The mid-band frequency range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is emerging as a key enabler for future communication systems. By exploiting the advent of new spectrum resources and degrees of freedom brought by the near-field propagation, the mid-band XL-MIMO system is expected to significantly enhance throughput and inherently support advanced functionalities such as integrated sensing and communication. Although theoretical studies have highlighted the benefits of mid-band XL-MIMO systems, the promised performance gains have yet to be validated in practical systems, posing a major challenge to the standardization. In this paper, preliminaries including frame structure, channel modeling, and signal models are first discussed, followed by an analysis of key challenges in constructing a real-time prototype system. Subsequently, the design and implementation of a real-time mid-band XL-MIMO prototype system are presented. Underpinned by a novel architecture, the proposed prototype system supports specifications aligned with standardization, including a bandwidth of 200 MHz, up to 1024 antenna elements, and up to 256 transceiver chains. Operating in time-division duplexing mode, the prototype enables multiuser communication for up to 12 users, while retaining standard communication procedures. Built on hybrid software-defined radio and field programmable gate array platforms, the prototype is programmable and allows for flexible deployment of advanced algorithms. Moreover, the modular architecture ensures high scalability, making the prototype adaptable to various configurations, including distributed deployments and decentralized signal processing. Experimental results demonstrate that the prototype handles real-time digital sample processing at 1453.33 Gbps and achieves a peak data throughput of 15.81 Gbps for 12 users.

Mid-band extra-large-scale multiple-input multiple-output (XL-MIMO), emerging as a critical enabler for future communication systems, is expected to deliver significantly higher throughput by leveraging the extended bandwidth and enlarged antenna aperture. However, power consumption remains a significant concern due to the expanded system dimension, underscoring the need for thorough investigations into efficient system design and deployment. To this end, an in-depth study is conducted on mid-band XL-MIMO systems. Specifically, a comprehensive power consumption model is proposed, encompassing the power consumption of major hardware components and signal processing procedures, while capturing the influence of key system parameters. Considering typical near-field propagation characteristics, closed-form approximations of throughput are derived, providing an analytical framework for assessing energy efficiency (EE). Based on the proposed framework, the scaling law of EE with respect to key system configurations is derived, offering valuable insights for system design. Subsequently, extensions and comparisons are conducted among representative multi-antenna technologies, demonstrating the superiority of mid-band XL-MIMO in EE. Extensive numerical results not only verify the tightness of the throughput analysis but also validate the EE evaluations, unveiling the potential of energy-efficient mid-band XL-MIMO systems.

Mid-band extra large-scale multiple-input multiple-output (XL-MIMO) systems are considered a key enabler for future wireless communications, offering enhanced throughput and extended coverage. Combined with subarray-based architecture and distributed signal processing, the computational complexity and implementation overhead are reduced. However, uncertain channel characteristics associated with the novel frequency band present significant bottlenecks, hindering the development of hardware architecture and algorithm design. Meanwhile, channel characteristics across distributed processing units remain insufficiently explored. In response, a mid-band channel sounder is constructed, and extensive measurement campaigns are carried out across various typical scenarios. Initially, mid-band channel characteristics are unveiled and compared across different scenarios. Subsequently, the mid-band XL-MIMO channel characteristics are analyzed using a virtual array comprising 256 array antennas and 64 transceiver chains. Moreover, motivated by the potential of distributed processing, mid-band XL-MIMO channel characteristics are particularly investigated from the perspectives of subarrays and sub-bands, encompassing subarray-wise non-stationarities, consistencies, far-field approximations, and sub-band characteristics. Through the combination of analysis and measurement validation, several insights and benefits are revealed, particularly relevant to distributed architecture and processing, which provides practical guidance for the real-world deployment of mid-band XL-MIMO systems.

The mid-band frequency range, combined with extra-large-scale antenna arrays (ELAA), is emerging as a critical enabler for future communication systems. However, deploying mid-band ELAA systems presents significant challenges due to the high complexity and overhead associated with signal processing tasks such as channel state information (CSI) acquisition. This paper introduces an efficient transmission framework that incorporates a distributed hardware architecture, distributed channel modeling, and a dual time-scale transmission protocol. Building upon this framework, a practical implementation is proposed, leveraging the discrete Fourier transform (DFT)-based radio frequency (RF) front-ends and linear receivers as the hardware foundation. Additionally, a novel transmission strategy is developed, exploiting both statistical and instantaneous CSI. The proposed framework includes approximations of the ergodic spectral efficiency (SE) to guide DFT beam selection based on statistical CSI. Furthermore, two user scheduling strategies are introduced, utilizing statistical CSI and location information, respectively, with angular division implemented in a distributed manner. Reduced-dimensional instantaneous CSI is then employed for both local and centralized processing. To support system design, the proposed transmission strategy’s ergodic SE performance is analyzed, focusing on the DFT RF front-end and the eigenvalue characteristics of channel correlation matrices. Numerical results reveal that the proposed framework and transmission strategy achieve SE comparable to fully-digital architectures, while significantly reducing overhead and complexity.

Mid-band extra large-scale massive multiple-input multiple-output (XL-MIMO) systems are regarded as potential enablers in future communication systems, which are also trapped in high complexity and channel state information (CSI) acquisition overhead. In this paper, an efficient distributed XL-MIMO structure is first considered, and a novel transmission strategy is proposed by utilizing the joint instantaneous and statistical CSI. Specifically, a user scheduling scheme based on user locations is first presented. Subsequently, approximations of ergodic spectral efficiency (SE) are derived, serving as the basis of analog beamforming. Additionally, the signal processing at the local units and the central unit is carried out utilizing instantaneous CSI. Numerical results demonstrate that the proposed distributed XLMIMO structure and transmission strategy, leveraging angular division in a distributed manner, are capable of achieving SE comparable to that of a fully digital structure.

In pursuit of enhanced quality of service and higher transmission rates, communication within the mid-band spectrum, such as bands in the 6-15 GHz range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is considered a potential enabler for future communication systems. However, the characteristics introduced by mid-band XL-MIMO systems pose challenges for channel modeling and performance analysis. In this paper, we first analyze the potential characteristics of mid-band MIMO channels. Then, an analytical channel model incorporating novel channel characteristics is proposed, based on a review of classical analytical channel models. This model is convenient for theoretical analysis and compatible with other analytical channel models. Subsequently, based on the proposed channel model, we analyze key metrics of wireless communication, including the ergodic spectral efficiency (SE) and outage probability (OP) of MIMO maximal-ratio combining systems. Specifically, we derive closed-form approximations and performance bounds for two typical scenarios, aiming to illustrate the influence of mid-band XL-MIMO systems. Finally, comparisons between systems under different practical configurations are carried out through simulations. The theoretical analysis and simulations demonstrate that mid-band XL-MIMO systems excel in SE and OP due to the increased array elements, moderate large-scale fading, and enlarged transmission bandwidth.

A reconfigurable intelligent surface (RIS) with active elements is an augmented version of an RIS. By equipping all or part of RIS elements with signal processing capabilities, the channel estimation and the design of RIS phases can be further extended, yielding an improvement in the spectral efficiency (SE). In this paper, we first present a novel sensing RIS structure which is efficient for hardware implementation. Unlike partial active elements in previous structures, all elements are available to the RF chains via switches, which enables the traditional channel estimation methods and channel extrapolation to be implemented. Moreover, we make a comprehensive analysis and comparison with other RIS structures from the perspective of channel state information (CSI) acquisition. Considering the large-scale of RIS and base station (BS) array, we model the channel between the user and the RIS, the RIS and the BS using a near-field channel model. Based on the structured channel model, we propose a low-overhead channel reconstruction protocol through a parameter-extracting method, while the training overhead and complexity are also analyzed. In addition, we investigate the RIS elements’ activation strategy to further reduce the training overhead. Finally, numerical results demonstrate that the proposed scheme achieves accurate channel estimation with low overhead, which can also enhance the achievable SE.