenging for ultra-massive multiple-input multiple-output (MIMO) systems, which are expected to be the backbone of 6G networks [2]. The rigidity of hardware solutions hampers efficient resource allocation and optimization, thus motivating the shift towards virtualization techniques. The dawn of 6G networks is poised to revolutionize the telecommunications landscape, offering unprecedented speeds, ultra-low latency, and enhanced connectivity for a variety of applications, from autonomous systems to immersive virtual reality experiences [3]. Central to this evolution is the deployment of massive multiple-input multiple-output (MIMO) technology, a cornerstone of next-generation wireless communication systems [4]. Massive MIMO enables networks to handle vast amounts of data by transmitting multiple signals simultaneously, boosting spectral efficiency and network capacity [5]. However, the complexity of managing such large-scale MIMO systems presents significant challenges, particularly in optimizing beamforming techniques, power efficiency, and scalability within highly dynamic environments [6]. Current beamforming approaches, often tied to fixed hardware configurations, struggle to meet the agility and adaptability required in 6G [7]. To overcome these limitations, this paper proposes a novel framework for 6G Virtualized Beamforming [8], introducing a paradigm shift that leverages software-defined networking (SDN) and machine learning to optimize massive MIMO performance in 6G networks [9]. By virtualizing beamforming processes, this framework allows for more flexible, dynamic resource allocation, transforming the way massive MIMO is utilized in the future of wireless communication [10]. Massive MIMO technology has been a significant driver of the 5G era, facilitating a dramatic increase in network capacity, spectral efficiency, and data throughput [11]. A massive MIMO base station can use hundreds or even thousands of antennas to simultaneously serve multiple users, making it a key enabler for high-density urban environments [11]. For instance, compared to 4G networks, massive MIMO in 5G can offer up to 10 times higher data throughput and improve spectral efficiency by up to 5x [12]. Despite these advancements, the shift towards 6G networks, which are expected to support data rates as high as 1 Tbps and latency as low as 0.1 milliseconds, demands even more efficient and flexible management of these complex systems [13], One of the critical challenges in massive MIMO is beamforming, where the network needs to precisely direct multiple signals to users while minimizing interference [12]. Traditional beamforming techniques rely heavily on fixed hardware archit (...truncated)