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Showing papers on "Wireless published in 2020"


Journal ArticleDOI
TL;DR: In this paper, the authors provide an overview of the IRS technology, including its main applications in wireless communication, competitive advantages over existing technologies, hardware architecture as well as the corresponding new signal model.
Abstract: IRS is a new and revolutionizing technology that is able to significantly improve the performance of wireless communication networks, by smartly reconfiguring the wireless propagation environment with the use of massive low-cost passive reflecting elements integrated on a planar surface. Specifically, different elements of an IRS can independently reflect the incident signal by controlling its amplitude and/or phase and thereby collaboratively achieve fine-grained 3D passive beamforming for directional signal enhancement or nulling. In this article, we first provide an overview of the IRS technology, including its main applications in wireless communication, competitive advantages over existing technologies, hardware architecture as well as the corresponding new signal model. We then address the key challenges in designing and implementing the new IRS-aided hybrid (with both active and passive components) wireless network, as compared to the traditional network comprising active components only. Finally, numerical results are provided to show the great performance enhancement with the use of IRS in typical wireless networks.

1,897 citations


Journal ArticleDOI
TL;DR: An overview of HMIMOS communications including the available hardware architectures for reconfiguring such surfaces are provided, and the opportunities and key challenges in designingHMIMOS-enabled wireless communications are highlighted.
Abstract: Future wireless networks are expected to evolve toward an intelligent and software reconfigurable paradigm enabling ubiquitous communications between humans and mobile devices. They will also be capable of sensing, controlling, and optimizing the wireless environment to fulfill the visions of low-power, high-throughput, massively- connected, and low-latency communications. A key conceptual enabler that is recently gaining increasing popularity is the HMIMOS that refers to a low-cost transformative wireless planar structure comprised of sub-wavelength metallic or dielectric scattering particles, which is capable of shaping electromagnetic waves according to desired objectives. In this article, we provide an overview of HMIMOS communications including the available hardware architectures for reconfiguring such surfaces, and highlight the opportunities and key challenges in designing HMIMOS-enabled wireless communications.

925 citations


Journal ArticleDOI
TL;DR: A novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure, is proposed, which is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR).
Abstract: Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).

846 citations


Journal ArticleDOI
16 Jun 2020
TL;DR: In this article, the authors discuss the potential applications of RISs in wireless networks that operate at high-frequency bands, eg, millimeter wave (30-100 GHz) and sub-millimeter-wave (greater than 100 GHz) frequencies when used in a manner similar to relays.
Abstract: Reconfigurable intelligent surfaces (RISs) have the potential of realizing the emerging concept of smart radio environments by leveraging the unique properties of metamaterials and large arrays of inexpensive antennas In this article, we discuss the potential applications of RISs in wireless networks that operate at high-frequency bands, eg, millimeter wave (30-100 GHz) and sub-millimeter wave (greater than 100 GHz) frequencies When used in wireless networks, RISs may operate in a manner similar to relays The present paper, therefore, elaborates on the key differences and similarities between RISs that are configured to operate as anomalous reflectors and relays In particular, we illustrate numerical results that highlight the spectral efficiency gains of RISs when their size is sufficiently large as compared with the wavelength of the radio waves In addition, we discuss key open issues that need to be addressed for unlocking the potential benefits of RISs for application to wireless communications and networks

651 citations


Journal ArticleDOI
TL;DR: A literature review on recent applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks, and the joint optimization of the IRS’s phase control and the transceivers’ transmission control in different network design problems, e.g., rate maximization and power minimization problems.
Abstract: This paper presents a literature review on recent applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks. Conventionally, the network optimization has been limited to transmission control at two endpoints, i.e., end users and network controller. The fading wireless channel is uncontrollable and becomes one of the main limiting factors for performance improvement. The IRS is composed of a large array of scattering elements, which can be individually configured to generate additional phase shifts to the signal reflections. Hence, it can actively control the signal propagation properties in favor of signal reception, and thus realize the notion of a smart radio environment. As such, the IRS’s phase control, combined with the conventional transmission control, can potentially bring performance gain compared to wireless networks without IRS. In this survey, we first introduce basic concepts of the IRS and the realizations of its reconfigurability. Then, we focus on applications of the IRS in wireless communications. We overview different performance metrics and analytical approaches to characterize the performance improvement of IRS-assisted wireless networks. To exploit the performance gain, we discuss the joint optimization of the IRS’s phase control and the transceivers’ transmission control in different network design problems, e.g., rate maximization and power minimization problems. Furthermore, we extend the discussion of IRS-assisted wireless networks to some emerging use cases. Finally, we highlight important practical challenges and future research directions for realizing IRS-assisted wireless networks in beyond 5G communications.

642 citations


Journal ArticleDOI
TL;DR: Significant technological breakthroughs to achieve connectivity goals within 6G include: a network operating at the THz band with much wider spectrum resources, intelligent communication environments that enable a wireless propagation environment with active signal transmission and reception, and pervasive artificial intelligence.
Abstract: 6G and beyond will fulfill the requirements of a fully connected world and provide ubiquitous wireless connectivity for all. Transformative solutions are expected to drive the surge for accommodating a rapidly growing number of intelligent devices and services. Major technological breakthroughs to achieve connectivity goals within 6G include: (i) a network operating at the THz band with much wider spectrum resources, (ii) intelligent communication environments that enable a wireless propagation environment with active signal transmission and reception, (iii) pervasive artificial intelligence, (iv) large-scale network automation, (v) an all-spectrum reconfigurable front-end for dynamic spectrum access, (vi) ambient backscatter communications for energy savings, (vii) the Internet of Space Things enabled by CubeSats and UAVs, and (viii) cell-free massive MIMO communication networks. In this roadmap paper, use cases for these enabling techniques as well as recent advancements on related topics are highlighted, and open problems with possible solutions are discussed, followed by a development timeline outlining the worldwide efforts in the realization of 6G. Going beyond 6G, promising early-stage technologies such as the Internet of NanoThings, the Internet of BioNanoThings, and quantum communications, which are expected to have a far-reaching impact on wireless communications, have also been discussed at length in this paper.

595 citations


Journal ArticleDOI
TL;DR: This paper develops a DRL based algorithm, in which the joint design is obtained through trial-and-error interactions with the environment by observing predefined rewards, in the context of continuous state and action, and obtains the comparable performance compared with two state-of-the-art benchmarks.
Abstract: Recently, the reconfigurable intelligent surface (RIS), benefited from the breakthrough on the fabrication of programmable meta-material, has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology to achieve smart radio environments. Employed as reflecting arrays, RIS is able to assist MIMO transmissions without the need of radio frequency chains resulting in considerable reduction in power consumption. In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL). We first develop a DRL based algorithm, in which the joint design is obtained through trial-and-error interactions with the environment by observing predefined rewards, in the context of continuous state and action. Unlike the most reported works utilizing the alternating optimization techniques to alternatively obtain the transmit beamforming and phase shifts, the proposed DRL based algorithm obtains the joint design simultaneously as the output of the DRL neural network. Simulation results show that the proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks. It is also observed that, appropriate neural network parameter settings will improve significantly the performance and convergence rate of the proposed algorithm.

575 citations


Journal ArticleDOI
TL;DR: In this paper, the authors proposed a new type of high-gain yet low-cost RIS that bears 256 elements, where positive intrinsic negative (PIN) diodes are used to realize 2-bit phase shifting for beamforming.
Abstract: One of the key enablers of future wireless communications is constituted by massive multiple-input multiple-output (MIMO) systems, which can improve the spectral efficiency by orders of magnitude. In existing massive MIMO systems, however, conventional phased arrays are used for beamforming. This method results in excessive power consumption and high hardware costs. Recently, reconfigurable intelligent surface (RIS) has been considered as one of the revolutionary technologies to enable energy-efficient and smart wireless communications, which is a two-dimensional structure with a large number of passive elements. In this paper, we develop a new type of high-gain yet low-cost RIS that bears 256 elements. The proposed RIS combines the functions of phase shift and radiation together on an electromagnetic surface, where positive intrinsic-negative (PIN) diodes are used to realize 2-bit phase shifting for beamforming. This radical design forms the basis for the world's first wireless communication prototype using RIS having 256 two-bit elements. The prototype consists of modular hardware and flexible software that encompass the following: the hosts for parameter setting and data exchange, the universal software radio peripherals (USRPs) for baseband and radio frequency (RF) signal processing, as well as the RIS for signal transmission and reception. Our performance evaluation confirms the feasibility and efficiency of RISs in wireless communications. We show that, at 2.3 GHz, the proposed RIS can achieve a 21.7 dBi antenna gain. At the millimeter wave (mmWave) frequency, that is, 28.5 GHz, it attains a 19.1 dBi antenna gain. Furthermore, it has been shown that the RIS-based wireless communication prototype developed is capable of significantly reducing the power consumption.

523 citations


Journal ArticleDOI
20 Jul 2020
TL;DR: In this article, the authors present the vision of future 6G wireless communication and its network architecture and also describe potential applications with 6G communication requirements and possible technologies, as well as potential challenges and research directions for achieving this goal.
Abstract: The demand for wireless connectivity has grown exponentially over the last few decades. Fifth-generation (5G) communications, with far more features than fourth-generation communications, will soon be deployed worldwide. A new paradigm of wireless communication, the sixth-generation (6G) system, with the full support of artificial intelligence, is expected to be implemented between 2027 and 2030. Beyond 5G, some fundamental issues that need to be addressed are higher system capacity, higher data rate, lower latency, higher security, and improved quality of service (QoS) compared to the 5G system. This paper presents the vision of future 6G wireless communication and its network architecture. This article describes emerging technologies such as artificial intelligence, terahertz communications, wireless optical technology, free-space optical network, blockchain, three-dimensional networking, quantum communications, unmanned aerial vehicles, cell-free communications, integration of wireless information and energy transfer, integrated sensing and communication, integrated access-backhaul networks, dynamic network slicing, holographic beamforming, backscatter communication, intelligent reflecting surface, proactive caching, and big data analytics that can assist the 6G architecture development in guaranteeing the QoS. Besides, expected applications with 6G communication requirements and possible technologies are presented. We also describe potential challenges and research directions for achieving this goal.

514 citations


Journal ArticleDOI
TL;DR: This work introduces a novel analog scheme, called A-DSGD, which exploits the additive nature of the wireless MAC for over-the-air gradient computation, and provides convergence analysis for this approach.
Abstract: We study federated machine learning (ML) at the wireless edge, where power- and bandwidth-limited wireless devices with local datasets carry out distributed stochastic gradient descent (DSGD) with the help of a parameter server (PS). Standard approaches assume separate computation and communication, where local gradient estimates are compressed and transmitted to the PS over orthogonal links. Following this digital approach, we introduce D-DSGD, in which the wireless devices employ gradient quantization and error accumulation, and transmit their gradient estimates to the PS over a multiple access channel (MAC). We then introduce a novel analog scheme, called A-DSGD, which exploits the additive nature of the wireless MAC for over-the-air gradient computation, and provide convergence analysis for this approach. In A-DSGD, the devices first sparsify their gradient estimates, and then project them to a lower dimensional space imposed by the available channel bandwidth. These projections are sent directly over the MAC without employing any digital code. Numerical results show that A-DSGD converges faster than D-DSGD thanks to its more efficient use of the limited bandwidth and the natural alignment of the gradient estimates over the channel. The improvement is particularly compelling at low power and low bandwidth regimes. We also illustrate for a classification problem that, A-DSGD is more robust to bias in data distribution across devices, while D-DSGD significantly outperforms other digital schemes in the literature. We also observe that both D-DSGD and A-DSGD perform better with the number of devices, showing their ability in harnessing the computation power of edge devices.

494 citations


Journal ArticleDOI
TL;DR: This article describes the working principles of reconfigurable intelligent surfaces (RIS) and elaborate on different candidate implementations using metasurfaces and reflectarrays, and discusses the channel models suitable for both implementations and the feasibility of obtaining accurate channel estimates.
Abstract: Recently there has been a flurry of research on the use of reconfigurable intelligent surfaces (RIS) in wireless networks to create smart radio environments. In a smart radio environment, surfaces are capable of manipulating the propagation of incident electromagnetic waves in a programmable manner to actively alter the channel realization, which turns the wireless channel into a controllable system block that can be optimized to improve overall system performance. In this article, we provide a tutorial overview of reconfigurable intelligent surfaces (RIS) for wireless communications. We describe the working principles of reconfigurable intelligent surfaces (RIS) and elaborate on different candidate implementations using metasurfaces and reflectarrays. We discuss the channel models suitable for both implementations and examine the feasibility of obtaining accurate channel estimates. Furthermore, we discuss the aspects that differentiate RIS optimization from precoding for traditional MIMO arrays highlighting both the arising challenges and the potential opportunities associated with this emerging technology. Finally, we present numerical results to illustrate the power of an RIS in shaping the key properties of a MIMO channel.

Journal ArticleDOI
TL;DR: Simulation results demonstrate the effectiveness of employing multiple IRSs for enhancing the performance of SWIPT systems as well as the significant performance gains achieved by the proposed algorithms over benchmark schemes.
Abstract: Intelligent reflecting surface (IRS) is a new and revolutionizing technology for achieving spectrum and energy efficient wireless networks. By leveraging massive low-cost passive elements that are able to reflect radio-frequency (RF) signals with adjustable phase shifts, IRS can achieve high passive beamforming gains, which are particularly appealing for improving the efficiency of RF-based wireless power transfer. Motivated by the above, we study in this paper an IRS-assisted simultaneous wireless information and power transfer (SWIPT) system. Specifically, a set of IRSs are deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), respectively. We aim to minimize the transmit power at the AP via jointly optimizing its transmit precoders and the reflect phase shifts at all IRSs, subject to the quality-of-service (QoS) constraints at all users, namely, the individual signal-to-interference-plus-noise ratio (SINR) constraints at IUs and the energy harvesting constraints at EUs. However, this optimization problem is non-convex with intricately coupled variables, for which the existing alternating optimization approach is shown to be inefficient as the number of QoS constraints increases. To tackle this challenge, we first apply proper transformations on the QoS constraints and then propose an efficient iterative algorithm by applying the penalty-based optimization method. Moreover, by exploiting the short-range coverage of IRS, we further propose a more computationally efficient algorithm by optimizing the phase shifts at all IRSs in parallel. Simulation results demonstrate the effectiveness of employing multiple IRSs for enhancing the performance of SWIPT systems as well as the significant performance gains achieved by our proposed algorithms over benchmark schemes. The impact of IRS on the transmitter/receiver design for SWIPT is also unveiled.

Journal ArticleDOI
TL;DR: In this article, a Deep Reinforcement Learning-based Online Offloading (DROO) framework is proposed to optimize task offloading decisions and wireless resource allocation to the time-varying wireless channel conditions.
Abstract: Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor networks and internet of things (IoT). In this paper, we consider a wireless powered MEC network that adopts a binary offloading policy, so that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server. Our goal is to acquire an online algorithm that optimally adapts task offloading decisions and wireless resource allocations to the time-varying wireless channel conditions. This requires quickly solving hard combinatorial optimization problems within the channel coherence time, which is hardly achievable with conventional numerical optimization methods. To tackle this problem, we propose a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network as a scalable solution that learns the binary offloading decisions from the experience. It eliminates the need of solving combinatorial optimization problems, and thus greatly reduces the computational complexity especially in large-size networks. To further reduce the complexity, we propose an adaptive procedure that automatically adjusts the parameters of the DROO algorithm on the fly. Numerical results show that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computation time by more than an order of magnitude compared with existing optimization methods. For example, the CPU execution latency of DROO is less than 0.1 second in a 30-user network, making real-time and optimal offloading truly viable even in a fast fading environment.

Journal ArticleDOI
TL;DR: An accessible introduction to the general idea of federated learning is provided, several possible applications in 5G networks are discussed, and key technical challenges and open problems for future research on Federated learning in the context of wireless communications are described.
Abstract: There is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always feasible in wireless communications applications because of the inaccessibility of private data and large communication overhead required to transmit raw data to central ML processors. As a result, decentralized ML approaches that keep the data where it is generated are much more appealing. Due to its privacy-preserving nature, federated learning is particularly relevant for many wireless applications, especially in the context of fifth generation (5G) networks. In this article, we provide an accessible introduction to the general idea of federated learning, discuss several possible applications in 5G networks, and describe key technical challenges and open problems for future research on federated learning in the context of wireless communications.

Journal ArticleDOI
TL;DR: Results show that intelligent reflecting surface (IRS) can help create effective virtual line-of-sight (LOS) paths and thus substantially improve robustness against blockages in mmWave communications.
Abstract: Millimeter wave (MmWave) communications is capable of supporting multi-gigabit wireless access thanks to its abundant spectrum resource. However, severe path loss and high directivity make it vulnerable to blockage events, which can be frequent in indoor and dense urban environments. To address this issue, in this paper, we introduce intelligent reflecting surface (IRS) as a new technology to provide effective reflected paths to enhance the coverage of mmWave signals. In this framework, we study joint active and passive precoding design for IRS-assisted mmWave systems, where multiple IRSs are deployed to assist the data transmission from a base station (BS) to a single-antenna receiver. Our objective is to maximize the received signal power by jointly optimizing the BS's transmit precoding vector and IRSs’ phase shift coefficients. Although such an optimization problem is generally non-convex, we show that, by exploiting some important characteristics of mmWave channels, an optimal closed-form solution can be derived for the single IRS case and a near-optimal analytical solution can be obtained for the multi-IRS case. Our analysis reveals that the received signal power increases quadratically with the number of reflecting elements for both the single IRS and multi-IRS cases. Simulation results are included to verify the optimality and near-optimality of our proposed solutions. Results also show that IRSs can help create effective virtual line-of-sight (LOS) paths and thus substantially improve robustness against blockages in mmWave communications.

Journal ArticleDOI
TL;DR: In this article, an analytical model is developed to characterize the performance of FL in wireless networks, accounting for effects from both scheduling schemes and inter-cell interference, and it is shown that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low.
Abstract: Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private data, a new machine learning model has emerged, namely federated learning (FL), that allows a decoupling of data acquisition and computation at the central unit. Unlike centralized learning taking place in a data center, FL usually operates in a wireless edge network where the communication medium is resource-constrained and unreliable. Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration. Due to the shared nature of the wireless medium, transmissions are subjected to interference and are not guaranteed. The performance of FL system in such a setting is not well understood. In this paper, an analytical model is developed to characterize the performance of FL in wireless networks. Particularly, tractable expressions are derived for the convergence rate of FL in a wireless setting, accounting for effects from both scheduling schemes and inter-cell interference. Using the developed analysis, the effectiveness of three different scheduling policies, i.e., random scheduling (RS), round robin (RR), and proportional fair (PF), are compared in terms of FL convergence rate. It is shown that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low. Moreover, the FL convergence rate decreases rapidly as the SINR threshold increases, thus confirming the importance of compression and quantization of the update parameters. The analysis also reveals a trade-off between the number of scheduled UEs and subchannel bandwidth under a fixed amount of available spectrum.

Journal ArticleDOI
TL;DR: In this article, the authors proposed a distributed stochastic gradient descent (DSGD) over a shared noisy wireless channel for federated machine learning at the wireless network edge, where limited power wireless devices, each with its own dataset, build a joint model with the help of a remote parameter server.
Abstract: We study federated machine learning at the wireless network edge, where limited power wireless devices, each with its own dataset, build a joint model with the help of a remote parameter server (PS). We consider a bandwidth-limited fading multiple access channel (MAC) from the wireless devices to the PS, and propose various techniques to implement distributed stochastic gradient descent (DSGD) over this shared noisy wireless channel. We first propose a digital DSGD (D-DSGD) scheme, in which one device is selected opportunistically for transmission at each iteration based on the channel conditions; the scheduled device quantizes its gradient estimate to a finite number of bits imposed by the channel condition, and transmits these bits to the PS in a reliable manner. Next, motivated by the additive nature of the wireless MAC, we propose a novel analog communication scheme, referred to as the compressed analog DSGD (CA-DSGD), where the devices first sparsify their gradient estimates while accumulating error from previous iterations, and project the resultant sparse vector into a low-dimensional vector for bandwidth reduction. We also design a power allocation scheme to align the received gradient vectors at the PS in an efficient manner. Numerical results show that D-DSGD outperforms other digital approaches in the literature; however, in general the proposed CA-DSGD algorithm converges faster than the D-DSGD scheme, and reaches a higher level of accuracy. We have observed that the gap between the analog and digital schemes increases when the datasets of devices are not independent and identically distributed (i.i.d.). Furthermore, the performance of the CA-DSGD scheme is shown to be robust against imperfect channel state information (CSI) at the devices. Overall these results show clear advantages for the proposed analog over-the-air DSGD scheme, which suggests that learning and communication algorithms should be designed jointly to achieve the best end-to-end performance in machine learning applications at the wireless edge.

Journal ArticleDOI
TL;DR: This paper proposes an efficient online CSI prediction scheme, called OCEAN, for predicting CSI from historical data in 5G wireless communication systems, and designs a learning framework that is an integration of a CNN and a long short term with memory (LSTM) network.
Abstract: Channel state information (CSI) estimation is one of the most fundamental problems in wireless communication systems. Various methods, so far, have been developed to conduct CSI estimation. However, they usually require high computational complexity, which makes them unsuitable for 5G wireless communications due to employing many new techniques (e.g., massive MIMO, OFDM, and millimeter-Wave (mmWave)). In this paper, we propose an efficient online CSI prediction scheme, called OCEAN, for predicting CSI from historical data in 5G wireless communication systems. Specifically, we first identify several important features affecting the CSI of a radio link and a data sample consists of the information of the features and the CSI. We then design a learning framework that is an integration of a CNN (convolutional neural network) and a long short term with memory (LSTM) network. We also further develop an offline-online two-step training mechanism, enabling the prediction results to be more stable when applying it to practical 5G wireless communication systems. To validate OCEAN's efficacy, we consider four typical case studies, and conduct extensive experiments in the four scenarios, i.e., two outdoor and two indoor scenarios. The experiment results show that OCEAN not only obtains the predicted CSI values very quickly but also achieves highly accurate CSI prediction with up to 2.650-3.457 percent average difference ratio (ADR) between the predicted and measured CSI.

Journal ArticleDOI
TL;DR: In this paper, the authors investigated whether the use of artificial noise (AN) is helpful to enhance the secrecy rate of an intelligent reflecting surface (IRS) assisted wireless communication system and proposed an efficient algorithm based on alternating optimization to solve the problem sub-optimally.
Abstract: In this letter, we investigate whether the use of artificial noise (AN) is helpful to enhance the secrecy rate of an intelligent reflecting surface (IRS) assisted wireless communication system. Specifically, an IRS is deployed nearby a single-antenna receiver to assist in the transmission from a multi-antenna transmitter, in the presence of multiple single-antenna eavesdroppers. Aiming to maximize the achievable secrecy rate, a design problem for jointly optimizing transmit beamforming with AN or jamming and IRS reflect beamforming is formulated, which is however difficult to solve due to its non-convexity and coupled variables. We thus propose an efficient algorithm based on alternating optimization to solve the problem sub-optimally. Simulation results show that incorporating AN in transmit beamforming is beneficial under the new setup with IRS reflect beamforming. In particular, it is unveiled that the IRS-aided design without AN even performs worse than the AN-aided design without IRS as the number of eavesdroppers near the IRS increases.

Journal ArticleDOI
TL;DR: Simulation results validate the effectiveness of system security enhancement via an IRS via the block coordinate descent (BCD) algorithm to solve the secrecy rate maximization (SRM) problem.
Abstract: This article considers an artificial noise (AN)-aided secure MIMO wireless communication system. To enhance the system security performance, the advanced intelligent reflecting surface (IRS) is invoked, and the base station (BS), legitimate information receiver (IR) and eavesdropper (Eve) are equipped with multiple antennas. With the aim for maximizing the secrecy rate (SR), the transmit precoding (TPC) matrix at the BS, covariance matrix of AN and phase shifts at the IRS are jointly optimized subject to constrains of transmit power limit and unit modulus of IRS phase shifts. Then, the secrecy rate maximization (SRM) problem is formulated, which is a non-convex problem with multiple coupled variables. To tackle it, we propose to utilize the block coordinate descent (BCD) algorithm to alternately update the variables while keeping SR non-decreasing. Specifically, the optimal TPC matrix and AN covariance matrix are derived by Lagrangian multiplier method, and the optimal phase shifts are obtained by Majorization-Minimization (MM) algorithm. Since all variables can be calculated in closed form, the proposed algorithm is very efficient. We also extend the SRM problem to the more general multiple-IRs scenario and propose a BCD algorithm to solve it. Simulation results validate the effectiveness of system security enhancement via an IRS.

Journal ArticleDOI
TL;DR: Novel communication frameworks of NOMA, massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated.
Abstract: The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, current communication systems, which were designed on the basis of conventional communication theories, significantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learning-based communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of NOMA, massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We envision that the appealing deep learning- based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.

Journal ArticleDOI
TL;DR: An end-to-end wireless communication system using deep neural networks using DNNs is developed, where a conditional generative adversarial net (GAN) is employed to model the channel effects in a data-driven way, where the received signal corresponding to the pilot symbols is added as a part of the conditioning information of the GAN.
Abstract: In this article, we develop an end-to-end wireless communication system using deep neural networks (DNNs), where DNNs are employed to perform several key functions, including encoding, decoding, modulation, and demodulation. However, an accurate estimation of instantaneous channel transfer function, i.e. , channel state information (CSI), is needed in order for the transmitter DNN to learn to optimize the receiver gain in decoding. This is very much a challenge since CSI varies with time and location in wireless communications and is hard to obtain when designing transceivers. We propose to use a conditional generative adversarial net (GAN) to represent channel effects and to bridge the transmitter DNN and the receiver DNN so that the gradient of the transmitter DNN can be back-propagated from the receiver DNN. In particular, a conditional GAN is employed to model the channel effects in a data-driven way, where the received signal corresponding to the pilot symbols is added as a part of the conditioning information of the GAN. To address the curse of dimensionality when the transmit symbol sequence is long, convolutional layers are utilized. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) channels, Rayleigh fading channels, and frequency-selective channels, which opens a new door for building data-driven DNNs for end-to-end communication systems.

Journal ArticleDOI
TL;DR: This survey provides a comprehensive overview of several emerging technologies for 5G systems, such as massive multiple-input multiple-output (MIMO) technologies, multiple access technologies, hybrid analog-digital precoding and combining, non-orthogonal multiple access (NOMA), cell-free massive MIMO, and simultaneous wireless information and power transfer (SWIPT) technologies.
Abstract: Fifth-generation (5G) cellular networks will almost certainly operate in the high-bandwidth, underutilized millimeter-wave (mmWave) frequency spectrum, which offers the potentiality of high-capacity wireless transmission of multi-gigabit-per-second (Gbps) data rates. Despite the enormous available bandwidth potential, mmWave signal transmissions suffer from fundamental technical challenges like severe path loss, sensitivity to blockage, directivity, and narrow beamwidth, due to its short wavelengths. To effectively support system design and deployment, accurate channel modeling comprising several 5G technologies and scenarios is essential. This survey provides a comprehensive overview of several emerging technologies for 5G systems, such as massive multiple-input multiple-output (MIMO) technologies, multiple access technologies, hybrid analog-digital precoding and combining, non-orthogonal multiple access (NOMA), cell-free massive MIMO, and simultaneous wireless information and power transfer (SWIPT) technologies. These technologies induce distinct propagation characteristics and establish specific requirements on 5G channel modeling. To tackle these challenges, we first provide a survey of existing solutions and standards and discuss the radio-frequency (RF) spectrum and regulatory issues for mmWave communications. Second, we compared existing wireless communication techniques like sub-6-GHz WiFi and sub-6 GHz 4G LTE over mmWave communications which come with benefits comprising narrow beam, high signal quality, large capacity data transmission, and strong detection potential. Third, we describe the fundamental propagation characteristics of the mmWave band and survey the existing channel models for mmWave communications. Fourth, we track evolution and advancements in hybrid beamforming for massive MIMO systems in terms of system models of hybrid precoding architectures, hybrid analog and digital precoding/combining matrices, with the potential antenna configuration scenarios and mmWave channel estimation (CE) techniques. Fifth, we extend the scope of the discussion by including multiple access technologies for mmWave systems such as non-orthogonal multiple access (NOMA) and space-division multiple access (SDMA), with limited RF chains at the base station. Lastly, we explore the integration of SWIPT in mmWave massive MIMO systems, with limited RF chains, to realize spectrally and energy-efficient communications.

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TL;DR: The theoretical framework for the performance comparison of reconfigurable intelligent surfaces (RISs) and amplify-and-forward (AF) relaying wireless systems is provided and it is highlighted that RIS-assisted wireless systems outperform the corresponding AF-relaying ones in terms of average SNR, OP, SER and EC.
Abstract: In this paper, we provide the theoretical framework for the performance comparison of reconfigurable intelligent surfaces (RISs) and amplify-and-forward (AF) relaying wireless systems. In particular, after statistically characterizing the end-to-end (e2e) wireless channel coefficient of the RIS-assisted wireless system, in terms of probability density function (PDF) and cumulative density function (CDF), we extract novel closed-form expressions for the instantaneous and average e2e signal-to-noise ratio (SNR) for both the RIS-assisted and AF-relaying wireless systems. Building upon these expressions, we derive the diversity gain of the RIS-assisted wireless system as well as the outage probability (OP) and symbol error rate (SER) for a large variety of Gray-mapped modulation schemes of both systems under investigation. Additionally, the diversity order of the RIS-assisted wireless system is presented as well as the ergodic capacity (EC) of both the RIS-assisted and AF-relaying wireless systems. Likewise, high-SNR and high-number of metasurfaces (MS) approximations for the SER and EC for the RIS-assisted wireless system are reported. Finally, for the sake of completeness, the special case in which the RIS is equipped with only one MS is also investigated. For this case, the instantaneous and average e2e SNR are derived, as well as the OP, SER and EC. Our analysis is verified through respective Monte Carlo simulations, which reveal the accuracy of the presented theoretical framework. Moreover, our results highlight that, in general, RIS-assisted wireless systems outperform the corresponding AF-relaying ones in terms of average SNR, OP, SER and EC.

Posted Content
TL;DR: In this article, a channel estimation framework based on the PARAllel FACtor (PARAFAC) decomposition is proposed to unfold the resulting cascaded channel model for the downlink of a RIS-empowered multi-user MISO downlink communication systems.
Abstract: Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the downlink of a RIS-empowered multi-user Multiple Input Single Output (MISO) downlink communication systems and propose a channel estimation framework based on the PARAllel FACtor (PARAFAC) decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cramer-Rao Bound (CRB). We also discuss the achievable sum-rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieve the CRB. It is also demonstrated that the sum rate using the estimated channels reached that of perfect channel estimation under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms.

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TL;DR: This article comprehensively survey 6G wireless channel measurements, characteristics, and models for all frequency bands and all scenarios, focusing on millimeter-wave, terahertz, and optical wireless communication channels under all spectra.
Abstract: In this article, we present our vision of the application scenarios, performance metrics, and potential key technologies of 6G wireless communication networks. We then comprehensively survey 6G wireless channel measurements, characteristics, and models for all frequency bands and all scenarios, focusing on millimeter-wave (mm-wave), terahertz, and optical wireless communication channels under all spectra; satellite, unmanned aerial vehicle (UAV), maritime, and underwater acoustic communication channels under global coverage scenarios; and high-speed train (HST), vehicle-to-vehicle (V2V), ultra-massive multiple-input, multiple-output (MIMO), orbital angular momentum (OAM), and industry Internet of Things (IoT) communication channels under full application scenarios. We also provide future research challenges of 6G channel measurements, a general standard 6G channel model framework, and models for intelligent reflection surface (IRS)-based 6G technologies and artificial intelligence (AI)-enabled channel measurements and models.

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TL;DR: The existing wireless sensing systems are surveyed in terms of their basic principles, techniques and system structures to describe how the wireless signals could be utilized to facilitate an array of applications including intrusion detection, room occupancy monitoring, daily activity recognition, gesture recognition, vital signs monitoring, user identification and indoor localization.
Abstract: With the advancement of wireless technologies and sensing methodologies, many studies have shown the success of re-using wireless signals (e.g., WiFi) to sense human activities and thereby realize a set of emerging applications, ranging from intrusion detection, daily activity recognition, gesture recognition to vital signs monitoring and user identification involving even finer-grained motion sensing. These applications arguably can brace various domains for smart home and office environments, including safety protection, well-being monitoring/management, smart healthcare and smart-appliance interaction. The movements of the human body impact the wireless signal propagation (e.g., reflection, diffraction and scattering), which provide great opportunities to capture human motions by analyzing the received wireless signals. Researchers take the advantage of the existing wireless links among mobile/smart devices (e.g., laptops, smartphones, smart thermostats, smart refrigerators and virtual assistance systems) by either extracting the ready-to-use signal measurements or adopting frequency modulated signals to detect the frequency shift. Due to the low-cost and non-intrusive sensing nature, wireless-based human activity sensing has drawn considerable attention and become a prominent research field over the past decade. In this paper, we survey the existing wireless sensing systems in terms of their basic principles, techniques and system structures. Particularly, we describe how the wireless signals could be utilized to facilitate an array of applications including intrusion detection, room occupancy monitoring, daily activity recognition, gesture recognition, vital signs monitoring, user identification and indoor localization. The future research directions and limitations of using wireless signals for human activity sensing are also discussed.

Journal ArticleDOI
TL;DR: This paper proposes CiFi, deep convolutional neural networks (DCNN) for indoor localization with commodity 5GHz WiFi, and implements the system with commodity Wi-Fi devices in the 5GHz band and verifies its performance with extensive experiments in two representative indoor environments.
Abstract: With the increasing demand of location-based services, Wi-Fi based localization has attracted great interest because it provides ubiquitous access in indoor environments. In this paper, we propose CiFi, deep convolutional neural networks (DCNN) for indoor localization with commodity 5GHz WiFi. Leveraging a modified device driver, we extract phase data of channel state information (CSI), which is used to estimate the angle of arrival (AoA). We then create estimated AoA images as input to a DCNN, to train the weights in the offline phase. The location of mobile device is predicted based using the trained DCNN and new CSI AoA images. We implement the proposed CiFi system with commodity Wi-Fi devices in the 5GHz band and verify its performance with extensive experiments in two representative indoor environments.

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TL;DR: This paper 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.
Abstract: 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.

Journal ArticleDOI
TL;DR: Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.
Abstract: In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its surface reflecting elements to guarantee secure communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated given the different quality of service (QoS) requirements and time-varying channel condition. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning-based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.