Sensing emerges as a critical challenge in 6G networks, which require simultaneous communication and target sensing capabilities. State-of-the-art super-resolution techniques for the direction of arrival (DoA) estimation encounter significant performance limitations when the number of targets exceeds antenna array dimensions. This paper introduces a novel sensing parameter estimation algorithm for orthogonal frequencydivision multiplexing (OFDM) multiple-input multiple-output (MIMO) radar systems. The proposed approach implements a strategic two-stage methodology: first, discriminating targets through delay and Doppler domain filtering to reduce the number of effective targets for super-resolution DoA estimation, and second, introducing a fusion technique to mitigate sidelobe interferences. The algorithm enables robust DoA estimation, particularly in high-density target environments with limitedsize antenna arrays. Numerical simulations validate the superior performance of the proposed method compared to conventional DoA estimation approaches.

The initial 6G networks will likely operate in the upper mid-band (7-24 GHz), which has decent propagation conditions but underwhelming new spectrum availability. In this paper, we explore whether we can anyway reach the ambitious 6G performance goals by evolving the multiple-input multipleoutput (MIMO) technology from massive in 5G to gigantic in 6G. We describe how many antennas are needed to reach the envisioned 6G peak user rates, how many can realistically be deployed in practical radio equipment, and what the practical spatial degrees-of-freedom might become. We further suggest a new deployment strategy that enables the utilization of radiative near-field effects in these bands for precise beamfocusing, localization, and sensing from a single base station site. Finally, we identify open research and standardization challenges that must be overcome to efficiently use gigantic MIMO dimensions in 6G from hardware, cost, and algorithmic perspectives.

This article provides a comprehensive analysis aimed at addressing the impact of power amplifier nonlinearities on massive multiple-input multiple-output (MIMO) communication systems. Specifically, we derive a closed-form expression for the received distortion power, a critical aspect that has not been explored in literature. Our derived expression takes into account the combined influence of spatial diversity and frequency-selectivity on received distortion, with a particular emphasis on the slowly varying parameters, primarily the locations of users and delay spread. In the context of massive MIMO systems, characterized by an exceptionally large number of antennas, our work highlights the crucial role of distortion correlation among antennas. Neglecting distortion correlation can result in significant inaccuracies in system evaluation and algorithmic design. Utilizing the analytical framework we have developed, we formulate a power allocation problem that explicitly exploits the distortion correlation. The proposed solution outperforms methods that neglect the spatial distortion correlation, particularly in terms of spectral efficiency (SE). This superior performance is crucial in optimizing the overall system performance and ensuring desirable power allocation decisions.

Low-resolution quantization constrains the maximum achievable gains of multiple-input multiple-output (MIMO) systems. While the adverse effects and mitigation strategies have been thoroughly analyzed for communication systems, the impact of low-resolution quantization on integrated sensing and communication (ISAC) systems remains insufficiently explored in the existing literature. In this paper, we propose an analysis and design framework to investigate and mitigate the effects of 1-bit digital to analog converters (DACs) for ISAC systems. Firstly, an analytical sensing signal-to-noise ratio (SNR) expression is derived by using the Bussgang decomposition. Furthermore, two different methodologies are proposed to design a transmit waveform that satisfies both communication and sensing requirements simultaneously. The first method uses a separate constant modulus (CM) sensing signal since CM signals are known to be more robust to nonlinear distortion than orthogonal frequency division multiplexing (OFDM) modulated signals. The second method employs the squared-infinity norm Douglas-Rachford splitting (SQUID) approach to construct the transmit waveform using nonlinear quantized precoding. Finally, the performance of the proposed methods are validated via numerical simulations to indicate the complexity-performance tradeoff between two different methods.

This article proposes a reduced complexity digital predistortion (DPD) scheme for fully-connected hybrid beamforming systems for massive multiple-input multiple-output (MIMO) channels impaired by nonlinear power amplifiers (PAs). Due to the inherent physical structure of the fully-connected hybrid beamforming architecture, we consider a two-stage vector DPD method to linearize the received signal at the user terminal by performing single input DPD operations at the virtual antenna elements and combining the predistorted virtual outputs properly. The required computational complexity is reduced by exploiting the spatial correlation of the antenna input signals such that redundant computation of the nonlinear complex DPD implementation is eliminated. Our results demonstrate that we can outperform the existing DPD solutions for fully-connected hybrid MIMO in terms of out-of-band emissions, the error-vector magnitude of the received signals, and computational complexity.

This paper investigates beamforming strategies for integrated sensing and communication (ISAC) systems, where shared signal resources are employed for these services. The proposed framework focuses on maximizing the signal-to-noise ratio (SNR) of the target echo while satisfying the communication rate requirement. Addressing the challenges posed by the higher peak-to-average-power ratio (PAPR) of communication waveforms than sensing waveforms, we provide a detailed analysis and offer valuable insights into power assignment techniques that account for PAPR considerations. We reveal that the integration of constant-modulus sensing signals results in a substantial enhancement in sensing SNR, and numerical results underscore the effectiveness of leveraging sensing signals within the ISAC framework. These findings highlight the potential for achieving superior sensing performance by using dedicated constant-modulus sensing signals.