This paper presents the first experimental validation of reflective near-field beamfocusing using a reconfigurable intelligent surface (RIS). While beamfocusing has been theoretically established as a key feature of large-aperture RISs, its practical realization has remained unexplored. We derive new analytical expressions for the array gain achieved with a $b$-bit RIS in near-field line-of-sight scenarios, characterizing both the finite depth and angular width of the focal region. The theoretical results are validated through a series of measurements in an indoor office environment at 28 GHz using a one-bit 1024-element RIS. The experiments confirm that beamfocusing can be dynamically achieved and accurately predicted by the proposed simplified analytical model, despite the presence of hardware imperfections and multipath propagation. These findings demonstrate that near-field beamfocusing is a robust and practically viable feature of RIS-assisted wireless communications.

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.

This paper introduces a sensing management method for integrated sensing and communications (ISAC) in cell-free massive multiple-input multiple-output (MIMO) systems. Conventional communication systems employ channel estimation procedures that impose significant overhead during data transmission, consuming resources that could otherwise be utilized for data. To address this challenge, we propose a state-based approach that leverages sensing capabilities to track the user when there is no communication request. Upon receiving a communication request, predictive beamforming is employed based on the tracked user position, thereby reducing the need for channel estimation. Our framework incorporates an extended Kalman filter (EKF) based tracking algorithm with adaptive sensing management to perform sensing operations only when necessary to maintain high tracking accuracy. The simulation results demonstrate that our proposed sensing management approach provides uniform downlink communication rates that are higher than with existing methods by achieving overhead-free predictive beamforming.

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.

Nonlinear distortion stemming from low-cost power amplifiers may severely affect wireless communication performance through out-of-band (OOB) radiation and in-band distortion. The distortion is correlated between different transmit antennas in an antenna array, which results in a beamforming gain at the receiver side that grows with the number of antennas. In this paper, we investigate how the strength of the distortion is affected by the frequency selectivity of the channel. A closed-form expression for the received distortion power is derived as a function of the number of multipath components (MPCs) and the delay spread, which highlight their impact. The performed analysis, which is verified via numerical simulations, reveals that as the number of MPCs increases, distortion exhibits distinct characteristics for in-band and OOB frequencies. It is shown that the received in-band and OOB distortion power is inversely proportional to the number of MPCs, and it is reported that as the delay spread gets narrower, the in-band distortion power is beamformed towards the intended user, which yields higher received in-band distortion compared to the OOB distortion.

In this study, a novel iterative turbo digital self interference cancellation (DSIC) algorithm is proposed to eliminate remaining self interference (SI) signal, which is contaminated by signal of interest (SoI), subject to time varying SI channel. SoI creates a noise floor for the estimation of SI signal; hence, estimation accuracy significantly degrades. In order to improve the estimation accuracy, it is proposed that for each block, SoI is decoded via signal decomposition based on generalized memory polynomial (GMP) and neural network (NN) nonlinear regressors and its copy subtracted from received signal iteratively so that SoI contamination is decreased. This procedure is performed in an online manner during data transmission stage; therefore, it does not yield extra training overhead. In addition, since each block is processed independently, the proposed algorithm can track varying channel efficiently. Both numerical results and hardware implementations are presented to demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose a detector structure for fully connected hybrid massive MIMO systems with transceiver impairments at user equipments. In literature, memoryless compensation of nonlinear effects at the receiver side is recently proposed for systems employing frequency domain equalization. However, in these studies, the system model is restricted to be symbol sampled, where pulse shaping and fractional delays in channel impulse response (CIR) are not taken into consideration. However, memory effects of nonlinear distortion become more apparent when the system is considered in the upsampled signal domain. In this study, we propose a receiver architecture, where the received signal of each user is nonlinearly modified by the post-distortion unit with memory to recover the transmitted symbols. In addition, based on the proposed receiver structure, a novel detection criterion is introduced in order to compensate the distortion amplification due to fractional delays in the CIR. Lastly, performance improvement brought by the proposed detector is verified by numerical simulations.

In this paper, we propose a receiver structure for single-carrier uplink transmission with frequency domain equalization (FDE) that is exposed to power amplifier (PA) nonlinearities. A two-stage approach is adopted, in which linear communication channel is equalized at the first stage, and it is followed by post-distortion, where nonlinear distortion is reduced. In the literature, nonlinear processing techniques are proposed, which perform memoryless compensation of nonlinear distortion together with FDE. However, in this study, we show that even if memoryless nonlinearity exists, the received signal is impaired by nonlinear inter-symbol-interference. Therefore, we propose a class of symbol rate post-distortion techniques, which use neighboring received symbols to suppress the nonlinear interference. Three different post-distortion methods, Gaussian process regression (GPR), neural network (NN) and Volterra series (VS) based post-distorters, are considered. Also, a combiner, which intelligently combines the outputs of fractional delayed bank of FDE’s after post-distortion, is proposed to overcome performance degradation of FDE for frequency selective channels under nonlinear distortion. Performances of the proposed techniques are compared with state-of-the-art approaches in terms of bit error rate (BER) and achievable information rate (AIR) metrics. Simulation results demonstrate that post-distortion methods together with bank of FDE outperform state-of-the-art techniques.

In this paper, we present a general framework for the investigation of the effects of power amplifier (PA) non-linearities on the hybrid beamforming based massive multiple-input multiple-output (MIMO) systems. In literature, much of the attention is devoted to phase shifters as the analog beamformer stage; however, in this work, a more comprehensive scenario, based on fully connected analog beamformer architecture, is considered. Firstly, out-of-band (OOB) radiation pattern is evaluated to investigate the spatial characteristics of the undesired radiation. It is observed that OOB radiation due to nonlinear distortion is concentrated on the beamforming directions. In addition, a framework is proposed to design digital predistorters (DPD) and a conventional DPD architecture is adopted to the proposed design framework. It is shown that conventional DPD can solve the OOB problem for a single user environment; however, its performance degrades in the multi-user case. Even though, there are many studies on the effects of nonlinear PA distortions, a few of them consider the achievable information rate (AIR). In this work, a framework is adopted to study the capacity of the considered system under PA nonlinearities.