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This paper presents the first machine learning based real-world demonstration for radar-aided beam prediction in a practical vehicular communication scenario. Leveraging radar sensory data at the communication terminals provides important awareness about the transmitter/receiver locations and the surrounding environment. This awareness could be utilized to reduce or even eliminate the beam training overhead in millimeter wave (mmWave) and sub-terahertz (THz) MIMO communication systems, which enables a wide range of highly-mobile low-latency applications. In this paper, we develop deep learning based radar-aided beam prediction approaches for mmWave/sub-THz systems. The developed solutions leverage domain knowledge for radar signal processing to extract the relevant features fed to the learning models. This optimizes their performance, complexity, and inference time. The proposed radar-aided beam prediction solutions are evaluated using the large-scale real-world dataset DeepSense 6G, which comprises co-existing mmWave beam training and radar measurements. In addition to completely eliminating the radar/communication calibration overhead, the experimental results showed that 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 beam management overhead challenges in mmWave/THz communication systems., Comment: Submitted to IEEE. The dataset and code files will be available on the DeepSense 6G website http://deepsense6g.net/

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., Comment: Submitted to IEEE. The dataset and code files will be available on the DeepSense 6G website: https://www.deepsense6g.net/

Overcoming the link blockage challenges is essential for enhancing the reliability and latency of millimeter wave (mmWave) and sub-terahertz (sub-THz) communication networks. Previous approaches relied mainly on either (i) multiple-connectivity, which under-utilizes the network resources, or on (ii) the use of out-of-band and non-RF sensors to predict link blockages, which is associated with increased cost and system complexity. 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, that we call pre-blockage wireless signatures to infer future blockages. Specifically, the developed machine learning models attempt to predict: (i) If a future blockage will occur? (ii) When will this blockage happen? (iii) What is the type of the blockage? And (iv) what is the direction of the moving blockage? To evaluate our proposed approach, we build a large-scale real-world dataset comprising nearly $0.5$ million data points (mmWave measurements) for both indoor and outdoor blockage scenarios. The results, using this dataset, show that the proposed approach can successfully predict the occurrence of future dynamic blockages with more than 85\% accuracy. Further, for the outdoor scenario with highly-mobile vehicular blockages, the proposed model can predict the exact time of the future blockage with less than $80$ms error for blockages happening within the future $500$ms. These results, among others, highlight the promising gains of the proposed proactive blockage prediction solution which could potentially enhance the reliability and latency of future wireless networks., Comment: Submitted to IEEE. The dataset and code files will be available on the DeepSense 6G website https://www.deepsense6g.net/

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 $\approx$ 75\% top-1 beam prediction accuracy and close to 100\% top-3 beam prediction accuracy in realistic communication scenarios., Comment: Dataset and code files will be available on the DeepSense 6G website http://deepsense6g.net/

Cell-free massive MIMO systems have promising data rate and uniform coverage gains. These systems, however, typically rely on optical fiber based fronthaul for the communication between the central processing unit (CPU) and the distributed access points (APs), which increases the infrastructure cost, leads to high installation time, and limits the deployment flexibility and adaptability. To address these challenges, this paper proposes two architectures for cell-free massive MIMO systems based on wireless fronthaul that is operating at a \textit{higher-band} compared to the access links: (i) A wireless-only fronthaul architecture where the CPU has a wireless fronthaul link to each AP, and (ii) a mixed-fronthaul architecture where the CPU has a wireless link to each cluster of APs that are connected together via optical fibers. These dual-band architectures ensure high-data rate fronthaul and provide high capability to synchronize the distributed APs. Further, the wireless fronthaul reduces the infrastructure cost and installation time, and enhances the flexibility, adaptability, and scalability of the network deployment. 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., Comment: This paper is accepted in IEEE Transactions on Wireless Communications

Reconfigurable intelligent surfaces (RIS) are expected to play an important role in future wireless communication systems. These surfaces typically rely on their reflection beamforming codebooks to reflect and focus the signal on the target receivers. Prior work has mainly considered pre-defined RIS beamsteering codebooks that do not adapt to the environment and hardware and lead to large beam training overhead. In this work, a novel deep reinforcement learning based framework is developed to efficiently construct the RIS reflection beam codebook. This framework adopts a multi-level design approach that transfers the learning between the multiple RIS subarrays, which speeds up the learning convergence and highly reduces the computational complexity for large RIS surfaces. The proposed approach is generic for co-located/distributed RIS surfaces with arbitrary array geometries and with stationary/non-stationary channels. Further, the developed solution does not require explicitly channel knowledge and adapts the codebook beams to the surrounding environment, user distribution, and hardware characteristics. Simulation results show that the proposed learning framework can learn optimized interaction codebooks within reasonable iterations. Besides, with only 6 beams, the learned codebook outperforms a 256-beam DFT codebook, which significantly reduces the beam training overhead., Comment: Submitted to IEEE Wireless Communications Letters, 5 pages, 3 figures

Reconfigurable intelligent surfaces (RISs) have promising coverage and data rate gains for wireless communication systems in 5G and beyond. Prior work has mainly focused on analyzing the performance of these surfaces using computer simulations or lab-level prototypes. To draw accurate insights about the actual performance of these systems, this paper develops an RIS proof-of-concept prototype and extensively evaluates its potential gains in the field and under realistic wireless communication settings. In particular, a 160-element reconfigurable surface, operating at a 5.8GHz band, is first designed, fabricated, and accurately measured in the anechoic chamber. This surface is then integrated into a wireless communication system and the beamforming gains, path-loss, and coverage improvements are evaluated in realistic outdoor communication scenarios. When both the transmitter and receiver employ directional antennas and with 5m and 10m distances between the transmitter-RIS and RIS-receiver, the developed RIS achieves $15$-$20$dB gain in the signal-to-noise ratio (SNR) in a range of $\pm60^\circ$ beamforming angles. In terms of coverage, and considering a far-field experiment with a blockage between a base station and a grid of mobile users and with an average distance of $35m$ between base station (BS) and the user (through the RIS), the RIS provides an average SNR improvement of $6$dB (max $8$dB) within an area $> 75$m$^2$. Thanks to the scalable RIS design, these SNR gains can be directly increased with larger RIS areas. For example, a 1,600-element RIS with the same design is expected to provide around $26$dB SNR gain for a similar deployment. These results, among others, draw useful insights into the design and performance of RIS systems and provide an important proof for their potential gains in real-world far-field wireless communication environments., Comment: Submitted to IEEE Open Journal of the Communications Society, 29 pages, 20 figures

The support of coexisting ultra-reliable and low-latency (URLL) and enhanced Mobile BroadBand (eMBB) services is a key challenge for the current and future wireless communication networks. Those two types of services introduce strict, and in some time conflicting, resource allocation requirements that may result in a power-struggle between reliability, latency, and resource utilization in wireless networks. The difficulty in addressing that challenge could be traced back to the predominant reactive approach in allocating the wireless resources. This allocation operation is carried out based on received service requests and global network statistics, which may not incorporate a sense of \textit{proaction}. Therefore, this paper proposes a novel framework termed \textit{service identification} to develop novel proactive resource allocation algorithms. The developed framework is based on visual data (captured for example by RGB cameras) and deep learning (e.g., deep neural networks). The ultimate objective of this framework is to equip future wireless networks with the ability to analyze user behavior, anticipate incoming services, and perform proactive resource allocation. To demonstrate the potential of the proposed framework, a wireless network scenario with two coexisting URLL and eMBB services is considered, and two deep learning algorithms are designed to utilize RGB video frames and predict incoming service type and its request time. An evaluation dataset based on the considered scenario is developed and used to evaluate the performance of the two algorithms. The results confirm the anticipated value of proaction to wireless networks; the proposed models enable efficient network performance ensuring more than $85\%$ utilization of the network resources at $\sim 98\%$ reliability. This highlights a promising direction for the future vision-aided wireless communication networks., Comment: The datasets and code files will be available soon on the ViWi Dataset website https://viwi-dataset.net/

Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. Being pre-defined, however, these codebooks 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 increases the initial access/tracking latency and 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 iteratively optimize the codebook beam patterns (shapes) relying only on the receive power measurements and without requiring any explicit channel knowledge. The developed model learns how to autonomously adapt the beam patterns to best match the surrounding environment, user distribution, hardware impairments, and array geometry. Further, this approach does not require any knowledge about the channel, array geometry, RF hardware, or user positions. To reduce the learning time, the proposed model designs a novel Wolpertinger-variant architecture that is capable of efficiently searching for an optimal policy in a large discrete action space, which is important for large antenna arrays with quantized phase shifters. This complex-valued neural network architecture design respects the practical RF hardware constraints such as the constant-modulus and quantized phase shifter constraints. Simulation results based on the publicly available DeepMIMO dataset confirm the ability of the developed framework to learn near-optimal beam patterns for both line-of-sight (LOS) and non-LOS scenarios and for arrays with hardware impairments without requiring any channel knowledge., Comment: Submitted to IEEE, 30 pages, 9 figures. The dataset/code files will be available soon on the DeepMIMO website https://www.deepmimo.net/. arXiv admin note: text overlap with arXiv:2102.09084

We consider the problem of proactive handoff and beam selection in Terahertz (THz) drone communication networks assisted with reconfigurable intelligent surfaces (RIS). Drones have emerged as critical assets for next-generation wireless networks to provide seamless connectivity and extend the coverage, and can largely benefit from operating in the THz band to achieve high data rates (such as considered for 6G). However, THz communications are highly susceptible to channel impairments and blockage effects that become extra challenging when accounting for drone mobility. RISs offer flexibility to extend coverage by adapting to channel dynamics. To integrate RISs into THz drone communications, we propose a novel deep learning solution based on a recurrent neural network, namely the Gated Recurrent Unit (GRU), that proactively predicts the serving base station/RIS and the serving beam for each drone based on the prior observations of drone location/beam trajectories. This solution has the potential to extend the coverage of drones and enhance the reliability of next-generation wireless communications. Predicting future beams based on the drone beam/position trajectory significantly reduces the beam training overhead and its associated latency, and thus emerges as a viable solution to serve time-critical applications. Numerical results based on realistic 3D ray-tracing simulations show that the proposed deep learning solution is promising for future RIS-assisted THz networks by achieving near-optimal proactive hand-off performance and more than 90% accuracy for beam prediction.

The sensitivity to blockages is a key challenge for the high-frequency (5G millimeter wave and 6G sub-terahertz) wireless networks. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle the reliability and latency challenges lies in enabling proaction in wireless networks. Proaction basically allows the network to anticipate blockages, especially dynamic blockages, and initiate user hand-off beforehand. This paper presents a complete machine learning framework for enabling proaction in wireless networks relying on visual data captured, for example, by RGB cameras deployed at the base stations. In particular, the paper proposes a vision-aided wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. The bedrock of this solution is a deep learning algorithm that learns from visual and wireless data how to predict incoming blockages. The predictions of this algorithm are used by the wireless network to proactively initiate hand-off decisions and avoid any unnecessary latency. The algorithm is developed on a vision-wireless dataset generated using the ViWi data-generation framework. Experimental results on two basestations with different cameras indicate that the algorithm is capable of accurately detecting incoming blockages more than $\sim 90\%$ of the time. Such blockage prediction ability is directly reflected in the accuracy of proactive hand-off, which also approaches $87\%$. This highlights a promising direction for enabling high reliability and low latency in future wireless networks., Comment: Submitted to IEEE, 30 pages, 11 figures. The dataset will be available soon on the ViWi website https://viwi-dataset.net/

Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. However, due to the adoption of fully analog or hybrid analog/digital architectures, as well as non-ideal hardware or arbitrary/unknown array geometries, the accurate channel state information becomes hard to acquire. This impedes the design of beamforming/combining vectors that are crucial to fully exploit the potential of large-scale antenna arrays in providing sufficient receive signal power. In this paper, we develop a novel framework that leverages deep reinforcement learning (DRL) and a Wolpertinger-variant architecture and learns how to iteratively optimize the beam pattern (shape) for serving one or a small set of users relying only on the receive power measurements and without requiring any explicit channel knowledge. The proposed model accounts for key hardware constraints such as the phase-only, constant-modulus, and quantized-angle constraints. Further, the proposed framework can efficiently optimize the beam patterns for systems with non-ideal hardware and for arrays with unknown or arbitrary array geometries. Simulation results show that the developed solution is capable of finding near-optimal beam patterns based only on the receive power measurements., Comment: Asilomar 2020, dataset and code files will be available soon on the DeepMIMO website https://www.deepmimo.net/

Augmented and virtual reality systems (AR/VR) are rapidly becoming key components of the wireless landscape. For immersive AR/VR experience, these devices should be able to construct accurate depth perception of the surrounding environment. Current AR/VR devices rely heavily on using RGB-D depth cameras to achieve this goal. The performance of these depth cameras, however, has clear limitations in several scenarios, such as the cases with shiny objects, dark surfaces, and abrupt color transition among other limitations. In this paper, we propose a novel solution for AR/VR depth map construction using mmWave MIMO communication transceivers. This is motivated by the deployment of advanced mmWave communication systems in future AR/VR devices for meeting the high data rate demands and by the interesting propagation characteristics of mmWave signals. Accounting for the constraints on these systems, we develop a comprehensive framework for constructing accurate and high-resolution depth maps using mmWave systems. In this framework, we developed new sensing beamforming codebook approaches that are specific for the depth map construction objective. Using these codebooks, and leveraging tools from successive interference cancellation, we develop a joint beam processing approach that can construct high-resolution depth maps using practical mmWave antenna arrays. Extensive simulation results highlight the potential of the proposed solution in building accurate depth maps. Further, these simulations show the promising gains of mmWave based depth perception compared to RGB-based approaches in several important use cases., Comment: Submitted to IEEE, 43 pages, 24 figures

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., Comment: 31 pages, 20 figures

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.

Millimeter wave (mmWave) communication is a key component of 5G and beyond. Harvesting the gains of the large bandwidth and low latency at mmWave systems, however, is challenged by the sensitivity of mmWave signals to blockages; a sudden blockage in the line of sight (LOS) link leads to abrupt disconnection, which affects the reliability of the network. In addition, searching for an alternative base station to re-establish the link could result in needless latency overhead. In this paper, we address these challenges collectively by utilizing machine learning to anticipate dynamic blockages proactively. The proposed approach sees a machine learning algorithm learning to predict future blockages by observing what we refer to as the pre-blockage signature. To evaluate our proposed approach, we build a mmWave communication setup with a moving blockage and collect a dataset of received power sequences. Simulation results on a real dataset show that blockage occurrence could be predicted with more than 85% accuracy and the exact time instance of blockage occurrence can be obtained with low error. This highlights the potential of the proposed solution for dynamic blockage prediction and proactive hand-off, which enhances the reliability and latency of future wireless networks., Comment: 6 pages, 8 figures

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. Simulations results demonstrate that the proposed DML framework can effectively exploit the constructive and complementary information of multimodal sensory data in various wireless communication scenarios., Comment: accepted by IEEE Journal on Selected Areas in Communications

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, even if these directions are never used. This leads to very large training overhead. Further, these codebooks do not normally account for the hardware impairments or the possible non-uniform array geometries, and their calibration is an expensive process. 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 novel complex-valued neural network architecture in which the neuron weights directly model the beamforming weights of the analog phase shifters, accounting for the key hardware constraints such as the constant-modulus and quantized-angles. 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 either unavailable, imperfect, or hard to obtain, especially in the presence of hardware impairments. Simulation results highlight the capability of the proposed solution in learning environment and hardware aware beam codebooks, which can significantly reduce the training overhead, enhance the achievable data rates, and improve the robustness against possible hardware impairments., Comment: Submitted to IEEE Transactions on Communications. The dataset and code files will be available soon on the DeepMIMO website: https://www.deepmimo.net/

Unlocking the full potential of millimeter-wave and sub-terahertz wireless communication networks hinges on realizing unprecedented low-latency and high-reliability requirements. The challenge in meeting those requirements lies partly in the sensitivity of signals in the millimeter-wave and sub-terahertz frequency ranges to blockages. One promising way to tackle that challenge is to help a wireless network develop a sense of its surrounding using machine learning. This paper attempts to do that by utilizing deep learning and computer vision. It proposes a novel solution that proactively predicts \textit{dynamic} link blockages. More specifically, it develops a deep neural network architecture that learns from observed sequences of RGB images and beamforming vectors how to predict possible future link blockages. The proposed architecture is evaluated on a publicly available dataset that represents a synthetic dynamic communication scenario with multiple moving users and blockages. It scores a link-blockage prediction accuracy in the neighborhood of 86\%, a performance that is unlikely to be matched without utilizing visual data., Comment: The dataset and code files will be available soon on the ViWi website: https://www.viwi-dataset.net/

This paper proposes a novel relay-aided intelligent reconfigurable surface (IRS) architecture for future wireless communication systems. The proposed architecture, which consists of two side-by-side intelligent surfaces connected via a full-duplex relay, has the potential of achieving the promising gains of intelligent surfaces while requiring much smaller numbers of reflecting elements. Consequently, the proposed IRS architecture needs significantly less channel estimation and beam training overhead and provides higher robustness compared to classical IRS approaches. Further, thanks to dividing the IRS reflection process over two surfaces, the position and orientation of these surfaces can be optimized to extend the wireless communication coverage and enhance the system performance. In this paper, the achievable rates and required numbers of elements using the proposed relay-aided IRS architecture are first analytically characterized and then evaluated using numerical simulations. The results show that the proposed architecture can achieve the data rate targets with much smaller numbers of elements compared to typical IRS solutions, which highlights a promising path towards the practical deployment of these intelligent surfaces., Comment: 8 pages; 7 figures

Employing low-resolution analog-to-digital converters (ADCs) for millimeter wave receivers with large antenna arrays provides opportunity to efficiently reduce power consumption of the receiver. Reducing ADC resolution, however, results in performance degradation due to non-negligible quantization error. In addition, the large number of radio frequency (RF) chains is still not desirable. Accordingly, conventional low-resolution ADC systems require more efficient designs to minimize the cost and complexity while maximizing performance. In this article, we discuss advanced low-resolution ADC receiver architectures that further improve the spectral and energy efficiency tradeoff. To reduce both the numbers of RF chains and ADC bits, hybrid analog-and-digital beamforming is jointly considered with low-resolution ADCs. We explore the challenges in designing such receivers and present key insights on how the advanced architectures overcome such challenges. As an alternative low-resolution ADC receiver, we also introduce receivers with learning-based detection. The receiver does not require explicit channel estimation, thereby is suitable for one-bit ADC systems. Finally, future challenges and research issues are discussed., Comment: Accepted to IEEE Communications Magazine (7 pages, 5 figures)

The promising coverage and spectral efficiency gains of intelligent reflecting surfaces (IRSs) are attracting increasing interest. In order to realize these surfaces in practice, however, several challenges need to be addressed. One of these main challenges is how to configure the reflecting coefficients on these passive surfaces without requiring massive channel estimation or beam training overhead. Earlier work suggested leveraging supervised learning tools to design the IRS reflection matrices. While this approach has the potential of reducing the beam training overhead, it requires collecting large datasets for training the neural network models. In this paper, we propose a novel deep reinforcement learning framework for predicting the IRS reflection matrices with minimal training overhead. Simulation results show that the proposed online learning framework can converge to the optimal rate that assumes perfect channel knowledge. This represents an important step towards realizing a standalone IRS operation, where the surface configures itself without any control from the infrastructure., Comment: Submitted to IEEE SPAWC 2020

Scaling the number of antennas up is a key characteristic of current and future wireless communication systems. The hardware cost and power consumption, however, motivate large-scale MIMO systems, especially at millimeter wave (mmWave) bands, to rely on analog-only or hybrid analog/digital transceiver architectures. With these architectures, mmWave base stations normally use pre-defined beamforming codebooks for both initial access and data transmissions. Current beam codebooks, however, generally adopt single-lobe narrow beams and scan the entire angular space. This leads to high beam training overhead and loss in the achievable beamforming gains. In this paper, we propose a new machine learning framework for learning beamforming codebooks in hardware-constrained large-scale MIMO systems. More specifically, we develop a neural network architecture that accounts for the hardware constraints and learns beam codebooks that adapt to the surrounding environment and the user locations. Simulation results highlight the capability of the proposed solution in learning multi-lobe beams and reducing the codebook size, which leads to noticeable gains compared to classical codebook design approaches., Comment: submitted to IEEE SPAWC 2020

Vision-aided wireless communication is motivated by the recent advances in deep learning and computer vision as well as the increasing dependence on line-of-sight links in millimeter wave (mmWave) and terahertz systems. By leveraging vision, this new research direction enables an interesting set of new capabilities such as vision-aided mmWave beam and blockage prediction, proactive hand-off, and resource allocation among others. These capabilities have the potential of reliably supporting highly-mobile applications such as vehicular/drone communications and wireless virtual/augmented reality in mmWave and terahertz systems. Investigating these interesting applications, however, requires the development of special dataset and machine learning tasks. Based on the Vision-Wireless (ViWi) dataset generation framework [1], this paper develops an advanced and realistic scenario/dataset that features multiple base stations, mobile users, and rich dynamics. Enabled by this dataset, the paper defines the vision-wireless mmWave beam tracking task (ViWi-BT) and proposes a baseline solution that can provide an initial benchmark for the future ViWi-BT algorithms., Comment: The ViWi-BT Challenge at ICC 2020 - https://www.viwi-dataset.net/viwi-bt.html

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, where each learning task aims to predict the downlink CSI from the uplink CSI for one single environment. Specifically, we develop the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained on the data from all previous environments 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 in terms of both prediction accuracy and stability, which validates its effectiveness and superiority., Comment: Accepted by IEEE Transactions on Communications

In this paper, we present a novel framework of 3D scene based beam selection for mmWave communications that relies only on the environmental data and deep learning techniques. Different from other out-of-band side-information aided communication strategies, the proposed one fully utilizes the environmental information, e.g., the shape, the position, and even the materials of the surrounding buildings/cars/trees that are obtained from 3D scene reconstruction. Specifically, we build the neural networks with the input as point cloud of the 3D scene and the output as the beam indices. Compared with the LIDAR aided technique, the reconstructed 3D scene here is achieved from multiple images taken offline from cameras and thus significantly lowers down the cost and makes itself applicable for small mobile terminals. Simulation results show that the proposed 3D scene based beam selection can outperform the LIDAR method in terms of accuracy., Comment: 5 pages, 6 figures

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., Comment: submitted to IEEE Vehicular Technology Conference, Oct. 2019. The datasets/codes are available at https://www.viwi-dataset.net

The growing role that 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 \textit{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., Comment: IEEE VTC 2020. The ViWi datasets and applications are available at https://www.viwi-dataset.net

This paper considers uplink massive MIMO systems with 1-bit analog-to-digital converters (ADCs) and develops a deep-learning based channel estimation framework. In this framework, the prior channel estimation observations and deep neural network models are leveraged to learn the non-trivial mapping from quantized received measurements to channels. For that, we derive the sufficient length and structure of the pilot sequence to guarantee the existence of this mapping function. This leads to the interesting, and \textit{counter-intuitive}, observation that when more antennas are employed by the massive MIMO base station, our proposed deep learning approach achieves better channel estimation performance, for the same pilot sequence length. Equivalently, for the same channel estimation performance, this means that when more antennas are employed, fewer pilots are required. This observation is also analytically proved for some special channel models. Simulation results confirm our observations and show that more antennas lead to better channel estimation both in terms of the normalized mean squared error and the achievable signal-to-noise ratio per antenna., Comment: Accepted in IEEE Wireless Communications Letters; 5 pages; 3 figures

Predicting the millimeter wave (mmWave) beams and blockages using sub-6GHz channels has the potential of enabling mobility and reliability in scalable mmWave systems. These gains attracted increasing interest in the last few years. Prior work, however, has focused on extracting spatial channel characteristics at the sub-6GHz band first and then use them to reduce the mmWave beam training overhead. This approach has a number of limitations: (i) It still requires a beam search at mmWave, (ii) its performance is sensitive to the error associated with extracting the sub-6GHz channel characteristics, and (iii) it 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 correct blockage status directly from the sub-6GHz channel, which overcome the limitations in prior work. 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 use the sub-6GHz channel to directly predict the optimal mmWave beam and the correct blockage status with success probabilities that can be made arbitrarily close to one. Then, we develop an efficient deep learning model and empirically evaluate its beam/blockage prediction performance using the publicly available dataset DeepMIMO. The results show that the proposed solution can predict the mmWave blockages with more than 90$\%$ success probability. Further, these results confirm the capability of the proposed deep learning model in predicting the optimal mmWave beams and approaching the optimal data rates, that assume perfect channel knowledge, while requiring no beam training overhead..., Comment: Submitted to IEEE Transactions on Communications. The code files and datasets are available at http://www.deepmimo.net/DeepMIMO_applications.html

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., Comment: 31 pages, 15 figures, revised and resubmitted to IEEE Transactions on Wireless Communications on October 2, 2019, March 9, 2020, July 2, 2020, and September 1, 2020

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., Comment: (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

This paper proposes a novel neural network architecture, that we call an auto-precoder, and a deep-learning based approach that jointly senses the millimeter wave (mmWave) channel and designs the hybrid precoding matrices with only a few training pilots. More specifically, the proposed machine learning model leverages the prior observations of the channel to achieve two objectives. First, it optimizes the compressive channel sensing vectors based on the surrounding environment in an unsupervised manner to focus the sensing power on the most promising spatial directions. This is enabled by a novel neural network architecture that accounts for the constraints on the RF chains and models the transmitter/receiver measurement matrices as two complex-valued convolutional layers. Second, the proposed model learns how to construct the RF beamforming vectors of the hybrid architectures directly from the projected channel vector (the received sensing vector). The auto-precoder neural network that incorporates both the channel sensing and beam prediction is trained end-to-end as a multi-task classification problem. Thanks to this design methodology that leverages the prior channel observations and the implicit awareness about the surrounding environment/user distributions, the proposed approach significantly reduces the training overhead compared to classical (non-machine learning) solutions. For example, for a system of 64 transmit and 64 receive antennas, with 3 RF chains at both sides, the proposed solution needs only 8 or 16 channel training pilots to directly predict the RF beamforming/combining vectors of the hybrid architectures and achieve near-optimal achievable rates. This highlights a promising solution for the channel estimation and hybrid precoding design problem in mmWave and massive MIMO systems., Comment: The results are generated using the DeepMIMO dataset. The code files will be available soon at http://deepmimo.net/DeepMIMO_applications.html

Can we map the channels at one set of antennas and one frequency band to the channels at another set of antennas---possibly at a different location and a different frequency band? If this channel-to-channel mapping is possible, we can expect dramatic gains for massive MIMO systems. For example, in FDD massive MIMO, the uplink channels can be mapped to the downlink channels or the downlink channels at one subset of antennas can be mapped to the downlink channels at all the other antennas. This can significantly reduce (or even eliminate) the downlink training/feedback overhead. In the context of cell-free/distributed massive MIMO systems, this channel mapping can be leveraged to reduce the fronthaul signaling overhead as only the channels at a subset of the distributed terminals need to be fed to the central unit which can map them to the channels at all the other terminals. This mapping can also find interesting applications in mmWave beam prediction, MIMO radar, and massive MIMO based positioning. In this paper, we introduce the new concept of channel mapping in space and frequency, where the channels at one set of antennas and one frequency band are mapped to the channels at another set of antennas and frequency band. First, we prove that this channel-to-channel mapping function exists under the condition that the mapping from the candidate user positions to the channels at the first set of antennas is bijective; a condition that can be achieved with high probability in several practical MIMO communication scenarios. Then, we note that the channel-to-channel mapping function, even if it exists, is typically unknown and very hard to characterize analytically as it heavily depends on the various elements of the surrounding environment. With this motivation, we propose to leverage the powerful learning capabilities of deep neural networks ...., Comment: An extended version of an (invited) Asilomar paper; the dataset and code files are available online at http://deepmimo.net/DeepMIMO_applications.html

Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise a massive number of nearly-passive elements that interact with the incident signals, for example by reflecting them, in a smart way that improves the wireless system performance. Prior work focused on the design of the LIS reflection matrices assuming full knowledge of the channels. Estimating these channels at the LIS, however, is a key challenging problem, and is associated with large training overhead given the massive number of LIS elements. This paper proposes efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband of the LIS controller). We then develop two solutions that design the LIS reflection matrices with negligible training overhead. In the first approach, we leverage compressive sensing tools to construct the channels at all the LIS elements from the channels seen only at the active elements. These full channels can then be used to design the LIS reflection matrices with no training overhead. In the second approach, we develop a deep learning based solution where the LIS learns how to optimally interact with the incident signal given the channels at the active elements, which represent the current state of the environment and transmitter/receiver locations. We show that the achievable rates of the proposed compressive sensing and deep learning solutions approach the upper bound, that assumes perfect channel knowledge, with negligible training overhead and with less than 1% of the elements being active., Comment: Submitted to IEEE Access. The code will be available soon

Machine learning tools are finding interesting applications in millimeter wave (mmWave) and massive MIMO systems. This is mainly thanks to their powerful capabilities in learning unknown models and tackling hard optimization problems. To advance the machine learning research in mmWave/massive MIMO, however, there is a need for a common dataset. This dataset can be used to evaluate the developed algorithms, reproduce the results, set benchmarks, and compare the different solutions. In this work, we introduce the DeepMIMO dataset, which is a generic dataset for mmWave/massive MIMO channels. The DeepMIMO dataset generation framework has two important features. First, the DeepMIMO channels are constructed based on accurate ray-tracing data obtained from Remcom Wireless InSite. The DeepMIMO channels, therefore, capture the dependence on the environment geometry/materials and transmitter/receiver locations, which is essential for several machine learning applications. Second, the DeepMIMO dataset is generic/parameterized as the researcher can adjust a set of system and channel parameters to tailor the generated DeepMIMO dataset for the target machine learning application. The DeepMIMO dataset can then be completely defined by the (i) the adopted ray-tracing scenario and (ii) the set of parameters, which enables the accurate definition and reproduction of the dataset. In this paper, an example DeepMIMO dataset is described based on an outdoor ray-tracing scenario of 18 base stations and more than one million users. The paper also shows how this dataset can be used in an example deep learning application of mmWave beam prediction., Comment: to appear in Proc. of Information Theory and Applications Workshop (ITA), Feb., 2019

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., Comment: submitted to IEEE ICASSP 2019

Enabling highly-mobile millimeter wave (mmWave) systems is challenging because of the huge training overhead associated with acquiring the channel knowledge or designing the narrow beams. Current mmWave beam training and channel estimation techniques do not normally make use of the prior beam training or channel estimation observations. Intuitively, though, the channel matrices are functions of the various elements of the environment. Learning these functions can dramatically reduce the training overhead needed to obtain the channel knowledge. In this paper, a novel solution that exploits machine learning tools, namely conditional generative adversarial networks (GAN), is developed to learn these functions between the environment and the channel covariance matrices. More specifically, the proposed machine learning model treats the covariance matrices as 2D images and learns the mapping function relating the uplink received pilots, which act as RF signatures of the environment, and these images. Simulation results show that the developed strategy efficiently predicts the covariance matrices of the large-dimensional mmWave channels with negligible training overhead., Comment: to appear in Asilomar Conference on Signals, Systems, and Computers, Oct. 2018

The sensitivity of millimeter wave (mmWave) signals to blockages is a fundamental challenge for mobile mmWave communication systems. The sudden blockage of the line-of-sight (LOS) link between the base station and the mobile user normally leads to disconnecting the communication session, which highly impacts the system reliability. Further, reconnecting the user to another LOS base station incurs high beam training overhead and critical latency problems. In this paper, we leverage machine learning tools and propose a novel solution for these reliability and latency challenges in mmWave MIMO systems. In the developed solution, the base stations learn how to predict that a certain link will experience blockage in the next few time frames using their past observations of adopted beamforming vectors. This allows the serving base station to proactively hand-over the user to another base station with a highly probable LOS link. Simulation results show that the developed deep learning based strategy successfully predicts blockage/hand-off in close to 95% of the times. This reduces the probability of communication session disconnection, which ensures high reliability and low latency in mobile mmWave systems., Comment: submitted to IEEE GlobalSIP 2018 (Invited paper)

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., Comment: to appear in VTC 2018, Chicago, USA

Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming several challenges. First, the use of narrow beams and the sensitivity of mmWave signals to blockage greatly impact the coverage and reliability of highly-mobile links. Second, highly-mobile users in dense mmWave deployments need to frequently hand-off between base stations (BSs), which is associated with critical control and latency overhead. Further, identifying the optimal beamforming vectors in large antenna array mmWave systems requires considerable training overhead, which significantly affects the efficiency of these mobile systems. In this paper, a novel integrated machine learning and coordinated beamforming solution is developed to overcome these challenges and enable highly-mobile mmWave applications. In the proposed solution, a number of distributed yet coordinating BSs simultaneously serve a mobile user. This user ideally needs to transmit only one uplink training pilot sequence that will be jointly received at the coordinating BSs using omni or quasi-omni beam patterns. These received signals draw a defining signature not only for the user location, but also for its interaction with the surrounding environment. The developed solution then leverages a deep learning model that learns how to use these signatures to predict the beamforming vectors at the BSs. This renders a comprehensive solution that supports highly-mobile mmWave applications with reliable coverage, low latency, and negligible training overhead. Simulation results show that the proposed deep-learning coordinated beamforming strategy approaches the achievable rate of the genie-aided solution that knows the optimal beamforming vectors with no training overhead., Comment: 42 pages, 14 figures, accepted in IEEE Access

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 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 SC-FDE and OFDM 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., Comment: 31 pages, 13 figures

Massive multiple-input multiple-output (MIMO) systems achieve high sum spectral efficiency by offering an order of magnitude increase in multiplexing gains. In time division duplexing systems, however, the reuse of uplink training pilots among cells results in additional channel estimation error, which causes downlink inter-cell interference, even when large numbers of antennas are employed. Handling this interference with conventional network MIMO techniques is challenging due to the large channel dimensionality. Further, the implementation of large antenna precoding/combining matrices is associated with high hardware complexity and power consumption. 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 is 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, each one called a layer. Multi-layer precoding (i) leverages the directional characteristics of large-scale MIMO channels to manage inter-cell interference with low channel knowledge requirements, and (ii) allows for an efficient implementation using low-complexity hybrid analog/digital architectures. We present a specific multi-layer precoding design for full-dimensional massive MIMO systems. The performance of this precoding design is analyzed and the per-user achievable rate is characterized for general channel models. The asymptotic optimality of the proposed multi-layer precoding 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 to traditional pilot-contaminated massive MIMO solutions., Comment: submitted to IEEE Transactions on Wireless Communications

Hybrid analog/digital precoding architectures can address the trade-off between achievable spectral efficiency and power consumption in large-scale MIMO systems. This makes it a promising candidate for millimeter wave systems, which require deploying large antenna arrays at both the transmitter and receiver to guarantee sufficient received signal power. Most prior work on hybrid precoding focused on narrowband channels and assumed fully-connected hybrid architectures. 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 them, a novel technique that dynamically constructs the hybrid subarrays based on 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. Further, the results indicate that the developed dynamic subarray solution outperforms the fixed hybrid subarray structures in various system and channel conditions., Comment: submitted to IEEE Transactions on Wireless Communications

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-6GHz cellular systems are (i) vulnerability to blocking, and (ii) 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: (a) mmWave systems are significantly more noise-limited than at Sub-6GHz for most parameter configurations; (b) initial access is much more difficult in mmWave; (c) self-backhauling is more viable than in Sub-6GHz systems which makes ultra-dense deployments more viable, but this leads to increasingly interference-limited behavior; and (d) in sharp contrast to Sub-6GHz 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., Comment: 50 pages, 10 figures, submitted to IEEE Trans. Communications, invited paper

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 MIMO systems. Most prior work represents two extreme cases in which either a small number of 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 finite number of ADC bits is proposed. For this architecture, achievable rates with channel inversion and SVD 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 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 to both hybrid combining with full-resolutions ADCs and 1-bit ADC combining., Comment: 30 pages, 8 figures, submitted to IEEE Transactions on Wireless Communications

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. To explain economic trade-offs, 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.

Enabling the high data rates of millimeter wave (mmWave) cellular systems requires deploying large antenna arrays at both the basestations and mobile users. The beamforming weights of these large arrays need to be tuned to guarantee sufficient beamforming gains. Prior work on coverage and rate of mmWave cellular networks focused mainly on the case when basestations and mobile users beamfomring vectors are perfectly designed for maximum beamforming gains. Designing beamforming/combining vectors, though, requires training which may impact both the SINR coverage and rate of mmWave cellular systems. This paper characterizes and evaluates the performance of mmWave cellular networks 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 into system performance, a new metric, called the effective reliable rate, is defined and adopted. Using stochastic geometry, the effective reliable rate of mmWave cellular networks is derived for two special cases: with near-orthogonal control pilots and with full pilot reuse. Analytical and simulation results provide insights into the answers of three important questions: (i) What is the impact of beam association on mmWave network performance? (ii) Should orthogonal or reused control pilots be employed in the initial beam association phase? (iii) Should exhaustive or hierarchical search be adopted for the beam training phase? The results show that unless the employed beams are very wide or the system coherence block length is very small, exhaustive search with full pilot reuse is nearly as good as perfect beam alignment., Comment: 30 pages, 9 figures, submitted to IEEE Transactions on Wireless Communications

Massive multiple-input multiple-output (MIMO) is expected to play a central role in future wireless systems. The deployment of large antenna arrays at the base station and the mobile users offers multiplexing and beamforming gains that boost system spectral efficiency. Unfortunately, the high cost and power consumption of components like analog-to-digital converters makes assigning an RF chain per antenna and applying typical fully digital precoding/combining solutions difficult. In this paper, a novel architecture for massive MIMO receivers, consisting of arrays of switches and constant (non-tunable) phase shifters, is proposed. This architecture applies a quasi-coherent combining in the RF domain to reduce the number of required RF chains. An algorithm that designs the RF combining for this architecture is developed and analyzed. Results show that the proposed massive MIMO combining model can achieve a comparable performance to the fully-digital receiver architecture in single-user and multi-user massive MIMO setups., Comment: 4 pages, 3 figures, accepted in IEEE Wireless Communications Letters