Next generation wireless networks focus on improving spectral efficiency (SE) while reducing power consumption and hardware cost. Reconfigurable intelligent surfaces (RISs) offer a viable solution to meet these requirements. In order to enhance the SE, index modulation (IM) has been regarded as one of the enabling technologies via the transmission of additional information bits over the transmission media such as subcarriers, antennas and spatial paths. In this work, we explore the usage of spatial paths and introduce spatial path IM (SPIM) for RIS-aided massive multiple-input multiple-output (mMIMO) systems. Thus, the proposed framework improves the network efficiency and the coverage with the use of RIS while SPIM provides SE improvement. In order to perform SPIM, we exploit the spatial diversity of the millimeter wave channel and assign the index bits to the spatial patterns of the channel between the base station and the users through RIS. We introduce a low complexity approach for the design of hybrid beamformers, which are constructed by the steering vectors corresponding to the selected spatial path indices for SPIM-mMIMO. Furthermore, we conduct a theoretical analysis on the SE of the proposed SPIM approach, and derive the SE relationship between the SPIM-based hybrid beamforming and fully digital (FD) beamforming. Via numerical simulations, we validate our theoretical results and show that the proposed SPIM approach presents an improved SE performance, even higher than that of the use of FD beamformers while using a few RF chains.

The next generation wireless networks focus on improving the energy and spectral efficiency (SE/EE) of the communication systems in response to the demand for massive number of users and data rate. In this work, we aim to achieve the enhancement of SE and EE by employing index modulation (IM) techniques for reconfigurable intelligent surface (RIS)-aided communication systems. While RIS offers the network efficiency and improve the coverage, IM provides SE improvement by the transmission of additional index bits. In IM, we utilize the indices of the spatial paths between the base station and the user through the RIS. We introduce a low complexity approach for the design of hybrid beamformers, which are constructed by the steering vectors corresponding to the selected spatial path indices for IM. Via numerical experiments, we show that the proposed approach presents an improved SE performance, even higher than that of the use of fully-digital beamformers while using a few RF chains.

Cell-free massive MIMO (multiple-input multiple-output) is a promising infrastructure for 6G and beyond, offering significantly higher spectral efficiency than traditional cellular systems. In cell-free massive MIMO, a large number of low-cost access points (APs) are densely deployed, making hardware impairments inevitable due to cost-effective radio hardware. While the impact of quantization and other impairments has been extensively studied for narrowband channels, their effects in wideband scenarios remain relatively unexamined. This paper presents the first analysis of how low-resolution analog-to-digital converters (ADCs) affect the uplink performance of a cell-free massive MIMO system using an orthogonal frequency division multiplexing (OFDM) waveform. Both quantization-impaired channel estimation and data detection are considered, and the quantization-unaware and quantization-aware linear receivers are developed. To further mitigate the adverse effects of quantization at the bit level, an alternating direction method of multipliers (ADMM)-based receiver is proposed. Simulation results demonstrate that the ADMM-based receiver outperforms conventional linear receivers by orders of magnitude.

As extremely large-scale (XL) arrays advance, near-field (NF) communications have gained significant attention.With this shift, traditional far-field techniques are being revised for compatibility with new XL NF communication paradigms. This work presents NF hybrid beamforming (NF-HBF) approaches for XL-MIMO, focusing on challenges like near-field effects and spatial non-stationarity. First, it redefines the sparse recovery-based NF-HBF problem, shifting from angular- to polar-domain code-books, leading to direct greedy hybrid beamforming (DG-HBF). However, challenges such as high computational complexity, phase shifter (PS) resolution, and spatial non-stationarities persist. To overcome these, this study proposes stepwise-individual and stepwise-joint greedy HBF methods, namely SIG-HBF and SJG-HBF. These methods simplify the process by approximating spherical-wave beams with planar-wave beams, promising lower PS resolution needs, reduced complexity, and the ability to tackle spatially non-stationary channels. Moreover, by exploring conjugate symmetric sequency-ordered Hadamard transforms, NF-HBF can be efficiently achieved using 2-bit PSs with values in {1,−1,j,−j}, facilitated by the SJG-HBF and SJG-HBF methods. Numerical simulations on the proposed methods demonstrate that DG-HBF can approach NF fully-digital beamforming, while SIG-HBF and SJG-HBF highlight the feasibility of utilizing angular-domain codebooks with low PS cost and low memory storage for NF-HBF.

Beam-squint is a challenging issue in ultra-wideband systems, e.g., terahertz (THz) integrated sensing and communications (ISAC). In order to compensate for the loss due to beam-squint, this paper leverages index modulation in spatial domain, which enables the transmission of additional information bits to improve the spectral efficiency (SE). Specifically, a joint antenna and spatial path index modulation (JASPIM) technique is proposed by exploiting the spatial diversity of both antenna and path indices. We present a hybrid beamforming technique with JASPIM for ISAC, wherein the analog beamformers are designed in accordance with the radar targets and the communications user. Numerical simulations demonstrate that our JASPIM-ISAC approach exhibits a significant SE improvement even higher than that of the use of fully digital beamformers in the presence of beam-squint.

In terahertz (THz) wideband systems, beam-squint causes deviations in the generated beam directions at different subcarriers due to the use of subcarrier-independent analog beamformers. In order to combat the performance loss due to beam-squint effect, this work employs spatial path index modulation (SPIM) to improve the spectral efficiency (SE) performance of the overall system, thereby compensating the loss due to beam-squint. Specifically, SPIM allows the transmission of additional information bits to the receiver via modulating the indices of the spatial paths. The proposed approach is evaluated in a THz integrated sensing and communications (THz-ISAC) scenario, wherein the beamformer design allows generating multiple beams toward both radar targets and the communications user. Numerical simulations demonstrate that the proposed approach exhibits significant SE performance even higher than that of the use of fully digital beamformers without SPIM in the presence of beam-squint.

As the demand for wireless connectivity continues to soar, the fifth generation and beyond wireless networks are exploring new ways to efficiently utilize the wireless spectrum and reduce hardware costs. One such approach is the integration of sensing and communications (ISAC) paradigms to jointly access the spectrum. Recent ISAC studies have focused on upper millimeter-wave and low terahertz bands to exploit ultrawide bandwidths. At these frequencies, hybrid beamformers that employ fewer radio-frequency chains are employed to offset expensive hardware but at the cost of lower multiplexing gains. Wideband hybrid beamforming also suffers from the beam-split effect arising from the subcarrier-independent (SI) analog beamformers. To overcome these limitations, we introduce a spatial path index modulation (SPIM) ISAC architecture, which transmits additional information bits via modulating the spatial paths between the base station and communications users. We design the SPIM-ISAC beamformers by estimating both radar and communications parameters through our proposed beam-split-aware algorithms. We then develop a family of hybrid beamforming techniques – hybrid, SI, subcarrier-dependent analog-only, and beam-split-aware beamformers – for SPIM-ISAC. Numerical experiments demonstrate that the proposed approach exhibits significantly improved spectral efficiency performance in the presence of beam-split when compared with even fully digital non-SPIM beamformers.

The sixth-generation networks envision the terahertz (THz) band as one of the key enabling technologies because of its ultrawide bandwidth. To combat the severe attenuation, the THz wireless systems employ large arrays, wherein the near-field beam-split (NB) severely degrades the accuracy of channel acquisition. Contrary to prior works that examine only either narrowband beamforming or far-field models, we estimate the wideband THz channel via an NB-aware orthogonal matching pursuit (NBA-OMP) approach. We design an NBA dictionary of near-field steering vectors by exploiting the corresponding angular and range deviation. Our OMP algorithm accounts for this deviation thereby ipso facto mitigating the effect of NB. Numerical experiments demonstrate the effectiveness of the proposed channel estimation technique for wideband THz systems.

Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output. In previous works, model training is mostly done via centralized learning (CL), where the whole training dataset is collected from the users at the base station (BS). This approach introduces huge communication overhead for data collection. In this paper, to address this challenge, we propose a federated learning (FL) framework for channel estimation. We design a convolutional neural network (CNN) trained on the local datasets of the users without sending them to the BS. We develop FL-based channel estimation schemes for both conventional and RIS (intelligent reflecting surface) assisted massive MIMO (multiple-input multiple-output) systems, where a single CNN is trained for two different datasets for both scenarios. We evaluate the performance for noisy and quantized model transmission and show that the proposed approach provides approximately 16 times lower overhead than CL, while maintaining satisfactory performance close to CL. Furthermore, the proposed architecture exhibits lower estimation error than the state-of-the-art ML-based schemes.

Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with small number of radio-frequency (RF) chains have limited multiplexing gain. Spatial path index modulation (SPIM) is helpful in improving this gain by utilizing additional signal bits modulated by the indices of spatial paths. In this paper, we introduce model-based and model-free frameworks for beamformer design in multi-user SPIM-MIMO systems. We first design the beamformers via model-based manifold optimization algorithm. Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on the local dataset of users, who estimate the beamformers by feeding the model with their channel data. The DL randomly selects different set of model parameters during training, thereby further reducing the transmission overhead compared to conventional FL. Numerical experiments show that the proposed framework exhibits higher spectral efficiency than the state-of-the-art SPIM-MIMO methods and mmWave-MIMO, which relies on the strongest propagation path. Furthermore, the proposed FL approach provides at least 10 times lower transmission overhead than the centralized learning techniques.

In millimeter-wave communications, multiple-input-multiple-output (MIMO) systems use large antenna arrays to achieve high gain and spectral efficiency. These massive MIMO systems employ hybrid beamformers to reduce power consumption associated with fully digital beamforming in large arrays. Further savings in cost and power are possible through the use of subarrays. Unlike prior works that resort to large latency methods such as optimization and greedy search for subarray selection, we propose a deep-learning-based approach in order to overcome the complexity issue without causing significant performance loss. We formulate antenna selection and hybrid beamformer design as a classification/prediction problem for convolutional neural networks (CNNs). For antenna selection, the CNN accepts the channel matrix as input and outputs a subarray with optimal spectral efficiency. The resultant subarray channel matrix is then again fed to a CNN to obtain analog and baseband beamformers. We train the CNNs with several noisy channel matrices that have different channel statistics in order to achieve a robust performance at the network output. Numerical experiments show that our CNN framework provides an order better spectral efficiency and is 10 times faster than the conventional techniques. Further investigations with quantized-CNNs show that the proposed network, saved in no more than 5 bits, is also suited for digital mobile devices.