Learning site-specific beams that adapt to the deployment environment, interference sources, and hardware imperfections can lead to noticeable performance gains in coverage, data rate, and power saving, among other interesting advantages. This learning process, however, typically requires a large number of active interactions/iterations, which limits its practical feasibility and leads to excessive overhead. To address these challenges, we propose a digital twin aided codebook learning framework, where a site-specific digital twin is leveraged to generate synthetic channel data for codebook learning. We also propose to learn separate codebooks for line-of-sight and non-line-of-sight users, leveraging the geometric information provided by the digital twin. Simulation results demonstrate that the codebook learned from the digital twin can adapt to the environment geometry and user distribution, leading to high received signal-to-noise ratio performance. Moreover, we identify the ray-tracing accuracy as the most critical factor in digital twin fidelity that impacts the learned codebook performance.

Massive MIMO systems can enhance spectral and energy efficiency, but they require accurate channel state information (CSI), which becomes costly as the number of antennas increases. While machine learning (ML) autoencoders show promise for CSI reconstruction and reducing feedback overhead, they introduce new challenges with standardization, interoperability, and backward compatibility. Also, the significant data collection needed for training makes real-world deployment difficult. To overcome these drawbacks, we propose an ML-based, decoder-only solution for compressed CSI. Our approach uses a standardized encoder for CSI compression on the user side and a site-specific generative decoder at the base station to refine the compressed CSI using environmental knowledge. We introduce two training schemes for the generative decoder: An end-to-end method and a two-stage method, both utilizing a goal-oriented loss function. Furthermore, we reduce the data collection overhead by using a site-specific digital twin to generate synthetic CSI data for training. Our simulations highlight the effectiveness of this solution across various feedback overhead regimes.

Using environmental sensory data can enhance communications beam training and reduce its overhead compared to conventional methods. However, the availability of fresh sensory data during inference may be limited due to sensing constraints or sensor failures, necessitating a realistic model for multimodal sensing. This paper proposes a joint multimodal sensing and beam prediction framework that operates under a constraint on the average sensing rate, i.e., how often fresh sensory data should be obtained. The proposed method combines deep reinforcement learning, i.e., a deep Q-network (DQN), with a neural network (NN)-based beam predictor. The DQN determines the sensing decisions, while the NN predicts the best beam from the codebook. To capture the effect of limited fresh data during inference, the age of information (AoI) is incorporated into the training of both the DQN and the beam predictor. Lyapunov optimization is employed to design a reward function that enforces the average sensing constraint. Simulation results on a real-world dataset show that AoI-aware training improves top-1 and top-3 inference accuracy by 44.16% and 52.96%, respectively, under a strict sensing constraint. The performance gain, however, diminishes as the sensing constraint is relaxed.

In this paper, we propose a precoding framework for frequency division duplex (FDD) integrated sensing and communication (ISAC) systems with multiple-input multiple-output (MIMO). Specifically, we aim to maximize ergodic sum spectral efficiency (SE) while satisfying a sensing beam pattern constraint defined by the mean squared error (MSE). Our method reconstructs downlink (DL) channel state information (CSI) from uplink (UL) training signals using partial reciprocity, eliminating the need for CSI feedback. To obtain the error covariance matrix of the reconstructed DL CSI, we devise anobserved Fisher information-based estimation technique. Leveraging this, to mitigate interference caused by imperfect DL CSI reconstruction and sensing operations, we propose a rate-splitting multiple access (RSMA) aided precoder optimization method. This method jointly updates the precoding vector and Lagrange multipliers by solving the nonlinear eigenvalue problem with eigenvector dependency to maximize SE. The numerical results show that the proposed design achieves precise beam pattern control, maximizes SE, and significantly improves the sensing-communication trade-off compared to the state-of-the-art methods in FDD ISAC scenarios.

This paper considers a cell-free integrated sensing and communication (ISAC) MIMO system, where distributed MIMO access points (APs) jointly serve the communication users and sense the target. For this setup, we derive a sensing SNR for multi-static sensing where both joint communication and sensing signals transmitted by different APs are utilized. With this sensing objective, we develop two baseline approaches that separately design the sensing and communication beamforming vectors, namely communication-prioritized sensing beamforming and sensing-prioritized communication beamforming. Then, we consider the joint sensing and communication (JSC) beamforming design and derive the optimal structure of these beamforming vectors based on a max-min fairness formulation. In addition, considering any pre-determined JSC beam design, we devise a power allocation approach. The results show that the developed JSC beamforming is capable of achieving nearly the same communication signal-to-interference-plus-noise ratio (SINR) of the communication-prioritized sensing beamforming solution with almost the same sensing SNR of the sensing-prioritized communication beamforming approach. The proposed JSC beamforming optimization also provides a noticeable gain over the power allocation with regularized zero-forcing beamforming, yielding a promising strategy for cell-free ISAC MIMO systems.

Deep learning (DL) techniques have demonstrated strong performance in compressing and reconstructing channel state information (CSI) while reducing feedback overhead in massive MIMO systems. A key challenge, however, is their reliance on extensive site-specific training data, whose real-world collection incurs significant overhead and limits scalability across deployment sites. To address this, we propose leveraging site-specific digital twins to assist the training of DL-based CSI compression models. The digital twin integrates an electromagnetic (EM) 3D model of the environment, a hardware model, and ray tracing to produce site-specific synthetic CSI data, allowing DL models to be trained without the need for extensive real-world measurements. We further develop a fidelity analysis framework that decomposes digital twin quality into four key aspects: 3D geometry, material properties, ray tracing, and hardware modeling. We explore how these factors influence the reliability of the data and model performance. To enhance the adaptability to real-world environments, we propose a refinement strategy that incorporates a limited amount of real-world data to fine-tune the DL model pre-trained on the digital twin dataset. Evaluation results show that models trained on site-specific digital twins outperform those trained on generic datasets, with the proposed refinement method effectively enhancing performance using limited real-world data. The simulations also highlight the importance of digital twin fidelity, especially in 3D geometry, ray tracing, and hardware modeling, for improving CSI reconstruction quality. This analysis framework offers valuable insights into the critical fidelity aspects, and facilitates more efficient digital twin development and deployment strategies for various wireless communication tasks.

Beam codebooks are integral components of future millimeter wave MIMO systems. Therefore, it is critical to optimize these codebooks for efficient and reliable communications. Prior work has focused on single-cell codebook learning problems and under stationary interference. In this work, we generalize the interference-aware codebook learning problem to networks with multiple cells/basestations. One of the key differences is that the underlying environment becomes non-stationary, as the behavior of one basestation may influence the learning of the others. Further, we avoid information exchange between different learning nodes which leads to a fully decentralized system with increased learning difficulties. To tackle the non-stationarity, the averaging of measurements is used to estimate the interference nulling performance of a particular beam, based on which a decision rule is provided. Furthermore, we theoretically justify the adoption of such estimator, and prove that it is a sufficient statistic for the underlying quantity of interest in an asymptotic sense. Finally, a novel reward function is proposed to decouple the learning of the multiple agents running at different nodes. Results show that the developed solution is capable of learning well-shaped codebook patterns for different networks and significantly suppress the interference without requiring any information exchange between basestations.

In frequency-division duplexing (FDD) multiple-input multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) for precoding is vastly challenging due to the tremendous feedback overhead with the growing number of antennas. Utilizing uplink pilots for downlink CSI estimation is a promising approach that can eliminate CSI feedback. However, the downlink CSI estimation accuracy diminishes significantly as the number of channel paths increases, resulting in reduced spectral efficiency. In this paper, we demonstrate that achieving downlink spectral efficiency comparable to perfect CSI is feasible by combining uplink CSI with limited downlink CSI feedback information. Our proposed downlink CSI feedback strategy transmits quantized phase information of downlink channel paths, deviating from conventional limited methods. We put forth a mean square error (MSE)-optimal downlink channel reconstruction method by jointly exploiting the uplink CSI and the limited downlink CSI. Armed with the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for phase quantization. Subsequently, we present an optimal feedback bit allocation method for minimizing the MSE in the reconstructed channel through phase quantization. Utilizing a robust downlink precoding technique, we establish that the proposed downlink channel reconstruction method is sufficient for attaining a sum-spectral efficiency comparable to perfect CSI.

This paper presents an end-to-end (E2E) autoencoder learning framework that relies on unsupervised deep learning for the joint design of millimeter wave (mmWave) probing beams and hybrid precoding matrices in multi-user communication systems. Our model utilizes prior channel observations to achieve two main objectives: designing a compact set of probing beams and predicting off-grid radio frequency (RF) beamforming vectors. The E2E learning framework optimizes probing beams in an unsupervised manner, concentrating sensing power on promising spatial directions based on the environment. To this aim, we develop a neural network architecture respecting RF chain constraints and model received signal strength (RSS) using complex-valued convolutional layers. The autoencoder is trained to directly produce RF beamforming vectors for hybrid architectures based on projected RSS indicators (RSSIs). Once RF beamforming vectors for multi-users are predicted, baseband digital precoders are designed by accounting for multi-user interference. The autoencoder neural network is trained E2E in an unsupervised manner with a customized loss function aimed at maximizing RSS. In a system with 64 antennas, 4 RF chains, and 4 users, our approach requires only 8 probing beams to design RF beamforming vectors, compared to the conventional predefined codebooks with 64 or 128 beams.

Accurate localization is crucial for various applications, including autonomous vehicles and next-generation wireless networks. However, the reliability and precision of Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), are compromised by multi-path errors and non-line-of-sight scenarios. This paper presents a novel approach to enhance GPS accuracy by combining visual data from RGB cameras with wireless signals captured at millimeter-wave (mmWave) and sub-terahertz (sub-THz) basestations. We propose a sensing-aided framework for (i) site-specific GPS data characterization and (ii) GPS position de-noising that utilizes multi-modal visual and wireless information. Our approach is validated in a realistic Vehicle-to-Infrastructure (V2I) scenario using a comprehensive real-world dataset, demonstrating a substantial reduction in localization error to sub-meter levels. This method represents a significant advancement in achieving precise localization, particularly beneficial for high-mobility applications in 5G and beyond networks.

In frequency-division duplexing (FDD) multipleinput multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) becomes challenging due to the tremendous feedback overhead that increases with the number of antennas. Using uplink pilots to estimate downlink CSI is a promising approach that can eliminate the need for CSI feedback, but its accuracy decreases significantly as the number of channel paths increases. In this paper, we propose a mean square error (MSE)-optimal downlink channel reconstruction method that jointly utilizes uplink CSI and limited downlink CSI. With the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for channel phase quantization and show the optimal feedback bit allocation method to minimize the MSE. Harnessing robust downlink precoding, we demonstrate that the proposed downlink channel reconstruction is sufficient to achieve a sum-spectral efficiency comparable to that with perfect downlink CSI.

Deep learning (DL) approaches have demonstrated high performance in compressing and reconstructing the channel state information (CSI) and reducing the CSI feedback overhead in massive MIMO systems. One key challenge, however, with the DL approaches is the demand for extensive training data. Collecting this real-world CSI data incurs significant overhead that hinders the DL approaches from scaling to a large number of communication sites. To address this challenge, we propose a novel direction that utilizes site-specific digital twins to aid the training of DL models. The proposed digital twin approach generates site-specific synthetic CSI data from the EM 3D model and ray tracing, which can then be used to train the DL model without real-world data collection. To further improve the performance, we adopt online data selection to refine the DL model training with a small real-world CSI dataset. Results show that a DL model trained solely on the digital twin data can achieve high performance when tested in a real-world deployment. Further, leveraging domain adaptation techniques, the proposed approach requires orders of magnitude less real-world data to approach the same performance of the model trained completely on a real-world CSI dataset.

In this paper, we explore an integrated sensing and communication (ISAC) system with backscattering RFID tags. In this setup, an access point employs a communication beam to serve a user while leveraging a sensing beam to detect an RFID tag. Under the total transmit power constraint of the system, our objective is to design sensing and communication beams by considering the tag detection and communication requirements. First, we adopt zero-forcing to design the beamforming vectors, followed by solving a convex optimization problem to determine the power allocation between sensing and communication. Then, we study a joint beamforming design problem with the goal of minimizing the total transmit power while satisfying the tag detection and communication requirements. To resolve this, we reformulate the non-convex constraints into convex second-order cone constraints. The simulation results demonstrate that, under different communication SINR requirements, joint beamforming optimization outperforms the zero-forcing-based method in terms of achievable detection distance, offering a promising approach for the ISAC-backscattering systems.

Reconfigurable intelligent surfaces (RISs) are envisioned to play a key role in future wireless communication networks. However, channel estimation in RIS-aided wireless networks is challenging due to their passive nature and the large number of reflective elements, leading to high channel estimation overhead. Additionally, conventional methods like beam sweeping, which do not rely on explicit channel state information, often struggle in managing interference in multi-user networks. In this paper, we propose a novel approach that leverages digital twins (DTs) of the physical environments to approximate channels using electromagnetic 3D models and ray tracing, thus relaxing the need for channel estimation and extensive over-the-air computations in RIS-aided wireless networks. To address the digital twins channel approximation errors, we further refine this approach with a DT-specific robust transmission design that reliably meets minimum desired rates. The results show that our method secures these rates over 90% of the time, significantly outperforming beam sweeping, which achieves these rates less than 8% of the time due to its poor management of transmitting power and interference.

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, and message) to serve multiple users/devices/machines/ services, ideally in the most efficient way. Given the increasing need of multifunctional wireless networks for integrated communications, sensing, localization, and computing, coupled with the surge of machine learning (ML)/artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this article, we provide a tutorial, survey, and outlook on past, emerging, and future MA techniques and pay particular attention to how wireless network intelligence and multifunctionality will lead to a rethinking of those techniques. This article starts with an overview of orthogonal, physical-layer multicasting, space domain, power domain (PD), rate-splitting, code-domain MAs, MAs in other domains, and random access (RA), and highlights the importance of conducting research in universal MA (UMA) to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, and receiver designs, for different MA schemes, and MA for AI such as federated learning (FL)/edge intelligence and over-the-air computation (AirComp). We then discuss MA for network multifunctionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications (SeComs), virtual reality (VR), and smart radio and reconfigurable intelligent surfaces (RISs), before presenting a roadmap toward 6G standardization. Throughout the text, we also point out numerous directions that are promising for future research.

Integrated sensing and communication (ISAC) is widely recognized as a fundamental enabler for future wireless communications. In this paper, we present a joint communication and radar beamforming framework for maximizing a sum spectral efficiency (SE) while guaranteeing desired radar performance with imperfect channel state information (CSI) in multi-user and multi-target ISAC systems. To this end, we adopt either a radar transmit beam mean square error (MSE) or receive signal-to-clutter-plus-noise ratio (SCNR) as a radar performance constraint of a sum SE maximization problem. To resolve inherent challenges such as non-convexity and imperfect CSI, we reformulate the problems and identify first-order optimality conditions for the joint radar and communication beamformer. Turning the condition to a nonlinear eigenvalue problem with eigenvector dependency (NEPv), we develop an alternating method which finds the joint beamformer through power iteration and a Lagrangian multiplier through binary search. The proposed framework encompasses both the radar metrics and is robust to channel estimation error with low complexity. Simulations validate the proposed methods. In particular, we observe that the MSE and SCNR constraints exhibit complementary performance depending on the operating environment, which manifests the importance of the proposed comprehensive and robust optimization framework.

Noteworthy strides continue to be made in the development of full-duplex millimeter wave (mmWave) communication systems, but most of this progress has been built on theoretical models and validated through simulation. In this work, we conduct a long overdue real-world evaluation of full-duplex mmWave systems using off-the-shelf 60 GHz phased arrays. We collect over 200,000 measurements of self-interference by electronically sweeping the transmit and receive beams of an experimental base station across a dense spatial profile, shedding light on the effects of the environment, array positioning, and beam steering direction. Then, we call attention to five key challenges faced by practical full-duplex mmWave systems and, with these in mind, propose a general framework for beamforming-based full-duplex solutions. Guided by this framework, we introduce a novel solution called STEER+, a more robust version of recent work called STEER, and experimentally evaluate both in a real-world setting with actual downlink and uplink users. Rather than purely minimize self-interference as with STEER, STEER+ makes use of additional measurements to maximize spectral efficiency, which proves to make it much less sensitive to one’s choice of design parameters. Experimentally, we demonstrate that STEER+ can reliably reduce self-interference to near or below the noise floor while maintaining high SNR on the downlink and uplink, thus enabling full-duplex operation purely via beamforming.

Deep neural networks (DNNs) in the wireless communication domain have been shown to be hardly generalizable to scenarios where the train and test datasets follow a different distribution. This lack of generalization poses a significant hurdle to the practical utilization of DNNs in wireless communication. In this paper, we propose a generalizable deep learning approach for millimeter wave (mmWave) beam selection using sub-6 GHz channel state information (CSI) measurements, referred to as PARAMOUNT. First, we provide a detailed discussion on physical aspects of the electromagnetic wave scattering in the mmWave and sub-6 GHz bands. Based on this discussion, we develop the augmented discrete angle delay profile (ADADP) which is a novel linear transformation for the sub-6 GHz CSI that extracts the angle-delay attributes and provides a semantic visual representation of the multi-path clusters. Next, we introduce a convolutional neural network (CNN) structure that can learn the signatures of the path clusters in the sub-6 GHz ADADP representation and transform it to mmWave band beam indices. We demonstrate by extensive simulations on several different datasets that PARAMOUNT can generalize beyond the training dataset which is mainly due to transfer learning principles that allow transferring information from previously learned tasks to the learning of new unseen tasks.

Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. Due to the hardware constraints and the lack of channel knowledge, codebook based beamforming/combining is normally adopted to achieve the desired array gain. However, most of the existing codebooks focus only on improving the gain of their target user, without taking interference into account. This can incur critical performance degradation in dense networks. In this paper, we propose a sample-efficient online reinforcement learning based beam pattern design algorithm that learns how to shape the beam pattern to null the interfering directions. The proposed approach does not require any explicit channel knowledge or any coordination with the interferers. Simulation results show that the developed solution is capable of learning well-shaped beam patterns that significantly suppress the interference while sacrificing tolerable beamforming/combing gain from the desired user. Furthermore, a hardware proof-of-concept prototype based on mmWave phased arrays is built and used to implement and evaluate the developed online beam learning solutions in realistic scenarios. The learned beam patterns, measured in an anechoic chamber, show the performance gains of the developed framework and highlight a promising machine learning based beam/codebook optimization direction for mmWave and terahertz systems.

In this paper, we address the problem of estimating the delays and Doppler shifts introduced by a sparse wireless channel for orthogonal time frequency space (OTFS) modulation. We show that in the discrete time-frequency (TF) domain, the received signal resulting from an OTFS pilot signal is a superposition of two-dimensional (2D) complex exponentials, where the 2D frequencies of the complex exponentials are given by the delays and Doppler shifts of the scatterers. Thus, estimating the delays and Doppler shifts can be formulated as a 2D sinusoidal frequency estimation problem. We apply a 2D TF domain version of the well-known MUSIC algorithm to recover the delays and Doppler shifts. Since the reduced guard interval frame structure eliminates most of the interference between the pilot and data symbols, the data symbols can be filtered out from the received signal such that only the pilot signal component is used for the 2D MUSIC algorithm.

Reconfigurable intelligent surfaces (RISs) have attracted increasing interest due to their ability to improve the coverage, reliability, and energy efficiency of millimeter wave (mmWave) communication systems. However, designing the RIS beamforming typically requires large channel estimation or beam training overhead, which degrades the efficiency of these systems. In this paper, we propose to equip the RIS surfaces with visual sensors (cameras) that obtain sensing information about the surroundings and user/basestation locations, guide the RIS beam selection, and reduce the beam training overhead. We develop a machine learning (ML) framework that leverages this visual sensing information to efficiently select the optimal RIS reflection beams that reflect the signals between the basestation and mobile users. To evaluate the developed approach, we build a high-fidelity synthetic dataset that comprises co-existing wireless and visual data. Based on this dataset, the results show that the proposed vision-aided machine learning solution can accurately predict the RIS beams and achieve near-optimal achievable rate while significantly reducing the beam training overhead.

Millimeter-wave (mmWave) communication systems rely on narrow beams to achieve sufficient receive signal power. Adjusting these beams is typically associated with large training overhead, which becomes particularly critical for highly-mobile applications. Beam selection can benefit from the knowledge of user positions to reduce the overhead in mm Wave beam training. Prior work, however, studied this problem using only synthetic data that does not accurately represent real-world measurements. In this paper, we revisit the position-aided beam prediction problem in light of real-world measurements with commercial-off-the-shelf GPS to derive insights into how much beam training overhead can be saved in practice. We also compare algorithms that perform well in synthetic data but fail to generalize with real data, and attempt to answer what factors cause inference degradation. Further, we propose a machine learning evaluation metric that better captures the end communication system objective. This work aims at closing the gap between reality and simulations in position-aided beam alignment.

Current scene depth estimation approaches mainly rely on optical sensing, which carries privacy concerns and suffers from estimation ambiguity for distant, shiny, and transparent surfaces/objects. Reconfigurable intelligent surfaces (RISs) provide a path for employing a massive number of antennas using low-cost and energy-efficient architectures. This has the potential for realizing RIS-aided wireless sensing with high spatial resolution. In this paper, we propose to employ RIS-aided wireless sensing systems for scene depth estimation. We develop a comprehensive framework for building accurate depth maps using RIS-aided mmWave sensing systems. In this framework, we propose a new RIS interaction codebook capable of creating a sensing grid of reflected beams that meets the desirable characteristics of efficient scene depth map construction. Using the designed codebook, the received signals are processed to build high-resolution depth maps. Simulation results compare the proposed solution against RGB-based approaches and highlight the promise of adopting RIS-aided mmWave sensing in scene depth perception.

Can reconfigurable intelligent surfaces (RISs) operate in a standalone mode that is completely transparent to the 3GPP 5G initial access process? Realizing that may greatly simplify the deployment and operation of these surfaces and reduce the infrastructure control overhead. This paper investigates the feasibility of building standalone/transparent RIS systems and shows that one key challenge lies in determining the user equipment (UE)-side RIS beam reflection direction. To address this challenge, we propose to equip the RISs with multi-modal sensing capabilities (e.g., using wireless and visual sensors) that enable them to develop some perception of the surrounding environment and the mobile users. Based on that, we develop a machine learning framework that leverages the wireless and visual sensors at the RIS to select the high-performance beams between the base station (BS) and UEs and enable standalone/transparent RIS operation for 5G high-frequency systems. Using a high-fidelity synthetic dataset with co-existing wireless and visual data, we extensively evaluate the performance of the proposed framework. Experimental results demonstrate that the proposed approach can accurately predict the BS and UE-side candidate beams, and that the standalone RIS beam selection solution is capable of realizing near-optimal achievable rates with significantly reduced beam training overhead.

mmWave communications systems usually rely on analog or hybrid analog/digital architectures and thus need a predefined codebook to perform beamforming. Traditional codebooks are designed for universal environments, although in practice a particular BS will only serve a particular environment. In this paper, we propose novel site-specific codebook design methods by utilizing the visual information captured through cameras. Different from other site-specific codebook design methods that require a large amount of measured channel state information (CSI), the proposed ones need only a simple snapshot of the environment followed by efficient computer vision (CV) techniques. Thus the proposed CV-aided codebook design reduces the overhead of communications system, such as the cost of time, human resources, as well as the hardware installation and calibration. Specifically, we propose a CV-based approach that detects the LOS area around the BS and reconstructs the LOS channel vectors set (CVS). With this knowledge, we build a vision-based beam codebook using Lloyd algorithm. Further, we design a FusionNet to generate the codebook that can serve the non-line-of-sight (NLOS) users. The simulation results demonstrate the effectiveness of the proposed CV-aided codebook design methods and their superiority compared to the conventional methods.

Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status. In this paper, we propose a novel beam alignment framework that leverages images taken by cameras installed at the mobile user. Specifically, we utilize 3D object detection techniques to extract the size and location information of the dynamic vehicles around the mobile user, and design a deep neural network (DNN) to infer the optimal beam pair for transceivers without any pilot signal overhead. Moreover, to avoid performing beam alignment too frequently or too slowly, a beam coherence time (BCT) prediction method is developed based on the vision information. This can effectively improve the transmission rate compared with the beam alignment approach with the fixed BCT. Simulation results show that the proposed vision based beam alignment methods outperform the existing LIDAR and vision based solutions, and demand for much lower hardware cost and communication overhead.

Millimeter-wave (mmWave) and terahertz (THz) communication systems typically deploy large antenna arrays to guarantee sufficient receive signal power. The beam training overhead associated with these arrays, however, make it hard for these systems to support highly-mobile applications such as drone communication. To overcome this challenge, this paper proposes a machine learning based approach that leverages additional sensory data, such as visual and positional data, for fast and accurate mmWave/THz beam prediction. The developed framework is evaluated on a real-world multi-modal mmWave drone communication dataset comprising co-existing camera, practical GPS, and mmWave beam training data. The proposed sensing-aided solution achieves a top-1 beam prediction accuracy of 86.32% and close to 100% top-3 and top-5 accuracies, while considerably reducing the beam training overhead. This highlights a promising solution for enabling highly-mobile 6G drone communications.

While initial beam alignment (BA) in millimeter-wave networks has been thoroughly investigated, most research assumes a simplified terminal model based on uniform linear/planar arrays with isotropic antennas. Devices with non-isotropic antenna elements need multiple panels to provide good spherical coverage, and exhaustive search over all beams of all the panels leads to unacceptable overhead. This paper proposes a location- and orientation-aware solution that manages the initial BA for multi-panel devices. We present three different neural network structures that provide efficient BA with a wide range of training dataset sizes, complexity, and feedback message sizes. Our proposed methods outperform the generalized inverse fingerprinting and hierarchical panel-beam selection methods for two considered edge and edge-face antenna placement designs.

Millimeter wave (mmWave) and sub-terahertz communication systems rely mainly on line-of-sight (LOS) links between the transmitters and receivers. The sensitivity of these high-frequency LOS links to blockages, however, challenges the reliability and latency requirements of these communication networks. In this paper, we propose to utilize radar sensors to provide sensing information about the surrounding environment and moving objects, and leverage this information to proactively predict future link blockages before they happen. This is motivated by the low cost of the radar sensors, their ability to efficiently capture important features such as range, angle, velocity of the moving scatterers (candidate blockages), and their capability to capture radar frames at relatively high speed. We formulate the radar-aided proactive blockage prediction problem and develop a solution with deep neural networks. To accurately evaluate the proposed solutions, we build a large-scale real-world dataset, based on the DeepSense framework, gathering co-existing radar and mmWave communication measurements of more than 10 thousand data points and various blockage objects (vehicles, bikes, humans). The evaluation results, based on this dataset, show that the proposed approaches can predicted future blockages 1 second before they happen with more than 90% F1score (and more than 90% accuracy). These results, among others, highlight a promising solution for blockage prediction and reliability enhancement in future wireless mmWave and terahertz communication systems.

Millimeter wave (mmWave) communication is a key component of 5G systems and beyond. Such systems provide high bandwidth and high data rate but are sensitive to blockages. A sudden blockage in the line of sight (LOS) link leads to abrupt disconnection. Thus addressing blockage problems is essential for enhancing the reliability and latency of mmWave communication networks. In this paper, we propose a novel solution that relies only on in-band mmWave wireless measurements to proactively predict future dynamic line-of-sight (LOS) link blockages. The proposed solution utilizes deep neural networks and special patterns of received signal power, which we call pre-blockage wireless signatures, to infer future blockages. Specifically, the machine learning models attempt to predict: (i) Whether a blockage will occur in the next few seconds? (ii) At what time instance will this blockage occur? To evaluate our proposed approach, we build a mmWave communication setup with moving blockage in an indoor scenario and collect received power sequences. Simulation results on a real dataset show that blockage occurrence can be predicted with more than 85% accuracy, and the exact time instance of blockage occurrence can be obtained with less than 2 time instances (1.66s) error for prediction interval of 10 time instances (8.8s). This demonstrates the potential of the proposed solution for dynamic blockage prediction and proactive hand-off.

Enabling highly-mobile millimeter wave (mmWave) and terahertz (THz) wireless communication applications requires overcoming the critical challenges associated with the large antenna arrays deployed at these systems. In particular, adjusting the narrow beams of these antenna arrays typically incurs high beam training overhead that scales with the number of antennas. To address these challenges, this paper proposes a multi-modal machine learning based approach that leverages positional and visual (camera) data collected from the wireless communication environment for fast beam prediction. The developed framework has been tested on a real-world vehicular dataset comprising practical GPS, camera, and mmWave beam training data. The results show the proposed approach achieves more than 75% top-1 beam prediction accuracy and close to 100% top-3≈beam prediction accuracy in realistic communication scenarios.

Adjusting the narrow beams at millimeter wave (mmWave) and terahertz (THz) MIMO communication systems is associated with high beam training overhead, which makes it hard for these systems to support highly-mobile applications. This overhead can potentially be reduced or eliminated if sufficient awareness about the transmitter/receiver locations and the surrounding environment is available. In this paper, efficient deep learning solutions that leverage radar sensory data are developed to guide the mmWave beam prediction and significantly reduce the beam training overhead. Our solutions integrate radar signal processing approaches to extract the relevant features for the learning models, and hence optimize their complexity and inference time. The proposed machine learning based radar-aided beam prediction solutions are evaluated using a large-scale real-world mmWave radar/communication dataset and their capabilities were demonstrated in a realistic vehicular communication scenario. In addition to completely eliminating the radar/communication calibration overhead, the proposed algorithms are able to achieve around 90% top-5 beam prediction accuracy while saving 93% of the beam training overhead. This highlights a promising direction for addressing the training overhead challenge in mmWave/THz communication systems.

Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. This paper proposes to leverage LiDAR sensory data to provide awareness about the communication environment and proactively predict dynamic link blockages before they happen. This allows the network to make proactive decisions for hand-off/beam switching which enhances its reliability and latency. We formulate the LiDAR-aided blockage prediction problem and present the first real-world demonstration for LiDAR-aided blockage prediction in mmWave systems. In particular, we construct a large-scale real-world dataset, based on the DeepSense 6G structure, that comprises co-existing LiDAR and mmWave communication measurements in outdoor vehicular scenarios. Then, we develop an efficient LiDAR data denoising (static cluster removal) algorithm and a machine learning model that proactively predicts dynamic link blockages. Based on the real-world dataset, our LiDAR-aided approach is shown to achieve 95% accuracy in predicting blockages happening within 100ms and more than 80% prediction accuracy for blockages happening within one second. If used for proactive hand-off, the proposed solutions can potentially provide an order of magnitude saving in the network latency, which highlights a promising direction for addressing the blockage challenges in mmWave/sub-THz networks.

Millimeter wave (mmWave) and massive MIMO systems are intrinsic components of 5G and beyond. These systems rely on using beamforming codebooks for both initial access and data transmission. Current beam codebooks, however, generally consist of a large number of narrow beams that scan all possible directions, leading to large training overhead. Further, these codebooks do not normally account for hardware impairments or possible non-uniform array geometries, and their calibration process is expensive. To overcome these limitations, this paper develops an efficient online machine learning framework that learns how to adapt the codebook beam patterns to the specific deployment, surrounding environment, user distribution, and hardware characteristics. This is done by designing a novelcomplex-valued neural networkarchitecture in which the neuron weights directly model the beamforming weights of the analog phase shifters, accounting for the key hardware constraints. This model learns the codebook beams through online and self-supervised training avoiding the need for explicit channel state information. This respects the practical situations where the channel is imperfect or hard to obtain. Simulation results highlight the capability of the proposed solution in learning environment and hardware aware beam codebooks, which reduce the training overhead and improve the robustness against possible hardware impairments.

Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. These pre-defined codebooks, however, are commonly not optimized for specific environments, user distributions, and/or possible hardware impairments. This leads to large codebook sizes with high beam training overhead which makes it hard for these systems to support highly mobile applications. To overcome these limitations, this paper develops a deep reinforcement learning framework that learns how to optimize the codebook beam patterns relying only on the receive power measurements. The developed model learns how to adapt the beam patterns based on the surrounding environment, user distribution, hardware impairments, and array geometry. Further, this approach does not require any knowledge about the channel, RF hardware, or user positions. To reduce the learning time, the proposed model designs a novelWolpertinger-variant architecture that is capable of efficiently searching the large discrete action space. The proposed learning framework respects the RF hardware constraints such as the constant-modulus and quantized phase shifter constraints. Simulation results confirm the ability of the developed framework to learn near-optimal beam patterns for line-of-sight (LOS), non-LOS (NLOS), mixed LOS/NLOS scenarios and for arrays with hardware impairments without requiring any channel knowledge.

Cell-free massive MIMO systems have promising data rate and coverage gains. These systems, however, typically rely on fiber based fronthaul for the communication between the central processing unit and the distributed access points (APs), which increases the infrastructure cost and installation complexity. To address these challenges, this paper proposes two architectures for cell-free massive MIMO systems based on wireless fronthaul that is operating at a higher-band compared to the access links. These dual-band architectures ensure high data rate fronthaul while reducing the infrastructure cost and enhancing the deployment flexibility and adaptability. To investigate the achievable data rates with the proposed architectures, we formulate the end-to-end data rate optimization problem accounting for the various practical aspects of the fronthaul and access links. Then, we develop a low-complexity yet efficient joint beamforming and resource allocation solution for the proposed architectures based on user-centric AP grouping. With this solution, we show that the proposed architectures can achieve comparable data rates to those obtained with optical fiber-based fronthaul under realistic assumptions on the fronthaul bandwidth, hardware constraints, and deployment scenarios. This highlights a promising path for realizing the cell-free massive MIMO gains in practice while reducing the infrastructure and deployment overhead.

Existing deep learning (DL) based downlink channel prediction algorithms for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems mainly utilize single-source sensing information, e.g., the uplink channels, to predict the downlink channels. With the aid of multi-source sensing information (MSI) in communication systems, this paper explores deep multimodal learning (DML) technologies to improve the accuracy of downlink channel prediction. By leveraging various modality combinations and fusion levels, we design several DML based architectures for downlink channel prediction, which can also be easily extended to other communication problems like beam prediction. Simulation results demonstrate that the proposed DML based architectures can effectively exploit the constructive and complementary information of multimodal sensory data, thus achieving better performance than existing works.

Existing work in intelligent communications has recently made preliminary attempts to utilize multi-source sensing information (MSI) to improve the system performance. However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML). In this paper, we first present complete descriptions and heuristic understandings on the framework of DML based wireless communications, where core design choices are analyzed in the view of communications. Then, we develop several DML based architectures for channel prediction in massive multiple-input multiple-output (MIMO) systems that leverage various modality combinations and fusion levels. The case study of massive MIMO channel prediction offers an important example that can be followed in developing other DML based communication technologies. Simulation results demonstrate that the proposed DML framework can effectively exploit the constructive and complementary information of multimodal sensory data to assist the current wireless communications.

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user’s movement, we design a deep neural network (DNN) to predict CCM from user location and user speed, and the corresponding estimation method is named as ULCCME. A location denoising method is further developed to reduce the positioning error and improve the robustness of ULCCME. For cases when user location information is not available, we propose an interesting way that uses the environmental 3D images to predict the CCM, and the corresponding estimation method is named as SICCME. Simulation results show that both the proposed methods are effective and will benefit the subsequent channel estimation.

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection.

In cellular systems, the user equipment (UE) can request a change in the frequency band when its rate drops below a threshold on the current band. The UE is then instructed by the base station (BS) to measure the quality of candidate bands, which requires a measurement gap in the data transmission, thus lowering the data rate. We propose an online-learning based band switching approach that does not require any measurement gap. Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location. We focus on switching between a lower (e.g., 3.5 GHz) band and a millimeter wave band (e.g., 28 GHz), and design and evaluate two classification models that are trained on a ray-tracing dataset. A key insight is that measurement gaps are overkill, in that only the relative order of the bands is necessary for band selection, rather than a full channel estimate. Our proposed machine learning-based policies achieve roughly 30% improvement in mean effective rates over those of the industry standard policy, while achieving misclassification errors well below 0.5% and maintaining resilience against blockage uncertainty.

This paper investigates a novel research direction that leverages vision to help overcome the critical wireless communication challenges. In particular, this paper considers millimeter wave (mmWave) communication systems, which are principal components of 5G and beyond. These systems face two important challenges: (i) the large training overhead associated with selecting the optimal beam and (ii) the reliability challenge due to the high sensitivity to link blockages. Interestingly, most of the devices that employ mmWave arrays will likely also use cameras, such as 5G phones, self-driving vehicles, and virtual/augmented reality headsets. Therefore, we investigate the potential gains of employing cameras at the mmWave base stations and leveraging their visual data to help overcome the beam selection and blockage prediction challenges. To do that, this paper exploits computer vision and deep learning tools to predict mmWave beams and blockages directly from the camera RGB images and the sub-6GHz channels. The experimental results reveal interesting insights into the effectiveness of such solutions. For example, the deep learning model is capable of achieving over 90% beam prediction accuracy, which only requires snapping a shot of the scene and zero overhead.

The growing role artificial intelligence and specifically machine learning is playing in shaping the future of wireless communications has opened up many new and intriguing research directions. This paper motivates the research in the novel direction of vision-aided wireless communications, which aims at leveraging visual sensory information in tackling wireless communication problems. Like any new research direction driven by machine learning, obtaining a development dataset poses the first and most important challenge to vision-aided wireless communications. This paper addresses this issue by introducing the Vision-Wireless (ViWi) dataset framework. It is developed to be a parametric, systematic, and scalable data generation framework. It utilizes advanced 3D-modeling and ray-tracing softwares to generate high-fidelity synthetic wireless and vision data samples for the same scenes. The result is a framework that does not only offer a way to generate training and testing datasets but helps provide a common ground on which the quality of different machine learning-powered solutions could be assessed.

Predicting the millimeter wave (mmWave) beams and blockages using sub-6 GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. Prior work has focused on extracting spatial channel characteristics at the sub-6 GHz band and then use them to reduce the mmWave beam training overhead. This approach still requires beam refinement at mmWave and does not normally account for the different dielectric properties at the different bands. In this paper, we first prove that under certain conditions, there exist mapping functions that can predict the optimal mmWave beam and blockage status directly from the sub-6 GHz channel. These mapping functions, however, are hard to characterize analytically which motivates exploiting deep neural network models to learn them. For that, we prove that a large enough neural network can predict mmWave beams and blockages with success probabilities that can be made arbitrarily close to one. Then, we develop a deep learning model and empirically evaluate its beam/blockage prediction performance using a publicly available dataset. The results show that the proposed solution can predict the mmWave blockages with more than 90% success probability and can predict the optimal mmWave beams to approach the upper bounds while requiring no beam training overhead.

The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination as a non-convex optimization problem to maximize the signal to interference plus noise ratio (SINR) and solve this problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future rewards of actions and using the reported coordinates of the users served by the network, we propose an algorithm for voice bearers and data bearers in sub-6 GHz and millimeter wave (mmWave) frequency bands, respectively. The algorithm improves the performance measured by SINR and sum-rate capacity. In realistic cellular environments, the simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers. For data bearers in the mmWave frequency band, our algorithm approaches the maximum sum rate capacity, but with less than 4% of the required run time.

Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems has attracted growing attention recently. However, existing works focus on the downlink CSI prediction for the users under a given environment and is hard to adapt to users in new environment especially when labeled data is limited. To address this issue, we formulate the downlink channel prediction as a deep transfer learning (DTL) problem, and propose the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained in the manner of classical deep learning and is then fine-tuned for new environments. To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and across-task updates and then adapts to a new environment with a small number of labeled data. Simulation results show that the direct-transfer algorithm achieves better performance than the deep learning algorithm, which implies that the transfer learning benefits the downlink channel prediction in new environments. Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm, which validates its effectiveness and superiority.

As a promising candidate for future wireless systems, large intelligent surfaces (LISs) recently emerged to serve considerate improvements in both spectral and energy efficiencies. These surfaces consist of large numbers of passive elements capable of intelligently reflecting the incident signals. Since the LIS employs passive elements, critical challenges are inherent in the channel training/estimation process in order to properly design the LIS reflection matrices. One challenge particularly is how to acquire the channel knowledge with low training overhead and power consumption solutions. In this paper, we first propose an energy-efficient novel LIS architecture where all the LIS elements are passive except few non-uniformly distributed active elements (connected to the baseband). Then, we develop an efficient solution to design the LIS reflection matrices, with negligible training overhead, leveraging deep learning tools. Given what we call environment descriptors, the LIS has the ability to learn the optimal LIS reflection matrices. The simulation results show that the developed solution can approach the optimal upper bound, when only a small fraction of the LIS elements are active, yielding a promising solution for LIS systems from both energy efficiency and training overhead perspectives.

In this work, we propose a novel approach for high accuracy user localization by merging tools from both millimeter wave (mmWave) imaging and communications. The key idea of the proposed solution is to leverage mmWave imaging to construct a high-resolution 3D image of the line-of-sight (LOS) and non-line-of-sight (NLOS) objects in the environment at one antenna array. Then, uplink pilot signaling with the user is used to estimate the angle-of-arrival and time-of-arrival of the dominant channel paths. By projecting the AoA and ToA information on the 3D mmWave images of the environment, the proposed solution can locate the user with a sub-centimeter accuracy. This approach has several gains. First, it allows accurate simultaneous localization and mapping (SLAM) from a single standpoint, i.e., using only one antenna array. Second, it does not require any prior knowledge of the surrounding environment. Third, it can locate NLOS users, even if their signals experience more than one reflection and without requiring an antenna array at the user. The approach is evaluated using a hardware setup and its ability to provide sub-centimeter localization accuracy is shown.

Demonstrating the feasibility of large antenna array beamforming is essential for realizing mmWave communication systems. This is due to the dependency of these systems on the large array beamforming gains to provide sufficient received signal power. In this paper, the design of a proof-of-concept prototype that demonstrates these gains in practice is explained in detail. We develop a mmWave system with digitally controlled analog front-end. The developed prototype uses 60 GHz phased arrays and universal software radio peripheral (USRP) controllers. The software interface of our design is easily reproducible and can be leveraged for future mmWave prototypes and demonstrations.

Millimeter wave (mmWave) systems will likely employ large antennas at both the transmitter and receiver for directional beamforming. Hybrid analog/digital MIMO architectures have been proposed previously for leveraging both array gain and multiplexing gain, while reducing the power consumption in analog-to-digital converters. Channel knowledge is needed to design the hybrid precoders/combiners, which is difficult to obtain due to the large antenna arrays and the frequency selective nature of the channel. In this paper, we propose a sparse recovery based time-domain channel estimation technique for hybrid architecture based frequency selective mmWave systems. The proposed compressed sensing channel estimation algorithm is shown to provide good estimation error performance, while requiring small training overhead. The simulation results show that using multiple RF chains at the receiver and the transmitter further reduces the training overhead.

Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on the knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures-based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing-based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both single carrier-frequency domain equalization and orthogonal frequency-division multiplexing systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates.

We provide a comprehensive overview of mathematical models and analytical techniques for millimeter wave (mmWave) cellular systems. The two fundamental physical differences from conventional sub-6-GHz cellular systems are: 1) vulnerability to blocking and 2) the need for significant directionality at the transmitter and/or receiver, which is achieved through the use of large antenna arrays of small individual elements. We overview and compare models for both of these factors, and present a baseline analytical approach based on stochastic geometry that allows the computation of the statistical distributions of the downlink signal-to-interference-plus-noise ratio (SINR) and also the per link data rate, which depends on the SINR as well as the average load. There are many implications of the models and analysis: 1) mmWave systems are significantly more noise-limited than at sub-6 GHz for most parameter configurations; 2) initial access is much more difficult in mmWave; 3) self-backhauling is more viable than in sub-6-GHz systems, which makes ultra-dense deployments more viable, but this leads to increasingly interference-limited behavior; and 4) in sharp contrast to sub-6-GHz systems cellular operators can mutually benefit by sharing their spectrum licenses despite the uncontrolled interference that results from doing so. We conclude by outlining several important extensions of the baseline model, many of which are promising avenues for future research.

Massive MIMO systems achieve high sum spectral efficiency by simultaneously serving large numbers of users. In time division duplexing systems, however, the reuse of uplink training pilots among cells results in channel estimation errors, which causes downlink inter-cell interference. Handling this interference is challenging due to the large channel dimensionality and the high complexity associated with implementing large precoding/combining matrices. In this paper, we propose multi-layer precoding to enable efficient and low-complexity operation in full-dimensional massive MIMO, where a large number of antennas are used in two dimensions. In multi-layer precoding, the precoding matrix of each base station is written as a product of a number of precoding matrices. Multi-layer precoding: 1) leverages the directional characteristics of large-scale MIMO channels to manage inter-cell interference with low channel knowledge requirements and 2) allows for an efficient implementation using hybrid analog/digital architectures. We present and analyze a specific multi-layer precoding design for full-dimensional massive MIMO systems. The asymptotic optimality of the proposed design is then proved for some special yet important channels. Numerical simulations verify the analytical results and illustrate the potential gains of multi-layer precoding compared with other multi-cell precoding solutions.

Enabling the high data rates of millimeter wave (mmWave) cellular systems requires deploying large antenna arrays at both the basestations and mobile users. Prior work on coverage and rate of mmWave cellular networks focused on the case when basestations and mobile beamforming vectors are predesigned for maximum beamforming gains. Designing beamforming/combining vectors, though, requires training, which may impact both the SINR coverage and rate of mmWave systems. This paper evaluates mmWave cellular network performance while accounting for the beam training/association overhead. First, a model for the initial beam association is developed based on beam sweeping and downlink control pilot reuse. To incorporate the impact of beam training, a new metric, called the effective reliable rate, is defined and adopted. Using stochastic geometry, the effective rate of mmWave cellular networks is derived for two special cases: near-orthogonal pilots and full pilot reuse. Analytical and simulation results provide insights into the answers of two important questions. First, what is the impact of beam association on mmWave network performance? Then, should orthogonal or reused pilots be employed? The results show that unless the employed beams are very wide, initial beam training with full pilot reuse is nearly as good as perfect beam alignment.

Hybrid analog/digital architectures and receivers with low-resolution analog-to-digital converters (ADCs) are two low power solutions for wireless systems with large antenna arrays, such as millimeter wave and massive multiple-input multiple-output systems. Most prior work represents two extreme cases in which either a small number of radio frequency (RF) chains with full-resolution ADCs, or low-resolution ADC with a number of RF chains equal to the number of antennas is assumed. In this paper, a generalized hybrid architecture with a small number of RF chains and a finite number of ADC bits is proposed. For this architecture, achievable rates with channel inversion and singular value decomposition-based transmission methods are derived. Results show that the achievable rate is comparable to that obtained by full-precision ADC receivers at low and medium SNRs. A trade-off between the achievable rate and power consumption for the different numbers of bits and RF chains is devised. This enables us to draw some conclusions on the number of ADC bits needed to maximize the system energy efficiency. Numerical simulations show that coarse ADC quantization is optimal under various system configurations. This means that hybrid combining with coarse quantization achieves better energy-rate trade-off compared with both hybrid combining with full-resolutions ADCs and 1-bit ADC combining.

Hybrid analog/digital precoding architectures can address the tradeoff between achievable spectral efficiency and power consumption in large-scale MIMO systems. This makes them a promising candidate for millimeter wave systems, which deploy large antenna arrays at both the transmitter and the receiver to guarantee sufficient received signal power. Most prior work on hybrid precoding focused on narrowband channels and assumed fully connected hybrid architectures. Millimeter wave (mmWave) systems, though, are expected to be wideband with frequency selectivity. In this paper, a closed-form solution for fully connected OFDM-based hybrid analog/digital precoding is developed for frequency selective mmWave systems. This solution is then extended to partially connected but fixed architectures in which each RF chain is connected to a specific subset of the antennas. The derived solutions give insights into how the hybrid subarray structures should be designed. Based on this, a novel technique that dynamically constructs the hybrid subarrays knowing the long-term channel characteristics is developed. Simulation results show that the proposed hybrid precoding solutions achieve spectral efficiencies close to that obtained with fully digital architectures in wideband mmWave channels. Furthermore, the results indicate that the developed dynamic subarray solution outperforms the fixed hybrid subarray structures in various system and channel conditions.

Sharing the spectrum among multiple operators seems promising in millimeter wave (mmWave) systems. One explanation is the highly directional transmission in mmWave, which reduces the interference caused by one network on the other networks sharing the same resources. In this paper, we model a mmWave cellular system where an operator that primarily owns an exclusive-use license of a certain band can sell a restricted secondary license of the same band to another operator. This secondary network has a restriction on the maximum interference it can cause to the original network. Using stochastic geometry, we derive expressions for the coverage and rate of both networks, and establish the feasibility of secondary licensing in licensed mmWave bands. Results show that the restricted secondary operator can achieve good coverage with a small impact on the original operator. Our results also illustrate that the spectrum sharing gains increase with narrow beams and when the network densifies.

Hybrid analog/digital precoding allows millimeter wave MIMO systems to leverage large antenna array gains while permitting low cost and power consumption hardware. Most prior work has focused on hybrid precoding for narrow-band mmWave systems. MmWave systems, however, will likely operate on wideband channels with frequency selectivity. Therefore, this paper considers frequency selective hybrid precoding with RF beamforming vectors taken from a quantized codebook. For this system, a low-complexity yet near-optimal greedy algorithm is developed for the design of the hybrid analog/digital precoders. The proposed algorithm greedily selects the RF beamforming vectors using Gram-Schmidt orthogonalization. Simulation results show that the developed precoding design algorithm achieves very good performance compared with the unconstrained solutions while requiring less complexity.

Sharing the spectrum among multiple operators seems promising in millimeter wave (mmWave) systems. One explanation is the highly directional transmission in mmWave, which reduces the interference caused by one network on the other networks sharing the same resources. In this paper, we model a mmWave cellular system, where an operator that primarily owns an exclusive-use license of a certain band can sell a restricted secondary license of the same band to another operator. This secondary network has a restriction on the maximum interference it can cause to the original network. Using stochastic geometry, we derive expressions for the coverage and the rate of both networks, and establish the feasibility of secondary licensing in licensed mmWave bands. To explain economic tradeoffs, we consider a revenue-pricing model for both operators in the presence of a central licensing authority. Our results show that the original operator and central network authority can benefit from secondary licensing when the maximum interference threshold is properly adjusted. This means that the original operator and central licensing authority have an incentive to permit a secondary network to restrictively share the spectrum. Our results also illustrate that the spectrum sharing gains increase with narrow beams and when the network densifies.

Hybrid analog/digital precoding offers a compromise between hardware complexity and system performance in millimeter wave (mmWave) systems. This type of precoding allows mmWave systems to leverage large antenna array gains that are necessary for sufficient link margin, while permitting low cost and power consumption hardware. Most prior work has focused on hybrid precoding for narrow-band mmWave systems, with perfect or estimated channel knowledge at the transmitter. MmWave systems, however, will likely operate on wideband channels with frequency selectivity. Therefore, this paper considers wideband mmWave systems with a limited feedback channel between the transmitter and receiver. First, the optimal hybrid precoding design for a given RF codebook is derived. This provides a benchmark for any other heuristic algorithm and gives useful insights into codebook designs. Second, efficient hybrid analog/digital codebooks are developed for spatial multiplexing in wideband mmWave systems. Finally, a low-complexity yet near-optimal greedy frequency selective hybrid precoding algorithm is proposed based on Gram-Schmidt orthogonalization. Simulation results show that the developed hybrid codebooks and precoder designs achieve very-good performance compared with the unconstrained solutions while requiring much less complexity.

The millimeter wave (mmWave) band, a prime candidate for 5G cellular networks, seems attractive for wireless energy harvesting since it will feature large antenna arrays and extremely dense base station (BS) deployments. The viability of mmWave for energy harvesting though is unclear, due to the differences in propagation characteristics, such as extreme sensitivity to building blockages. This paper considers a scenario where low-power devices extract energy and/or information from the mmWave signals. Using stochastic geometry, analytical expressions are derived for the energy coverage probability, the average harvested power, and the overall (energy-and-information) coverage probability at a typical wireless-powered device in terms of the BS density, the antenna geometry parameters, and the channel parameters. Numerical results reveal several network and device level design insights. At the BSs, optimizing the antenna geometry parameters, such as beamwidth, can maximize the network-wide energy coverage for a given user population. At the device level, the performance can be substantially improved by optimally splitting the received signal for energy and information extraction, and by deploying multi-antenna arrays. For the latter, an efficient low-power multi-antenna mmWave receiver architecture is proposed for simultaneous energy and information transfer. Overall, simulation results suggest that mmWave energy harvesting generally outperforms lower frequency solutions.

Millimeter wave (mmWave) systems will likely employ directional beamforming with large antenna arrays at both the transmitters and receivers. Acquiring channel knowledge to design these beamformers, however, is challenging due to the large antenna arrays and small signal-to-noise ratio before beamforming. In this paper, we propose and evaluate a downlink system operation for multi-user mmWave systems based on compressed sensing channel estimation and conjugate analog beamforming. Adopting the achievable sum-rate as a performance metric, we show how many compressed sensing measurements are needed to approach the perfect channel knowledge performance. The results illustrate that the proposed algorithm requires an order of magnitude less training overhead compared with traditional lower-frequency solutions, while employing mmWave-suitable hardware. They also show that the number of measurements need to be optimized to handle the trade-off between the channel estimate quality and the training overhead.

Antenna arrays will be an important ingredient in millimeter-wave (mmWave) cellular systems. A natural application of antenna arrays is simultaneous transmission to multiple users. Unfortunately, the hardware constraints in mmWave systems make it difficult to apply conventional lower frequency multiuser MIMO precoding techniques at mmWave. This paper develops low-complexity hybrid analog/digital precoding for downlink multiuser mmWave systems. Hybrid precoding involves a combination of analog and digital processing that is inspired by the power consumption of complete radio frequency and mixed signal hardware. The proposed algorithm configures hybrid precoders at the transmitter and analog combiners at multiple receivers with a small training and feedback overhead. The performance of the proposed algorithm is analyzed in the large dimensional regime and in single-path channels. When the analog and digital precoding vectors are selected from quantized codebooks, the rate loss due to the joint quantization is characterized, and insights are given into the performance of hybrid precoding compared with analog-only beamforming solutions. Analytical and simulation results show that the proposed techniques offer higher sum rates compared with analog-only beamforming solutions, and approach the performance of the unconstrained digital beamforming with relatively small codebooks.